From 8b462e8c988780572ea2d6332b76900fcca39c68 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Fri, 4 Oct 2024 23:07:41 +0000
Subject: [PATCH 01/21] use nep rule

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 8f9aa165e5a..0240992fe1a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -90,6 +90,7 @@ select = [
     "UP007",
     # Import from `collections.abc` instead: `Callable`
     "UP035",
+    "NPY002",
 ]
 ignore = [
     # whitespace before :

From 763ae358330426727846c9d0fb3b4e92c58d672d Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Fri, 4 Oct 2024 23:13:41 +0000
Subject: [PATCH 02/21] update pre-commit config

---
 .pre-commit-config.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f861fb57916..5a4162ce32b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -141,6 +141,7 @@ repos:
     hooks:
       - id: ruff
         files: python/.*$
+        args: [ --fix ]
       - id: ruff-format
         files: python/.*$
   - repo: https://github.com/rapidsai/pre-commit-hooks

From 027e1488f42abb0b521004d1fdd661b61a4e38fa Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Sat, 5 Oct 2024 02:41:34 +0000
Subject: [PATCH 03/21] first pass

---
 .pre-commit-config.yaml                       |   2 +-
 python/cudf/cudf/tests/test_binops.py         |  22 +--
 python/cudf/cudf/tests/test_categorical.py    |   6 +-
 python/cudf/cudf/tests/test_column.py         |   1 +
 python/cudf/cudf/tests/test_concat.py         |  12 +-
 python/cudf/cudf/tests/test_copying.py        |   9 +-
 python/cudf/cudf/tests/test_csv.py            |   6 +-
 python/cudf/cudf/tests/test_dataframe.py      | 136 +++++++++---------
 python/cudf/cudf/tests/test_dataframe_copy.py |  34 +++--
 python/cudf/cudf/tests/test_datetime.py       |   6 +-
 python/cudf/cudf/tests/test_dlpack.py         |  10 +-
 python/cudf/cudf/tests/test_dropna.py         |   6 +-
 python/cudf/cudf/tests/test_duplicates.py     |   3 +-
 python/cudf/cudf/tests/test_factorize.py      |   8 +-
 python/cudf/cudf/tests/test_feather.py        |   5 +-
 python/cudf/cudf/tests/test_groupby.py        |  78 +++++-----
 python/cudf/cudf/tests/test_index.py          |   6 +-
 python/cudf/cudf/tests/test_indexing.py       |  80 ++++++-----
 python/cudf/cudf/tests/test_joining.py        |  40 +++---
 python/cudf/cudf/tests/test_json.py           |   2 +-
 python/cudf/cudf/tests/test_multiindex.py     |  12 +-
 python/cudf/cudf/tests/test_orc.py            |  30 ++--
 python/cudf/cudf/tests/test_parquet.py        |  24 ++--
 python/cudf/cudf/tests/test_query.py          |   2 +-
 python/cudf/cudf/tests/test_reductions.py     |   4 +-
 python/cudf/cudf/tests/test_repr.py           |  20 +--
 python/cudf/cudf/tests/test_resampling.py     |   6 +-
 python/cudf/cudf/tests/test_reshape.py        |  32 ++---
 python/cudf/cudf/tests/test_serialize.py      |  14 +-
 python/cudf/cudf/tests/test_series.py         |  32 ++---
 python/cudf/cudf/tests/test_sorting.py        |  10 +-
 python/cudf/cudf/tests/test_stats.py          |  32 ++---
 python/cudf/cudf/tests/test_string.py         |   6 +-
 python/cudf/cudf/tests/test_unique.py         |   2 +-
 python/dask_cudf/dask_cudf/tests/test_join.py |   1 +
 .../dask_cudf/tests/test_reductions.py        |   6 +-
 python/dask_cudf/dask_cudf/tests/test_sort.py |   4 +-
 python/dask_cudf/dask_cudf/tests/utils.py     |   3 +-
 38 files changed, 366 insertions(+), 346 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5a4162ce32b..09f441b143b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -141,7 +141,7 @@ repos:
     hooks:
       - id: ruff
         files: python/.*$
-        args: [ --fix ]
+        args: [ "--fix" ]
       - id: ruff-format
         files: python/.*$
   - repo: https://github.com/rapidsai/pre-commit-hooks
diff --git a/python/cudf/cudf/tests/test_binops.py b/python/cudf/cudf/tests/test_binops.py
index 2e8519509e2..c1f4d9ac7e5 100644
--- a/python/cudf/cudf/tests/test_binops.py
+++ b/python/cudf/cudf/tests/test_binops.py
@@ -271,10 +271,9 @@ def test_series_bitwise_binop(binop, obj_class, lhs_dtype, rhs_dtype):
     "dtype", ["int8", "int32", "int64", "float32", "float64", "datetime64[ms]"]
 )
 def test_series_compare(cmpop, obj_class, dtype):
-    arr1 = np.random.randint(0, 100, 100).astype(dtype)
-    arr2 = np.random.randint(0, 100, 100).astype(dtype)
-    sr1 = Series(arr1)
-    sr2 = Series(arr2)
+    rng = np.random.default_rng(seed=0)
+    sr1 = Series(rng.integers(0, 100, 100).astype(dtype))
+    sr2 = Series(rng.integers(0, 100, 100).astype(dtype))
 
     if obj_class == "Index":
         sr1 = Index(sr1)
@@ -438,9 +437,10 @@ def test_str_series_compare_num_reflected(
 def test_series_compare_scalar(
     nelem, cmpop, obj_class, dtype, use_cudf_scalar
 ):
-    arr1 = np.random.randint(0, 100, 100).astype(dtype)
+    rng = np.random.default_rng(seed=0)
+    arr1 = rng.integers(0, 100, 100).astype(dtype)
     sr1 = Series(arr1)
-    rhs = random.choice(arr1).item()
+    rhs = rng.choice(arr1).item()
 
     if use_cudf_scalar:
         rhs = cudf.Scalar(rhs)
@@ -774,7 +774,7 @@ def test_df_different_index_shape(df2, binop):
 
 @pytest.mark.parametrize("op", [operator.eq, operator.ne])
 def test_boolean_scalar_binop(op):
-    psr = pd.Series(np.random.choice([True, False], 10))
+    psr = pd.Series(rng.choice([True, False], 10))
     gsr = cudf.from_pandas(psr)
     assert_eq(op(psr, True), op(gsr, True))
     assert_eq(op(psr, False), op(gsr, False))
@@ -926,13 +926,13 @@ def gen_df():
         pdf = pd.DataFrame()
         from string import ascii_lowercase
 
-        cols = np.random.choice(num_cols + 5, num_cols, replace=False)
+        cols = rng.choice(num_cols + 5, num_cols, replace=False)
 
         for i in range(num_cols):
             colname = ascii_lowercase[cols[i]]
             data = utils.gen_rand("float64", num_rows) * 10000
             if nulls == "some":
-                idx = np.random.choice(
+                idx = rng.choice(
                     num_rows, size=int(num_rows / 2), replace=False
                 )
                 data[idx] = np.nan
@@ -962,13 +962,13 @@ def gen_df():
         pdf = pd.DataFrame()
         from string import ascii_lowercase
 
-        cols = np.random.choice(num_cols + 5, num_cols, replace=False)
+        cols = rng.choice(num_cols + 5, num_cols, replace=False)
 
         for i in range(num_cols):
             colname = ascii_lowercase[cols[i]]
             data = utils.gen_rand("float64", num_rows) * 10000
             if nulls == "some":
-                idx = np.random.choice(
+                idx = rng.choice(
                     num_rows, size=int(num_rows / 2), replace=False
                 )
                 data[idx] = np.nan
diff --git a/python/cudf/cudf/tests/test_categorical.py b/python/cudf/cudf/tests/test_categorical.py
index cd1ad21ae59..aa43453fcb8 100644
--- a/python/cudf/cudf/tests/test_categorical.py
+++ b/python/cudf/cudf/tests/test_categorical.py
@@ -255,7 +255,7 @@ def test_categorical_unique(num_elements):
     np.random.seed(12)
     pd_cat = pd.Categorical(
         pd.Series(
-            np.random.choice(
+            rng.choice(
                 list(string.ascii_letters + string.digits), num_elements
             ),
             dtype="category",
@@ -282,9 +282,7 @@ def test_categorical_unique_count(nelem):
     np.random.seed(12)
     pd_cat = pd.Categorical(
         pd.Series(
-            np.random.choice(
-                list(string.ascii_letters + string.digits), nelem
-            ),
+            rng.choice(list(string.ascii_letters + string.digits), nelem),
             dtype="category",
         )
     )
diff --git a/python/cudf/cudf/tests/test_column.py b/python/cudf/cudf/tests/test_column.py
index 4aa7fb27c9b..732dbeaab34 100644
--- a/python/cudf/cudf/tests/test_column.py
+++ b/python/cudf/cudf/tests/test_column.py
@@ -37,6 +37,7 @@ def pandas_input(request):
     def random_ints(dtype, size):
         dtype_min = np.iinfo(dtype).min
         dtype_max = np.iinfo(dtype).max
+        rng = np.random.default_rng(seed=0)
         return rng.integers(dtype_min, dtype_max, size=size, dtype=dtype)
 
     try:
diff --git a/python/cudf/cudf/tests/test_concat.py b/python/cudf/cudf/tests/test_concat.py
index 8da589ba45b..8acb296e4e9 100644
--- a/python/cudf/cudf/tests/test_concat.py
+++ b/python/cudf/cudf/tests/test_concat.py
@@ -203,10 +203,9 @@ def test_concat_misordered_columns():
 
 @pytest.mark.parametrize("axis", [1, "columns"])
 def test_concat_columns(axis):
-    pdf1 = pd.DataFrame(np.random.randint(10, size=(5, 3)), columns=[1, 2, 3])
-    pdf2 = pd.DataFrame(
-        np.random.randint(10, size=(5, 4)), columns=[4, 5, 6, 7]
-    )
+    rng = np.random.default_rng(seed=0)
+    pdf1 = pd.DataFrame(rng.integers(10, size=(5, 3)), columns=[1, 2, 3])
+    pdf2 = pd.DataFrame(rng.integers(10, size=(5, 4)), columns=[4, 5, 6, 7])
     gdf1 = cudf.from_pandas(pdf1)
     gdf2 = cudf.from_pandas(pdf2)
 
@@ -1398,11 +1397,12 @@ def test_concat_single_object(ignore_index, typ):
     ],
 )
 def test_concat_decimal_dataframe(ltype, rtype):
+    rng = np.random.default_rng(seed=0)
     gdf1 = cudf.DataFrame(
-        {"id": np.random.randint(0, 10, 3), "val": ["22.3", "59.5", "81.1"]}
+        {"id": rng.integers(0, 10, 3), "val": ["22.3", "59.5", "81.1"]}
     )
     gdf2 = cudf.DataFrame(
-        {"id": np.random.randint(0, 10, 3), "val": ["2.35", "5.59", "8.14"]}
+        {"id": rng.integers(0, 10, 3), "val": ["2.35", "5.59", "8.14"]}
     )
 
     gdf1["val"] = gdf1["val"].astype(ltype)
diff --git a/python/cudf/cudf/tests/test_copying.py b/python/cudf/cudf/tests/test_copying.py
index 9b6f82ec705..2d883916d58 100644
--- a/python/cudf/cudf/tests/test_copying.py
+++ b/python/cudf/cudf/tests/test_copying.py
@@ -16,8 +16,9 @@
 
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES + OTHER_TYPES)
 def test_repeat(dtype):
+    rng = np.random.default_rng(seed=0)
     arr = np.random.rand(10) * 10
-    repeats = np.random.randint(10, size=10)
+    repeats = rng.integers(10, size=10)
     psr = pd.Series(arr).astype(dtype)
     gsr = cudf.from_pandas(psr)
 
@@ -25,18 +26,20 @@ def test_repeat(dtype):
 
 
 def test_repeat_index():
+    rng = np.random.default_rng(seed=0)
     arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]]
     psr = pd.MultiIndex.from_arrays(arrays, names=("number", "color"))
     gsr = cudf.from_pandas(psr)
-    repeats = np.random.randint(10, size=4)
+    repeats = rng.integers(10, size=4)
 
     assert_eq(psr.repeat(repeats), gsr.repeat(repeats))
 
 
 def test_repeat_dataframe():
+    rng = np.random.default_rng(seed=0)
     psr = pd.DataFrame({"a": [1, 1, 2, 2]})
     gsr = cudf.from_pandas(psr)
-    repeats = np.random.randint(10, size=4)
+    repeats = rng.integers(10, size=4)
 
     # pd.DataFrame doesn't have repeat() so as a workaround, we are
     # comparing pd.Series.repeat() with cudf.DataFrame.repeat()['a']
diff --git a/python/cudf/cudf/tests/test_csv.py b/python/cudf/cudf/tests/test_csv.py
index b6efc8ebd88..d371ddff09d 100644
--- a/python/cudf/cudf/tests/test_csv.py
+++ b/python/cudf/cudf/tests/test_csv.py
@@ -1764,11 +1764,11 @@ def test_csv_writer_multiindex(tmpdir):
     pdf_df_fname = tmpdir.join("pdf_df_3.csv")
     gdf_df_fname = tmpdir.join("gdf_df_3.csv")
 
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame(
         {
-            "a": np.random.randint(0, 5, 20),
-            "b": np.random.randint(0, 5, 20),
+            "a": rng.integers(0, 5, 20),
+            "b": rng.integers(0, 5, 20),
             "c": range(20),
             "d": np.random.random(20),
         }
diff --git a/python/cudf/cudf/tests/test_dataframe.py b/python/cudf/cudf/tests/test_dataframe.py
index 6f88d942746..831c491b6bc 100644
--- a/python/cudf/cudf/tests/test_dataframe.py
+++ b/python/cudf/cudf/tests/test_dataframe.py
@@ -1534,14 +1534,12 @@ def test_dataframe_hash_values_xxhash64():
 @pytest.mark.parametrize("nparts", [1, 2, 8, 13])
 @pytest.mark.parametrize("nkeys", [1, 2])
 def test_dataframe_hash_partition(nrows, nparts, nkeys):
-    np.random.seed(123)
-    gdf = cudf.DataFrame()
-    keycols = []
-    for i in range(nkeys):
-        keyname = f"key{i}"
-        gdf[keyname] = np.random.randint(0, 7 - i, nrows)
-        keycols.append(keyname)
-    gdf["val1"] = np.random.randint(0, nrows * 2, nrows)
+    rng = np.random.default_rng(seed=0)
+    gdf = cudf.DataFrame(
+        {f"key{i}": rng.integers(0, 7 - i, nrows) for i in range(nkeys)}
+    )
+    keycols = gdf.columns.to_list()
+    gdf["val1"] = rng.integers(0, nrows * 2, nrows)
 
     got = gdf.partition_by_hash(keycols, nparts=nparts)
     # Must return a list
@@ -1787,13 +1785,14 @@ def test_concat_with_axis():
         check_index_type=True,
     )
 
+    rng = np.random.default_rng(seed=0)
     # concat groupby multi index
     gdf1 = cudf.DataFrame(
         {
-            "x": np.random.randint(0, 10, 10),
-            "y": np.random.randint(0, 10, 10),
-            "z": np.random.randint(0, 10, 10),
-            "v": np.random.randint(0, 10, 10),
+            "x": rng.integers(0, 10, 10),
+            "y": rng.integers(0, 10, 10),
+            "z": rng.integers(0, 10, 10),
+            "v": rng.integers(0, 10, 10),
         }
     )
     gdf2 = gdf1[5:]
@@ -1833,14 +1832,14 @@ def test_concat_with_axis():
 
 @pytest.mark.parametrize("nrows", [0, 3, 10, 100, 1000])
 def test_nonmatching_index_setitem(nrows):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     gdf = cudf.DataFrame()
-    gdf["a"] = np.random.randint(2147483647, size=nrows)
-    gdf["b"] = np.random.randint(2147483647, size=nrows)
+    gdf["a"] = rng.integers(2147483647, size=nrows)
+    gdf["b"] = rng.integers(2147483647, size=nrows)
     gdf = gdf.set_index("b")
 
-    test_values = np.random.randint(2147483647, size=nrows)
+    test_values = rng.integers(2147483647, size=nrows)
     gdf["c"] = test_values
     assert len(test_values) == len(gdf["c"])
     gdf_series = cudf.Series(test_values, index=gdf.index, name="c")
@@ -1974,10 +1973,11 @@ def test_index_in_dataframe_constructor():
 @pytest.mark.parametrize("nelem", [0, 2, 3, 100, 1000])
 @pytest.mark.parametrize("data_type", dtypes)
 def test_from_arrow(nelem, data_type):
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "a": np.random.randint(0, 1000, nelem).astype(data_type),
-            "b": np.random.randint(0, 1000, nelem).astype(data_type),
+            "a": rng.integers(0, 1000, nelem).astype(data_type),
+            "b": rng.integers(0, 1000, nelem).astype(data_type),
         }
     )
     padf = pa.Table.from_pandas(
@@ -2012,10 +2012,11 @@ def test_from_arrow_chunked_categories():
 @pytest.mark.parametrize("nelem", [0, 2, 3, 100, 1000])
 @pytest.mark.parametrize("data_type", dtypes)
 def test_to_arrow(nelem, data_type):
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "a": np.random.randint(0, 1000, nelem).astype(data_type),
-            "b": np.random.randint(0, 1000, nelem).astype(data_type),
+            "a": rng.integers(0, 1000, nelem).astype(data_type),
+            "b": rng.integers(0, 1000, nelem).astype(data_type),
         }
     )
     gdf = cudf.DataFrame.from_pandas(df)
@@ -2119,17 +2120,16 @@ def test_to_arrow_missing_categorical():
 
 @pytest.mark.parametrize("data_type", dtypes)
 def test_from_scalar_typing(data_type):
+    rng = np.random.default_rng(seed=0)
     if data_type == "datetime64[ms]":
         scalar = (
-            np.dtype("int64")
-            .type(np.random.randint(0, 5))
-            .astype("datetime64[ms]")
+            np.dtype("int64").type(rng.integers(0, 5)).astype("datetime64[ms]")
         )
     elif data_type.startswith("datetime64"):
         scalar = np.datetime64(datetime.date.today()).astype("datetime64[ms]")
         data_type = "datetime64[ms]"
     else:
-        scalar = np.dtype(data_type).type(np.random.randint(0, 5))
+        scalar = np.dtype(data_type).type(rng.integers(0, 5))
 
     gdf = cudf.DataFrame()
     gdf["a"] = [1, 2, 3, 4, 5]
@@ -2140,7 +2140,8 @@ def test_from_scalar_typing(data_type):
 
 @pytest.mark.parametrize("data_type", NUMERIC_TYPES)
 def test_from_python_array(data_type):
-    np_arr = np.random.randint(0, 100, 10).astype(data_type)
+    rng = np.random.default_rng(seed=0)
+    np_arr = rng.integers(0, 100, 10).astype(data_type)
     data = memoryview(np_arr)
     data = arr.array(data.format, data)
 
@@ -2228,13 +2229,11 @@ def test_dataframe_transpose(nulls, num_cols, num_rows, dtype):
     for i in range(num_cols):
         colname = string.ascii_lowercase[i]
         data = pd.Series(
-            np.random.randint(0, 26, num_rows).astype(np_dtype),
+            rng.integers(0, 26, num_rows).astype(np_dtype),
             dtype=dtype,
         )
         if nulls == "some":
-            idx = np.random.choice(
-                num_rows, size=int(num_rows / 2), replace=False
-            )
+            idx = rng.choice(num_rows, size=int(num_rows / 2), replace=False)
             if len(idx):
                 data[idx] = null_rep
         elif nulls == "all":
@@ -2729,9 +2728,10 @@ def test_quantile(q, numeric_only):
     [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
 )
 def test_decimal_quantile(q, interpolation, decimal_type):
+    rng = np.random.default_rng(seed=0)
     data = ["244.8", "32.24", "2.22", "98.14", "453.23", "5.45"]
     gdf = cudf.DataFrame(
-        {"id": np.random.randint(0, 10, size=len(data)), "val": data}
+        {"id": rng.integers(0, 10, size=len(data)), "val": data}
     )
     gdf["id"] = gdf["id"].astype("float64")
     gdf["val"] = gdf["val"].astype(decimal_type(7, 2))
@@ -2844,8 +2844,7 @@ def test_cuda_array_interface(dtype):
 @pytest.mark.parametrize("data_type", dtypes)
 def test_from_arrow_chunked_arrays(nelem, nchunks, data_type):
     np_list_data = [
-        np.random.randint(0, 100, nelem).astype(data_type)
-        for i in range(nchunks)
+        rng.integers(0, 100, nelem).astype(data_type) for i in range(nchunks)
     ]
     pa_chunk_array = pa.chunked_array(np_list_data)
 
@@ -2855,8 +2854,7 @@ def test_from_arrow_chunked_arrays(nelem, nchunks, data_type):
     assert_eq(expect, got)
 
     np_list_data2 = [
-        np.random.randint(0, 100, nelem).astype(data_type)
-        for i in range(nchunks)
+        rng.integers(0, 100, nelem).astype(data_type) for i in range(nchunks)
     ]
     pa_chunk_array2 = pa.chunked_array(np_list_data2)
     pa_table = pa.Table.from_arrays(
@@ -2885,7 +2883,8 @@ def query_GPU_memory(note=""):
     colNames = ["col" + str(iCol) for iCol in range(nCols)]
     pandasDF = pd.DataFrame(data=dataNumpy, columns=colNames, dtype=np.float32)
     cudaDF = cudf.core.DataFrame.from_pandas(pandasDF)
-    boolmask = cudf.Series(np.random.randint(1, 2, len(cudaDF)).astype("bool"))
+    rng = np.random.default_rng(seed=0)
+    boolmask = cudf.Series(rng.integers(1, 2, len(cudaDF)).astype("bool"))
 
     memory_used = query_GPU_memory()
     cudaDF = cudaDF[boolmask]
@@ -2903,7 +2902,8 @@ def query_GPU_memory(note=""):
 
 
 def test_boolmask(pdf, gdf):
-    boolmask = np.random.randint(0, 2, len(pdf)) > 0
+    rng = np.random.default_rng(seed=0)
+    boolmask = rng.integers(0, 2, len(pdf)) > 0
     gdf = gdf[boolmask]
     pdf = pdf[boolmask]
     assert_eq(pdf, gdf)
@@ -2922,12 +2922,11 @@ def test_boolmask(pdf, gdf):
     ],
 )
 def test_dataframe_boolmask(mask_shape):
-    pdf = pd.DataFrame()
-    for col in "abc":
-        pdf[col] = np.random.randint(0, 10, 3)
-    pdf_mask = pd.DataFrame()
-    for col in mask_shape[1]:
-        pdf_mask[col] = np.random.randint(0, 2, mask_shape[0]) > 0
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame({col: rng.integers(0, 10, 3) for col in "abc"})
+    pdf_mask = pd.DataFrame(
+        {col: rng.integers(0, 2, mask_shape[0]) > 0 for col in mask_shape[1]}
+    )
     gdf = cudf.DataFrame.from_pandas(pdf)
     gdf_mask = cudf.DataFrame.from_pandas(pdf_mask)
     gdf = gdf[gdf_mask]
@@ -3052,10 +3051,11 @@ def test_series_rename():
 @pytest.mark.parametrize("data_type", dtypes)
 @pytest.mark.parametrize("nelem", [0, 100])
 def test_head_tail(nelem, data_type):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "a": np.random.randint(0, 1000, nelem).astype(data_type),
-            "b": np.random.randint(0, 1000, nelem).astype(data_type),
+            "a": rng.integers(0, 1000, nelem).astype(data_type),
+            "b": rng.integers(0, 1000, nelem).astype(data_type),
         }
     )
     gdf = cudf.from_pandas(pdf)
@@ -3308,15 +3308,15 @@ def test_set_index_verify_integrity(data, index, verify_integrity):
 @pytest.mark.parametrize("drop", [True, False])
 @pytest.mark.parametrize("nelem", [10, 200, 1333])
 def test_set_index_multi(drop, nelem):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     a = np.arange(nelem)
     np.random.shuffle(a)
     df = pd.DataFrame(
         {
             "a": a,
-            "b": np.random.randint(0, 4, size=nelem),
-            "c": np.random.uniform(low=0, high=4, size=nelem),
-            "d": np.random.choice(["green", "black", "white"], nelem),
+            "b": rng.integers(0, 4, size=nelem),
+            "c": rng.uniform(low=0, high=4, size=nelem),
+            "d": rng.choice(["green", "black", "white"], nelem),
         }
     )
     df["e"] = df["d"].astype("category")
@@ -3898,9 +3898,9 @@ def test_dataframe_describe_exclude():
     data_length = 10000
 
     df = cudf.DataFrame()
-    df["x"] = np.random.normal(10, 1, data_length)
+    df["x"] = rng.normal(10, 1, data_length)
     df["x"] = df.x.astype("int64")
-    df["y"] = np.random.normal(10, 1, data_length)
+    df["y"] = rng.normal(10, 1, data_length)
     pdf = df.to_pandas()
 
     gdf_results = df.describe(exclude=["float"])
@@ -3914,9 +3914,9 @@ def test_dataframe_describe_include():
     data_length = 10000
 
     df = cudf.DataFrame()
-    df["x"] = np.random.normal(10, 1, data_length)
+    df["x"] = rng.normal(10, 1, data_length)
     df["x"] = df.x.astype("int64")
-    df["y"] = np.random.normal(10, 1, data_length)
+    df["y"] = rng.normal(10, 1, data_length)
     pdf = df.to_pandas()
     gdf_results = df.describe(include=["int"])
     pdf_results = pdf.describe(include=["int"])
@@ -3929,8 +3929,8 @@ def test_dataframe_describe_default():
     data_length = 10000
 
     df = cudf.DataFrame()
-    df["x"] = np.random.normal(10, 1, data_length)
-    df["y"] = np.random.normal(10, 1, data_length)
+    df["x"] = rng.normal(10, 1, data_length)
+    df["y"] = rng.normal(10, 1, data_length)
     pdf = df.to_pandas()
     gdf_results = df.describe()
     pdf_results = pdf.describe()
@@ -3943,10 +3943,10 @@ def test_series_describe_include_all():
     data_length = 10000
 
     df = cudf.DataFrame()
-    df["x"] = np.random.normal(10, 1, data_length)
+    df["x"] = rng.normal(10, 1, data_length)
     df["x"] = df.x.astype("int64")
-    df["y"] = np.random.normal(10, 1, data_length)
-    df["animal"] = np.random.choice(["dog", "cat", "bird"], data_length)
+    df["y"] = rng.normal(10, 1, data_length)
+    df["animal"] = rng.choice(["dog", "cat", "bird"], data_length)
 
     pdf = df.to_pandas()
     gdf_results = df.describe(include="all")
@@ -3967,8 +3967,8 @@ def test_dataframe_describe_percentiles():
     sample_percentiles = [0.0, 0.1, 0.33, 0.84, 0.4, 0.99]
 
     df = cudf.DataFrame()
-    df["x"] = np.random.normal(10, 1, data_length)
-    df["y"] = np.random.normal(10, 1, data_length)
+    df["x"] = rng.normal(10, 1, data_length)
+    df["y"] = rng.normal(10, 1, data_length)
     pdf = df.to_pandas()
     gdf_results = df.describe(percentiles=sample_percentiles)
     pdf_results = pdf.describe(percentiles=sample_percentiles)
@@ -4101,7 +4101,7 @@ def test_dataframe_round(decimals):
     gdf = cudf.DataFrame(
         {
             "floats": np.arange(0.5, 10.5, 1),
-            "ints": np.random.normal(-100, 100, 10),
+            "ints": rng.normal(-100, 100, 10),
             "floats_with_na": np.array(
                 [
                     14.123,
@@ -4117,9 +4117,9 @@ def test_dataframe_round(decimals):
                 ]
             ),
             "floats_same": np.repeat([-0.6459412758761901], 10),
-            "bools": np.random.choice([True, None, False], 10),
-            "strings": np.random.choice(["abc", "xyz", None], 10),
-            "struct": np.random.choice([{"abc": 1}, {"xyz": 2}, None], 10),
+            "bools": rng.choice([True, None, False], 10),
+            "strings": rng.choice(["abc", "xyz", None], 10),
+            "struct": rng.choice([{"abc": 1}, {"xyz": 2}, None], 10),
             "list": [[1], [2], None, [4], [3]] * 2,
         }
     )
@@ -5814,7 +5814,7 @@ def test_memory_usage_string():
     df = pd.DataFrame(
         {
             "A": np.arange(rows, dtype="int32"),
-            "B": np.random.choice(["apple", "banana", "orange"], rows),
+            "B": rng.choice(["apple", "banana", "orange"], rows),
         }
     )
     gdf = cudf.from_pandas(df)
@@ -5840,7 +5840,7 @@ def test_memory_usage_cat():
     df = pd.DataFrame(
         {
             "A": np.arange(rows, dtype="int32"),
-            "B": np.random.choice(["apple", "banana", "orange"], rows),
+            "B": rng.choice(["apple", "banana", "orange"], rows),
         }
     )
     df["B"] = df.B.astype("category")
@@ -5873,10 +5873,10 @@ def test_memory_usage_multi(rows):
     df = pd.DataFrame(
         {
             "A": np.arange(rows, dtype="int32"),
-            "B": np.random.choice(
+            "B": rng.choice(
                 np.arange(rows, dtype="int64"), rows, replace=False
             ),
-            "C": np.random.choice(
+            "C": rng.choice(
                 np.arange(rows, dtype="float64"), rows, replace=False
             ),
         }
@@ -10161,7 +10161,7 @@ def df_eval(request):
             }
         )
     int_max = 10
-    rng = cupy.random.default_rng(0)
+    rng = cupy.random.default_rng(seed=0)
     return cudf.DataFrame(
         {
             "a": rng.integers(N, size=int_max),
diff --git a/python/cudf/cudf/tests/test_dataframe_copy.py b/python/cudf/cudf/tests/test_dataframe_copy.py
index 45bd31ef58e..3aedbf8365b 100644
--- a/python/cudf/cudf/tests/test_dataframe_copy.py
+++ b/python/cudf/cudf/tests/test_dataframe_copy.py
@@ -93,11 +93,15 @@ def test_dataframe_deep_copy_and_insert(copy_parameters):
 @pytest.mark.parametrize("ncols", [0, 1, 10])
 @pytest.mark.parametrize("data_type", ALL_TYPES)
 def test_cudf_dataframe_copy(copy_fn, ncols, data_type):
-    pdf = pd.DataFrame()
-    for i in range(ncols):
-        pdf[chr(i + ord("a"))] = pd.Series(
-            np.random.randint(0, 1000, 20)
-        ).astype(data_type)
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(
+        {
+            chr(i + ord("a")): pd.Series(rng.integers(0, 1000, 20)).astype(
+                data_type
+            )
+            for i in range(ncols)
+        }
+    )
     df = DataFrame.from_pandas(pdf)
     copy_df = copy_fn(df)
     assert_eq(df, copy_df)
@@ -116,18 +120,20 @@ def test_cudf_dataframe_copy(copy_fn, ncols, data_type):
 @pytest.mark.parametrize("ncols", [0, 1, 10])
 @pytest.mark.parametrize("data_type", ALL_TYPES)
 def test_cudf_dataframe_copy_then_insert(copy_fn, ncols, data_type):
-    pdf = pd.DataFrame()
-    for i in range(ncols):
-        pdf[chr(i + ord("a"))] = pd.Series(
-            np.random.randint(0, 1000, 20)
-        ).astype(data_type)
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(
+        {
+            chr(i + ord("a")): pd.Series(rng.integers(0, 1000, 20)).astype(
+                data_type
+            )
+            for i in range(ncols)
+        }
+    )
     df = DataFrame.from_pandas(pdf)
     copy_df = copy_fn(df)
     copy_pdf = copy_fn(pdf)
-    copy_df["aa"] = pd.Series(np.random.randint(0, 1000, 20)).astype(data_type)
-    copy_pdf["aa"] = pd.Series(np.random.randint(0, 1000, 20)).astype(
-        data_type
-    )
+    copy_df["aa"] = pd.Series(rng.integers(0, 1000, 20)).astype(data_type)
+    copy_pdf["aa"] = pd.Series(rng.integers(0, 1000, 20)).astype(data_type)
     assert not copy_pdf.to_string().split() == pdf.to_string().split()
     assert not copy_df.to_string().split() == df.to_string().split()
 
diff --git a/python/cudf/cudf/tests/test_datetime.py b/python/cudf/cudf/tests/test_datetime.py
index 4a2345fc009..e09a9a386a7 100644
--- a/python/cudf/cudf/tests/test_datetime.py
+++ b/python/cudf/cudf/tests/test_datetime.py
@@ -432,11 +432,12 @@ def test_datetime_to_arrow(dtype):
 )
 @pytest.mark.parametrize("nulls", ["none", "some"])
 def test_datetime_unique(data, nulls):
+    rng = np.random.default_rng(seed=0)
     psr = data.copy()
 
     if len(data) > 0:
         if nulls == "some":
-            p = np.random.randint(0, len(data), 2)
+            p = rng.integers(0, len(data), 2)
             psr[p] = None
 
     gsr = cudf.from_pandas(psr)
@@ -461,10 +462,11 @@ def test_datetime_unique(data, nulls):
 @pytest.mark.parametrize("nulls", ["none", "some"])
 def test_datetime_nunique(data, nulls):
     psr = data.copy()
+    rng = np.random.default_rng(seed=0)
 
     if len(data) > 0:
         if nulls == "some":
-            p = np.random.randint(0, len(data), 2)
+            p = rng.integers(0, len(data), 2)
             psr[p] = None
 
     gsr = cudf.from_pandas(psr)
diff --git a/python/cudf/cudf/tests/test_dlpack.py b/python/cudf/cudf/tests/test_dlpack.py
index ebcc35784ee..20c24bd7564 100644
--- a/python/cudf/cudf/tests/test_dlpack.py
+++ b/python/cudf/cudf/tests/test_dlpack.py
@@ -42,9 +42,10 @@ def data_1d(request):
     nelems = request.param[0]
     dtype = request.param[1]
     nulls = request.param[2]
-    a = np.random.randint(10, size=nelems).astype(dtype)
+    rng = np.random.default_rng(seed=0)
+    a = rng.integers(10, size=nelems).astype(dtype)
     if nulls == "some" and a.size != 0 and np.issubdtype(dtype, np.floating):
-        idx = np.random.choice(a.size, size=int(a.size * 0.2), replace=False)
+        idx = rng.choice(a.size, size=int(a.size * 0.2), replace=False)
         a[idx] = np.nan
     return a
 
@@ -55,9 +56,10 @@ def data_2d(request):
     nrows = request.param[1]
     dtype = request.param[2]
     nulls = request.param[3]
-    a = np.random.randint(10, size=(nrows, ncols)).astype(dtype)
+    rng = np.random.default_rng(seed=0)
+    a = rng.integers(10, size=(nrows, ncols)).astype(dtype)
     if nulls == "some" and a.size != 0 and np.issubdtype(dtype, np.floating):
-        idx = np.random.choice(a.size, size=int(a.size * 0.2), replace=False)
+        idx = rng.choice(a.size, size=int(a.size * 0.2), replace=False)
         a.ravel()[idx] = np.nan
     return np.ascontiguousarray(a)
 
diff --git a/python/cudf/cudf/tests/test_dropna.py b/python/cudf/cudf/tests/test_dropna.py
index 5b1ee0ffac6..eeac78dbebc 100644
--- a/python/cudf/cudf/tests/test_dropna.py
+++ b/python/cudf/cudf/tests/test_dropna.py
@@ -22,13 +22,13 @@
 @pytest.mark.parametrize("inplace", [True, False])
 def test_dropna_series(data, nulls, inplace):
     psr = pd.Series(data)
-
+    rng = np.random.default_rng(seed=0)
     if len(data) > 0:
         if nulls == "one":
-            p = np.random.randint(0, 4)
+            p = rng.integers(0, 4)
             psr[p] = None
         elif nulls == "some":
-            p1, p2 = np.random.randint(0, 4, (2,))
+            p1, p2 = rng.integers(0, 4, (2,))
             psr[p1] = None
             psr[p2] = None
         elif nulls == "all":
diff --git a/python/cudf/cudf/tests/test_duplicates.py b/python/cudf/cudf/tests/test_duplicates.py
index 0b4ed52ba96..dcff4f2f07a 100644
--- a/python/cudf/cudf/tests/test_duplicates.py
+++ b/python/cudf/cudf/tests/test_duplicates.py
@@ -585,7 +585,8 @@ def test_drop_duplicates_multi_index():
     ]
 
     idx = pd.MultiIndex.from_tuples(list(zip(*arrays)), names=["a", "b"])
-    pdf = pd.DataFrame(np.random.randint(0, 2, (8, 4)), index=idx)
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.integers(0, 2, (8, 4)), index=idx)
     gdf = cudf.DataFrame.from_pandas(pdf)
 
     expected = pdf.drop_duplicates()
diff --git a/python/cudf/cudf/tests/test_factorize.py b/python/cudf/cudf/tests/test_factorize.py
index 47f9180dcb1..cd558cc1415 100644
--- a/python/cudf/cudf/tests/test_factorize.py
+++ b/python/cudf/cudf/tests/test_factorize.py
@@ -13,10 +13,10 @@
 @pytest.mark.parametrize("ncats,nelem", [(2, 2), (2, 10), (10, 100)])
 def test_factorize_series_obj(ncats, nelem):
     df = DataFrame()
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # initialize data frame
-    df["cats"] = arr = np.random.randint(2, size=10, dtype=np.int32)
+    df["cats"] = arr = rng.integers(2, size=10, dtype=np.int32)
 
     uvals, labels = df["cats"].factorize()
     np.testing.assert_array_equal(labels.to_numpy(), sorted(set(arr)))
@@ -31,10 +31,10 @@ def test_factorize_series_obj(ncats, nelem):
 @pytest.mark.parametrize("ncats,nelem", [(2, 2), (2, 10), (10, 100)])
 def test_factorize_index_obj(ncats, nelem):
     df = DataFrame()
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # initialize data frame
-    df["cats"] = arr = np.random.randint(2, size=10, dtype=np.int32)
+    df["cats"] = arr = rng.integers(2, size=10, dtype=np.int32)
     df = df.set_index("cats")
 
     uvals, labels = df.index.factorize()
diff --git a/python/cudf/cudf/tests/test_feather.py b/python/cudf/cudf/tests/test_feather.py
index 7e5523bb8c7..f93bd2c5d32 100644
--- a/python/cudf/cudf/tests/test_feather.py
+++ b/python/cudf/cudf/tests/test_feather.py
@@ -15,13 +15,14 @@
 
 @pytest.fixture(params=[0, 1, 10, 100])
 def pdf(request):
+    rng = np.random.default_rng(seed=0)
     types = NUMERIC_TYPES + ["bool"]
     nrows = request.param
 
     # Create a pandas dataframe with random data of mixed types
     test_pdf = pd.DataFrame(
         {
-            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            f"col_{typ}": rng.integers(0, nrows, nrows).astype(typ)
             for typ in types
         }
     )
@@ -30,7 +31,7 @@ def pdf(request):
     test_pdf.index.name = "index"
 
     # Create non-numeric categorical data otherwise may get typecasted
-    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    data = [ascii_letters[rng.integers(0, 52)] for i in range(nrows)]
     test_pdf["col_category"] = pd.Series(data, dtype="category")
 
     # Feather can't handle indexes properly
diff --git a/python/cudf/cudf/tests/test_groupby.py b/python/cudf/cudf/tests/test_groupby.py
index 14ba9894fd3..75eeb72a3af 100644
--- a/python/cudf/cudf/tests/test_groupby.py
+++ b/python/cudf/cudf/tests/test_groupby.py
@@ -77,21 +77,21 @@ def make_frame(
     extra_vals=(),
     with_datetime=False,
 ):
-    np.random.seed(seed)
+    rng = np.random.default_rng(seed=seed)
 
     df = dataframe_class()
 
-    df["x"] = np.random.randint(0, 5, nelem)
-    df["y"] = np.random.randint(0, 3, nelem)
+    df["x"] = rng.integers(0, 5, nelem)
+    df["y"] = rng.integers(0, 3, nelem)
     for lvl in extra_levels:
-        df[lvl] = np.random.randint(0, 2, nelem)
+        df[lvl] = rng.integers(0, 2, nelem)
 
     df["val"] = np.random.random(nelem)
     for val in extra_vals:
         df[val] = np.random.random(nelem)
 
     if with_datetime:
-        df["datetime"] = np.random.randint(
+        df["datetime"] = rng.integers(
             _now, _tomorrow, nelem, dtype=np.int64
         ).astype("datetime64[ns]")
 
@@ -285,13 +285,16 @@ def test_groupby_cats():
 
 
 def test_groupby_iterate_groups():
-    np.random.seed(0)
-    df = DataFrame()
+    rng = np.random.default_rng(seed=0)
     nelem = 20
-    df["key1"] = np.random.randint(0, 3, nelem)
-    df["key2"] = np.random.randint(0, 2, nelem)
-    df["val1"] = np.random.random(nelem)
-    df["val2"] = np.random.random(nelem)
+    df = DataFrame(
+        {
+            "key1": rng.integers(0, 3, nelem),
+            "key2": rng.integers(0, 2, nelem),
+            "val1": np.random.random(nelem),
+            "val2": np.random.random(nelem),
+        }
+    )
 
     def assert_values_equal(arr):
         np.testing.assert_array_equal(arr[0], arr)
@@ -307,13 +310,16 @@ def assert_values_equal(arr):
     reason="Fails in older versions of pandas",
 )
 def test_groupby_apply():
-    np.random.seed(0)
-    df = DataFrame()
+    rng = np.random.default_rng(seed=0)
     nelem = 20
-    df["key1"] = np.random.randint(0, 3, nelem)
-    df["key2"] = np.random.randint(0, 2, nelem)
-    df["val1"] = np.random.random(nelem)
-    df["val2"] = np.random.random(nelem)
+    df = DataFrame(
+        {
+            "key1": rng.integers(0, 3, nelem),
+            "key2": rng.integers(0, 2, nelem),
+            "val1": np.random.random(nelem),
+            "val2": np.random.random(nelem),
+        }
+    )
 
     expect_grpby = df.to_pandas().groupby(
         ["key1", "key2"], as_index=False, group_keys=False
@@ -351,13 +357,16 @@ def f3(df, k, L, m):
     reason="Fails in older versions of pandas",
 )
 def test_groupby_apply_args(func, args):
-    np.random.seed(0)
-    df = DataFrame()
+    rng = np.random.default_rng(seed=0)
     nelem = 20
-    df["key1"] = np.random.randint(0, 3, nelem)
-    df["key2"] = np.random.randint(0, 2, nelem)
-    df["val1"] = np.random.random(nelem)
-    df["val2"] = np.random.random(nelem)
+    df = DataFrame(
+        {
+            "key1": rng.integers(0, 3, nelem),
+            "key2": rng.integers(0, 2, nelem),
+            "val1": np.random.random(nelem),
+            "val2": np.random.random(nelem),
+        }
+    )
 
     expect_grpby = df.to_pandas().groupby(
         ["key1", "key2"], as_index=False, group_keys=False
@@ -1315,7 +1324,7 @@ def test_empty_groupby(func):
 def test_groupby_unsupported_columns():
     np.random.seed(12)
     pd_cat = pd.Categorical(
-        pd.Series(np.random.choice(["a", "b", 1], 3), dtype="category")
+        pd.Series(rng.choice(["a", "b", 1], 3), dtype="category")
     )
     pdf = pd.DataFrame(
         {
@@ -1421,10 +1430,11 @@ def test_groupby_apply_basic_agg_single_column():
 
 
 def test_groupby_multi_agg_single_groupby_series():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=10000),
-            "y": np.random.normal(size=10000),
+            "x": rng.integers(0, 5, size=10000),
+            "y": rng.normal(size=10000),
         }
     )
     gdf = cudf.from_pandas(pdf)
@@ -1435,12 +1445,13 @@ def test_groupby_multi_agg_single_groupby_series():
 
 
 def test_groupby_multi_agg_multi_groupby():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "a": np.random.randint(0, 5, 10),
-            "b": np.random.randint(0, 5, 10),
-            "c": np.random.randint(0, 5, 10),
-            "d": np.random.randint(0, 5, 10),
+            "a": rng.integers(0, 5, 10),
+            "b": rng.integers(0, 5, 10),
+            "c": rng.integers(0, 5, 10),
+            "d": rng.integers(0, 5, 10),
         }
     )
     gdf = cudf.from_pandas(pdf)
@@ -1450,6 +1461,7 @@ def test_groupby_multi_agg_multi_groupby():
 
 
 def test_groupby_datetime_multi_agg_multi_groupby():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
             "a": pd.date_range(
@@ -1457,9 +1469,9 @@ def test_groupby_datetime_multi_agg_multi_groupby():
                 datetime.datetime.now() + datetime.timedelta(9),
                 freq="D",
             ),
-            "b": np.random.randint(0, 5, 10),
-            "c": np.random.randint(0, 5, 10),
-            "d": np.random.randint(0, 5, 10),
+            "b": rng.integers(0, 5, 10),
+            "c": rng.integers(0, 5, 10),
+            "d": rng.integers(0, 5, 10),
         }
     )
     gdf = cudf.from_pandas(pdf)
diff --git a/python/cudf/cudf/tests/test_index.py b/python/cudf/cudf/tests/test_index.py
index 3f483219423..40dbf7a1813 100644
--- a/python/cudf/cudf/tests/test_index.py
+++ b/python/cudf/cudf/tests/test_index.py
@@ -2645,8 +2645,10 @@ def test_isin_multiindex(data, values, level, err):
         )
 
 
+rng = np.random.default_rng(seed=0)
+
 range_data = [
-    range(np.random.randint(0, 100)),
+    range(rng.integers(0, 100)),
     range(9, 12, 2),
     range(20, 30),
     range(100, 1000, 10),
@@ -2831,7 +2833,7 @@ def test_rangeindex_append_return_rangeindex():
 
 
 index_data = [
-    range(np.random.randint(0, 100)),
+    range(rng.integers(0, 100)),
     range(0, 10, -2),
     range(0, -10, 2),
     range(0, -10, -2),
diff --git a/python/cudf/cudf/tests/test_indexing.py b/python/cudf/cudf/tests/test_indexing.py
index 00ae99466bb..305d679ba2f 100644
--- a/python/cudf/cudf/tests/test_indexing.py
+++ b/python/cudf/cudf/tests/test_indexing.py
@@ -212,12 +212,17 @@ def test_dataframe_column_name_indexing():
         df[1].to_numpy(), np.asarray(range(10), dtype=np.int32)
     )
 
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame()
     nelem = 10
-    pdf["key1"] = np.random.randint(0, 5, nelem)
-    pdf["key2"] = np.random.randint(0, 3, nelem)
-    pdf[1] = np.arange(1, 1 + nelem)
-    pdf[2] = np.random.random(nelem)
+    pdf = pd.DataFrame(
+        {
+            "key1": rng.integers(0, 5, nelem),
+            "key2": rng.integers(0, 3, nelem),
+            1: np.arange(1, 1 + nelem),
+            2: np.random.random(nelem),
+        }
+    )
     df = cudf.from_pandas(pdf)
 
     assert_eq(df[df.columns], df)
@@ -239,15 +244,12 @@ def test_dataframe_column_name_indexing():
 
 
 def test_dataframe_slicing():
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame()
     size = 123
-    df["a"] = ha = np.random.randint(low=0, high=100, size=size).astype(
-        np.int32
-    )
+    df["a"] = ha = rng.integers(low=0, high=100, size=size).astype(np.int32)
     df["b"] = hb = np.random.random(size).astype(np.float32)
-    df["c"] = hc = np.random.randint(low=0, high=100, size=size).astype(
-        np.int64
-    )
+    df["c"] = hc = rng.integers(low=0, high=100, size=size).astype(np.int64)
     df["d"] = hd = np.random.random(size).astype(np.float64)
 
     # Row slice first 10
@@ -287,9 +289,10 @@ def test_dataframe_slicing():
 @pytest.mark.parametrize("scalar", [0, 20, 100])
 def test_dataframe_loc(scalar, step):
     size = 123
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "a": np.random.randint(low=0, high=100, size=size),
+            "a": rng.integers(low=0, high=100, size=size),
             "b": np.random.random(size).astype(np.float32),
             "c": np.random.random(size).astype(np.float64),
             "d": np.random.random(size).astype(np.float64),
@@ -392,11 +395,10 @@ def test_dataframe_loc_mask(mask, arg):
 
 
 def test_dataframe_loc_outbound():
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame()
     size = 10
-    df["a"] = ha = np.random.randint(low=0, high=100, size=size).astype(
-        np.int32
-    )
+    df["a"] = ha = rng.integers(low=0, high=100, size=size).astype(np.int32)
     df["b"] = hb = np.random.random(size).astype(np.float32)
 
     pdf = pd.DataFrame()
@@ -625,11 +627,10 @@ def test_series_iloc(nelem):
 
 @pytest.mark.parametrize("nelem", [2, 5, 20, 100])
 def test_dataframe_iloc(nelem):
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame()
 
-    gdf["a"] = ha = np.random.randint(low=0, high=100, size=nelem).astype(
-        np.int32
-    )
+    gdf["a"] = ha = rng.integers(low=0, high=100, size=nelem).astype(np.int32)
     gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
 
     pdf = pd.DataFrame()
@@ -679,11 +680,10 @@ def test_dataframe_iloc(nelem):
 
 
 def test_dataframe_iloc_tuple():
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame()
     nelem = 123
-    gdf["a"] = ha = np.random.randint(low=0, high=100, size=nelem).astype(
-        np.int32
-    )
+    gdf["a"] = ha = rng.integers(low=0, high=100, size=nelem).astype(np.int32)
     gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
 
     pdf = pd.DataFrame()
@@ -695,11 +695,10 @@ def test_dataframe_iloc_tuple():
 
 
 def test_dataframe_iloc_index_error():
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame()
     nelem = 123
-    gdf["a"] = ha = np.random.randint(low=0, high=100, size=nelem).astype(
-        np.int32
-    )
+    gdf["a"] = ha = rng.integers(low=0, high=100, size=nelem).astype(np.int32)
     gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
 
     pdf = pd.DataFrame()
@@ -714,14 +713,16 @@ def test_dataframe_iloc_index_error():
 
 @pytest.mark.parametrize("ntake", [0, 1, 10, 123, 122, 200])
 def test_dataframe_take(ntake):
-    np.random.seed(0)
-    df = cudf.DataFrame()
-
+    rng = np.random.default_rng(seed=0)
     nelem = 123
-    df["ii"] = np.random.randint(0, 20, nelem)
-    df["ff"] = np.random.random(nelem)
+    df = cudf.DataFrame(
+        {
+            "ii": rng.integers(0, 20, nelem),
+            "ff": np.random.random(nelem),
+        }
+    )
 
-    take_indices = np.random.randint(0, len(df), ntake)
+    take_indices = rng.integers(0, len(df), ntake)
 
     actual = df.take(take_indices)
     expected = df.to_pandas().take(take_indices)
@@ -733,7 +734,7 @@ def test_dataframe_take(ntake):
 
 @pytest.mark.parametrize("ntake", [1, 2, 8, 9])
 def test_dataframe_take_with_multiindex(ntake):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame(
         index=cudf.MultiIndex(
             levels=[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
@@ -742,10 +743,10 @@ def test_dataframe_take_with_multiindex(ntake):
     )
 
     nelem = 9
-    df["ii"] = np.random.randint(0, 20, nelem)
+    df["ii"] = rng.integers(0, 20, nelem)
     df["ff"] = np.random.random(nelem)
 
-    take_indices = np.random.randint(0, len(df), ntake)
+    take_indices = rng.integers(0, len(df), ntake)
 
     actual = df.take(take_indices)
     expected = df.to_pandas().take(take_indices)
@@ -755,13 +756,13 @@ def test_dataframe_take_with_multiindex(ntake):
 
 @pytest.mark.parametrize("ntake", [0, 1, 10, 123, 122, 200])
 def test_series_take(ntake):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     nelem = 123
 
-    psr = pd.Series(np.random.randint(0, 20, nelem))
+    psr = pd.Series(rng.integers(0, 20, nelem))
     gsr = cudf.Series(psr)
 
-    take_indices = np.random.randint(0, len(gsr), ntake)
+    take_indices = rng.integers(0, len(gsr), ntake)
 
     actual = gsr.take(take_indices)
     expected = psr.take(take_indices)
@@ -841,14 +842,15 @@ def test_empty_boolean_mask(dtype):
 )
 @pytest.mark.parametrize("nulls", ["one", "some", "all", "none"])
 def test_series_apply_boolean_mask(data, mask, nulls):
+    rng = np.random.default_rng(seed=0)
     psr = pd.Series(data)
 
     if len(data) > 0:
         if nulls == "one":
-            p = np.random.randint(0, 4)
+            p = rng.integers(0, 4)
             psr[p] = None
         elif nulls == "some":
-            p1, p2 = np.random.randint(0, 4, (2,))
+            p1, p2 = rng.integers(0, 4, (2,))
             psr[p1] = None
             psr[p2] = None
         elif nulls == "all":
@@ -1816,7 +1818,7 @@ def test_loc_timestamp_issue_8585(index_type):
     )
     end = pd.Timestamp(datetime.strptime("2021-03-12 11:00", "%Y-%m-%d %H:%M"))
     timestamps = pd.date_range(start, end, periods=12)
-    value = np.random.normal(size=12)
+    value = rng.normal(size=12)
     df = pd.DataFrame(value, index=timestamps, columns=["value"])
     cdf = cudf.from_pandas(df)
     if index_type == "single":
@@ -1861,7 +1863,7 @@ def test_loc_multiindex_timestamp_issue_8585(index_type):
     index = pd.MultiIndex.from_product(
         [timestamps, labels], names=["timestamp", "label"]
     )
-    value = np.random.normal(size=12)
+    value = rng.normal(size=12)
     df = pd.DataFrame(value, index=index, columns=["value"])
     cdf = cudf.from_pandas(df)
     start = pd.Timestamp(
diff --git a/python/cudf/cudf/tests/test_joining.py b/python/cudf/cudf/tests/test_joining.py
index b1ce69e58ef..32a90b25791 100644
--- a/python/cudf/cudf/tests/test_joining.py
+++ b/python/cudf/cudf/tests/test_joining.py
@@ -22,7 +22,7 @@
 
 
 def make_params():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     hows = _JOIN_TYPES
 
@@ -39,8 +39,8 @@ def make_params():
         yield (aa, bb, how)
 
     # Test large random integer inputs
-    aa = np.random.randint(0, 50, 100)
-    bb = np.random.randint(0, 50, 100)
+    aa = rng.integers(0, 50, 100)
+    bb = rng.integers(0, 50, 100)
     for how in hows:
         yield (aa, bb, how)
 
@@ -164,7 +164,7 @@ def _check_series(expect, got):
 def test_dataframe_join_suffix():
     np.random.seed(0)
 
-    df = cudf.DataFrame(np.random.randint(0, 5, (5, 3)), columns=list("abc"))
+    df = cudf.DataFrame(rng.integers(0, 5, (5, 3)), columns=list("abc"))
 
     left = df.set_index("a")
     right = df.set_index("c")
@@ -286,14 +286,14 @@ def test_dataframe_merge_on(on):
     # Make cuDF
     df_left = cudf.DataFrame()
     nelem = 500
-    df_left["key1"] = np.random.randint(0, 40, nelem)
-    df_left["key2"] = np.random.randint(0, 50, nelem)
+    df_left["key1"] = rng.integers(0, 40, nelem)
+    df_left["key2"] = rng.integers(0, 50, nelem)
     df_left["left_val"] = np.arange(nelem)
 
     df_right = cudf.DataFrame()
     nelem = 500
-    df_right["key1"] = np.random.randint(0, 30, nelem)
-    df_right["key2"] = np.random.randint(0, 50, nelem)
+    df_right["key1"] = rng.integers(0, 30, nelem)
+    df_right["key2"] = rng.integers(0, 50, nelem)
     df_right["right_val"] = np.arange(nelem)
 
     # Make pandas DF
@@ -352,14 +352,14 @@ def test_dataframe_merge_on_unknown_column():
     # Make cuDF
     df_left = cudf.DataFrame()
     nelem = 500
-    df_left["key1"] = np.random.randint(0, 40, nelem)
-    df_left["key2"] = np.random.randint(0, 50, nelem)
+    df_left["key1"] = rng.integers(0, 40, nelem)
+    df_left["key2"] = rng.integers(0, 50, nelem)
     df_left["left_val"] = np.arange(nelem)
 
     df_right = cudf.DataFrame()
     nelem = 500
-    df_right["key1"] = np.random.randint(0, 30, nelem)
-    df_right["key2"] = np.random.randint(0, 50, nelem)
+    df_right["key1"] = rng.integers(0, 30, nelem)
+    df_right["key2"] = rng.integers(0, 50, nelem)
     df_right["right_val"] = np.arange(nelem)
 
     with pytest.raises(KeyError) as raises:
@@ -373,14 +373,14 @@ def test_dataframe_merge_no_common_column():
     # Make cuDF
     df_left = cudf.DataFrame()
     nelem = 500
-    df_left["key1"] = np.random.randint(0, 40, nelem)
-    df_left["key2"] = np.random.randint(0, 50, nelem)
+    df_left["key1"] = rng.integers(0, 40, nelem)
+    df_left["key2"] = rng.integers(0, 50, nelem)
     df_left["left_val"] = np.arange(nelem)
 
     df_right = cudf.DataFrame()
     nelem = 500
-    df_right["key3"] = np.random.randint(0, 30, nelem)
-    df_right["key4"] = np.random.randint(0, 50, nelem)
+    df_right["key3"] = rng.integers(0, 30, nelem)
+    df_right["key4"] = rng.integers(0, 50, nelem)
     df_right["right_val"] = np.arange(nelem)
 
     with pytest.raises(ValueError) as raises:
@@ -465,9 +465,9 @@ def test_dataframe_pairs_of_triples(pairs, max, rows, how):
     pdf_left = pd.DataFrame()
     pdf_right = pd.DataFrame()
     for left_column in pairs[0]:
-        pdf_left[left_column] = np.random.randint(0, max, rows)
+        pdf_left[left_column] = rng.integers(0, max, rows)
     for right_column in pairs[1]:
-        pdf_right[right_column] = np.random.randint(0, max, rows)
+        pdf_right[right_column] = rng.integers(0, max, rows)
     gdf_left = cudf.from_pandas(pdf_left)
     gdf_right = cudf.from_pandas(pdf_right)
     if not set(pdf_left.columns).intersection(pdf_right.columns):
@@ -510,9 +510,9 @@ def test_safe_merging_with_left_empty():
     pdf_left = pd.DataFrame()
     pdf_right = pd.DataFrame()
     for left_column in pairs[0]:
-        pdf_left[left_column] = np.random.randint(0, 10, 0)
+        pdf_left[left_column] = rng.integers(0, 10, 0)
     for right_column in pairs[1]:
-        pdf_right[right_column] = np.random.randint(0, 10, 5)
+        pdf_right[right_column] = rng.integers(0, 10, 5)
     gdf_left = cudf.from_pandas(pdf_left)
     gdf_right = cudf.from_pandas(pdf_right)
 
diff --git a/python/cudf/cudf/tests/test_json.py b/python/cudf/cudf/tests/test_json.py
index c81c2d1d94b..9a967c54202 100644
--- a/python/cudf/cudf/tests/test_json.py
+++ b/python/cudf/cudf/tests/test_json.py
@@ -38,7 +38,7 @@ def pdf(request):
     # Create a pandas dataframe with random data of mixed types
     test_pdf = pd.DataFrame(
         {
-            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            f"col_{typ}": rng.integers(0, nrows, nrows).astype(typ)
             for typ in types
         }
     )
diff --git a/python/cudf/cudf/tests/test_multiindex.py b/python/cudf/cudf/tests/test_multiindex.py
index c41be3e4428..65e898d5769 100644
--- a/python/cudf/cudf/tests/test_multiindex.py
+++ b/python/cudf/cudf/tests/test_multiindex.py
@@ -523,8 +523,8 @@ def test_multiindex_from_product(arrays):
 
 def test_multiindex_index_and_columns():
     gdf = cudf.DataFrame()
-    gdf["x"] = np.random.randint(0, 5, 5)
-    gdf["y"] = np.random.randint(0, 5, 5)
+    gdf["x"] = rng.integers(0, 5, 5)
+    gdf["y"] = rng.integers(0, 5, 5)
     pdf = gdf.to_pandas()
     mi = cudf.MultiIndex(
         levels=[[0, 1, 2], [3, 4]],
@@ -546,7 +546,7 @@ def test_multiindex_multiple_groupby():
         {
             "a": [4, 17, 4, 9, 5],
             "b": [1, 4, 4, 3, 2],
-            "x": np.random.normal(size=5),
+            "x": rng.normal(size=5),
         }
     )
     gdf = cudf.DataFrame.from_pandas(pdf)
@@ -568,9 +568,9 @@ def test_multiindex_multiple_groupby():
 def test_multi_column(func):
     pdf = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=1000),
-            "y": np.random.randint(0, 10, size=1000),
-            "z": np.random.normal(size=1000),
+            "x": rng.integers(0, 5, size=1000),
+            "y": rng.integers(0, 10, size=1000),
+            "z": rng.normal(size=1000),
         }
     )
     gdf = cudf.DataFrame.from_pandas(pdf)
diff --git a/python/cudf/cudf/tests/test_orc.py b/python/cudf/cudf/tests/test_orc.py
index 1dd732c7191..e7ca0c67148 100644
--- a/python/cudf/cudf/tests/test_orc.py
+++ b/python/cudf/cudf/tests/test_orc.py
@@ -999,12 +999,12 @@ def generate_list_struct_buff(size=100_000):
                 [
                     [
                         [
-                            rd.choice([None, np.random.randint(1, 3)])
-                            for _ in range(np.random.randint(1, 3))
+                            rd.choice([None, rng.integers(1, 3)])
+                            for _ in range(rng.integers(1, 3))
                         ]
-                        for _ in range(np.random.randint(0, 3))
+                        for _ in range(rng.integers(0, 3))
                     ]
-                    for _ in range(np.random.randint(0, 3))
+                    for _ in range(rng.integers(0, 3))
                 ],
             ]
         )
@@ -1012,8 +1012,8 @@ def generate_list_struct_buff(size=100_000):
     ]
     lvl1_list = [
         [
-            rd.choice([None, np.random.randint(0, 3)])
-            for _ in range(np.random.randint(1, 4))
+            rd.choice([None, rng.integers(0, 3)])
+            for _ in range(rng.integers(1, 4))
         ]
         for _ in range(size)
     ]
@@ -1021,7 +1021,7 @@ def generate_list_struct_buff(size=100_000):
         rd.choice(
             [
                 None,
-                {"a": np.random.randint(0, 3), "b": np.random.randint(0, 3)},
+                {"a": rng.integers(0, 3), "b": rng.integers(0, 3)},
             ]
         )
         for _ in range(size)
@@ -1030,11 +1030,11 @@ def generate_list_struct_buff(size=100_000):
         rd.choice(
             [
                 None,
-                {"a": rd.choice([None, np.random.randint(0, 3)])},
+                {"a": rd.choice([None, rng.integers(0, 3)])},
                 {
                     "lvl1_struct": {
-                        "c": rd.choice([None, np.random.randint(0, 3)]),
-                        "d": np.random.randint(0, 3),
+                        "c": rd.choice([None, rng.integers(0, 3)]),
+                        "d": rng.integers(0, 3),
                     },
                 },
             ]
@@ -1044,7 +1044,7 @@ def generate_list_struct_buff(size=100_000):
     list_nests_struct = [
         [
             {"a": rd.choice(lvl1_struct), "b": rd.choice(lvl1_struct)}
-            for _ in range(np.random.randint(1, 4))
+            for _ in range(rng.integers(1, 4))
         ]
         for _ in range(size)
     ]
@@ -1146,7 +1146,7 @@ def gen_map_buff(size):
                     None,
                     {
                         rd.choice(al): rd.choice(
-                            [None, np.random.randint(1, 1500)]
+                            [None, rng.integers(1, 1500)]
                         ),
                     },
                 ]
@@ -1167,7 +1167,7 @@ def gen_map_buff(size):
                                     None,
                                     [
                                         rd.choice(
-                                            [None, np.random.randint(1, 1500)]
+                                            [None, rng.integers(1, 1500)]
                                         )
                                         for _ in range(5)
                                     ],
@@ -1194,10 +1194,10 @@ def gen_map_buff(size):
                                     None,
                                     {
                                         "a": rd.choice(
-                                            [None, np.random.randint(1, 1500)]
+                                            [None, rng.integers(1, 1500)]
                                         ),
                                         "b": rd.choice(
-                                            [None, np.random.randint(1, 1500)]
+                                            [None, rng.integers(1, 1500)]
                                         ),
                                     },
                                 ]
diff --git a/python/cudf/cudf/tests/test_parquet.py b/python/cudf/cudf/tests/test_parquet.py
index 7f1b0b1cd46..55c20ef4f8d 100644
--- a/python/cudf/cudf/tests/test_parquet.py
+++ b/python/cudf/cudf/tests/test_parquet.py
@@ -72,7 +72,7 @@ def simple_pdf(request):
     # Create a pandas dataframe with random data of mixed types
     test_pdf = pd.DataFrame(
         {
-            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            f"col_{typ}": rng.integers(0, nrows, nrows).astype(typ)
             for typ in types
         },
         # Need to ensure that this index is not a RangeIndex to get the
@@ -114,7 +114,7 @@ def build_pdf(num_columns, day_resolution_timestamps):
     # Create a pandas dataframe with random data of mixed types
     test_pdf = pd.DataFrame(
         {
-            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            f"col_{typ}": rng.integers(0, nrows, nrows).astype(typ)
             for typ in types
         },
         # Need to ensure that this index is not a RangeIndex to get the
@@ -142,7 +142,7 @@ def build_pdf(num_columns, day_resolution_timestamps):
         },
     ]:
         data = [
-            np.random.randint(0, (0x7FFFFFFFFFFFFFFF / t["nsDivisor"]))
+            rng.integers(0, (0x7FFFFFFFFFFFFFFF / t["nsDivisor"]))
             for i in range(nrows)
         ]
         if day_resolution_timestamps:
@@ -152,11 +152,11 @@ def build_pdf(num_columns, day_resolution_timestamps):
         )
 
     # Create non-numeric categorical data otherwise parquet may typecast it
-    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    data = [ascii_letters[rng.integers(0, 52)] for i in range(nrows)]
     test_pdf["col_category"] = pd.Series(data, dtype="category")
 
     # Create non-numeric str data
-    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    data = [ascii_letters[rng.integers(0, 52)] for i in range(nrows)]
     test_pdf["col_str"] = pd.Series(data, dtype="str")
 
     return test_pdf
@@ -1917,8 +1917,8 @@ def test_parquet_partitioned(tmpdir_factory, cols, filename):
     pdf = pd.DataFrame(
         {
             "a": np.arange(0, stop=size, dtype="int64"),
-            "b": np.random.choice(list("abcd"), size=size),
-            "c": np.random.choice(np.arange(4), size=size),
+            "b": rng.choice(list("abcd"), size=size),
+            "c": rng.choice(np.arange(4), size=size),
         }
     )
     pdf.to_parquet(pdf_dir, index=False, partition_cols=cols)
@@ -1961,8 +1961,8 @@ def test_parquet_partitioned_notimplemented(tmpdir_factory, kwargs):
     pdf = pd.DataFrame(
         {
             "a": np.arange(0, stop=size, dtype="int64"),
-            "b": np.random.choice(list("abcd"), size=size),
-            "c": np.random.choice(np.arange(4), size=size),
+            "b": rng.choice(list("abcd"), size=size),
+            "c": rng.choice(np.arange(4), size=size),
         }
     )
     pdf.to_parquet(pdf_dir, index=False, partition_cols=["b"])
@@ -2139,7 +2139,7 @@ def test_parquet_write_to_dataset(tmpdir_factory, cols, store_schema):
     gdf = cudf.DataFrame(
         {
             "a": np.arange(0, stop=size),
-            "b": np.random.choice(np.arange(4), size=size),
+            "b": rng.choice(np.arange(4), size=size),
         }
     )
     gdf.to_parquet(dir1, partition_cols=cols, store_schema=store_schema)
@@ -3217,8 +3217,8 @@ def test_parquet_writer_zstd():
     expected = cudf.DataFrame(
         {
             "a": np.arange(0, stop=size, dtype="float64"),
-            "b": np.random.choice(list("abcd"), size=size),
-            "c": np.random.choice(np.arange(4), size=size),
+            "b": rng.choice(list("abcd"), size=size),
+            "c": rng.choice(np.arange(4), size=size),
         }
     )
 
diff --git a/python/cudf/cudf/tests/test_query.py b/python/cudf/cudf/tests/test_query.py
index b12209fd3b9..84f735bf5b7 100644
--- a/python/cudf/cudf/tests/test_query.py
+++ b/python/cudf/cudf/tests/test_query.py
@@ -123,7 +123,7 @@ def test_query_local_dict():
 def test_query_splitted_combine():
     np.random.seed(0)
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=10), "y": np.random.normal(size=10)}
+        {"x": rng.integers(0, 5, size=10), "y": rng.normal(size=10)}
     )
     gdf = DataFrame.from_pandas(df)
 
diff --git a/python/cudf/cudf/tests/test_reductions.py b/python/cudf/cudf/tests/test_reductions.py
index f276f394cd0..2cf6a9517a7 100644
--- a/python/cudf/cudf/tests/test_reductions.py
+++ b/python/cudf/cudf/tests/test_reductions.py
@@ -79,7 +79,7 @@ def test_product(dtype, nelem):
         data = np.ones(nelem, dtype=dtype)
         # Set at most 30 items to [0..2) to keep the value within 2^32
         for _ in range(30):
-            data[np.random.randint(low=0, high=nelem, size=1)] = (
+            data[rng.integers(low=0, high=nelem, size=1)] = (
                 np.random.uniform() * 2
             )
     else:
@@ -256,7 +256,7 @@ def test_sum_boolean():
 
 
 def test_date_minmax():
-    np_data = np.random.normal(size=10**3)
+    np_data = rng.normal(size=10**3)
     gdf_data = Series(np_data)
 
     np_casted = np_data.astype("datetime64[ms]")
diff --git a/python/cudf/cudf/tests/test_repr.py b/python/cudf/cudf/tests/test_repr.py
index 95e19fae501..a892fe62b8b 100644
--- a/python/cudf/cudf/tests/test_repr.py
+++ b/python/cudf/cudf/tests/test_repr.py
@@ -26,8 +26,8 @@
 @pytest.mark.parametrize("nrows", [0, 5, 10])
 def test_null_series(nrows, dtype):
     size = 5
-    sr = cudf.Series(np.random.randint(1, 9, size)).astype(dtype)
-    sr[np.random.choice([False, True], size=size)] = None
+    sr = cudf.Series(rng.integers(1, 9, size)).astype(dtype)
+    sr[rng.choice([False, True], size=size)] = None
     if dtype != "category" and cudf.dtype(dtype).kind in {"u", "i"}:
         ps = pd.Series(
             sr._column.data_array_view(mode="read").copy_to_host(),
@@ -63,8 +63,8 @@ def test_null_dataframe(ncols):
     size = 20
     gdf = cudf.DataFrame()
     for idx, dtype in enumerate(dtype_categories):
-        sr = cudf.Series(np.random.randint(0, 128, size)).astype(dtype)
-        sr[np.random.choice([False, True], size=size)] = None
+        sr = cudf.Series(rng.integers(0, 128, size)).astype(dtype)
+        sr[rng.choice([False, True], size=size)] = None
         gdf[dtype] = sr
     pdf = gdf.to_pandas()
     pd.options.display.max_columns = int(ncols)
@@ -77,7 +77,7 @@ def test_null_dataframe(ncols):
 @pytest.mark.parametrize("nrows", [None, 0, 1, 2, 9, 10, 11, 19, 20, 21])
 def test_full_series(nrows, dtype):
     size = 20
-    ps = pd.Series(np.random.randint(0, 100, size)).astype(dtype)
+    ps = pd.Series(rng.integers(0, 100, size)).astype(dtype)
     sr = cudf.from_pandas(ps)
     pd.options.display.max_rows = nrows
     assert repr(ps) == repr(sr)
@@ -90,7 +90,7 @@ def test_full_series(nrows, dtype):
 @pytest.mark.parametrize("dtype", repr_categories)
 def test_full_dataframe_20(dtype, size, nrows, ncols):
     pdf = pd.DataFrame(
-        {idx: np.random.randint(0, 100, size) for idx in range(size)}
+        {idx: rng.integers(0, 100, size) for idx in range(size)}
     ).astype(dtype)
     gdf = cudf.from_pandas(pdf)
 
@@ -180,9 +180,9 @@ def test_mixed_series(mixed_pdf, mixed_gdf):
 def test_MI():
     gdf = cudf.DataFrame(
         {
-            "a": np.random.randint(0, 4, 10),
-            "b": np.random.randint(0, 4, 10),
-            "c": np.random.randint(0, 4, 10),
+            "a": rng.integers(0, 4, 10),
+            "b": rng.integers(0, 4, 10),
+            "c": rng.integers(0, 4, 10),
         }
     )
     levels = [["a", "b", "c", "d"], ["w", "x", "y", "z"], ["m", "n"]]
@@ -225,7 +225,7 @@ def test_groupby_MI(nrows, ncols):
 def test_generic_index(length, dtype):
     psr = pd.Series(
         range(length),
-        index=np.random.randint(0, high=100, size=length).astype(dtype),
+        index=rng.integers(0, high=100, size=length).astype(dtype),
         dtype="float64" if length == 0 else None,
     )
     gsr = cudf.Series.from_pandas(psr)
diff --git a/python/cudf/cudf/tests/test_resampling.py b/python/cudf/cudf/tests/test_resampling.py
index a61477981f8..8a366ed9cd2 100644
--- a/python/cudf/cudf/tests/test_resampling.py
+++ b/python/cudf/cudf/tests/test_resampling.py
@@ -51,7 +51,7 @@ def test_series_upsample_simple():
 @pytest.mark.parametrize("rule", ["2s", "10s"])
 def test_series_resample_ffill(rule):
     rng = pd.date_range("1/1/2012", periods=10, freq="5s")
-    ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
+    ts = pd.Series(rng.integers(0, 500, len(rng)), index=rng)
     gts = cudf.from_pandas(ts)
     assert_resample_results_equal(
         ts.resample(rule).ffill(), gts.resample(rule).ffill()
@@ -61,7 +61,7 @@ def test_series_resample_ffill(rule):
 @pytest.mark.parametrize("rule", ["2s", "10s"])
 def test_series_resample_bfill(rule):
     rng = pd.date_range("1/1/2012", periods=10, freq="5s")
-    ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
+    ts = pd.Series(rng.integers(0, 500, len(rng)), index=rng)
     gts = cudf.from_pandas(ts)
     assert_resample_results_equal(
         ts.resample(rule).bfill(), gts.resample(rule).bfill()
@@ -71,7 +71,7 @@ def test_series_resample_bfill(rule):
 @pytest.mark.parametrize("rule", ["2s", "10s"])
 def test_series_resample_asfreq(rule):
     rng = pd.date_range("1/1/2012", periods=100, freq="5s")
-    ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
+    ts = pd.Series(rng.integers(0, 500, len(rng)), index=rng)
     gts = cudf.from_pandas(ts)
     assert_resample_results_equal(
         ts.resample(rule).asfreq(), gts.resample(rule).asfreq()
diff --git a/python/cudf/cudf/tests/test_reshape.py b/python/cudf/cudf/tests/test_reshape.py
index 4235affd4d1..779f23bb5ab 100644
--- a/python/cudf/cudf/tests/test_reshape.py
+++ b/python/cudf/cudf/tests/test_reshape.py
@@ -48,11 +48,9 @@ def test_melt(nulls, num_id_vars, num_value_vars, num_rows, dtype):
     id_vars = []
     for i in range(num_id_vars):
         colname = "id" + str(i)
-        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        data = rng.integers(0, 26, num_rows).astype(dtype)
         if nulls == "some":
-            idx = np.random.choice(
-                num_rows, size=int(num_rows / 2), replace=False
-            )
+            idx = rng.choice(num_rows, size=int(num_rows / 2), replace=False)
             data[idx] = np.nan
         elif nulls == "all":
             data[:] = np.nan
@@ -62,11 +60,9 @@ def test_melt(nulls, num_id_vars, num_value_vars, num_rows, dtype):
     value_vars = []
     for i in range(num_value_vars):
         colname = "val" + str(i)
-        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        data = rng.integers(0, 26, num_rows).astype(dtype)
         if nulls == "some":
-            idx = np.random.choice(
-                num_rows, size=int(num_rows / 2), replace=False
-            )
+            idx = rng.choice(num_rows, size=int(num_rows / 2), replace=False)
             data[idx] = np.nan
         elif nulls == "all":
             data[:] = np.nan
@@ -132,11 +128,9 @@ def test_df_stack(nulls, num_cols, num_rows, dtype):
     pdf = pd.DataFrame()
     for i in range(num_cols):
         colname = str(i)
-        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        data = rng.integers(0, 26, num_rows).astype(dtype)
         if nulls == "some":
-            idx = np.random.choice(
-                num_rows, size=int(num_rows / 2), replace=False
-            )
+            idx = rng.choice(num_rows, size=int(num_rows / 2), replace=False)
             data[idx] = np.nan
         pdf[colname] = data
 
@@ -301,12 +295,10 @@ def test_interleave_columns(nulls, num_cols, num_rows, dtype):
     pdf = pd.DataFrame(dtype=dtype)
     for i in range(num_cols):
         colname = str(i)
-        data = pd.Series(np.random.randint(0, 26, num_rows)).astype(dtype)
+        data = pd.Series(rng.integers(0, 26, num_rows)).astype(dtype)
 
         if nulls == "some":
-            idx = np.random.choice(
-                num_rows, size=int(num_rows / 2), replace=False
-            )
+            idx = rng.choice(num_rows, size=int(num_rows / 2), replace=False)
             data[idx] = np.nan
         pdf[colname] = data
 
@@ -337,14 +329,10 @@ def test_tile(nulls, num_cols, num_rows, dtype, count):
     pdf = pd.DataFrame(dtype=dtype)
     for i in range(num_cols):
         colname = str(i)
-        data = pd.Series(np.random.randint(num_cols, 26, num_rows)).astype(
-            dtype
-        )
+        data = pd.Series(rng.integers(num_cols, 26, num_rows)).astype(dtype)
 
         if nulls == "some":
-            idx = np.random.choice(
-                num_rows, size=int(num_rows / 2), replace=False
-            )
+            idx = rng.choice(num_rows, size=int(num_rows / 2), replace=False)
             data[idx] = np.nan
         pdf[colname] = data
 
diff --git a/python/cudf/cudf/tests/test_serialize.py b/python/cudf/cudf/tests/test_serialize.py
index 0b892a51895..542007ac34e 100644
--- a/python/cudf/cudf/tests/test_serialize.py
+++ b/python/cudf/cudf/tests/test_serialize.py
@@ -204,7 +204,7 @@ def test_serialize_multi_index():
         {
             "a": [4, 17, 4, 9, 5],
             "b": [1, 4, 4, 3, 2],
-            "x": np.random.normal(size=5),
+            "x": rng.normal(size=5),
         }
     )
     gdf = cudf.DataFrame.from_pandas(pdf)
@@ -230,8 +230,8 @@ def test_serialize_masked_series():
 
 def test_serialize_groupby_df():
     df = cudf.DataFrame()
-    df["key_1"] = np.random.randint(0, 20, 100)
-    df["key_2"] = np.random.randint(0, 20, 100)
+    df["key_1"] = rng.integers(0, 20, 100)
+    df["key_2"] = rng.integers(0, 20, 100)
     df["val"] = np.arange(100, dtype=np.float32)
     gb = df.groupby(["key_1", "key_2"], sort=True)
     outgb = gb.deserialize(*gb.serialize())
@@ -243,7 +243,7 @@ def test_serialize_groupby_df():
 def test_serialize_groupby_external():
     df = cudf.DataFrame()
     df["val"] = np.arange(100, dtype=np.float32)
-    gb = df.groupby(cudf.Series(np.random.randint(0, 20, 100)))
+    gb = df.groupby(cudf.Series(rng.integers(0, 20, 100)))
     outgb = gb.deserialize(*gb.serialize())
     expect = gb.mean()
     got = outgb.mean()
@@ -262,7 +262,7 @@ def test_serialize_groupby_level():
 
 
 def test_serialize_groupby_sr():
-    sr = cudf.Series(np.random.randint(0, 20, 100))
+    sr = cudf.Series(rng.integers(0, 20, 100))
     gb = sr.groupby(sr // 2)
     outgb = gb.deserialize(*gb.serialize())
     got = gb.mean()
@@ -273,7 +273,7 @@ def test_serialize_groupby_sr():
 def test_serialize_datetime():
     # Make frame with datetime column
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+        {"x": rng.integers(0, 5, size=20), "y": rng.normal(size=20)}
     )
     ts = np.arange(0, len(df), dtype=np.dtype("datetime64[ms]"))
     df["timestamp"] = ts
@@ -287,7 +287,7 @@ def test_serialize_datetime():
 def test_serialize_string():
     # Make frame with string column
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=5), "y": np.random.normal(size=5)}
+        {"x": rng.integers(0, 5, size=5), "y": rng.normal(size=5)}
     )
     str_data = ["a", "bc", "def", "ghij", "klmno"]
     df["timestamp"] = str_data
diff --git a/python/cudf/cudf/tests/test_series.py b/python/cudf/cudf/tests/test_series.py
index a24002dc38e..9fb492c4e25 100644
--- a/python/cudf/cudf/tests/test_series.py
+++ b/python/cudf/cudf/tests/test_series.py
@@ -522,10 +522,10 @@ def test_series_datetime_value_counts(data, nulls, normalize, dropna):
 
     if len(data) > 0:
         if nulls == "one":
-            p = np.random.randint(0, len(data))
+            p = rng.integers(0, len(data))
             psr[p] = None
         elif nulls == "some":
-            p = np.random.randint(0, len(data), 2)
+            p = rng.integers(0, len(data), 2)
             psr[p] = None
 
     gsr = cudf.from_pandas(psr)
@@ -549,7 +549,7 @@ def test_categorical_value_counts(dropna, normalize, num_elements):
     np.random.seed(12)
     pd_cat = pd.Categorical(
         pd.Series(
-            np.random.choice(list(ascii_letters + digits), num_elements),
+            rng.choice(list(ascii_letters + digits), num_elements),
             dtype="category",
         )
     )
@@ -587,7 +587,7 @@ def test_categorical_value_counts(dropna, normalize, num_elements):
 @pytest.mark.parametrize("normalize", [True, False])
 def test_series_value_counts(dropna, normalize):
     for size in [10**x for x in range(5)]:
-        arr = np.random.randint(low=-1, high=10, size=size)
+        arr = rng.integers(low=-1, high=10, size=size)
         mask = arr != -1
         sr = cudf.Series.from_masked_array(
             arr, cudf.Series(mask)._column.as_mask()
@@ -714,8 +714,8 @@ def test_series_mode(gs, dropna):
 @pytest.mark.parametrize(
     "arr",
     [
-        np.random.normal(-100, 100, 1000),
-        np.random.randint(-50, 50, 1000),
+        rng.normal(-100, 100, 1000),
+        rng.integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat([-0.6459412758761901], 100),
         np.repeat(np.nan, 100),
@@ -734,9 +734,9 @@ def test_series_round(arr, decimals):
 
     # with nulls, maintaining existing null mask
     arr = arr.astype("float64")  # for pandas nulls
-    arr.ravel()[
-        np.random.choice(arr.shape[0], arr.shape[0] // 2, replace=False)
-    ] = np.nan
+    arr.ravel()[rng.choice(arr.shape[0], arr.shape[0] // 2, replace=False)] = (
+        np.nan
+    )
 
     pser = pd.Series(arr)
     ser = cudf.Series(arr)
@@ -1726,7 +1726,7 @@ def test_series_truncate_datetimeindex():
         [],
         [0, 12, 14],
         [0, 14, 12, 12, 3, 10, 12, 14],
-        np.random.randint(-100, 100, 200),
+        rng.integers(-100, 100, 200),
         pd.Series([0.0, 1.0, None, 10.0]),
         [None, None, None, None],
         [np.nan, None, -1, 2, 3],
@@ -1735,7 +1735,7 @@ def test_series_truncate_datetimeindex():
 @pytest.mark.parametrize(
     "values",
     [
-        np.random.randint(-100, 100, 10),
+        rng.integers(-100, 100, 10),
         [],
         [np.nan, None, -1, 2, 3],
         [1.0, 12.0, None, None, 120],
@@ -1746,7 +1746,7 @@ def test_series_truncate_datetimeindex():
     ],
 )
 def test_isin_numeric(data, values):
-    index = np.random.randint(0, 100, len(data))
+    index = rng.integers(0, 100, len(data))
     psr = pd.Series(data, index=index)
     gsr = cudf.Series.from_pandas(psr, nan_as_null=False)
 
@@ -1943,8 +1943,8 @@ def test_diff_many_dtypes(data):
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["bool"])
 @pytest.mark.parametrize("series_bins", [True, False])
 def test_series_digitize(num_rows, num_bins, right, dtype, series_bins):
-    data = np.random.randint(0, 100, num_rows).astype(dtype)
-    bins = np.unique(np.sort(np.random.randint(2, 95, num_bins).astype(dtype)))
+    data = rng.integers(0, 100, num_rows).astype(dtype)
+    bins = np.unique(np.sort(rng.integers(2, 95, num_bins).astype(dtype)))
     s = cudf.Series(data)
     if series_bins:
         s_bins = cudf.Series(bins)
@@ -1957,7 +1957,7 @@ def test_series_digitize(num_rows, num_bins, right, dtype, series_bins):
 
 
 def test_series_digitize_invalid_bins():
-    s = cudf.Series(np.random.randint(0, 30, 80), dtype="int32")
+    s = cudf.Series(rng.integers(0, 30, 80), dtype="int32")
     bins = cudf.Series([2, None, None, 50, 90], dtype="int32")
 
     with pytest.raises(
@@ -2038,7 +2038,7 @@ def test_default_float_bitwidth_construction(default_float_bitwidth, data):
 
 def test_series_ordered_dedup():
     # part of https://github.com/rapidsai/cudf/issues/11486
-    sr = cudf.Series(np.random.randint(0, 100, 1000))
+    sr = cudf.Series(rng.integers(0, 100, 1000))
     # pandas unique() preserves order
     expect = pd.Series(sr.to_pandas().unique())
     got = cudf.Series._from_column(sr._column.unique())
diff --git a/python/cudf/cudf/tests/test_sorting.py b/python/cudf/cudf/tests/test_sorting.py
index 2cf2259d9ec..09b83ff37b7 100644
--- a/python/cudf/cudf/tests/test_sorting.py
+++ b/python/cudf/cudf/tests/test_sorting.py
@@ -222,7 +222,7 @@ def test_dataframe_multi_column(
 
     for i in range(5):
         colname = string.ascii_lowercase[i]
-        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        data = rng.integers(0, 26, num_rows).astype(dtype)
         pdf[colname] = data
 
     gdf = DataFrame.from_pandas(pdf)
@@ -250,11 +250,11 @@ def test_dataframe_multi_column_nulls(
 
     for i in range(3):
         colname = string.ascii_lowercase[i]
-        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        data = rng.integers(0, 26, num_rows).astype(dtype)
         if nulls == "some":
             idx = np.array([], dtype="int64")
             if num_rows > 0:
-                idx = np.random.choice(
+                idx = rng.choice(
                     num_rows, size=int(num_rows / 4), replace=False
                 )
             data[idx] = np.nan
@@ -310,9 +310,9 @@ def test_dataframe_scatter_by_map(map_size, nelem, keep):
     strlist = ["dog", "cat", "fish", "bird", "pig", "fox", "cow", "goat"]
     np.random.seed(0)
     df = DataFrame()
-    df["a"] = np.random.choice(strlist[:map_size], nelem)
+    df["a"] = rng.choice(strlist[:map_size], nelem)
     df["b"] = np.random.uniform(low=0, high=map_size, size=nelem)
-    df["c"] = np.random.randint(map_size, size=nelem)
+    df["c"] = rng.integers(map_size, size=nelem)
     df["d"] = df["a"].astype("category")
 
     def _check_scatter_by_map(dfs, col):
diff --git a/python/cudf/cudf/tests/test_stats.py b/python/cudf/cudf/tests/test_stats.py
index f952cea07f8..5a01d81b012 100644
--- a/python/cudf/cudf/tests/test_stats.py
+++ b/python/cudf/cudf/tests/test_stats.py
@@ -85,7 +85,7 @@ def test_series_std(ddof):
 
 def test_series_unique():
     for size in [10**x for x in range(5)]:
-        arr = np.random.randint(low=-1, high=10, size=size)
+        arr = rng.integers(low=-1, high=10, size=size)
         mask = arr != -1
         sr = cudf.Series(arr)
         sr[~mask] = None
@@ -129,7 +129,7 @@ def test_series_nunique(nan_as_null, dropna):
 
 
 def test_series_scale():
-    arr = pd.Series(np.random.randint(low=-10, high=10, size=100))
+    arr = pd.Series(rng.integers(low=-10, high=10, size=100))
     sr = cudf.Series(arr)
 
     vmin = arr.min()
@@ -229,8 +229,8 @@ def test_misc_quantiles(data, q):
 @pytest.mark.parametrize(
     "data",
     [
-        {"data": np.random.normal(-100, 100, 1000)},
-        {"data": np.random.randint(-50, 50, 1000)},
+        {"data": rng.normal(-100, 100, 1000)},
+        {"data": rng.integers(-50, 50, 1000)},
         {"data": (np.zeros(100))},
         {"data": np.repeat(np.nan, 100)},
         {"data": np.array([1.123, 2.343, np.nan, 0.0])},
@@ -280,8 +280,8 @@ def test_kurt_skew_error(op):
 @pytest.mark.parametrize(
     "data",
     [
-        cudf.Series(np.random.normal(-100, 100, 1000)),
-        cudf.Series(np.random.randint(-50, 50, 1000)),
+        cudf.Series(rng.normal(-100, 100, 1000)),
+        cudf.Series(rng.integers(-50, 50, 1000)),
         cudf.Series(np.zeros(100)),
         cudf.Series(np.repeat(np.nan, 100)),
         cudf.Series(np.array([1.123, 2.343, np.nan, 0.0])),
@@ -344,8 +344,8 @@ def test_series_median(dtype, num_na):
 @pytest.mark.parametrize(
     "data",
     [
-        np.random.normal(-100, 100, 1000),
-        np.random.randint(-50, 50, 1000),
+        rng.normal(-100, 100, 1000),
+        rng.integers(-50, 50, 1000),
         np.zeros(100),
         np.array([1.123, 2.343, np.nan, 0.0]),
         np.array([-2, 3.75, 6, None, None, None, -8.5, None, 4.2]),
@@ -379,8 +379,8 @@ def test_series_pct_change(data, periods, fill_method):
 @pytest.mark.parametrize(
     "data1",
     [
-        np.random.normal(-100, 100, 1000),
-        np.random.randint(-50, 50, 1000),
+        rng.normal(-100, 100, 1000),
+        rng.integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
@@ -393,8 +393,8 @@ def test_series_pct_change(data, periods, fill_method):
 @pytest.mark.parametrize(
     "data2",
     [
-        np.random.normal(-100, 100, 1000),
-        np.random.randint(-50, 50, 1000),
+        rng.normal(-100, 100, 1000),
+        rng.integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
@@ -423,8 +423,8 @@ def test_cov1d(data1, data2):
 @pytest.mark.parametrize(
     "data1",
     [
-        np.random.normal(-100, 100, 1000),
-        np.random.randint(-50, 50, 1000),
+        rng.normal(-100, 100, 1000),
+        rng.integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
@@ -437,8 +437,8 @@ def test_cov1d(data1, data2):
 @pytest.mark.parametrize(
     "data2",
     [
-        np.random.normal(-100, 100, 1000),
-        np.random.randint(-50, 50, 1000),
+        rng.normal(-100, 100, 1000),
+        rng.integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
diff --git a/python/cudf/cudf/tests/test_string.py b/python/cudf/cudf/tests/test_string.py
index cc88cc79769..b806b9258a2 100644
--- a/python/cudf/cudf/tests/test_string.py
+++ b/python/cudf/cudf/tests/test_string.py
@@ -132,9 +132,9 @@ def test_string_get_item(ps_gs, item):
         np.array([False] * 5),
         cupy.asarray(np.array([True] * 5)),
         cupy.asarray(np.array([False] * 5)),
-        np.random.randint(0, 2, 5).astype("bool").tolist(),
-        np.random.randint(0, 2, 5).astype("bool"),
-        cupy.asarray(np.random.randint(0, 2, 5).astype("bool")),
+        rng.integers(0, 2, 5).astype("bool").tolist(),
+        rng.integers(0, 2, 5).astype("bool"),
+        cupy.asarray(rng.integers(0, 2, 5).astype("bool")),
     ],
 )
 def test_string_bool_mask(ps_gs, item):
diff --git a/python/cudf/cudf/tests/test_unique.py b/python/cudf/cudf/tests/test_unique.py
index 699b3340521..6a1366770b3 100644
--- a/python/cudf/cudf/tests/test_unique.py
+++ b/python/cudf/cudf/tests/test_unique.py
@@ -14,7 +14,7 @@ def df():
     df = cudf.DataFrame()
     np.random.seed(0)
 
-    arr = np.random.randint(2, size=10, dtype=np.int64)
+    arr = rng.integers(2, size=10, dtype=np.int64)
     df["foo"] = arr
     df["bar"] = cudf.Series([pd.Timestamp(x) for x in arr])
 
diff --git a/python/dask_cudf/dask_cudf/tests/test_join.py b/python/dask_cudf/dask_cudf/tests/test_join.py
index 3e078c47cdd..43fadbecd0a 100644
--- a/python/dask_cudf/dask_cudf/tests/test_join.py
+++ b/python/dask_cudf/dask_cudf/tests/test_join.py
@@ -257,6 +257,7 @@ def test_merge_should_fail():
         left.merge(right, how="left", on=["c"])
 
     # Same column names
+
     df2["b"] = np.random.randint(0, 12, 12)
     right = dask_cudf.from_cudf(df2, 1).groupby("a").b.min().to_frame()
 
diff --git a/python/dask_cudf/dask_cudf/tests/test_reductions.py b/python/dask_cudf/dask_cudf/tests/test_reductions.py
index d03e92319be..4351b672151 100644
--- a/python/dask_cudf/dask_cudf/tests/test_reductions.py
+++ b/python/dask_cudf/dask_cudf/tests/test_reductions.py
@@ -13,11 +13,11 @@
 
 
 def _make_random_frame(nelem, npartitions=2):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=nelem),
-            "y": np.random.normal(size=nelem) + 1,
+            "x": rng.integers(0, 5, size=nelem),
+            "y": rng.normal(loc=1.0, scale=1.0, size=nelem),
         }
     )
     gdf = cudf.DataFrame.from_pandas(df)
diff --git a/python/dask_cudf/dask_cudf/tests/test_sort.py b/python/dask_cudf/dask_cudf/tests/test_sort.py
index 9bbbbc79561..02c815427f3 100644
--- a/python/dask_cudf/dask_cudf/tests/test_sort.py
+++ b/python/dask_cudf/dask_cudf/tests/test_sort.py
@@ -28,7 +28,7 @@
 @pytest.mark.parametrize("nelem", [10, 500])
 @pytest.mark.parametrize("nparts", [1, 10])
 def test_sort_values(nelem, nparts, by, ascending):
-    np.random.seed(0)
+    _ = np.random.default_rng(seed=0)
     df = cudf.DataFrame()
     df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
     df["b"] = np.arange(100, nelem + 100)
@@ -82,7 +82,7 @@ def test_sort_repartition():
     ],
 )
 def test_sort_values_with_nulls(data, by, ascending, na_position):
-    np.random.seed(0)
+    _ = np.random.default_rng(seed=0)
     cp.random.seed(0)
     df = cudf.DataFrame(data)
     ddf = dd.from_pandas(df, npartitions=5)
diff --git a/python/dask_cudf/dask_cudf/tests/utils.py b/python/dask_cudf/dask_cudf/tests/utils.py
index cc0c6899804..1744b08cd8d 100644
--- a/python/dask_cudf/dask_cudf/tests/utils.py
+++ b/python/dask_cudf/dask_cudf/tests/utils.py
@@ -19,8 +19,9 @@
 
 
 def _make_random_frame(nelem, npartitions=2, include_na=False):
+    rng = np.random.default_rng()
     df = pd.DataFrame(
-        {"x": np.random.random(size=nelem), "y": np.random.random(size=nelem)}
+        {"x": rng.random(size=nelem), "y": rng.random(size=nelem)}
     )
 
     if include_na:

From 828706e413be44921acef381a2f9a07c17965afc Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 7 Oct 2024 18:24:09 +0000
Subject: [PATCH 04/21] Switch numpy random calls to latest API

---
 python/cudf/benchmarks/API/bench_functions.py |   7 +-
 .../cudf/benchmarks/API/bench_multiindex.py   |  12 +-
 python/cudf/cudf/core/dataframe.py            |   8 +-
 python/cudf/cudf/testing/_utils.py            |  24 ++--
 python/cudf/cudf/tests/test_array_function.py |  12 +-
 .../test_avro_reader_fastavro_integration.py  |   4 +-
 python/cudf/cudf/tests/test_binops.py         |  42 ++++---
 python/cudf/cudf/tests/test_categorical.py    |   4 +-
 python/cudf/cudf/tests/test_column.py         |   4 +-
 python/cudf/cudf/tests/test_concat.py         |   3 +-
 python/cudf/cudf/tests/test_copying.py        |   5 +-
 python/cudf/cudf/tests/test_csv.py            |   2 +-
 python/cudf/cudf/tests/test_dataframe.py      |  80 +++++++------
 python/cudf/cudf/tests/test_datetime.py       |  24 ++--
 python/cudf/cudf/tests/test_duplicates.py     |  10 +-
 python/cudf/cudf/tests/test_groupby.py        |  31 ++---
 python/cudf/cudf/tests/test_indexing.py       |  33 +++---
 python/cudf/cudf/tests/test_joining.py        |  16 +--
 python/cudf/cudf/tests/test_json.py           |   1 +
 python/cudf/cudf/tests/test_monotonic.py      |   3 +-
 python/cudf/cudf/tests/test_multiindex.py     |  27 +++--
 python/cudf/cudf/tests/test_orc.py            |   7 +-
 python/cudf/cudf/tests/test_pack.py           |  68 +++++------
 python/cudf/cudf/tests/test_parquet.py        |   6 +
 python/cudf/cudf/tests/test_pickling.py       |  18 +--
 python/cudf/cudf/tests/test_query.py          |  10 +-
 python/cudf/cudf/tests/test_rank.py           |  11 +-
 python/cudf/cudf/tests/test_reductions.py     |  12 +-
 python/cudf/cudf/tests/test_repr.py           |   6 +
 python/cudf/cudf/tests/test_resampling.py     |  17 ++-
 python/cudf/cudf/tests/test_reshape.py        |  13 ++-
 python/cudf/cudf/tests/test_serialize.py      |  37 ++++--
 python/cudf/cudf/tests/test_series.py         |   7 +-
 python/cudf/cudf/tests/test_seriesmap.py      |   4 +-
 python/cudf/cudf/tests/test_sorting.py        |  63 +++++-----
 python/cudf/cudf/tests/test_sparse_df.py      |   5 +-
 python/cudf/cudf/tests/test_stats.py          |  18 +--
 python/cudf/cudf/tests/test_string.py         |   3 +-
 python/cudf/cudf/tests/test_transform.py      |   4 +-
 python/cudf/cudf/tests/test_unaops.py         |  13 ++-
 python/cudf/cudf/tests/test_unique.py         |   2 +-
 python/cudf/cudf/utils/hash_vocab_utils.py    |  12 +-
 .../data/repr_slow_down_test.ipynb            |   2 +-
 .../cudf_pandas_tests/test_cudf_pandas.py     |   4 +-
 .../cudf/cudf_pandas_tests/test_profiler.py   |   8 +-
 .../tests/test_cuml.py                        |   2 +-
 .../tests/test_stumpy_distributed.py          |   8 +-
 .../dask_cudf/io/tests/test_parquet.py        |   8 +-
 .../dask_cudf/tests/test_accessor.py          |   2 +-
 .../dask_cudf/dask_cudf/tests/test_binops.py  |  13 +--
 python/dask_cudf/dask_cudf/tests/test_core.py | 110 +++++++++---------
 .../dask_cudf/tests/test_delayed_io.py        |  37 +++---
 .../dask_cudf/tests/test_dispatch.py          |   6 +-
 .../dask_cudf/dask_cudf/tests/test_groupby.py |  51 ++++----
 python/dask_cudf/dask_cudf/tests/test_join.py |  51 ++++----
 55 files changed, 552 insertions(+), 438 deletions(-)

diff --git a/python/cudf/benchmarks/API/bench_functions.py b/python/cudf/benchmarks/API/bench_functions.py
index 93109838900..f902111b0db 100644
--- a/python/cudf/benchmarks/API/bench_functions.py
+++ b/python/cudf/benchmarks/API/bench_functions.py
@@ -72,12 +72,13 @@ def bench_pivot_table_simple(benchmark, dataframe):
 
 @pytest_cases.parametrize("nr", NUM_ROWS)
 def bench_crosstab_simple(benchmark, nr):
+    rng = np.random.default_rng(seed=0)
     series_a = np.array(["foo", "bar"] * nr)
     series_b = np.array(["one", "two"] * nr)
     series_c = np.array(["dull", "shiny"] * nr)
-    np.random.shuffle(series_a)
-    np.random.shuffle(series_b)
-    np.random.shuffle(series_c)
+    rng.shuffle(series_a)
+    rng.shuffle(series_b)
+    rng.shuffle(series_c)
     series_a = cudf.Series(series_a)
     series_b = cudf.Series(series_b)
     series_c = cudf.Series(series_c)
diff --git a/python/cudf/benchmarks/API/bench_multiindex.py b/python/cudf/benchmarks/API/bench_multiindex.py
index 6268bcc4267..77004c3313e 100644
--- a/python/cudf/benchmarks/API/bench_multiindex.py
+++ b/python/cudf/benchmarks/API/bench_multiindex.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 """Benchmarks of MultiIndex methods."""
 
@@ -11,16 +11,18 @@
 @pytest.fixture
 def pidx():
     num_elements = int(1e3)
-    a = np.random.randint(0, num_elements // 10, num_elements)
-    b = np.random.randint(0, num_elements // 10, num_elements)
+    rng = np.random.default_rng(seed=0)
+    a = rng.integers(0, num_elements // 10, num_elements)
+    b = rng.integers(0, num_elements // 10, num_elements)
     return pd.MultiIndex.from_arrays([a, b], names=("a", "b"))
 
 
 @pytest.fixture
 def midx(pidx):
     num_elements = int(1e3)
-    a = np.random.randint(0, num_elements // 10, num_elements)
-    b = np.random.randint(0, num_elements // 10, num_elements)
+    rng = np.random.default_rng(seed=0)
+    a = rng.integers(0, num_elements // 10, num_elements)
+    b = rng.integers(0, num_elements // 10, num_elements)
     df = cudf.DataFrame({"a": a, "b": b})
     return cudf.MultiIndex.from_frame(df)
 
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index 16b0aa95c35..edad9bbdfc1 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -5118,11 +5118,11 @@ def info(
         useful for big DataFrames and fine-tune memory optimization:
 
         >>> import numpy as np
-        >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+        >>> random_strings_array = rng.choice(['a', 'b', 'c'], 10 ** 6)
         >>> df = cudf.DataFrame({
-        ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
-        ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
-        ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
+        ...     'column_1': rng.choice(['a', 'b', 'c'], 10 ** 6),
+        ...     'column_2': rng.choice(['a', 'b', 'c'], 10 ** 6),
+        ...     'column_3': rng.choice(['a', 'b', 'c'], 10 ** 6)
         ... })
         >>> df.info(memory_usage='deep')
         <class 'cudf.core.dataframe.DataFrame'>
diff --git a/python/cudf/cudf/testing/_utils.py b/python/cudf/cudf/testing/_utils.py
index 8cb9efa873c..8a30b0ae6d6 100644
--- a/python/cudf/cudf/testing/_utils.py
+++ b/python/cudf/cudf/testing/_utils.py
@@ -92,7 +92,8 @@ def random_bitmask(size):
         number of bits
     """
     sz = bitmask_allocation_size_bytes(size)
-    data = np.random.randint(0, 255, dtype="u1", size=sz)
+    rng = np.random.default_rng(seed=0)
+    data = rng.integers(0, 255, dtype="u1", size=sz)
     return data.view("i1")
 
 
@@ -209,9 +210,10 @@ def _get_args_kwars_for_assert_exceptions(func_args_and_kwargs):
 
 
 def gen_rand(dtype, size, **kwargs):
+    rng = np.random.default_rng(seed=0)
     dtype = cudf.dtype(dtype)
     if dtype.kind == "f":
-        res = np.random.random(size=size).astype(dtype)
+        res = rng.random(size=size).astype(dtype)
         if kwargs.get("positive_only", False):
             return res
         else:
@@ -219,25 +221,23 @@ def gen_rand(dtype, size, **kwargs):
     elif dtype == np.int8 or dtype == np.int16:
         low = kwargs.get("low", -32)
         high = kwargs.get("high", 32)
-        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+        return rng.integers(low=low, high=high, size=size).astype(dtype)
     elif dtype.kind == "i":
         low = kwargs.get("low", -10000)
         high = kwargs.get("high", 10000)
-        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+        return rng.integers(low=low, high=high, size=size).astype(dtype)
     elif dtype == np.uint8 or dtype == np.uint16:
         low = kwargs.get("low", 0)
         high = kwargs.get("high", 32)
-        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+        return rng.integers(low=low, high=high, size=size).astype(dtype)
     elif dtype.kind == "u":
         low = kwargs.get("low", 0)
         high = kwargs.get("high", 128)
-        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+        return rng.integers(low=low, high=high, size=size).astype(dtype)
     elif dtype.kind == "b":
         low = kwargs.get("low", 0)
         high = kwargs.get("high", 2)
-        return np.random.randint(low=low, high=high, size=size).astype(
-            np.bool_
-        )
+        return rng.integers(low=low, high=high, size=size).astype(np.bool_)
     elif dtype.kind == "M":
         low = kwargs.get("low", 0)
         time_unit, _ = np.datetime_data(dtype)
@@ -246,14 +246,14 @@ def gen_rand(dtype, size, **kwargs):
             int(1e18) / _unit_to_nanoseconds_conversion[time_unit],
         )
         return pd.to_datetime(
-            np.random.randint(low=low, high=high, size=size), unit=time_unit
+            rng.integers(low=low, high=high, size=size), unit=time_unit
         )
     elif dtype.kind in ("O", "U"):
         low = kwargs.get("low", 10)
         high = kwargs.get("high", 11)
-        nchars = np.random.randint(low=low, high=high, size=1)[0]
+        nchars = rng.integers(low=low, high=high, size=1)[0]
         char_options = np.array(list(string.ascii_letters + string.digits))
-        all_chars = "".join(np.random.choice(char_options, nchars * size))
+        all_chars = "".join(rng.choice(char_options, nchars * size))
         return np.array(
             [all_chars[nchars * i : nchars * (i + 1)] for i in range(size)]
         )
diff --git a/python/cudf/cudf/tests/test_array_function.py b/python/cudf/cudf/tests/test_array_function.py
index 979c936a182..af9a6c7e696 100644
--- a/python/cudf/cudf/tests/test_array_function.py
+++ b/python/cudf/cudf/tests/test_array_function.py
@@ -33,7 +33,7 @@ def __array_function__(self, *args, **kwargs):
 
 missing_arrfunc_reason = "NEP-18 support is not available in NumPy"
 
-np.random.seed(0)
+rng = np.random.default_rng(seed=0)
 
 
 @pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
@@ -49,7 +49,7 @@ def __array_function__(self, *args, **kwargs):
     ],
 )
 def test_array_func_cudf_series(func):
-    np_ar = np.random.random(100)
+    np_ar = rng.random(100)
     cudf_ser = cudf.Series(np_ar)
     expect = func(np_ar)
     got = func(cudf_ser)
@@ -74,7 +74,7 @@ def test_array_func_cudf_series(func):
     ],
 )
 def test_array_func_cudf_dataframe(func):
-    pd_df = pd.DataFrame(np.random.uniform(size=(100, 10)))
+    pd_df = pd.DataFrame(rng.uniform(size=(100, 10)))
     cudf_df = cudf.from_pandas(pd_df)
     expect = func(pd_df)
     got = func(cudf_df)
@@ -91,7 +91,7 @@ def test_array_func_cudf_dataframe(func):
     ],
 )
 def test_array_func_missing_cudf_dataframe(func):
-    pd_df = pd.DataFrame(np.random.uniform(size=(100, 10)))
+    pd_df = pd.DataFrame(rng.uniform(size=(100, 10)))
     cudf_df = cudf.from_pandas(pd_df)
     with pytest.raises(TypeError):
         func(cudf_df)
@@ -105,7 +105,7 @@ def test_array_func_missing_cudf_dataframe(func):
     ],
 )
 def test_array_func_cudf_index(func):
-    np_ar = np.random.random(100)
+    np_ar = rng.random(100)
     cudf_index = cudf.Index(cudf.Series(np_ar))
     expect = func(np_ar)
     got = func(cudf_index)
@@ -125,7 +125,7 @@ def test_array_func_cudf_index(func):
     ],
 )
 def test_array_func_missing_cudf_index(func):
-    np_ar = np.random.random(100)
+    np_ar = rng.random(100)
     cudf_index = cudf.Index(cudf.Series(np_ar))
     with pytest.raises(TypeError):
         func(cudf_index)
diff --git a/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py b/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py
index 5acdf36de80..17ef033ea9e 100644
--- a/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py
+++ b/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py
@@ -600,12 +600,12 @@ def test_avro_reader_multiblock(
     else:
         assert dtype in ("float32", "float64")
         avro_type = "float" if dtype == "float32" else "double"
-        np.random.seed(0)
+        rng = np.random.default_rng(seed=0)
         # We don't use rand_dataframe() here, because it increases the
         # execution time of each test by a factor of 10 or more (it appears
         # to use a very costly approach to generating random data).
         # See also: https://github.com/rapidsai/cudf/issues/13128
-        values = np.random.rand(total_rows).astype(dtype)
+        values = rng.random(total_rows).astype(dtype)
         bytes_per_row = values.dtype.itemsize
 
     # The sync_interval is the number of bytes between sync blocks.  We know
diff --git a/python/cudf/cudf/tests/test_binops.py b/python/cudf/cudf/tests/test_binops.py
index c1f4d9ac7e5..474d64130eb 100644
--- a/python/cudf/cudf/tests/test_binops.py
+++ b/python/cudf/cudf/tests/test_binops.py
@@ -2,7 +2,6 @@
 
 import decimal
 import operator
-import random
 import warnings
 from itertools import combinations_with_replacement, product
 
@@ -204,7 +203,8 @@ def test_series_binop(binop, obj_class):
 @pytest.mark.parametrize("binop", _binops)
 def test_series_binop_concurrent(binop):
     def func(index):
-        arr = np.random.random(100) * 10
+        rng = np.random.default_rng(seed=0)
+        arr = rng.random(100) * 10
         sr = Series(arr)
 
         result = binop(sr.astype("int32"), sr)
@@ -223,8 +223,9 @@ def func(index):
 @pytest.mark.parametrize("obj_class", ["Series", "Index"])
 @pytest.mark.parametrize("nelem,binop", list(product([1, 2, 100], _binops)))
 def test_series_binop_scalar(nelem, binop, obj_class, use_cudf_scalar):
-    arr = np.random.random(nelem)
-    rhs = random.choice(arr).item()
+    rng = np.random.default_rng(seed=0)
+    arr = rng.random(nelem)
+    rhs = rng.choice(arr).item()
 
     sr = Series(arr)
     if obj_class == "Index":
@@ -247,10 +248,11 @@ def test_series_binop_scalar(nelem, binop, obj_class, use_cudf_scalar):
     "lhs_dtype,rhs_dtype", list(product(_int_types, _int_types))
 )
 def test_series_bitwise_binop(binop, obj_class, lhs_dtype, rhs_dtype):
-    arr1 = (np.random.random(100) * 100).astype(lhs_dtype)
+    rng = np.random.default_rng(seed=0)
+    arr1 = (rng.random(100) * 100).astype(lhs_dtype)
     sr1 = Series(arr1)
 
-    arr2 = (np.random.random(100) * 100).astype(rhs_dtype)
+    arr2 = (rng.random(100) * 100).astype(rhs_dtype)
     sr2 = Series(arr2)
 
     if obj_class == "Index":
@@ -272,8 +274,10 @@ def test_series_bitwise_binop(binop, obj_class, lhs_dtype, rhs_dtype):
 )
 def test_series_compare(cmpop, obj_class, dtype):
     rng = np.random.default_rng(seed=0)
-    sr1 = Series(rng.integers(0, 100, 100).astype(dtype))
-    sr2 = Series(rng.integers(0, 100, 100).astype(dtype))
+    arr1 = rng.integers(0, 100, 100).astype(dtype)
+    arr2 = rng.integers(0, 100, 100).astype(dtype)
+    sr1 = Series(arr1)
+    sr2 = Series(arr2)
 
     if obj_class == "Index":
         sr1 = Index(sr1)
@@ -465,9 +469,9 @@ def test_series_compare_scalar(
 @pytest.mark.parametrize("nelem", [1, 7, 8, 9, 32, 64, 128])
 @pytest.mark.parametrize("lhs_nulls,rhs_nulls", list(product(_nulls, _nulls)))
 def test_validity_add(nelem, lhs_nulls, rhs_nulls):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     # LHS
-    lhs_data = np.random.random(nelem)
+    lhs_data = rng.random(nelem)
     if lhs_nulls == "some":
         lhs_mask = utils.random_bitmask(nelem)
         lhs_bitmask = utils.expand_bits_to_bytes(lhs_mask)[:nelem]
@@ -478,7 +482,7 @@ def test_validity_add(nelem, lhs_nulls, rhs_nulls):
     else:
         lhs = Series(lhs_data)
     # RHS
-    rhs_data = np.random.random(nelem)
+    rhs_data = rng.random(nelem)
     if rhs_nulls == "some":
         rhs_mask = utils.random_bitmask(nelem)
         rhs_bitmask = utils.expand_bits_to_bytes(rhs_mask)[:nelem]
@@ -525,8 +529,9 @@ def test_validity_add(nelem, lhs_nulls, rhs_nulls):
 )
 def test_series_binop_mixed_dtype(binop, lhs_dtype, rhs_dtype, obj_class):
     nelem = 10
-    lhs = (np.random.random(nelem) * nelem).astype(lhs_dtype)
-    rhs = (np.random.random(nelem) * nelem).astype(rhs_dtype)
+    rng = np.random.default_rng(seed=0)
+    lhs = (rng.random(nelem) * nelem).astype(lhs_dtype)
+    rhs = (rng.random(nelem) * nelem).astype(rhs_dtype)
 
     sr1 = Series(lhs)
     sr2 = Series(rhs)
@@ -550,8 +555,9 @@ def test_series_binop_mixed_dtype(binop, lhs_dtype, rhs_dtype, obj_class):
 )
 def test_series_cmpop_mixed_dtype(cmpop, lhs_dtype, rhs_dtype, obj_class):
     nelem = 5
-    lhs = (np.random.random(nelem) * nelem).astype(lhs_dtype)
-    rhs = (np.random.random(nelem) * nelem).astype(rhs_dtype)
+    rng = np.random.default_rng(seed=0)
+    lhs = (rng.random(nelem) * nelem).astype(lhs_dtype)
+    rhs = (rng.random(nelem) * nelem).astype(rhs_dtype)
 
     sr1 = Series(lhs)
     sr2 = Series(rhs)
@@ -574,7 +580,6 @@ def test_series_cmpop_mixed_dtype(cmpop, lhs_dtype, rhs_dtype, obj_class):
 )
 def test_series_reflected_ops_scalar(func, dtype, obj_class):
     # create random series
-    np.random.seed(12)
     random_series = utils.gen_rand(dtype, 100, low=10)
 
     # gpu series
@@ -631,7 +636,6 @@ def test_series_reflected_ops_cudf_scalar(funcs, dtype, obj_class):
     cpu_func, gpu_func = funcs
 
     # create random series
-    np.random.seed(12)
     random_series = utils.gen_rand(dtype, 100, low=10)
 
     # gpu series
@@ -774,6 +778,7 @@ def test_df_different_index_shape(df2, binop):
 
 @pytest.mark.parametrize("op", [operator.eq, operator.ne])
 def test_boolean_scalar_binop(op):
+    rng = np.random.default_rng(seed=0)
     psr = pd.Series(rng.choice([True, False], 10))
     gsr = cudf.from_pandas(psr)
     assert_eq(op(psr, True), op(gsr, True))
@@ -923,6 +928,7 @@ def test_operator_func_dataframe(func, nulls, fill_value, other):
     num_cols = 3
 
     def gen_df():
+        rng = np.random.default_rng(seed=0)
         pdf = pd.DataFrame()
         from string import ascii_lowercase
 
@@ -954,7 +960,7 @@ def gen_df():
 @pytest.mark.parametrize("nulls", _nulls)
 @pytest.mark.parametrize("other", ["df", "scalar"])
 def test_logical_operator_func_dataframe(func, nulls, other):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     num_rows = 100
     num_cols = 3
 
diff --git a/python/cudf/cudf/tests/test_categorical.py b/python/cudf/cudf/tests/test_categorical.py
index aa43453fcb8..7d6c6c7d7b0 100644
--- a/python/cudf/cudf/tests/test_categorical.py
+++ b/python/cudf/cudf/tests/test_categorical.py
@@ -252,7 +252,7 @@ def test_cat_series_binop_error():
 @pytest.mark.parametrize("num_elements", [10, 100, 1000])
 def test_categorical_unique(num_elements):
     # create categorical series
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     pd_cat = pd.Categorical(
         pd.Series(
             rng.choice(
@@ -279,7 +279,7 @@ def test_categorical_unique(num_elements):
 @pytest.mark.parametrize("nelem", [20, 50, 100])
 def test_categorical_unique_count(nelem):
     # create categorical series
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     pd_cat = pd.Categorical(
         pd.Series(
             rng.choice(list(string.ascii_letters + string.digits), nelem),
diff --git a/python/cudf/cudf/tests/test_column.py b/python/cudf/cudf/tests/test_column.py
index 732dbeaab34..6073c4e6434 100644
--- a/python/cudf/cudf/tests/test_column.py
+++ b/python/cudf/cudf/tests/test_column.py
@@ -155,7 +155,9 @@ def test_column_slicing(pandas_input, offset, size):
     [cudf.Decimal128Dtype, cudf.Decimal64Dtype, cudf.Decimal32Dtype],
 )
 def test_decimal_column_slicing(offset, size, precision, scale, decimal_type):
-    col = cudf.core.column.as_column(pd.Series(np.random.rand(1000)))
+    col = cudf.core.column.as_column(
+        pd.Series(np.random.default_rng(seed=0).random(1000))
+    )
     col = col.astype(decimal_type(precision, scale))
     column_slicing_test(col, offset, size, True)
 
diff --git a/python/cudf/cudf/tests/test_concat.py b/python/cudf/cudf/tests/test_concat.py
index 8acb296e4e9..ab0f1767cd6 100644
--- a/python/cudf/cudf/tests/test_concat.py
+++ b/python/cudf/cudf/tests/test_concat.py
@@ -30,6 +30,7 @@ def _hide_concat_empty_dtype_warning():
 
 
 def make_frames(index=None, nulls="none"):
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
             "x": range(10),
@@ -51,7 +52,7 @@ def make_frames(index=None, nulls="none"):
         df2.y = np.full_like(df2.y, np.nan)
     if nulls == "some":
         mask = np.arange(10)
-        np.random.shuffle(mask)
+        rng.shuffle(mask)
         mask = mask[:5]
         df.loc[mask, "y"] = np.nan
         df2.loc[mask, "y"] = np.nan
diff --git a/python/cudf/cudf/tests/test_copying.py b/python/cudf/cudf/tests/test_copying.py
index 2d883916d58..f33cfe268a3 100644
--- a/python/cudf/cudf/tests/test_copying.py
+++ b/python/cudf/cudf/tests/test_copying.py
@@ -17,7 +17,7 @@
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES + OTHER_TYPES)
 def test_repeat(dtype):
     rng = np.random.default_rng(seed=0)
-    arr = np.random.rand(10) * 10
+    arr = rng.random(10) * 10
     repeats = rng.integers(10, size=10)
     psr = pd.Series(arr).astype(dtype)
     gsr = cudf.from_pandas(psr)
@@ -48,7 +48,8 @@ def test_repeat_dataframe():
 
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES)
 def test_repeat_scalar(dtype):
-    arr = np.random.rand(10) * 10
+    rng = np.random.default_rng(seed=0)
+    arr = rng.random(10) * 10
     repeats = 10
     psr = pd.Series(arr).astype(dtype)
     gsr = cudf.from_pandas(psr)
diff --git a/python/cudf/cudf/tests/test_csv.py b/python/cudf/cudf/tests/test_csv.py
index d371ddff09d..8800275bf67 100644
--- a/python/cudf/cudf/tests/test_csv.py
+++ b/python/cudf/cudf/tests/test_csv.py
@@ -1770,7 +1770,7 @@ def test_csv_writer_multiindex(tmpdir):
             "a": rng.integers(0, 5, 20),
             "b": rng.integers(0, 5, 20),
             "c": range(20),
-            "d": np.random.random(20),
+            "d": rng.random(20),
         }
     )
     gdg = gdf.groupby(["a", "b"]).mean()
diff --git a/python/cudf/cudf/tests/test_dataframe.py b/python/cudf/cudf/tests/test_dataframe.py
index 831c491b6bc..13260e1cb90 100644
--- a/python/cudf/cudf/tests/test_dataframe.py
+++ b/python/cudf/cudf/tests/test_dataframe.py
@@ -63,6 +63,9 @@
     pytest_xfail = pytest.mark.skipif
 
 
+rng = np.random.default_rng(seed=0)
+
+
 @contextmanager
 def _hide_ufunc_warnings(eval_str):
     # pandas raises warnings for some inputs to the following ufuncs:
@@ -428,7 +431,7 @@ def test_series_init_none():
 
 
 def test_dataframe_basic():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame()
 
     # Populate with cuda memory
@@ -437,7 +440,7 @@ def test_dataframe_basic():
     assert len(df) == 10
 
     # Populate with numpy array
-    rnd_vals = np.random.random(10)
+    rnd_vals = rng.random(10)
     df["vals"] = rnd_vals
     np.testing.assert_equal(df["vals"].to_numpy(), rnd_vals)
     assert len(df) == 10
@@ -1239,7 +1242,7 @@ def test_empty_dataframe_to_cupy():
     df = cudf.DataFrame()
     nelem = 123
     for k in "abc":
-        df[k] = np.random.random(nelem)
+        df[k] = rng.random(nelem)
 
     # Check all columns in empty dataframe.
     mat = df.head(0).to_cupy()
@@ -1251,7 +1254,7 @@ def test_dataframe_to_cupy():
 
     nelem = 123
     for k in "abcd":
-        df[k] = np.random.random(nelem)
+        df[k] = rng.random(nelem)
 
     # Check all columns
     mat = df.to_cupy()
@@ -1280,7 +1283,7 @@ def test_dataframe_to_cupy_null_values():
 
     refvalues = {}
     for k in "abcd":
-        df[k] = data = np.random.random(nelem)
+        df[k] = data = rng.random(nelem)
         bitmask = utils.random_bitmask(nelem)
         df[k] = df[k]._column.set_mask(bitmask)
         boolmask = np.asarray(
@@ -1321,10 +1324,10 @@ def test_dataframe_append_empty():
 
 
 def test_dataframe_setitem_from_masked_object():
-    ary = np.random.randn(100)
+    ary = rng.standard_normal(100)
     mask = np.zeros(100, dtype=bool)
     mask[:20] = True
-    np.random.shuffle(mask)
+    rng.shuffle(mask)
     ary[mask] = np.nan
 
     test1_null = cudf.Series(ary, nan_as_null=True)
@@ -1749,8 +1752,9 @@ def test_concat_with_axis():
 
     assert_eq(concat_cdf_s, concat_s, check_index_type=True)
 
+    rng = np.random.default_rng(seed=0)
     # concat series and dataframes
-    s3 = pd.Series(np.random.random(5))
+    s3 = pd.Series(rng.random(5))
     cs3 = cudf.Series.from_pandas(s3)
 
     concat_cdf_all = cudf.concat([cdf1, cs3, cdf2], axis=1)
@@ -2651,8 +2655,8 @@ def test_unaryops_df(pdf, unaryop, col_name, assign_col_name):
 
 
 def test_df_abs(pdf):
-    np.random.seed(0)
-    disturbance = pd.Series(np.random.rand(10))
+    rng = np.random.default_rng(seed=0)
+    disturbance = pd.Series(rng.random(10))
     pdf = pdf - 5 + disturbance
     d = pdf.apply(np.abs)
     g = cudf.from_pandas(pdf).abs()
@@ -2706,7 +2710,7 @@ def test_quantile(q, numeric_only):
     ts = pd.date_range("2018-08-24", periods=5, freq="D")
     td = pd.to_timedelta(np.arange(5), unit="h")
     pdf = pd.DataFrame(
-        {"date": ts, "delta": td, "val": np.random.randn(len(ts))}
+        {"date": ts, "delta": td, "val": rng.standard_normal(len(ts))}
     )
     gdf = cudf.DataFrame.from_pandas(pdf)
 
@@ -2879,7 +2883,8 @@ def query_GPU_memory(note=""):
     cuda.current_context().deallocations.clear()
     nRows = int(1e8)
     nCols = 2
-    dataNumpy = np.asfortranarray(np.random.rand(nRows, nCols))
+    rng = np.random.default_rng(seed=0)
+    dataNumpy = np.asfortranarray(rng.random(nRows, nCols))
     colNames = ["col" + str(iCol) for iCol in range(nCols)]
     pandasDF = pd.DataFrame(data=dataNumpy, columns=colNames, dtype=np.float32)
     cudaDF = cudf.core.DataFrame.from_pandas(pandasDF)
@@ -2991,7 +2996,8 @@ def test_arrow_handle_no_index_name(pdf, gdf):
 
 
 def test_pandas_non_contiguious():
-    arr1 = np.random.sample([5000, 10])
+    rng = np.random.default_rng(seed=0)
+    arr1 = rng.random(size=(5000, 10))
     assert arr1.flags["C_CONTIGUOUS"] is True
     df = pd.DataFrame(arr1)
     for col in df.columns:
@@ -3310,7 +3316,7 @@ def test_set_index_verify_integrity(data, index, verify_integrity):
 def test_set_index_multi(drop, nelem):
     rng = np.random.default_rng(seed=0)
     a = np.arange(nelem)
-    np.random.shuffle(a)
+    rng.shuffle(a)
     df = pd.DataFrame(
         {
             "a": a,
@@ -3894,7 +3900,7 @@ def test_select_dtype_datetime_with_frequency():
 
 
 def test_dataframe_describe_exclude():
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3910,7 +3916,7 @@ def test_dataframe_describe_exclude():
 
 
 def test_dataframe_describe_include():
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3925,7 +3931,7 @@ def test_dataframe_describe_include():
 
 
 def test_dataframe_describe_default():
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3939,7 +3945,7 @@ def test_dataframe_describe_default():
 
 
 def test_series_describe_include_all():
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3962,7 +3968,7 @@ def test_series_describe_include_all():
 
 
 def test_dataframe_describe_percentiles():
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     data_length = 10000
     sample_percentiles = [0.0, 0.1, 0.33, 0.84, 0.4, 0.99]
 
@@ -6698,8 +6704,8 @@ def test_dataframe_init_1d_list(data, columns):
         (cupy.array([11, 123, -2342, 232]), ["z"], [0, 1, 1, 0]),
         (cupy.array([11, 123, -2342, 232]), ["z"], [1, 2, 3, 4]),
         (cupy.array([11, 123, -2342, 232]), ["z"], ["a", "z", "d", "e"]),
-        (np.random.randn(2, 4), ["a", "b", "c", "d"], ["a", "b"]),
-        (np.random.randn(2, 4), ["a", "b", "c", "d"], [1, 0]),
+        (rng.standard_normal(size=(2, 4)), ["a", "b", "c", "d"], ["a", "b"]),
+        (rng.standard_normal(size=(2, 4)), ["a", "b", "c", "d"], [1, 0]),
         (cupy.random.randn(2, 4), ["a", "b", "c", "d"], ["a", "b"]),
         (cupy.random.randn(2, 4), ["a", "b", "c", "d"], [1, 0]),
     ],
@@ -6874,7 +6880,7 @@ def test_dataframe_info_basic():
     """
     )
     df = pd.DataFrame(
-        np.random.randn(10, 10),
+        rng.standard_normal(size=(10, 10)),
         index=["a", "2", "3", "4", "5", "6", "7", "8", "100", "1111"],
     )
     cudf.from_pandas(df).info(buf=buffer, verbose=True)
@@ -9374,8 +9380,8 @@ def test_dataframe_roundtrip_arrow_struct_dtype(gdf):
 
 
 def test_dataframe_setitem_cupy_array():
-    np.random.seed(0)
-    pdf = pd.DataFrame(np.random.randn(10, 2))
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.standard_normal(size=(10, 2)))
     gdf = cudf.from_pandas(pdf)
 
     gpu_array = cupy.array([True, False] * 5)
@@ -10529,11 +10535,12 @@ def test_dataframe_init_length_error(data, index):
 
 
 def test_dataframe_binop_with_mixed_date_types():
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
-        np.random.rand(2, 2),
+        rng.random(2, 2),
         columns=pd.Index(["2000-01-03", "2000-01-04"], dtype="datetime64[ns]"),
     )
-    ser = pd.Series(np.random.rand(3), index=[0, 1, 2])
+    ser = pd.Series(rng.random(3), index=[0, 1, 2])
     gdf = cudf.from_pandas(df)
     gser = cudf.from_pandas(ser)
     expected = df - ser
@@ -10542,9 +10549,10 @@ def test_dataframe_binop_with_mixed_date_types():
 
 
 def test_dataframe_binop_with_mixed_string_types():
-    df1 = pd.DataFrame(np.random.rand(3, 3), columns=pd.Index([0, 1, 2]))
+    rng = np.random.default_rng(seed=0)
+    df1 = pd.DataFrame(rng.random(size=(3, 3)), columns=pd.Index([0, 1, 2]))
     df2 = pd.DataFrame(
-        np.random.rand(6, 6),
+        rng.random(size=(6, 6)),
         columns=pd.Index([0, 1, 2, "VhDoHxRaqt", "X0NNHBIPfA", "5FbhPtS0D1"]),
     )
     gdf1 = cudf.from_pandas(df1)
@@ -10557,7 +10565,8 @@ def test_dataframe_binop_with_mixed_string_types():
 
 
 def test_dataframe_binop_and_where():
-    df = pd.DataFrame(np.random.rand(2, 2), columns=pd.Index([True, False]))
+    rng = np.random.default_rng(seed=0)
+    df = pd.DataFrame(rng.random(size=(2, 2)), columns=pd.Index([True, False]))
     gdf = cudf.from_pandas(df)
 
     expected = df > 1
@@ -10572,12 +10581,13 @@ def test_dataframe_binop_and_where():
 
 
 def test_dataframe_binop_with_datetime_index():
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
-        np.random.rand(2, 2),
+        rng.random(size=(2, 2)),
         columns=pd.Index(["2000-01-03", "2000-01-04"], dtype="datetime64[ns]"),
     )
     ser = pd.Series(
-        np.random.rand(2),
+        rng.random(2),
         index=pd.Index(
             [
                 "2000-01-04",
@@ -10615,8 +10625,8 @@ def test_dataframe_dict_like_with_columns(columns, index):
 
 
 def test_dataframe_init_columns_named_multiindex():
-    np.random.seed(0)
-    data = np.random.randn(2, 2)
+    rng = np.random.default_rng(seed=0)
+    data = rng.standard_normal(size=(2, 2))
     columns = cudf.MultiIndex.from_tuples(
         [("A", "one"), ("A", "two")], names=["y", "z"]
     )
@@ -10627,8 +10637,8 @@ def test_dataframe_init_columns_named_multiindex():
 
 
 def test_dataframe_init_columns_named_index():
-    np.random.seed(0)
-    data = np.random.randn(2, 2)
+    rng = np.random.default_rng(seed=0)
+    data = rng.standard_normal(size=(2, 2))
     columns = pd.Index(["a", "b"], name="custom_name")
     gdf = cudf.DataFrame(data, columns=columns)
     pdf = pd.DataFrame(data, columns=columns)
diff --git a/python/cudf/cudf/tests/test_datetime.py b/python/cudf/cudf/tests/test_datetime.py
index e09a9a386a7..14b53569860 100644
--- a/python/cudf/cudf/tests/test_datetime.py
+++ b/python/cudf/cudf/tests/test_datetime.py
@@ -216,17 +216,21 @@ def test_setitem_datetime():
 
 
 def test_sort_datetime():
-    df = pd.DataFrame()
-    df["date"] = np.array(
-        [
-            np.datetime64("2016-11-20"),
-            np.datetime64("2020-11-20"),
-            np.datetime64("2019-11-20"),
-            np.datetime64("1918-11-20"),
-            np.datetime64("2118-11-20"),
-        ]
+    rng = np.random.default_rng(seed=0)
+    df = pd.DataFrame(
+        {
+            "data": np.array(
+                [
+                    np.datetime64("2016-11-20"),
+                    np.datetime64("2020-11-20"),
+                    np.datetime64("2019-11-20"),
+                    np.datetime64("1918-11-20"),
+                    np.datetime64("2118-11-20"),
+                ]
+            ),
+            "vals": rng.random(5),
+        }
     )
-    df["vals"] = np.random.sample(len(df["date"]))
 
     gdf = cudf.from_pandas(df)
 
diff --git a/python/cudf/cudf/tests/test_duplicates.py b/python/cudf/cudf/tests/test_duplicates.py
index dcff4f2f07a..67dd7a8388b 100644
--- a/python/cudf/cudf/tests/test_duplicates.py
+++ b/python/cudf/cudf/tests/test_duplicates.py
@@ -368,9 +368,13 @@ def test_dataframe_drop_duplicates_method():
 
 
 def test_datetime_drop_duplicates():
-    date_df = cudf.DataFrame()
-    date_df["date"] = pd.date_range("11/20/2018", periods=6, freq="D")
-    date_df["value"] = np.random.sample(len(date_df))
+    rng = np.random.default_rng(seed=0)
+    date_df = cudf.DataFrame(
+        {
+            "date": pd.date_range("11/20/2018", periods=6, freq="D"),
+            "value": rng.random(6),
+        }
+    )
 
     df = concat([date_df, date_df[:4]])
     assert_eq(df[:-4], df.drop_duplicates())
diff --git a/python/cudf/cudf/tests/test_groupby.py b/python/cudf/cudf/tests/test_groupby.py
index 75eeb72a3af..6b9fad8e0b7 100644
--- a/python/cudf/cudf/tests/test_groupby.py
+++ b/python/cudf/cudf/tests/test_groupby.py
@@ -86,9 +86,9 @@ def make_frame(
     for lvl in extra_levels:
         df[lvl] = rng.integers(0, 2, nelem)
 
-    df["val"] = np.random.random(nelem)
+    df["val"] = rng.random(nelem)
     for val in extra_vals:
-        df[val] = np.random.random(nelem)
+        df[val] = rng.random(nelem)
 
     if with_datetime:
         df["datetime"] = rng.integers(
@@ -266,9 +266,10 @@ def test_groupby_getitem_getattr(as_index):
 
 
 def test_groupby_cats():
-    df = DataFrame()
-    df["cats"] = pd.Categorical(list("aabaacaab"))
-    df["vals"] = np.random.random(len(df))
+    rng = np.random.default_rng(seed=0)
+    df = DataFrame(
+        {"cats": pd.Categorical(list("aabaacaab")), "vals": rng.random(9)}
+    )
 
     cats = df["cats"].values_host
     vals = df["vals"].to_numpy()
@@ -291,8 +292,8 @@ def test_groupby_iterate_groups():
         {
             "key1": rng.integers(0, 3, nelem),
             "key2": rng.integers(0, 2, nelem),
-            "val1": np.random.random(nelem),
-            "val2": np.random.random(nelem),
+            "val1": rng.random(nelem),
+            "val2": rng.random(nelem),
         }
     )
 
@@ -316,8 +317,8 @@ def test_groupby_apply():
         {
             "key1": rng.integers(0, 3, nelem),
             "key2": rng.integers(0, 2, nelem),
-            "val1": np.random.random(nelem),
-            "val2": np.random.random(nelem),
+            "val1": rng.random(nelem),
+            "val2": rng.random(nelem),
         }
     )
 
@@ -363,8 +364,8 @@ def test_groupby_apply_args(func, args):
         {
             "key1": rng.integers(0, 3, nelem),
             "key2": rng.integers(0, 2, nelem),
-            "val1": np.random.random(nelem),
-            "val2": np.random.random(nelem),
+            "val1": rng.random(nelem),
+            "val2": rng.random(nelem),
         }
     )
 
@@ -378,7 +379,6 @@ def test_groupby_apply_args(func, args):
 
 
 def test_groupby_apply_grouped():
-    np.random.seed(0)
     df = DataFrame()
     nelem = 20
     df["key1"] = range(nelem)
@@ -1019,6 +1019,7 @@ def test_groupby_2keys_agg(nelem, func):
     # "func", ["min", "max", "idxmin", "idxmax", "count", "sum"],
 )
 def test_groupby_agg_decimal(num_groups, nelem_per_group, func):
+    rng = np.random.default_rng(seed=0)
     # The number of digits after the decimal to use.
     decimal_digits = 2
     # The number of digits before the decimal to use.
@@ -1035,8 +1036,8 @@ def test_groupby_agg_decimal(num_groups, nelem_per_group, func):
     # https://github.com/pandas-dev/pandas/issues/40685). However, if that is
     # ever enabled, then this issue will crop up again so we may as well have
     # it fixed now.
-    x = np.unique((np.random.rand(nelem) * scale).round(decimal_digits))
-    y = np.unique((np.random.rand(nelem) * scale).round(decimal_digits))
+    x = np.unique((rng.random(nelem) * scale).round(decimal_digits))
+    y = np.unique((rng.random(nelem) * scale).round(decimal_digits))
 
     if x.size < y.size:
         total_elements = x.size
@@ -1322,7 +1323,7 @@ def test_empty_groupby(func):
 
 
 def test_groupby_unsupported_columns():
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     pd_cat = pd.Categorical(
         pd.Series(rng.choice(["a", "b", 1], 3), dtype="category")
     )
diff --git a/python/cudf/cudf/tests/test_indexing.py b/python/cudf/cudf/tests/test_indexing.py
index 305d679ba2f..3145c48c0cb 100644
--- a/python/cudf/cudf/tests/test_indexing.py
+++ b/python/cudf/cudf/tests/test_indexing.py
@@ -32,7 +32,8 @@ def pdf_gdf():
 
 @pytest.fixture
 def pdf_gdf_multi():
-    pdf = pd.DataFrame(np.random.rand(7, 5))
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.random(size=(7, 5)))
     pdfIndex = pd.MultiIndex(
         [
             ["a", "b", "c"],
@@ -220,7 +221,7 @@ def test_dataframe_column_name_indexing():
             "key1": rng.integers(0, 5, nelem),
             "key2": rng.integers(0, 3, nelem),
             1: np.arange(1, 1 + nelem),
-            2: np.random.random(nelem),
+            2: rng.random(nelem),
         }
     )
     df = cudf.from_pandas(pdf)
@@ -248,9 +249,9 @@ def test_dataframe_slicing():
     df = cudf.DataFrame()
     size = 123
     df["a"] = ha = rng.integers(low=0, high=100, size=size).astype(np.int32)
-    df["b"] = hb = np.random.random(size).astype(np.float32)
+    df["b"] = hb = rng.random(size).astype(np.float32)
     df["c"] = hc = rng.integers(low=0, high=100, size=size).astype(np.int64)
-    df["d"] = hd = np.random.random(size).astype(np.float64)
+    df["d"] = hd = rng.random(size).astype(np.float64)
 
     # Row slice first 10
     first_10 = df[:10]
@@ -293,9 +294,9 @@ def test_dataframe_loc(scalar, step):
     pdf = pd.DataFrame(
         {
             "a": rng.integers(low=0, high=100, size=size),
-            "b": np.random.random(size).astype(np.float32),
-            "c": np.random.random(size).astype(np.float64),
-            "d": np.random.random(size).astype(np.float64),
+            "b": rng.random(size).astype(np.float32),
+            "c": rng.random(size).astype(np.float64),
+            "d": rng.random(size).astype(np.float64),
         }
     )
     pdf.index.name = "index"
@@ -399,7 +400,7 @@ def test_dataframe_loc_outbound():
     df = cudf.DataFrame()
     size = 10
     df["a"] = ha = rng.integers(low=0, high=100, size=size).astype(np.int32)
-    df["b"] = hb = np.random.random(size).astype(np.float32)
+    df["b"] = hb = rng.random(size).astype(np.float32)
 
     pdf = pd.DataFrame()
     pdf["a"] = ha
@@ -592,8 +593,8 @@ def test_dataframe_series_loc_multiindex(obj):
 @pytest.mark.parametrize("nelem", [2, 5, 20, 100])
 def test_series_iloc(nelem):
     # create random cudf.Series
-    np.random.seed(12)
-    ps = pd.Series(np.random.sample(nelem))
+    rng = np.random.default_rng(seed=0)
+    ps = pd.Series(rng.sample(nelem))
 
     # gpu cudf.Series
     gs = cudf.Series(ps)
@@ -631,7 +632,7 @@ def test_dataframe_iloc(nelem):
     gdf = cudf.DataFrame()
 
     gdf["a"] = ha = rng.integers(low=0, high=100, size=nelem).astype(np.int32)
-    gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
+    gdf["b"] = hb = rng.random(nelem).astype(np.float32)
 
     pdf = pd.DataFrame()
     pdf["a"] = ha
@@ -684,7 +685,7 @@ def test_dataframe_iloc_tuple():
     gdf = cudf.DataFrame()
     nelem = 123
     gdf["a"] = ha = rng.integers(low=0, high=100, size=nelem).astype(np.int32)
-    gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
+    gdf["b"] = hb = rng.random(nelem).astype(np.float32)
 
     pdf = pd.DataFrame()
     pdf["a"] = ha
@@ -699,7 +700,7 @@ def test_dataframe_iloc_index_error():
     gdf = cudf.DataFrame()
     nelem = 123
     gdf["a"] = ha = rng.integers(low=0, high=100, size=nelem).astype(np.int32)
-    gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
+    gdf["b"] = hb = rng.random(nelem).astype(np.float32)
 
     pdf = pd.DataFrame()
     pdf["a"] = ha
@@ -718,7 +719,7 @@ def test_dataframe_take(ntake):
     df = cudf.DataFrame(
         {
             "ii": rng.integers(0, 20, nelem),
-            "ff": np.random.random(nelem),
+            "ff": rng.random(nelem),
         }
     )
 
@@ -744,7 +745,7 @@ def test_dataframe_take_with_multiindex(ntake):
 
     nelem = 9
     df["ii"] = rng.integers(0, 20, nelem)
-    df["ff"] = np.random.random(nelem)
+    df["ff"] = rng.random(nelem)
 
     take_indices = rng.integers(0, len(df), ntake)
 
@@ -1812,6 +1813,7 @@ def test_boolean_mask_columns_iloc_series():
 
 @pytest.mark.parametrize("index_type", ["single", "slice"])
 def test_loc_timestamp_issue_8585(index_type):
+    rng = np.random.default_rng(seed=0)
     # https://github.com/rapidsai/cudf/issues/8585
     start = pd.Timestamp(
         datetime.strptime("2021-03-12 00:00", "%Y-%m-%d %H:%M")
@@ -1853,6 +1855,7 @@ def test_loc_timestamp_issue_8585(index_type):
     ],
 )
 def test_loc_multiindex_timestamp_issue_8585(index_type):
+    rng = np.random.default_rng(seed=0)
     # https://github.com/rapidsai/cudf/issues/8585
     start = pd.Timestamp(
         datetime.strptime("2021-03-12 00:00", "%Y-%m-%d %H:%M")
diff --git a/python/cudf/cudf/tests/test_joining.py b/python/cudf/cudf/tests/test_joining.py
index 32a90b25791..f6941ce7fae 100644
--- a/python/cudf/cudf/tests/test_joining.py
+++ b/python/cudf/cudf/tests/test_joining.py
@@ -45,8 +45,8 @@ def make_params():
         yield (aa, bb, how)
 
     # Test floating point inputs
-    aa = np.random.random(50)
-    bb = np.random.random(50)
+    aa = rng.random(50)
+    bb = rng.random(50)
     for how in hows:
         yield (aa, bb, how)
 
@@ -162,7 +162,7 @@ def _check_series(expect, got):
     reason="bug in older version of pandas",
 )
 def test_dataframe_join_suffix():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = cudf.DataFrame(rng.integers(0, 5, (5, 3)), columns=list("abc"))
 
@@ -281,7 +281,7 @@ def test_dataframe_join_mismatch_cats(how):
 
 @pytest.mark.parametrize("on", ["key1", ["key1", "key2"], None])
 def test_dataframe_merge_on(on):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # Make cuDF
     df_left = cudf.DataFrame()
@@ -347,7 +347,7 @@ def test_dataframe_merge_on(on):
 
 
 def test_dataframe_merge_on_unknown_column():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # Make cuDF
     df_left = cudf.DataFrame()
@@ -368,7 +368,7 @@ def test_dataframe_merge_on_unknown_column():
 
 
 def test_dataframe_merge_no_common_column():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # Make cuDF
     df_left = cudf.DataFrame()
@@ -460,7 +460,7 @@ def test_dataframe_merge_order():
 @pytest.mark.parametrize("rows", [1, 5, 100])
 @pytest.mark.parametrize("how", ["left", "inner", "outer"])
 def test_dataframe_pairs_of_triples(pairs, max, rows, how):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     pdf_left = pd.DataFrame()
     pdf_right = pd.DataFrame()
@@ -504,7 +504,7 @@ def test_dataframe_pairs_of_triples(pairs, max, rows, how):
 
 
 def test_safe_merging_with_left_empty():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     pairs = ("bcd", "b")
     pdf_left = pd.DataFrame()
diff --git a/python/cudf/cudf/tests/test_json.py b/python/cudf/cudf/tests/test_json.py
index 9a967c54202..47976fc4bac 100644
--- a/python/cudf/cudf/tests/test_json.py
+++ b/python/cudf/cudf/tests/test_json.py
@@ -32,6 +32,7 @@ def make_numeric_dataframe(nrows, dtype):
 
 @pytest.fixture(params=[0, 1, 10, 100])
 def pdf(request):
+    rng = np.random.default_rng(seed=0)
     types = NUMERIC_TYPES + DATETIME_TYPES + ["bool"]
     nrows = request.param
 
diff --git a/python/cudf/cudf/tests/test_monotonic.py b/python/cudf/cudf/tests/test_monotonic.py
index 790e84559a9..a34c89f55d3 100644
--- a/python/cudf/cudf/tests/test_monotonic.py
+++ b/python/cudf/cudf/tests/test_monotonic.py
@@ -164,7 +164,8 @@ def test_series(testlist):
 
 
 def test_multiindex():
-    pdf = pd.DataFrame(np.random.rand(7, 5))
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.random(size=(7, 5)))
     pdf.index = pd.MultiIndex(
         [
             ["a", "b", "c"],
diff --git a/python/cudf/cudf/tests/test_multiindex.py b/python/cudf/cudf/tests/test_multiindex.py
index 65e898d5769..ad0e0858c43 100644
--- a/python/cudf/cudf/tests/test_multiindex.py
+++ b/python/cudf/cudf/tests/test_multiindex.py
@@ -153,7 +153,8 @@ def test_multiindex_swaplevel():
 
 
 def test_string_index():
-    pdf = pd.DataFrame(np.random.rand(5, 5))
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.random(size=(5, 5)))
     gdf = cudf.from_pandas(pdf)
     stringIndex = ["a", "b", "c", "d", "e"]
     pdf.index = stringIndex
@@ -176,7 +177,8 @@ def test_string_index():
 
 
 def test_multiindex_row_shape():
-    pdf = pd.DataFrame(np.random.rand(0, 5))
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.random(size=(0, 5)))
     gdf = cudf.from_pandas(pdf)
     pdfIndex = pd.MultiIndex([["a", "b", "c"]], [[0]])
     pdfIndex.names = ["alpha"]
@@ -193,7 +195,8 @@ def test_multiindex_row_shape():
 
 @pytest.fixture
 def pdf():
-    return pd.DataFrame(np.random.rand(7, 5))
+    rng = np.random.default_rng(seed=0)
+    return pd.DataFrame(rng.random(size=(7, 5)))
 
 
 @pytest.fixture
@@ -271,7 +274,8 @@ def test_from_pandas_series():
 
 
 def test_series_multiindex(pdfIndex):
-    ps = pd.Series(np.random.rand(7))
+    rng = np.random.default_rng(seed=0)
+    ps = pd.Series(rng.random(7))
     gs = cudf.from_pandas(ps)
     ps.index = pdfIndex
     gs.index = cudf.from_pandas(pdfIndex)
@@ -439,7 +443,8 @@ def test_multiindex_loc_rows_1_1_key(pdf, gdf, pdfIndex):
 
 
 def test_multiindex_column_shape():
-    pdf = pd.DataFrame(np.random.rand(5, 0))
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(rng.random(size=(5, 0)))
     gdf = cudf.from_pandas(pdf)
     pdfIndex = pd.MultiIndex([["a", "b", "c"]], [[0]])
     pdfIndex.names = ["alpha"]
@@ -522,9 +527,13 @@ def test_multiindex_from_product(arrays):
 
 
 def test_multiindex_index_and_columns():
-    gdf = cudf.DataFrame()
-    gdf["x"] = rng.integers(0, 5, 5)
-    gdf["y"] = rng.integers(0, 5, 5)
+    rng = np.random.default_rng(seed=0)
+    gdf = cudf.DataFrame(
+        {
+            "x": rng.integers(0, 5, 5),
+            "y": rng.integers(0, 5, 5),
+        }
+    )
     pdf = gdf.to_pandas()
     mi = cudf.MultiIndex(
         levels=[[0, 1, 2], [3, 4]],
@@ -542,6 +551,7 @@ def test_multiindex_index_and_columns():
 
 
 def test_multiindex_multiple_groupby():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
             "a": [4, 17, 4, 9, 5],
@@ -566,6 +576,7 @@ def test_multiindex_multiple_groupby():
     ],
 )
 def test_multi_column(func):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
             "x": rng.integers(0, 5, size=1000),
diff --git a/python/cudf/cudf/tests/test_orc.py b/python/cudf/cudf/tests/test_orc.py
index e7ca0c67148..a259209bed9 100644
--- a/python/cudf/cudf/tests/test_orc.py
+++ b/python/cudf/cudf/tests/test_orc.py
@@ -681,7 +681,6 @@ def test_orc_write_statistics(tmpdir, datadir, nrows, stats_freq):
 def test_orc_chunked_write_statistics(tmpdir, datadir, nrows, stats_freq):
     from pyarrow import orc
 
-    np.random.seed(0)
     supported_stat_types = supported_numpy_dtypes + ["str"]
     # Writing bool columns to multiple row groups is disabled
     # until #6763 is fixed
@@ -845,7 +844,6 @@ def test_orc_reader_gmt_timestamps(datadir):
 
 
 def test_orc_bool_encode_fail():
-    np.random.seed(0)
     buffer = BytesIO()
 
     # Generate a boolean column longer than a single row group
@@ -927,7 +925,6 @@ def test_empty_string_columns(data):
     [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
 )
 def test_orc_writer_decimal(tmpdir, scale, decimal_type):
-    np.random.seed(0)
     fname = tmpdir / "decimal.orc"
 
     expected = cudf.DataFrame({"dec_val": gen_rand_series("i", 100)})
@@ -988,7 +985,7 @@ def test_orc_string_stream_offset_issue():
 
 def generate_list_struct_buff(size=100_000):
     rd = random.Random(1)
-    np.random.seed(seed=1)
+    rng = np.random.default_rng(seed=0)
 
     buff = BytesIO()
 
@@ -1135,7 +1132,7 @@ def gen_map_buff(size):
     from pyarrow import orc
 
     rd = random.Random(1)
-    np.random.seed(seed=1)
+    rng = np.random.default_rng(seed=0)
 
     buff = BytesIO()
 
diff --git a/python/cudf/cudf/tests/test_pack.py b/python/cudf/cudf/tests/test_pack.py
index ad78621c5fa..b474bbe9bd8 100644
--- a/python/cudf/cudf/tests/test_pack.py
+++ b/python/cudf/cudf/tests/test_pack.py
@@ -24,11 +24,11 @@
 
 
 def test_sizeof_packed_dataframe():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = DataFrame()
     nelem = 1000
     df["keys"] = hkeys = np.arange(nelem, dtype=np.float64)
-    df["vals"] = hvals = np.random.random(nelem)
+    df["vals"] = hvals = rng.random(nelem)
     packed = pack(df)
 
     nbytes = hkeys.nbytes + hvals.nbytes
@@ -67,46 +67,46 @@ def assert_packed_frame_equality(df):
 
 
 def test_packed_dataframe_equality_numeric():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     nelem = 10
     df["keys"] = np.arange(nelem, dtype=np.float64)
-    df["vals"] = np.random.random(nelem)
+    df["vals"] = rng.random(nelem)
 
     check_packed_equality(df)
 
 
 def test_packed_dataframe_equality_categorical():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = pd.Categorical(
         ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_equality(df)
 
 
 def test_packed_dataframe_equality_list():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_equality(df)
 
 
 def test_packed_dataframe_equality_struct():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(
         list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_equality(df)
 
@@ -135,46 +135,46 @@ def assert_packed_frame_unique_pointers(df):
 
 
 def test_packed_dataframe_unique_pointers_numeric():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     nelem = 10
     df["keys"] = np.arange(nelem, dtype=np.float64)
-    df["vals"] = np.random.random(nelem)
+    df["vals"] = rng.random(nelem)
 
     check_packed_unique_pointers(df)
 
 
 def test_packed_dataframe_unique_pointers_categorical():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = pd.Categorical(
         ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_unique_pointers(df)
 
 
 def test_packed_dataframe_unique_pointers_list():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_unique_pointers(df)
 
 
 def test_packed_dataframe_unique_pointers_struct():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(
         list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_unique_pointers(df)
 
@@ -208,46 +208,46 @@ def assert_packed_frame_picklable(df):
 
 
 def test_pickle_packed_dataframe_numeric():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     nelem = 10
     df["keys"] = np.arange(nelem, dtype=np.float64)
-    df["vals"] = np.random.random(nelem)
+    df["vals"] = rng.random(nelem)
 
     check_packed_pickled_equality(df)
 
 
 def test_pickle_packed_dataframe_categorical():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = pd.Categorical(
         ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_pickled_equality(df)
 
 
 def test_pickle_packed_dataframe_list():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_pickled_equality(df)
 
 
 def test_pickle_packed_dataframe_struct():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(
         list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_pickled_equality(df)
 
@@ -273,45 +273,45 @@ def assert_packed_frame_serializable(df):
 
 
 def test_serialize_packed_dataframe_numeric():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     nelem = 10
     df["keys"] = np.arange(nelem, dtype=np.float64)
-    df["vals"] = np.random.random(nelem)
+    df["vals"] = rng.random(nelem)
 
     check_packed_serialized_equality(df)
 
 
 def test_serialize_packed_dataframe_categorical():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = pd.Categorical(
         ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_serialized_equality(df)
 
 
 def test_serialize_packed_dataframe_list():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_serialized_equality(df)
 
 
 def test_serialize_packed_dataframe_struct():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = Series(
         list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_packed_serialized_equality(df)
diff --git a/python/cudf/cudf/tests/test_parquet.py b/python/cudf/cudf/tests/test_parquet.py
index 55c20ef4f8d..6d3ff918673 100644
--- a/python/cudf/cudf/tests/test_parquet.py
+++ b/python/cudf/cudf/tests/test_parquet.py
@@ -53,6 +53,7 @@ def datadir(datadir):
 
 @pytest.fixture(params=[1, 5, 10, 100000])
 def simple_pdf(request):
+    rng = np.random.default_rng(seed=0)
     types = [
         "bool",
         "int8",
@@ -92,6 +93,7 @@ def simple_gdf(simple_pdf):
 
 
 def build_pdf(num_columns, day_resolution_timestamps):
+    rng = np.random.default_rng(seed=0)
     types = [
         "bool",
         "int8",
@@ -1909,6 +1911,7 @@ def test_parquet_writer_dictionary_setting(use_dict, max_dict_size):
 @pytest.mark.parametrize("filename", ["myfile.parquet", None])
 @pytest.mark.parametrize("cols", [["b"], ["c", "b"]])
 def test_parquet_partitioned(tmpdir_factory, cols, filename):
+    rng = np.random.default_rng(seed=0)
     # Checks that write_to_dataset is wrapping to_parquet
     # as expected
     gdf_dir = str(tmpdir_factory.mktemp("gdf_dir"))
@@ -1954,6 +1957,7 @@ def test_parquet_partitioned(tmpdir_factory, cols, filename):
 
 @pytest.mark.parametrize("kwargs", [{"nrows": 1}, {"skip_rows": 1}])
 def test_parquet_partitioned_notimplemented(tmpdir_factory, kwargs):
+    rng = np.random.default_rng(seed=0)
     # Checks that write_to_dataset is wrapping to_parquet
     # as expected
     pdf_dir = str(tmpdir_factory.mktemp("pdf_dir"))
@@ -2127,6 +2131,7 @@ def test_parquet_writer_chunked_partitioned_context(tmpdir_factory):
 @pytest.mark.parametrize("cols", [None, ["b"]])
 @pytest.mark.parametrize("store_schema", [True, False])
 def test_parquet_write_to_dataset(tmpdir_factory, cols, store_schema):
+    rng = np.random.default_rng(seed=0)
     dir1 = tmpdir_factory.mktemp("dir1")
     dir2 = tmpdir_factory.mktemp("dir2")
     if cols is None:
@@ -3214,6 +3219,7 @@ def test_parquet_nested_struct_list():
 
 def test_parquet_writer_zstd():
     size = 12345
+    rng = np.random.default_rng(seed=0)
     expected = cudf.DataFrame(
         {
             "a": np.arange(0, stop=size, dtype="float64"),
diff --git a/python/cudf/cudf/tests/test_pickling.py b/python/cudf/cudf/tests/test_pickling.py
index 0f13a9e173a..2f10a5dfd74 100644
--- a/python/cudf/cudf/tests/test_pickling.py
+++ b/python/cudf/cudf/tests/test_pickling.py
@@ -40,33 +40,33 @@ def assert_frame_picklable(df):
 
 
 def test_pickle_dataframe_numeric():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = DataFrame()
     nelem = 10
     df["keys"] = np.arange(nelem, dtype=np.float64)
-    df["vals"] = np.random.random(nelem)
+    df["vals"] = rng.random(nelem)
 
     check_serialization(df)
 
 
 def test_pickle_dataframe_categorical():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = DataFrame()
     df["keys"] = pd.Categorical(
         ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
     )
-    df["vals"] = np.random.random(len(df))
+    df["vals"] = rng.random(len(df))
 
     check_serialization(df)
 
 
 def test_memory_usage_dataframe():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = DataFrame()
     nelem = 1000
     df["keys"] = hkeys = np.arange(nelem, dtype=np.float64)
-    df["vals"] = hvals = np.random.random(nelem)
+    df["vals"] = hvals = rng.random(nelem)
 
     nbytes = hkeys.nbytes + hvals.nbytes
     sizeof = df.memory_usage().sum()
@@ -98,11 +98,11 @@ def test_pickle_buffer():
 
 @pytest.mark.parametrize("named", [True, False])
 def test_pickle_series(named):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     if named:
-        ser = Series(np.random.random(10), name="a")
+        ser = Series(rng.random(10), name="a")
     else:
-        ser = Series(np.random.random(10))
+        ser = Series(rng.random(10))
 
     pickled = pickle.dumps(ser)
     out = pickle.loads(pickled)
diff --git a/python/cudf/cudf/tests/test_query.py b/python/cudf/cudf/tests/test_query.py
index 84f735bf5b7..7685d09203e 100644
--- a/python/cudf/cudf/tests/test_query.py
+++ b/python/cudf/cudf/tests/test_query.py
@@ -45,10 +45,10 @@ def test_query(data, fn, nulls):
     # prepare
     nelem, seed = data
     expect_fn, query_expr = fn
-    np.random.seed(seed)
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame()
     pdf["a"] = np.arange(nelem)
-    pdf["b"] = np.random.random(nelem) * nelem
+    pdf["b"] = rng.random(nelem) * nelem
     if nulls:
         pdf.loc[::2, "a"] = None
     gdf = cudf.from_pandas(pdf)
@@ -71,10 +71,10 @@ def test_query_ref_env(data, fn):
     # prepare
     nelem, seed = data
     expect_fn, query_expr = fn
-    np.random.seed(seed)
+    rng = np.random.default_rng(seed=0)
     df = DataFrame()
     df["a"] = aa = np.arange(nelem)
-    df["b"] = bb = np.random.random(nelem) * nelem
+    df["b"] = bb = rng.random(nelem) * nelem
     c = 2.3
     d = 1.2
     # udt
@@ -121,7 +121,7 @@ def test_query_local_dict():
 
 
 def test_query_splitted_combine():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {"x": rng.integers(0, 5, size=10), "y": rng.normal(size=10)}
     )
diff --git a/python/cudf/cudf/tests/test_rank.py b/python/cudf/cudf/tests/test_rank.py
index 4c1d8ce92ae..4e33f91b09b 100644
--- a/python/cudf/cudf/tests/test_rank.py
+++ b/python/cudf/cudf/tests/test_rank.py
@@ -125,9 +125,11 @@ def test_rank_error_arguments(pdf):
     )
 
 
+rng = np.random.default_rng(seed=0)
+
 sort_group_args = [
     np.full((3,), np.nan),
-    100 * np.random.random(10),
+    100 * rng.random(10),
     np.full((3,), np.inf),
     np.full((3,), -np.inf),
 ]
@@ -145,12 +147,9 @@ def test_rank_error_arguments(pdf):
     ),
 )
 def test_series_rank_combinations(elem, dtype):
-    np.random.seed(0)
     aa = np.fromiter(chain.from_iterable(elem), np.float64).astype(dtype)
-    gdf = DataFrame()
-    df = pd.DataFrame()
-    gdf["a"] = aa
-    df["a"] = aa
+    gdf = DataFrame({"a": aa})
+    df = pd.DataFrame({"a": aa})
     ranked_gs = gdf["a"].rank(method="first")
     ranked_ps = df["a"].rank(method="first")
     # Check
diff --git a/python/cudf/cudf/tests/test_reductions.py b/python/cudf/cudf/tests/test_reductions.py
index 2cf6a9517a7..7513bef69da 100644
--- a/python/cudf/cudf/tests/test_reductions.py
+++ b/python/cudf/cudf/tests/test_reductions.py
@@ -62,7 +62,6 @@ def test_sum_string():
 )
 @pytest.mark.parametrize("nelem", params_sizes)
 def test_sum_decimal(dtype, nelem):
-    np.random.seed(0)
     data = [str(x) for x in gen_rand("int64", nelem) / 100]
 
     expected = pd.Series([Decimal(x) for x in data]).sum()
@@ -73,15 +72,13 @@ def test_sum_decimal(dtype, nelem):
 
 @pytest.mark.parametrize("dtype,nelem", params)
 def test_product(dtype, nelem):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     dtype = cudf.dtype(dtype).type
     if cudf.dtype(dtype).kind in {"u", "i"}:
         data = np.ones(nelem, dtype=dtype)
         # Set at most 30 items to [0..2) to keep the value within 2^32
         for _ in range(30):
-            data[rng.integers(low=0, high=nelem, size=1)] = (
-                np.random.uniform() * 2
-            )
+            data[rng.integers(low=0, high=nelem, size=1)] = rng.uniform() * 2
     else:
         data = gen_rand(dtype, nelem)
 
@@ -104,7 +101,6 @@ def test_product(dtype, nelem):
     ],
 )
 def test_product_decimal(dtype):
-    np.random.seed(0)
     data = [str(x) for x in gen_rand("int8", 3) / 10]
 
     expected = pd.Series([Decimal(x) for x in data]).product()
@@ -153,7 +149,6 @@ def test_sum_of_squares(dtype, nelem):
     ],
 )
 def test_sum_of_squares_decimal(dtype):
-    np.random.seed(0)
     data = [str(x) for x in gen_rand("int8", 3) / 10]
 
     expected = pd.Series([Decimal(x) for x in data]).pow(2).sum()
@@ -186,7 +181,6 @@ def test_min(dtype, nelem):
 )
 @pytest.mark.parametrize("nelem", params_sizes)
 def test_min_decimal(dtype, nelem):
-    np.random.seed(0)
     data = [str(x) for x in gen_rand("int64", nelem) / 100]
 
     expected = pd.Series([Decimal(x) for x in data]).min()
@@ -219,7 +213,6 @@ def test_max(dtype, nelem):
 )
 @pytest.mark.parametrize("nelem", params_sizes)
 def test_max_decimal(dtype, nelem):
-    np.random.seed(0)
     data = [str(x) for x in gen_rand("int64", nelem) / 100]
 
     expected = pd.Series([Decimal(x) for x in data]).max()
@@ -256,6 +249,7 @@ def test_sum_boolean():
 
 
 def test_date_minmax():
+    rng = np.random.default_rng(seed=0)
     np_data = rng.normal(size=10**3)
     gdf_data = Series(np_data)
 
diff --git a/python/cudf/cudf/tests/test_repr.py b/python/cudf/cudf/tests/test_repr.py
index a892fe62b8b..bf0c97adb00 100644
--- a/python/cudf/cudf/tests/test_repr.py
+++ b/python/cudf/cudf/tests/test_repr.py
@@ -25,6 +25,7 @@
 @pytest.mark.parametrize("dtype", repr_categories)
 @pytest.mark.parametrize("nrows", [0, 5, 10])
 def test_null_series(nrows, dtype):
+    rng = np.random.default_rng(seed=0)
     size = 5
     sr = cudf.Series(rng.integers(1, 9, size)).astype(dtype)
     sr[rng.choice([False, True], size=size)] = None
@@ -60,6 +61,7 @@ def test_null_series(nrows, dtype):
 
 @pytest.mark.parametrize("ncols", [1, 2, 3, 4, 5, 10])
 def test_null_dataframe(ncols):
+    rng = np.random.default_rng(seed=0)
     size = 20
     gdf = cudf.DataFrame()
     for idx, dtype in enumerate(dtype_categories):
@@ -77,6 +79,7 @@ def test_null_dataframe(ncols):
 @pytest.mark.parametrize("nrows", [None, 0, 1, 2, 9, 10, 11, 19, 20, 21])
 def test_full_series(nrows, dtype):
     size = 20
+    rng = np.random.default_rng(seed=0)
     ps = pd.Series(rng.integers(0, 100, size)).astype(dtype)
     sr = cudf.from_pandas(ps)
     pd.options.display.max_rows = nrows
@@ -89,6 +92,7 @@ def test_full_series(nrows, dtype):
 @pytest.mark.parametrize("size", [20, 21])
 @pytest.mark.parametrize("dtype", repr_categories)
 def test_full_dataframe_20(dtype, size, nrows, ncols):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {idx: rng.integers(0, 100, size) for idx in range(size)}
     ).astype(dtype)
@@ -178,6 +182,7 @@ def test_mixed_series(mixed_pdf, mixed_gdf):
 
 
 def test_MI():
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame(
         {
             "a": rng.integers(0, 4, 10),
@@ -223,6 +228,7 @@ def test_groupby_MI(nrows, ncols):
 @pytest.mark.parametrize("dtype", utils.NUMERIC_TYPES)
 @pytest.mark.parametrize("length", [0, 1, 10, 100, 1000])
 def test_generic_index(length, dtype):
+    rng = np.random.default_rng(seed=0)
     psr = pd.Series(
         range(length),
         index=rng.integers(0, high=100, size=length).astype(dtype),
diff --git a/python/cudf/cudf/tests/test_resampling.py b/python/cudf/cudf/tests/test_resampling.py
index 8a366ed9cd2..4ac4484bf82 100644
--- a/python/cudf/cudf/tests/test_resampling.py
+++ b/python/cudf/cudf/tests/test_resampling.py
@@ -79,8 +79,9 @@ def test_series_resample_asfreq(rule):
 
 
 def test_dataframe_resample_aggregation_simple():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
-        np.random.randn(1000, 3),
+        rng.standard_normal(size=(1000, 3)),
         index=pd.date_range("1/1/2012", freq="s", periods=1000),
         columns=["A", "B", "C"],
     )
@@ -91,8 +92,9 @@ def test_dataframe_resample_aggregation_simple():
 
 
 def test_dataframe_resample_multiagg():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
-        np.random.randn(1000, 3),
+        rng.standard_normal(size=(1000, 3)),
         index=pd.date_range("1/1/2012", freq="s", periods=1000),
         columns=["A", "B", "C"],
     )
@@ -104,10 +106,11 @@ def test_dataframe_resample_multiagg():
 
 
 def test_dataframe_resample_on():
+    rng = np.random.default_rng(seed=0)
     # test resampling on a specified column
     pdf = pd.DataFrame(
         {
-            "x": np.random.randn(1000),
+            "x": rng.standard_normal(size=(1000)),
             "y": pd.date_range("1/1/2012", freq="s", periods=1000),
         }
     )
@@ -119,15 +122,16 @@ def test_dataframe_resample_on():
 
 
 def test_dataframe_resample_level():
+    rng = np.random.default_rng(seed=0)
     # test resampling on a specific level of a MultIndex
     pdf = pd.DataFrame(
         {
-            "x": np.random.randn(1000),
+            "x": rng.standard_normal(size=1000),
             "y": pd.date_range("1/1/2012", freq="s", periods=1000),
         }
     )
     pdi = pd.MultiIndex.from_frame(pdf)
-    pdf = pd.DataFrame({"a": np.random.randn(1000)}, index=pdi)
+    pdf = pd.DataFrame({"a": rng.standard_normal(size=1000)}, index=pdi)
     gdf = cudf.from_pandas(pdf)
     assert_resample_results_equal(
         pdf.resample("3min", level="y").mean(),
@@ -153,11 +157,12 @@ def test_dataframe_resample_level():
     reason="Fails in older versions of pandas",
 )
 def test_resampling_frequency_conversion(in_freq, sampling_freq, out_freq):
+    rng = np.random.default_rng(seed=0)
     # test that we cast to the appropriate frequency
     # when resampling:
     pdf = pd.DataFrame(
         {
-            "x": np.random.randn(100),
+            "x": rng.standard_normal(size=100),
             "y": pd.date_range("1/1/2012", freq=in_freq, periods=100),
         }
     )
diff --git a/python/cudf/cudf/tests/test_reshape.py b/python/cudf/cudf/tests/test_reshape.py
index 779f23bb5ab..1ddf517e041 100644
--- a/python/cudf/cudf/tests/test_reshape.py
+++ b/python/cudf/cudf/tests/test_reshape.py
@@ -35,6 +35,9 @@
     pytest_xfail = pytest.mark.skipif
 
 
+rng = np.random.default_rng(seed=0)
+
+
 @pytest.mark.parametrize("num_id_vars", [0, 1, 2])
 @pytest.mark.parametrize("num_value_vars", [0, 1, 2])
 @pytest.mark.parametrize("num_rows", [1, 2, 100])
@@ -266,7 +269,7 @@ def test_df_stack_multiindex_column_axis_pd_example(level):
         names=["exp", "animal", "hair_length"],
     )
 
-    df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
+    df = pd.DataFrame(rng.standard_normal(size=(4, 4)), columns=columns)
 
     with expect_warning_if(PANDAS_GE_220, FutureWarning):
         expect = df.stack(level=level, future_stack=False)
@@ -709,8 +712,8 @@ def test_pivot_duplicate_error():
             "A": ["one", "one", "two", "three"] * 6,
             "B": ["A", "B", "C"] * 8,
             "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
-            "D": np.random.randn(24),
-            "E": np.random.randn(24),
+            "D": rng.standard_normal(size=24),
+            "E": rng.standard_normal(size=24),
         }
     ],
 )
@@ -747,8 +750,8 @@ def test_pivot_table_simple(data, aggfunc, fill_value):
             "A": ["one", "one", "two", "three"] * 6,
             "B": ["A", "B", "C"] * 8,
             "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
-            "D": np.random.randn(24),
-            "E": np.random.randn(24),
+            "D": rng.standard_normal(size=24),
+            "E": rng.standard_normal(size=24),
         }
     ],
 )
diff --git a/python/cudf/cudf/tests/test_serialize.py b/python/cudf/cudf/tests/test_serialize.py
index 542007ac34e..68f2aaf9cab 100644
--- a/python/cudf/cudf/tests/test_serialize.py
+++ b/python/cudf/cudf/tests/test_serialize.py
@@ -170,11 +170,15 @@ def test_serialize_dataframe():
 
 
 def test_serialize_dataframe_with_index():
-    df = cudf.DataFrame()
-    df["a"] = np.arange(100)
-    df["b"] = np.random.random(100)
-    df["c"] = pd.Categorical(
-        ["a", "b", "c", "_", "_"] * 20, categories=["a", "b", "c"]
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame(
+        {
+            "a": np.arange(100),
+            "b": rng.random(100),
+            "c": pd.Categorical(
+                ["a", "b", "c", "_", "_"] * 20, categories=["a", "b", "c"]
+            ),
+        }
     )
     df = df.sort_values("b")
     outdf = cudf.DataFrame.deserialize(*df.serialize())
@@ -200,6 +204,7 @@ def test_serialize_generic_index():
 
 
 def test_serialize_multi_index():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
             "a": [4, 17, 4, 9, 5],
@@ -218,7 +223,8 @@ def test_serialize_multi_index():
 
 def test_serialize_masked_series():
     nelem = 50
-    data = np.random.random(nelem)
+    rng = np.random.default_rng(seed=0)
+    data = rng.random(nelem)
     mask = utils.random_bitmask(nelem)
     bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
     null_count = utils.count_zero(bitmask)
@@ -229,10 +235,14 @@ def test_serialize_masked_series():
 
 
 def test_serialize_groupby_df():
-    df = cudf.DataFrame()
-    df["key_1"] = rng.integers(0, 20, 100)
-    df["key_2"] = rng.integers(0, 20, 100)
-    df["val"] = np.arange(100, dtype=np.float32)
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame(
+        {
+            "key_1": rng.integers(0, 20, 100),
+            "key_2": rng.integers(0, 20, 100),
+            "val": np.arange(100, dtype=np.float32),
+        }
+    )
     gb = df.groupby(["key_1", "key_2"], sort=True)
     outgb = gb.deserialize(*gb.serialize())
     expect = gb.mean()
@@ -241,8 +251,8 @@ def test_serialize_groupby_df():
 
 
 def test_serialize_groupby_external():
-    df = cudf.DataFrame()
-    df["val"] = np.arange(100, dtype=np.float32)
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame({"val": np.arange(100, dtype=np.float32)})
     gb = df.groupby(cudf.Series(rng.integers(0, 20, 100)))
     outgb = gb.deserialize(*gb.serialize())
     expect = gb.mean()
@@ -262,6 +272,7 @@ def test_serialize_groupby_level():
 
 
 def test_serialize_groupby_sr():
+    rng = np.random.default_rng(seed=0)
     sr = cudf.Series(rng.integers(0, 20, 100))
     gb = sr.groupby(sr // 2)
     outgb = gb.deserialize(*gb.serialize())
@@ -271,6 +282,7 @@ def test_serialize_groupby_sr():
 
 
 def test_serialize_datetime():
+    rng = np.random.default_rng(seed=0)
     # Make frame with datetime column
     df = pd.DataFrame(
         {"x": rng.integers(0, 5, size=20), "y": rng.normal(size=20)}
@@ -285,6 +297,7 @@ def test_serialize_datetime():
 
 
 def test_serialize_string():
+    rng = np.random.default_rng(seed=0)
     # Make frame with string column
     df = pd.DataFrame(
         {"x": rng.integers(0, 5, size=5), "y": rng.normal(size=5)}
diff --git a/python/cudf/cudf/tests/test_series.py b/python/cudf/cudf/tests/test_series.py
index 9fb492c4e25..e7e45c574cc 100644
--- a/python/cudf/cudf/tests/test_series.py
+++ b/python/cudf/cudf/tests/test_series.py
@@ -27,6 +27,8 @@
     gen_rand,
 )
 
+rng = np.random.default_rng(seed=0)
+
 
 def _series_na_data():
     return [
@@ -546,7 +548,7 @@ def test_series_datetime_value_counts(data, nulls, normalize, dropna):
 @pytest.mark.parametrize("num_elements", [10, 100, 1000])
 def test_categorical_value_counts(dropna, normalize, num_elements):
     # create categorical series
-    np.random.seed(12)
+    rng = np.random.default_rng(seed=0)
     pd_cat = pd.Categorical(
         pd.Series(
             rng.choice(list(ascii_letters + digits), num_elements),
@@ -1943,6 +1945,7 @@ def test_diff_many_dtypes(data):
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["bool"])
 @pytest.mark.parametrize("series_bins", [True, False])
 def test_series_digitize(num_rows, num_bins, right, dtype, series_bins):
+    rng = np.random.default_rng(seed=0)
     data = rng.integers(0, 100, num_rows).astype(dtype)
     bins = np.unique(np.sort(rng.integers(2, 95, num_bins).astype(dtype)))
     s = cudf.Series(data)
@@ -1957,6 +1960,7 @@ def test_series_digitize(num_rows, num_bins, right, dtype, series_bins):
 
 
 def test_series_digitize_invalid_bins():
+    rng = np.random.default_rng(seed=0)
     s = cudf.Series(rng.integers(0, 30, 80), dtype="int32")
     bins = cudf.Series([2, None, None, 50, 90], dtype="int32")
 
@@ -2038,6 +2042,7 @@ def test_default_float_bitwidth_construction(default_float_bitwidth, data):
 
 def test_series_ordered_dedup():
     # part of https://github.com/rapidsai/cudf/issues/11486
+    rng = np.random.default_rng(seed=0)
     sr = cudf.Series(rng.integers(0, 100, 1000))
     # pandas unique() preserves order
     expect = pd.Series(sr.to_pandas().unique())
diff --git a/python/cudf/cudf/tests/test_seriesmap.py b/python/cudf/cudf/tests/test_seriesmap.py
index 3d8b6a79d2a..db1de7d0cf4 100644
--- a/python/cudf/cudf/tests/test_seriesmap.py
+++ b/python/cudf/cudf/tests/test_seriesmap.py
@@ -47,8 +47,8 @@ def test_series_map_callable_numeric_basic():
 @pytest.mark.parametrize("nelem", list(product([2, 10, 100, 1000])))
 def test_series_map_callable_numeric_random(nelem):
     # Generate data
-    np.random.seed(0)
-    data = np.random.random(nelem) * 100
+    rng = np.random.default_rng(seed=0)
+    data = rng.random(nelem) * 100
 
     sr = Series(data)
     pdsr = pd.Series(data)
diff --git a/python/cudf/cudf/tests/test_sorting.py b/python/cudf/cudf/tests/test_sorting.py
index 09b83ff37b7..7e5ce713c7e 100644
--- a/python/cudf/cudf/tests/test_sorting.py
+++ b/python/cudf/cudf/tests/test_sorting.py
@@ -34,10 +34,10 @@
     "nelem,dtype", list(product(sort_nelem_args, sort_dtype_args))
 )
 def test_dataframe_sort_values(nelem, dtype):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = DataFrame()
-    df["a"] = aa = (100 * np.random.random(nelem)).astype(dtype)
-    df["b"] = bb = (100 * np.random.random(nelem)).astype(dtype)
+    df["a"] = aa = (100 * rng.random(nelem)).astype(dtype)
+    df["b"] = bb = (100 * rng.random(nelem)).astype(dtype)
     sorted_df = df.sort_values(by="a")
     # Check
     sorted_index = np.argsort(aa, kind="mergesort")
@@ -85,9 +85,9 @@ def test_series_sort_values_ignore_index(ignore_index):
     "nelem,sliceobj", list(product([10, 100], sort_slice_args))
 )
 def test_dataframe_sort_values_sliced(nelem, sliceobj):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame()
-    df["a"] = np.random.random(nelem)
+    df["a"] = rng.random(nelem)
 
     expect = df[sliceobj]["a"].sort_values()
     gdf = DataFrame.from_pandas(df)
@@ -100,8 +100,8 @@ def test_dataframe_sort_values_sliced(nelem, sliceobj):
     list(product(sort_nelem_args, sort_dtype_args, [True, False])),
 )
 def test_series_argsort(nelem, dtype, asc):
-    np.random.seed(0)
-    sr = Series((100 * np.random.random(nelem)).astype(dtype))
+    rng = np.random.default_rng(seed=0)
+    sr = Series((100 * rng.random(nelem)).astype(dtype))
     res = sr.argsort(ascending=asc)
 
     if asc:
@@ -116,8 +116,8 @@ def test_series_argsort(nelem, dtype, asc):
     "nelem,asc", list(product(sort_nelem_args, [True, False]))
 )
 def test_series_sort_index(nelem, asc):
-    np.random.seed(0)
-    sr = Series(100 * np.random.random(nelem))
+    rng = np.random.default_rng(seed=0)
+    sr = Series(100 * rng.random(nelem))
     psr = sr.to_pandas()
 
     expected = psr.sort_index(ascending=asc)
@@ -167,9 +167,9 @@ def test_series_nsmallest(data, n):
 @pytest.mark.parametrize("op", ["nsmallest", "nlargest"])
 @pytest.mark.parametrize("columns", ["a", ["b", "a"]])
 def test_dataframe_nlargest_nsmallest(nelem, n, op, columns):
-    np.random.seed(0)
-    aa = np.random.random(nelem)
-    bb = np.random.random(nelem)
+    rng = np.random.default_rng(seed=0)
+    aa = rng.random(nelem)
+    bb = rng.random(nelem)
 
     df = DataFrame({"a": aa, "b": bb})
     pdf = df.to_pandas()
@@ -181,10 +181,10 @@ def test_dataframe_nlargest_nsmallest(nelem, n, op, columns):
 )
 def test_dataframe_nlargest_sliced(counts, sliceobj):
     nelem, n = counts
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame()
-    df["a"] = np.random.random(nelem)
-    df["b"] = np.random.random(nelem)
+    df["a"] = rng.random(nelem)
+    df["b"] = rng.random(nelem)
 
     expect = df[sliceobj].nlargest(n, "a")
     gdf = DataFrame.from_pandas(df)
@@ -197,10 +197,10 @@ def test_dataframe_nlargest_sliced(counts, sliceobj):
 )
 def test_dataframe_nsmallest_sliced(counts, sliceobj):
     nelem, n = counts
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame()
-    df["a"] = np.random.random(nelem)
-    df["b"] = np.random.random(nelem)
+    df["a"] = rng.random(nelem)
+    df["b"] = rng.random(nelem)
 
     expect = df[sliceobj].nsmallest(n, "a")
     gdf = DataFrame.from_pandas(df)
@@ -216,7 +216,7 @@ def test_dataframe_nsmallest_sliced(counts, sliceobj):
 def test_dataframe_multi_column(
     num_cols, num_rows, dtype, ascending, na_position
 ):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     by = list(string.ascii_lowercase[:num_cols])
     pdf = pd.DataFrame()
 
@@ -244,7 +244,7 @@ def test_dataframe_multi_column(
 def test_dataframe_multi_column_nulls(
     num_cols, num_rows, dtype, nulls, ascending, na_position
 ):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     by = list(string.ascii_lowercase[:num_cols])
     pdf = pd.DataFrame()
 
@@ -295,8 +295,8 @@ def test_dataframe_multi_column_nulls_multiple_ascending(
 
 @pytest.mark.parametrize("nelem", [1, 100])
 def test_series_nlargest_nelem(nelem):
-    np.random.seed(0)
-    elems = np.random.random(nelem)
+    rng = np.random.default_rng(seed=0)
+    elems = rng.random(nelem)
     gds = Series(elems).nlargest(nelem)
     pds = pd.Series(elems).nlargest(nelem)
 
@@ -308,11 +308,14 @@ def test_series_nlargest_nelem(nelem):
 @pytest.mark.parametrize("keep", [True, False])
 def test_dataframe_scatter_by_map(map_size, nelem, keep):
     strlist = ["dog", "cat", "fish", "bird", "pig", "fox", "cow", "goat"]
-    np.random.seed(0)
-    df = DataFrame()
-    df["a"] = rng.choice(strlist[:map_size], nelem)
-    df["b"] = np.random.uniform(low=0, high=map_size, size=nelem)
-    df["c"] = rng.integers(map_size, size=nelem)
+    rng = np.random.default_rng(seed=0)
+    df = DataFrame(
+        {
+            "a": rng.choice(strlist[:map_size], nelem),
+            "b": rng.uniform(low=0, high=map_size, size=nelem),
+            "c": rng.integers(map_size, size=nelem),
+        }
+    )
     df["d"] = df["a"].astype("category")
 
     def _check_scatter_by_map(dfs, col):
@@ -381,10 +384,10 @@ def _check_scatter_by_map(dfs, col):
     "kind", ["quicksort", "mergesort", "heapsort", "stable"]
 )
 def test_dataframe_sort_values_kind(nelem, dtype, kind):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = DataFrame()
-    df["a"] = aa = (100 * np.random.random(nelem)).astype(dtype)
-    df["b"] = bb = (100 * np.random.random(nelem)).astype(dtype)
+    df["a"] = aa = (100 * rng.random(nelem)).astype(dtype)
+    df["b"] = bb = (100 * rng.random(nelem)).astype(dtype)
     with expect_warning_if(kind != "quicksort", UserWarning):
         sorted_df = df.sort_values(by="a", kind=kind)
     # Check
diff --git a/python/cudf/cudf/tests/test_sparse_df.py b/python/cudf/cudf/tests/test_sparse_df.py
index 3248e7f72c0..8b68ae6480b 100644
--- a/python/cudf/cudf/tests/test_sparse_df.py
+++ b/python/cudf/cudf/tests/test_sparse_df.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+# Copyright (c) 2018-2024, NVIDIA CORPORATION.
 
 import numpy as np
 
@@ -6,7 +6,8 @@
 
 
 def test_to_dense_array():
-    data = np.random.random(8)
+    rng = np.random.default_rng(seed=0)
+    data = rng.random(8)
     mask = np.asarray([0b11010110]).astype(np.byte)
 
     sr = Series.from_masked_array(data=data, mask=mask, null_count=3)
diff --git a/python/cudf/cudf/tests/test_stats.py b/python/cudf/cudf/tests/test_stats.py
index 5a01d81b012..a4ff042e37c 100644
--- a/python/cudf/cudf/tests/test_stats.py
+++ b/python/cudf/cudf/tests/test_stats.py
@@ -19,13 +19,15 @@
 
 interpolation_methods = ["linear", "lower", "higher", "midpoint", "nearest"]
 
+rng = np.random.default_rng(seed=0)
+
 
 @pytest.mark.parametrize("method", methods)
 @pytest.mark.parametrize("dtype", params_dtypes)
 @pytest.mark.parametrize("skipna", [True, False])
 def test_series_reductions(method, dtype, skipna):
-    np.random.seed(0)
-    arr = np.random.random(100)
+    rng = np.random.default_rng(seed=0)
+    arr = rng.random(100)
     if np.issubdtype(dtype, np.integer):
         arr *= 100
         mask = arr > 10
@@ -56,8 +58,8 @@ def call_test(sr, skipna):
 def test_series_reductions_concurrency(method):
     e = ThreadPoolExecutor(10)
 
-    np.random.seed(0)
-    srs = [cudf.Series(np.random.random(10000)) for _ in range(1)]
+    rng = np.random.default_rng(seed=0)
+    srs = [cudf.Series(rng.random(10000)) for _ in range(1)]
 
     def call_test(sr):
         fn = getattr(sr, method)
@@ -74,8 +76,8 @@ def f(sr):
 
 @pytest.mark.parametrize("ddof", range(3))
 def test_series_std(ddof):
-    np.random.seed(0)
-    arr = np.random.random(100) - 0.5
+    rng = np.random.default_rng(seed=0)
+    arr = rng.random(100) - 0.5
     sr = cudf.Series(arr)
     pd = sr.to_pandas()
     got = sr.std(ddof=ddof)
@@ -311,8 +313,8 @@ def test_skew_series(data, null_flag, numeric_only):
 @pytest.mark.parametrize("dtype", params_dtypes)
 @pytest.mark.parametrize("num_na", [0, 1, 50, 99, 100])
 def test_series_median(dtype, num_na):
-    np.random.seed(0)
-    arr = np.random.random(100)
+    rng = np.random.default_rng(seed=0)
+    arr = rng.random(100)
     if np.issubdtype(dtype, np.integer):
         arr *= 100
     mask = np.arange(100) >= num_na
diff --git a/python/cudf/cudf/tests/test_string.py b/python/cudf/cudf/tests/test_string.py
index b806b9258a2..07b3ba6f772 100644
--- a/python/cudf/cudf/tests/test_string.py
+++ b/python/cudf/cudf/tests/test_string.py
@@ -36,6 +36,7 @@
 idx_list = [None, [10, 11, 12, 13, 14]]
 
 idx_id_list = ["None_index", "Set_index"]
+rng = np.random.default_rng(seed=0)
 
 
 def raise_builder(flags, exceptions):
@@ -1078,7 +1079,7 @@ def test_string_set_scalar(scalar):
 
 
 def test_string_index():
-    pdf = pd.DataFrame(np.random.rand(5, 5))
+    pdf = pd.DataFrame(rng.random(size=(5, 5)))
     gdf = cudf.DataFrame.from_pandas(pdf)
     stringIndex = ["a", "b", "c", "d", "e"]
     pdf.index = stringIndex
diff --git a/python/cudf/cudf/tests/test_transform.py b/python/cudf/cudf/tests/test_transform.py
index 88938457545..1305022d7fa 100644
--- a/python/cudf/cudf/tests/test_transform.py
+++ b/python/cudf/cudf/tests/test_transform.py
@@ -24,8 +24,8 @@ def _generic_function(a):
 )
 def test_apply_python_lambda(dtype, udf, testfunc):
     size = 500
-
-    lhs_arr = np.random.random(size).astype(dtype)
+    rng = np.random.default_rng(seed=0)
+    lhs_arr = rng.random(size).astype(dtype)
     lhs_ser = Series(lhs_arr)
 
     out_ser = lhs_ser.apply(udf)
diff --git a/python/cudf/cudf/tests/test_unaops.py b/python/cudf/cudf/tests/test_unaops.py
index 5f5d79c1dce..b714beb0069 100644
--- a/python/cudf/cudf/tests/test_unaops.py
+++ b/python/cudf/cudf/tests/test_unaops.py
@@ -17,7 +17,8 @@
 
 @pytest.mark.parametrize("dtype", utils.NUMERIC_TYPES)
 def test_series_abs(dtype):
-    arr = (np.random.random(1000) * 100).astype(dtype)
+    rng = np.random.default_rng(seed=0)
+    arr = (rng.random(1000) * 100).astype(dtype)
     sr = Series(arr)
     np.testing.assert_equal(sr.abs().to_numpy(), np.abs(arr))
     np.testing.assert_equal(abs(sr).to_numpy(), abs(arr))
@@ -25,22 +26,24 @@ def test_series_abs(dtype):
 
 @pytest.mark.parametrize("dtype", utils.INTEGER_TYPES)
 def test_series_invert(dtype):
-    arr = (np.random.random(1000) * 100).astype(dtype)
+    rng = np.random.default_rng(seed=0)
+    arr = (rng.random(1000) * 100).astype(dtype)
     sr = Series(arr)
     np.testing.assert_equal((~sr).to_numpy(), np.invert(arr))
     np.testing.assert_equal((~sr).to_numpy(), ~arr)
 
 
 def test_series_neg():
-    arr = np.random.random(100) * 100
+    rng = np.random.default_rng(seed=0)
+    arr = rng.random(100) * 100
     sr = Series(arr)
     np.testing.assert_equal((-sr).to_numpy(), -arr)
 
 
 @pytest.mark.parametrize("mth", ["min", "max", "sum", "product"])
 def test_series_pandas_methods(mth):
-    np.random.seed(0)
-    arr = (1 + np.random.random(5) * 100).astype(np.int64)
+    rng = np.random.default_rng(seed=0)
+    arr = (1 + rng.random(5) * 100).astype(np.int64)
     sr = Series(arr)
     psr = pd.Series(arr)
     np.testing.assert_equal(getattr(sr, mth)(), getattr(psr, mth)())
diff --git a/python/cudf/cudf/tests/test_unique.py b/python/cudf/cudf/tests/test_unique.py
index 6a1366770b3..9a1c3b213b8 100644
--- a/python/cudf/cudf/tests/test_unique.py
+++ b/python/cudf/cudf/tests/test_unique.py
@@ -12,7 +12,7 @@
 @pytest.fixture
 def df():
     df = cudf.DataFrame()
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     arr = rng.integers(2, size=10, dtype=np.int64)
     df["foo"] = arr
diff --git a/python/cudf/cudf/utils/hash_vocab_utils.py b/python/cudf/cudf/utils/hash_vocab_utils.py
index babe4be2715..66c9dd5135f 100644
--- a/python/cudf/cudf/utils/hash_vocab_utils.py
+++ b/python/cudf/cudf/utils/hash_vocab_utils.py
@@ -70,9 +70,10 @@ def _get_space_util(bins, init_bins):
 
 
 def _pick_initial_a_b(data, max_constant, init_bins):
+    rng = np.random.default_rng(seed=0)
     while True:
-        a = np.random.randint(2**12, 2**15)
-        b = np.random.randint(2**12, 2**15)
+        a = rng.integers(2**12, 2**15)
+        b = rng.integers(2**12, 2**15)
         bins = _make_bins(data, init_bins, a, b)
         score = _get_space_util(bins, init_bins) / len(data)
 
@@ -87,17 +88,18 @@ def _pick_initial_a_b(data, max_constant, init_bins):
 
 
 def _find_hash_for_internal(hash_bin):
+    rng = np.random.default_rng(seed=0)
     if not hash_bin:
         return [[], 0, 0]
 
     new_length = _new_bin_length(len(hash_bin))
 
     while True:
-        a = np.random.randint(
+        a = rng.integers(
             A_LBOUND_SECOND_LEVEL_HASH,
             A_HBOUND_SECOND_LEVEL_HASH,
         )
-        b = np.random.randint(
+        b = rng.integers(
             B_LBOUND_SECOND_LEVEL_HASH, B_HBOUND_SECOND_LEVEL_HASH
         )
         bins = _make_bins(hash_bin, new_length, a, b)
@@ -245,7 +247,7 @@ def hash_vocab(
     """
     Write the vocab vocabulary hashtable to the output_path
     """
-    np.random.seed(1243342)
+    _ = np.random.default_rng(seed=0)
     vocab = _load_vocab_dict(vocab_path)
     keys = list(map(_sdbm_hash, vocab.keys()))
 
diff --git a/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb b/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb
index c7d39b78810..cc03574155e 100644
--- a/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb
+++ b/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb
@@ -24,7 +24,7 @@
     "num_columns = 12\n",
     "\n",
     "# Create a DataFrame with random data\n",
-    "df = pd.DataFrame(np.random.randint(0, 100, size=(num_rows, num_columns)),\n",
+    "df = pd.DataFrame(rng.integers(0, 100, size=(num_rows, num_columns)),\n",
     "                  columns=[f'Column_{i}' for i in range(1, num_columns + 1)])"
    ]
   },
diff --git a/python/cudf/cudf_pandas_tests/test_cudf_pandas.py b/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
index 2bbed40e34e..06cc630e9af 100644
--- a/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
+++ b/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
@@ -1135,8 +1135,8 @@ def test_private_method_result_wrapped():
 
 
 def test_numpy_var():
-    np.random.seed(42)
-    data = np.random.rand(1000)
+    rng = np.random.default_rng(seed=0)
+    data = rng.random(1000)
     psr = pd.Series(data)
     sr = xpd.Series(data)
 
diff --git a/python/cudf/cudf_pandas_tests/test_profiler.py b/python/cudf/cudf_pandas_tests/test_profiler.py
index 5b7bde06d1d..b20c3fff346 100644
--- a/python/cudf/cudf_pandas_tests/test_profiler.py
+++ b/python/cudf/cudf_pandas_tests/test_profiler.py
@@ -23,12 +23,12 @@
     reason="function names change across versions of pandas, so making sure it only runs on latest version of pandas",
 )
 def test_profiler():
-    np.random.seed(42)
+    rng = np.random.default_rng(seed=0)
     with Profiler() as profiler:
         df = pd.DataFrame(
             {
-                "idx": np.random.randint(0, 10, 1000),
-                "data": np.random.rand(1000),
+                "idx": rng.integers(0, 10, 1000),
+                "data": rng.random(1000),
             }
         )
         sums = df.groupby("idx").sum()
@@ -58,7 +58,7 @@ def test_profiler():
     calls = [
         "pd.DataFrame",
         "",
-        "np.random.randint",
+        "rng.integers",
         "np.random.rand",
         'df.groupby("idx").sum',
         'df.sum()["data"]',
diff --git a/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_cuml.py b/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_cuml.py
index 892d0886596..27eaff87ba0 100644
--- a/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_cuml.py
+++ b/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_cuml.py
@@ -102,7 +102,7 @@ def test_random_forest(binary_classification_data):
 def test_clustering():
     rng = np.random.default_rng(42)
     nsamps = 300
-    X = rng.random((nsamps, 2))
+    X = rng.random(size=(nsamps, 2))
     data = pd.DataFrame(X, columns=["x", "y"])
 
     kmeans = KMeans(n_clusters=3, random_state=42)
diff --git a/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py b/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py
index 37e3cc34856..a81d1d01aa2 100644
--- a/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py
+++ b/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py
@@ -31,17 +31,17 @@ def dask_client():
 
 
 def test_1d_distributed(dask_client):
-    np.random.seed(42)
-    ts = pd.Series(np.random.rand(100))
+    rng = np.random.default_rng(seed=0)
+    ts = pd.Series(rng.random(100))
     m = 10
     return stumpy.stumped(dask_client, ts, m)
 
 
 def test_multidimensional_distributed_timeseries(dask_client):
-    np.random.seed(42)
+    rng = np.random.default_rng(seed=0)
     # Each row represents data from a different dimension while each column represents
     # data from the same dimension
-    your_time_series = np.random.rand(3, 1000)
+    your_time_series = rng.random(3, 1000)
     # Approximately, how many data points might be found in a pattern
     window_size = 50
 
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_parquet.py b/python/dask_cudf/dask_cudf/io/tests/test_parquet.py
index 620a917109e..896c4169f5b 100644
--- a/python/dask_cudf/dask_cudf/io/tests/test_parquet.py
+++ b/python/dask_cudf/dask_cudf/io/tests/test_parquet.py
@@ -371,12 +371,12 @@ def test_split_row_groups(tmpdir, row_groups, index):
     row_group_size = 5
     file_row_groups = 10  # Known apriori
     npartitions_expected = math.ceil(file_row_groups / row_groups) * 2
-
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "a": np.random.choice(["apple", "banana", "carrot"], size=df_size),
-            "b": np.random.random(size=df_size),
-            "c": np.random.randint(1, 5, size=df_size),
+            "a": rng.choice(["apple", "banana", "carrot"], size=df_size),
+            "b": rng.random(size=df_size),
+            "c": rng.integers(1, 5, size=df_size),
             "index": np.arange(0, df_size),
         }
     )
diff --git a/python/dask_cudf/dask_cudf/tests/test_accessor.py b/python/dask_cudf/dask_cudf/tests/test_accessor.py
index 6f04b5737da..3fbb2aacd2c 100644
--- a/python/dask_cudf/dask_cudf/tests/test_accessor.py
+++ b/python/dask_cudf/dask_cudf/tests/test_accessor.py
@@ -25,7 +25,7 @@ def data_dt_1():
 
 
 def data_dt_2():
-    return np.random.randn(100)
+    return np.random.default_rng(seed=0).standard_normal(size=100)
 
 
 dt_fields = ["year", "month", "day", "hour", "minute", "second"]
diff --git a/python/dask_cudf/dask_cudf/tests/test_binops.py b/python/dask_cudf/dask_cudf/tests/test_binops.py
index 87bd401accd..8c51f950765 100644
--- a/python/dask_cudf/dask_cudf/tests/test_binops.py
+++ b/python/dask_cudf/dask_cudf/tests/test_binops.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 import operator
 
@@ -21,10 +21,11 @@ def _make_empty_frame(npartitions=2):
 
 
 def _make_random_frame_float(nelem, npartitions=2):
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=nelem),
-            "y": np.random.normal(size=nelem) + 1,
+            "x": rng.integers(0, 5, size=nelem),
+            "y": rng.normal(size=nelem) + 1,
         }
     )
     gdf = cudf.from_pandas(df)
@@ -51,7 +52,6 @@ def _make_random_frame_float(nelem, npartitions=2):
 
 @pytest.mark.parametrize("binop", _binops)
 def test_series_binops_integer(binop):
-    np.random.seed(0)
     size = 1000
     lhs_df, lhs_gdf = _make_random_frame(size)
     rhs_df, rhs_gdf = _make_random_frame(size)
@@ -62,7 +62,6 @@ def test_series_binops_integer(binop):
 
 @pytest.mark.parametrize("binop", _binops)
 def test_series_binops_float(binop):
-    np.random.seed(0)
     size = 1000
     lhs_df, lhs_gdf = _make_random_frame_float(size)
     rhs_df, rhs_gdf = _make_random_frame_float(size)
@@ -73,10 +72,10 @@ def test_series_binops_float(binop):
 
 @pytest.mark.parametrize("operator", _binops)
 def test_df_series_bind_ops(operator):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     size = 1000
     lhs_df, lhs_gdf = _make_random_frame_float(size)
-    rhs = np.random.rand()
+    rhs = rng.random()
 
     for col in lhs_gdf.columns:
         got = getattr(lhs_gdf[col], operator.__name__)(rhs)
diff --git a/python/dask_cudf/dask_cudf/tests/test_core.py b/python/dask_cudf/dask_cudf/tests/test_core.py
index 5f0fae86691..8e42c847ddf 100644
--- a/python/dask_cudf/dask_cudf/tests/test_core.py
+++ b/python/dask_cudf/dask_cudf/tests/test_core.py
@@ -22,13 +22,15 @@
     xfail_dask_expr,
 )
 
+rng = np.random.default_rng(seed=0)
+
 
 def test_from_dict_backend_dispatch():
     # Test ddf.from_dict cudf-backend dispatch
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     data = {
-        "x": np.random.randint(0, 5, size=10000),
-        "y": np.random.normal(size=10000),
+        "x": rng.integers(0, 5, size=10000),
+        "y": rng.normal(size=10000),
     }
     expect = cudf.DataFrame(data)
     with dask.config.set({"dataframe.backend": "cudf"}):
@@ -62,10 +64,10 @@ def test_from_dask_dataframe_deprecated():
 
 
 def test_to_backend():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     data = {
-        "x": np.random.randint(0, 5, size=10000),
-        "y": np.random.normal(size=10000),
+        "x": rng.integers(0, 5, size=10000),
+        "y": rng.normal(size=10000),
     }
     with dask.config.set({"dataframe.backend": "pandas"}):
         ddf = dd.from_dict(data, npartitions=2)
@@ -114,12 +116,12 @@ def test_to_backend_kwargs():
 
 
 def test_from_pandas():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=10000),
-            "y": np.random.normal(size=10000),
+            "x": rng.integers(0, 5, size=10000),
+            "y": rng.normal(size=10000),
         }
     )
 
@@ -169,10 +171,10 @@ def _fragmented_gdf(df, nsplit):
 
 
 def test_query():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=10), "y": np.random.normal(size=10)}
+        {"x": rng.integers(0, 5, size=10), "y": rng.normal(size=10)}
     )
     gdf = cudf.DataFrame.from_pandas(df)
     expr = "x > 2"
@@ -188,9 +190,9 @@ def test_query():
 
 
 def test_query_local_dict():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=10), "y": np.random.normal(size=10)}
+        {"x": rng.integers(0, 5, size=10), "y": rng.normal(size=10)}
     )
     gdf = cudf.DataFrame.from_pandas(df)
     ddf = dask_cudf.from_cudf(gdf, npartitions=2)
@@ -204,11 +206,11 @@ def test_query_local_dict():
 
 
 def test_head():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=100),
-            "y": np.random.normal(size=100),
+            "x": rng.integers(0, 5, size=100),
+            "y": rng.normal(size=100),
         }
     )
     gdf = cudf.DataFrame.from_pandas(df)
@@ -220,13 +222,11 @@ def test_head():
 @pytest.mark.parametrize("nelem", [10, 200, 1333])
 def test_set_index(nelem):
     with dask.config.set(scheduler="single-threaded"):
-        np.random.seed(0)
+        rng = np.random.default_rng(seed=0)
         # Use unique index range as the sort may not be stable-ordering
         x = np.arange(nelem)
-        np.random.shuffle(x)
-        df = pd.DataFrame(
-            {"x": x, "y": np.random.randint(0, nelem, size=nelem)}
-        )
+        rng.shuffle(x)
+        df = pd.DataFrame({"x": x, "y": rng.integers(0, nelem, size=nelem)})
         ddf = dd.from_pandas(df, npartitions=2)
         ddf2 = ddf.to_backend("cudf")
 
@@ -242,7 +242,7 @@ def test_set_index(nelem):
 def test_set_index_quantile(nelem, nparts, by):
     df = cudf.DataFrame()
     df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
-    df["b"] = np.random.choice(cudf.datasets.names, size=nelem)
+    df["b"] = rng.choice(cudf.datasets.names, size=nelem)
     ddf = dd.from_pandas(df, npartitions=nparts)
 
     with pytest.warns(FutureWarning, match="deprecated"):
@@ -270,11 +270,11 @@ def assert_frame_equal_by_index_group(expect, got):
 @pytest.mark.parametrize("nelem", [10, 200, 1333])
 def test_set_index_2(nelem):
     with dask.config.set(scheduler="single-threaded"):
-        np.random.seed(0)
+        rng = np.random.default_rng(seed=0)
         df = pd.DataFrame(
             {
-                "x": 100 + np.random.randint(0, nelem // 2, size=nelem),
-                "y": np.random.normal(size=nelem),
+                "x": 100 + rng.integers(0, nelem // 2, size=nelem),
+                "y": rng.normal(size=nelem),
             }
         )
         expect = df.set_index("x").sort_index()
@@ -289,11 +289,11 @@ def test_set_index_2(nelem):
 def test_set_index_w_series():
     with dask.config.set(scheduler="single-threaded"):
         nelem = 20
-        np.random.seed(0)
+        rng = np.random.default_rng(seed=0)
         df = pd.DataFrame(
             {
-                "x": 100 + np.random.randint(0, nelem // 2, size=nelem),
-                "y": np.random.normal(size=nelem),
+                "x": 100 + rng.integers(0, nelem // 2, size=nelem),
+                "y": rng.normal(size=nelem),
             }
         )
         expect = df.set_index(df.x).sort_index()
@@ -327,12 +327,12 @@ def test_set_index_sorted():
 @pytest.mark.parametrize("index", [None, "myindex"])
 def test_rearrange_by_divisions(nelem, index):
     with dask.config.set(scheduler="single-threaded"):
-        np.random.seed(0)
+        rng = np.random.default_rng(seed=0)
         df = pd.DataFrame(
             {
-                "x": np.random.randint(0, 20, size=nelem),
-                "y": np.random.normal(size=nelem),
-                "z": np.random.choice(["dog", "cat", "bird"], nelem),
+                "x": rng.integers(0, 20, size=nelem),
+                "y": rng.normal(size=nelem),
+                "z": rng.choice(["dog", "cat", "bird"], nelem),
             }
         )
         df["z"] = df["z"].astype("category")
@@ -355,9 +355,9 @@ def test_rearrange_by_divisions(nelem, index):
 
 
 def test_assign():
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+        {"x": rng.integers(0, 5, size=20), "y": rng.normal(size=20)}
     )
 
     dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
@@ -372,10 +372,10 @@ def test_assign():
 
 @pytest.mark.parametrize("data_type", ["int8", "int16", "int32", "int64"])
 def test_setitem_scalar_integer(data_type):
-    np.random.seed(0)
-    scalar = np.random.randint(0, 100, dtype=data_type)
+    rng = np.random.default_rng(seed=0)
+    scalar = rng.integers(0, 100, dtype=data_type)
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+        {"x": rng.integers(0, 5, size=20), "y": rng.normal(size=20)}
     )
     dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
 
@@ -388,10 +388,10 @@ def test_setitem_scalar_integer(data_type):
 
 @pytest.mark.parametrize("data_type", ["float32", "float64"])
 def test_setitem_scalar_float(data_type):
-    np.random.seed(0)
-    scalar = np.random.randn(1).astype(data_type)[0]
+    rng = np.random.default_rng(seed=0)
+    scalar = rng.standard_normal(size=1).astype(data_type)[0]
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+        {"x": rng.integers(0, 5, size=20), "y": rng.normal(size=20)}
     )
     dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
 
@@ -403,10 +403,10 @@ def test_setitem_scalar_float(data_type):
 
 
 def test_setitem_scalar_datetime():
-    np.random.seed(0)
-    scalar = np.int64(np.random.randint(0, 100)).astype("datetime64[ms]")
+    rng = np.random.default_rng(seed=0)
+    scalar = np.int64(rng.integers(0, 100)).astype("datetime64[ms]")
     df = pd.DataFrame(
-        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+        {"x": rng.integers(0, 5, size=20), "y": rng.normal(size=20)}
     )
     dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
 
@@ -422,12 +422,12 @@ def test_setitem_scalar_datetime():
     "func",
     [
         lambda: pd.DataFrame(
-            {"A": np.random.rand(10), "B": np.random.rand(10)},
+            {"A": rng.random(10), "B": rng.random(10)},
             index=list("abcdefghij"),
         ),
         lambda: pd.DataFrame(
             {
-                "A": np.random.rand(10),
+                "A": rng.random(10),
                 "B": list("a" * 10),
                 "C": pd.Series(
                     [str(20090101 + i) for i in range(10)],
@@ -438,7 +438,7 @@ def test_setitem_scalar_datetime():
         ),
         lambda: pd.Series(list("abcdefghijklmnop")),
         lambda: pd.Series(
-            np.random.rand(10),
+            rng.random(10),
             index=pd.Index(
                 [str(20090101 + i) for i in range(10)], dtype="datetime64[ns]"
             ),
@@ -497,10 +497,11 @@ def test_repartition_hash_staged(npartitions):
     by = ["b"]
     datarange = 35
     size = 100
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame(
         {
             "a": np.arange(size, dtype="int64"),
-            "b": np.random.randint(datarange, size=size),
+            "b": rng.integers(datarange, size=size),
         }
     )
     # WARNING: Specific npartitions-max_branch combination
@@ -537,12 +538,13 @@ def test_repartition_hash(by, npartitions, max_branch):
     npartitions_i = 4
     datarange = 26
     size = 100
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame(
         {
             "a": np.arange(0, stop=size, dtype="int64"),
-            "b": np.random.randint(datarange, size=size),
-            "c": np.random.choice(list("abcdefgh"), size=size),
-            "d": np.random.choice(np.arange(26), size=size),
+            "b": rng.integers(datarange, size=size),
+            "c": rng.choice(list("abcdefgh"), size=size),
+            "d": rng.choice(np.arange(26), size=size),
         }
     )
     gdf.d = gdf.d.astype("datetime64[ms]")
@@ -768,6 +770,7 @@ def test_dataframe_series_replace(data):
 
 
 def test_dataframe_assign_col():
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame(list(range(100)))
     pdf = pd.DataFrame(list(range(100)))
 
@@ -780,7 +783,7 @@ def test_dataframe_assign_col():
     pddf = dd.from_pandas(pdf, npartitions=4)
     pddf["fold"] = 0
     pddf["fold"] = pddf["fold"].map_partitions(
-        lambda p_df: pd.Series(np.random.randint(0, 4, len(p_df)))
+        lambda p_df: pd.Series(rng.integers(0, 4, len(p_df)))
     )
 
     dd.assert_eq(ddf[0], pddf[0])
@@ -1015,10 +1018,11 @@ def test_to_backend_simplify():
 @pytest.mark.parametrize("numeric_only", [True, False])
 @pytest.mark.parametrize("op", ["corr", "cov"])
 def test_cov_corr(op, numeric_only):
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame.from_dict(
         {
-            "x": np.random.randint(0, 5, size=10),
-            "y": np.random.normal(size=10),
+            "x": rng.integers(0, 5, size=10),
+            "y": rng.normal(size=10),
         }
     )
     ddf = dd.from_pandas(df, npartitions=2)
diff --git a/python/dask_cudf/dask_cudf/tests/test_delayed_io.py b/python/dask_cudf/dask_cudf/tests/test_delayed_io.py
index e6fb58ad6df..84ed3b46598 100644
--- a/python/dask_cudf/dask_cudf/tests/test_delayed_io.py
+++ b/python/dask_cudf/dask_cudf/tests/test_delayed_io.py
@@ -51,9 +51,13 @@ def test_series_from_delayed():
 def test_dataframe_to_delayed():
     nelem = 100
 
-    df = cudf.DataFrame()
-    df["x"] = np.arange(nelem)
-    df["y"] = np.random.randint(nelem, size=nelem)
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame(
+        {
+            "x": np.arange(nelem),
+            "y": rng.integers(nelem, size=nelem),
+        }
+    )
 
     ddf = dask_cudf.from_cudf(df, npartitions=5)
 
@@ -80,8 +84,8 @@ def test_dataframe_to_delayed():
 
 def test_series_to_delayed():
     nelem = 100
-
-    sr = cudf.Series(np.random.randint(nelem, size=nelem))
+    rng = np.random.default_rng(seed=0)
+    sr = cudf.Series(rng.integers(nelem, size=nelem))
 
     dsr = dask_cudf.from_cudf(sr, npartitions=5)
 
@@ -109,11 +113,13 @@ def test_series_to_delayed():
 
 def test_mixing_series_frame_error():
     nelem = 20
-
-    df = cudf.DataFrame()
-    df["x"] = np.arange(nelem)
-    df["y"] = np.random.randint(nelem, size=nelem)
-
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame(
+        {
+            "x": np.arange(nelem),
+            "y": rng.integers(nelem, size=nelem),
+        }
+    )
     ddf = dask_cudf.from_cudf(df, npartitions=5)
 
     delay_frame = ddf.to_delayed()
@@ -128,10 +134,13 @@ def test_mixing_series_frame_error():
 
 def test_frame_extra_columns_error():
     nelem = 20
-
-    df = cudf.DataFrame()
-    df["x"] = np.arange(nelem)
-    df["y"] = np.random.randint(nelem, size=nelem)
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame(
+        {
+            "x": np.arange(nelem),
+            "y": rng.integers(nelem, size=nelem),
+        }
+    )
     ddf1 = dask_cudf.from_cudf(df, npartitions=5)
 
     df["z"] = np.arange(nelem)
diff --git a/python/dask_cudf/dask_cudf/tests/test_dispatch.py b/python/dask_cudf/dask_cudf/tests/test_dispatch.py
index a12481a7bb4..fe57d4a4f00 100644
--- a/python/dask_cudf/dask_cudf/tests/test_dispatch.py
+++ b/python/dask_cudf/dask_cudf/tests/test_dispatch.py
@@ -32,8 +32,9 @@ def test_pyarrow_conversion_dispatch(preserve_index, index):
         to_pyarrow_table_dispatch,
     )
 
+    rng = np.random.default_rng(seed=0)
     df1 = cudf.DataFrame(
-        np.random.randn(10, 3), columns=list("abc"), index=index
+        rng.standard_normal(size=(10, 3)), columns=list("abc"), index=index
     )
     df2 = from_pyarrow_table_dispatch(
         df1, to_pyarrow_table_dispatch(df1, preserve_index=preserve_index)
@@ -108,7 +109,8 @@ def test_pyarrow_schema_dispatch(preserve_index):
         to_pyarrow_table_dispatch,
     )
 
-    df = cudf.DataFrame(np.random.randn(10, 3), columns=list("abc"))
+    rng = np.random.default_rng(seed=0)
+    df = cudf.DataFrame(rng.standard_normal(size=(10, 3)), columns=list("abc"))
     df["d"] = cudf.Series(["cat", "dog"] * 5)
     table = to_pyarrow_table_dispatch(df, preserve_index=preserve_index)
     schema = pyarrow_schema_dispatch(df, preserve_index=preserve_index)
diff --git a/python/dask_cudf/dask_cudf/tests/test_groupby.py b/python/dask_cudf/dask_cudf/tests/test_groupby.py
index 7b9f0ca328a..e30474f6b94 100644
--- a/python/dask_cudf/dask_cudf/tests/test_groupby.py
+++ b/python/dask_cudf/dask_cudf/tests/test_groupby.py
@@ -30,21 +30,21 @@ def assert_cudf_groupby_layers(ddf):
 
 @pytest.fixture(params=["non_null", "null"])
 def pdf(request):
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # note that column name "x" is a substring of the groupby key;
     # this gives us coverage for cudf#10829
     pdf = pd.DataFrame(
         {
-            "xx": np.random.randint(0, 5, size=10000),
-            "x": np.random.normal(size=10000),
-            "y": np.random.normal(size=10000),
+            "xx": rng.integers(0, 5, size=10000),
+            "x": rng.normal(size=10000),
+            "y": rng.normal(size=10000),
         }
     )
 
     # insert nulls into dataframe at random
     if request.param == "null":
-        pdf = pdf.mask(np.random.choice([True, False], size=pdf.shape))
+        pdf = pdf.mask(rng.choice([True, False], size=pdf.shape))
 
     return pdf
 
@@ -173,11 +173,12 @@ def test_groupby_agg_empty_partition(tmpdir, split_out):
     ],
 )
 def test_groupby_multi_column(func):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "a": np.random.randint(0, 20, size=1000),
-            "b": np.random.randint(0, 5, size=1000),
-            "x": np.random.normal(size=1000),
+            "a": rng.integers(0, 20, size=1000),
+            "b": rng.integers(0, 5, size=1000),
+            "x": rng.normal(size=1000),
         }
     )
 
@@ -371,11 +372,12 @@ def test_groupby_string_index_name(myindex):
     ],
 )
 def test_groupby_split_out_multiindex(agg_func):
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame(
         {
-            "a": np.random.randint(0, 10, 100),
-            "b": np.random.randint(0, 5, 100),
-            "c": np.random.random(100),
+            "a": rng.integers(0, 10, 100),
+            "b": rng.integers(0, 5, 100),
+            "c": rng.random(100),
         }
     )
     ddf = dask_cudf.from_cudf(df, 5)
@@ -419,12 +421,13 @@ def test_groupby_multiindex_reset_index(npartitions):
     ],
 )
 def test_groupby_reset_index_multiindex(groupby_keys, agg_func):
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame(
         {
-            "a": np.random.randint(0, 10, 10),
-            "b": np.random.randint(0, 5, 10),
-            "c": np.random.randint(0, 5, 10),
-            "dd": np.random.randint(0, 5, 10),
+            "a": rng.integers(0, 10, 10),
+            "b": rng.integers(0, 5, 10),
+            "c": rng.integers(0, 5, 10),
+            "dd": rng.integers(0, 5, 10),
         }
     )
     ddf = dask_cudf.from_cudf(df, 5)
@@ -437,8 +440,9 @@ def test_groupby_reset_index_multiindex(groupby_keys, agg_func):
 
 
 def test_groupby_reset_index_drop_True():
+    rng = np.random.default_rng(seed=0)
     df = cudf.DataFrame(
-        {"a": np.random.randint(0, 10, 10), "b": np.random.randint(0, 5, 10)}
+        {"a": rng.integers(0, 10, 10), "b": rng.integers(0, 5, 10)}
     )
     ddf = dask_cudf.from_cudf(df, 5)
     pddf = dd.from_pandas(df.to_pandas(), 5)
@@ -653,10 +657,11 @@ def test_groupby_agg_params(npartitions, split_every, split_out, as_index):
     "aggregations", [(sum, "sum"), (max, "max"), (min, "min")]
 )
 def test_groupby_agg_redirect(aggregations):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=10000),
-            "y": np.random.normal(size=10000),
+            "x": rng.integers(0, 5, size=10000),
+            "y": rng.normal(size=10000),
         }
     )
 
@@ -758,10 +763,11 @@ def test_groupby_with_list_of_series():
     ],
 )
 def test_groupby_nested_dict(func):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=10000),
-            "y": np.random.normal(size=10000),
+            "x": rng.integers(0, 5, size=10000),
+            "y": rng.normal(size=10000),
         }
     )
 
@@ -794,10 +800,11 @@ def test_groupby_nested_dict(func):
     ],
 )
 def test_groupby_all_columns(func):
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {
-            "x": np.random.randint(0, 5, size=10000),
-            "y": np.random.normal(size=10000),
+            "x": rng.integers(0, 5, size=10000),
+            "y": rng.normal(size=10000),
         }
     )
 
diff --git a/python/dask_cudf/dask_cudf/tests/test_join.py b/python/dask_cudf/dask_cudf/tests/test_join.py
index 43fadbecd0a..61d0f8d7eb9 100644
--- a/python/dask_cudf/dask_cudf/tests/test_join.py
+++ b/python/dask_cudf/dask_cudf/tests/test_join.py
@@ -22,18 +22,18 @@
 def test_join_inner(left_nrows, right_nrows, left_nkeys, right_nkeys):
     chunksize = 50
 
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # cuDF
     left = cudf.DataFrame(
         {
-            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "x": rng.integers(0, left_nkeys, size=left_nrows),
             "a": np.arange(left_nrows),
         }
     )
     right = cudf.DataFrame(
         {
-            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "x": rng.integers(0, right_nkeys, size=right_nrows),
             "a": 1000 * np.arange(right_nrows),
         }
     )
@@ -84,18 +84,18 @@ def gather(df, grows):
 def test_join_left(left_nrows, right_nrows, left_nkeys, right_nkeys, how):
     chunksize = 50
 
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # cuDF
     left = cudf.DataFrame(
         {
-            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "x": rng.integers(0, left_nkeys, size=left_nrows),
             "a": np.arange(left_nrows, dtype=np.float64),
         }
     )
     right = cudf.DataFrame(
         {
-            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "x": rng.integers(0, right_nkeys, size=right_nrows),
             "a": 1000 * np.arange(right_nrows, dtype=np.float64),
         }
     )
@@ -153,20 +153,20 @@ def test_merge_left(
 ):
     chunksize = 3
 
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=42)
 
     # cuDF
     left = cudf.DataFrame(
         {
-            "x": np.random.randint(0, left_nkeys, size=left_nrows),
-            "y": np.random.randint(0, left_nkeys, size=left_nrows),
+            "x": rng.integers(0, left_nkeys, size=left_nrows),
+            "y": rng.integers(0, left_nkeys, size=left_nrows),
             "a": np.arange(left_nrows, dtype=np.float64),
         }
     )
     right = cudf.DataFrame(
         {
-            "x": np.random.randint(0, right_nkeys, size=right_nrows),
-            "y": np.random.randint(0, right_nkeys, size=right_nrows),
+            "x": rng.integers(0, right_nkeys, size=right_nrows),
+            "y": rng.integers(0, right_nkeys, size=right_nrows),
             "a": 1000 * np.arange(right_nrows, dtype=np.float64),
         }
     )
@@ -200,18 +200,18 @@ def test_merge_1col_left(
 ):
     chunksize = 3
 
-    np.random.seed(0)
+    rng = np.random.default_rng(seed=0)
 
     # cuDF
     left = cudf.DataFrame(
         {
-            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "x": rng.integers(0, left_nkeys, size=left_nrows),
             "a": np.arange(left_nrows, dtype=np.float64),
         }
     )
     right = cudf.DataFrame(
         {
-            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "x": rng.integers(0, right_nkeys, size=right_nrows),
             "a": 1000 * np.arange(right_nrows, dtype=np.float64),
         }
     )
@@ -238,13 +238,19 @@ def test_merge_1col_left(
 
 def test_merge_should_fail():
     # Expected failure cases described in #2694
-    df1 = cudf.DataFrame()
-    df1["a"] = [1, 2, 3, 4, 5, 6] * 2
-    df1["b"] = np.random.randint(0, 12, 12)
-
-    df2 = cudf.DataFrame()
-    df2["a"] = [7, 2, 3, 8, 5, 9] * 2
-    df2["c"] = np.random.randint(0, 12, 12)
+    rng = np.random.default_rng(seed=0)
+    df1 = pd.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5, 6] * 2,
+            "b": rng.integers(0, 12, 12),
+        }
+    )
+    df2 = pd.DataFrame(
+        {
+            "a": [7, 2, 3, 8, 5, 9] * 2,
+            "c": rng.integers(0, 12, 12),
+        }
+    )
 
     left = dask_cudf.from_cudf(df1, 1).groupby("a").b.min().to_frame()
     right = dask_cudf.from_cudf(df2, 1).groupby("a").c.min().to_frame()
@@ -257,8 +263,7 @@ def test_merge_should_fail():
         left.merge(right, how="left", on=["c"])
 
     # Same column names
-
-    df2["b"] = np.random.randint(0, 12, 12)
+    df2["b"] = np.random.default_rng(seed=0).integers(0, 12, 12)
     right = dask_cudf.from_cudf(df2, 1).groupby("a").b.min().to_frame()
 
     with pytest.raises(KeyError):

From 858afbbb55f4f9b0ded98e3cc6423b60015552ed Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 7 Oct 2024 22:04:52 +0000
Subject: [PATCH 05/21] improve

---
 python/cudf/cudf/testing/_utils.py        |  2 +-
 python/cudf/cudf/tests/test_binops.py     | 10 +++++++---
 python/cudf/cudf/tests/test_dataframe.py  |  4 ++--
 python/cudf/cudf/tests/test_datetime.py   |  2 +-
 python/cudf/cudf/tests/test_indexing.py   |  2 +-
 python/cudf/cudf/tests/test_resampling.py | 15 +++++++++------
 6 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/python/cudf/cudf/testing/_utils.py b/python/cudf/cudf/testing/_utils.py
index 8a30b0ae6d6..38b46ff74c7 100644
--- a/python/cudf/cudf/testing/_utils.py
+++ b/python/cudf/cudf/testing/_utils.py
@@ -217,7 +217,7 @@ def gen_rand(dtype, size, **kwargs):
         if kwargs.get("positive_only", False):
             return res
         else:
-            return res * 2 - 1
+            return 2 * res - 2
     elif dtype == np.int8 or dtype == np.int16:
         low = kwargs.get("low", -32)
         high = kwargs.get("high", 32)
diff --git a/python/cudf/cudf/tests/test_binops.py b/python/cudf/cudf/tests/test_binops.py
index 474d64130eb..138e6e92515 100644
--- a/python/cudf/cudf/tests/test_binops.py
+++ b/python/cudf/cudf/tests/test_binops.py
@@ -960,11 +960,11 @@ def gen_df():
 @pytest.mark.parametrize("nulls", _nulls)
 @pytest.mark.parametrize("other", ["df", "scalar"])
 def test_logical_operator_func_dataframe(func, nulls, other):
-    rng = np.random.default_rng(seed=0)
     num_rows = 100
     num_cols = 3
 
     def gen_df():
+        rng = np.random.default_rng(seed=0)
         pdf = pd.DataFrame()
         from string import ascii_lowercase
 
@@ -983,8 +983,12 @@ def gen_df():
 
     pdf1 = gen_df()
     pdf2 = gen_df() if other == "df" else 59.0
-    gdf1 = cudf.DataFrame.from_pandas(pdf1)
-    gdf2 = cudf.DataFrame.from_pandas(pdf2) if other == "df" else 59.0
+    gdf1 = cudf.DataFrame.from_pandas(pdf1, nan_as_null=False)
+    gdf2 = (
+        cudf.DataFrame.from_pandas(pdf2, nan_as_null=False)
+        if other == "df"
+        else 59.0
+    )
 
     got = getattr(gdf1, func)(gdf2)
     expect = getattr(pdf1, func)(pdf2)[list(got._data)]
diff --git a/python/cudf/cudf/tests/test_dataframe.py b/python/cudf/cudf/tests/test_dataframe.py
index 13260e1cb90..b26e6b3e9b2 100644
--- a/python/cudf/cudf/tests/test_dataframe.py
+++ b/python/cudf/cudf/tests/test_dataframe.py
@@ -10537,10 +10537,10 @@ def test_dataframe_init_length_error(data, index):
 def test_dataframe_binop_with_mixed_date_types():
     rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
-        rng.random(2, 2),
+        rng.random(size=(2, 2)),
         columns=pd.Index(["2000-01-03", "2000-01-04"], dtype="datetime64[ns]"),
     )
-    ser = pd.Series(rng.random(3), index=[0, 1, 2])
+    ser = pd.Series(rng.random(size=3), index=[0, 1, 2])
     gdf = cudf.from_pandas(df)
     gser = cudf.from_pandas(ser)
     expected = df - ser
diff --git a/python/cudf/cudf/tests/test_datetime.py b/python/cudf/cudf/tests/test_datetime.py
index 14b53569860..33a93b02a3f 100644
--- a/python/cudf/cudf/tests/test_datetime.py
+++ b/python/cudf/cudf/tests/test_datetime.py
@@ -219,7 +219,7 @@ def test_sort_datetime():
     rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
-            "data": np.array(
+            "date": np.array(
                 [
                     np.datetime64("2016-11-20"),
                     np.datetime64("2020-11-20"),
diff --git a/python/cudf/cudf/tests/test_indexing.py b/python/cudf/cudf/tests/test_indexing.py
index 3145c48c0cb..421bc0c298b 100644
--- a/python/cudf/cudf/tests/test_indexing.py
+++ b/python/cudf/cudf/tests/test_indexing.py
@@ -594,7 +594,7 @@ def test_dataframe_series_loc_multiindex(obj):
 def test_series_iloc(nelem):
     # create random cudf.Series
     rng = np.random.default_rng(seed=0)
-    ps = pd.Series(rng.sample(nelem))
+    ps = pd.Series(rng.random(nelem))
 
     # gpu cudf.Series
     gs = cudf.Series(ps)
diff --git a/python/cudf/cudf/tests/test_resampling.py b/python/cudf/cudf/tests/test_resampling.py
index 4ac4484bf82..5ff0098bcf4 100644
--- a/python/cudf/cudf/tests/test_resampling.py
+++ b/python/cudf/cudf/tests/test_resampling.py
@@ -50,8 +50,9 @@ def test_series_upsample_simple():
 
 @pytest.mark.parametrize("rule", ["2s", "10s"])
 def test_series_resample_ffill(rule):
-    rng = pd.date_range("1/1/2012", periods=10, freq="5s")
-    ts = pd.Series(rng.integers(0, 500, len(rng)), index=rng)
+    date_idx = pd.date_range("1/1/2012", periods=10, freq="5s")
+    rng = np.random.default_rng(seed=0)
+    ts = pd.Series(rng.integers(0, 500, len(date_idx)), index=date_idx)
     gts = cudf.from_pandas(ts)
     assert_resample_results_equal(
         ts.resample(rule).ffill(), gts.resample(rule).ffill()
@@ -60,8 +61,9 @@ def test_series_resample_ffill(rule):
 
 @pytest.mark.parametrize("rule", ["2s", "10s"])
 def test_series_resample_bfill(rule):
-    rng = pd.date_range("1/1/2012", periods=10, freq="5s")
-    ts = pd.Series(rng.integers(0, 500, len(rng)), index=rng)
+    date_idx = pd.date_range("1/1/2012", periods=10, freq="5s")
+    rng = np.random.default_rng(seed=0)
+    ts = pd.Series(rng.integers(0, 500, len(date_idx)), index=date_idx)
     gts = cudf.from_pandas(ts)
     assert_resample_results_equal(
         ts.resample(rule).bfill(), gts.resample(rule).bfill()
@@ -70,8 +72,9 @@ def test_series_resample_bfill(rule):
 
 @pytest.mark.parametrize("rule", ["2s", "10s"])
 def test_series_resample_asfreq(rule):
-    rng = pd.date_range("1/1/2012", periods=100, freq="5s")
-    ts = pd.Series(rng.integers(0, 500, len(rng)), index=rng)
+    date_range = pd.date_range("1/1/2012", periods=100, freq="5s")
+    rng = np.random.default_rng(seed=0)
+    ts = pd.Series(rng.integers(0, 500, len(date_range)), index=date_range)
     gts = cudf.from_pandas(ts)
     assert_resample_results_equal(
         ts.resample(rule).asfreq(), gts.resample(rule).asfreq()

From ebaa5624dad1d8032a1bfd0db156a99743e1b225 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Fri, 11 Oct 2024 19:31:34 +0000
Subject: [PATCH 06/21] more fixes

---
 python/cudf/cudf/_fuzz_testing/avro.py        |  15 +-
 python/cudf/cudf/_fuzz_testing/csv.py         |  40 ++---
 python/cudf/cudf/_fuzz_testing/io.py          |   5 +-
 python/cudf/cudf/_fuzz_testing/json.py        |  14 +-
 python/cudf/cudf/_fuzz_testing/orc.py         |  28 ++-
 python/cudf/cudf/_fuzz_testing/parquet.py     |  14 +-
 .../_fuzz_testing/tests/fuzz_test_parquet.py  |   6 +-
 python/cudf/cudf/_fuzz_testing/utils.py       |  15 +-
 python/cudf/cudf/testing/dataset_generator.py | 169 ++++++++++--------
 .../groupby/test_ordering_pandas_compat.py    |   5 +-
 10 files changed, 169 insertions(+), 142 deletions(-)

diff --git a/python/cudf/cudf/_fuzz_testing/avro.py b/python/cudf/cudf/_fuzz_testing/avro.py
index d9974037daa..4236ecb0219 100644
--- a/python/cudf/cudf/_fuzz_testing/avro.py
+++ b/python/cudf/cudf/_fuzz_testing/avro.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 import copy
 import io
@@ -68,12 +68,12 @@ def generate_input(self):
                 # https://github.com/rapidsai/cudf/issues/6604
                 - cudf.utils.dtypes.TIMEDELTA_TYPES
             )
-
+            seed = random.randint(0, 2**32 - 1)
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
-            seed = random.randint(0, 2**32 - 1)
+
             self._current_params["seed"] = seed
             self._current_params["num_rows"] = num_rows
             self._current_params["num_cols"] = num_cols
@@ -100,17 +100,18 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "columns":
                     col_size = self._rand(len(self._df.columns))
                     params_dict[param] = list(
-                        np.unique(np.random.choice(self._df.columns, col_size))
+                        np.unique(rng.choice(self._df.columns, col_size))
                     )
                 elif param in ("skiprows", "num_rows"):
-                    params_dict[param] = np.random.choice(
+                    params_dict[param] = rng.choice(
                         [None, self._rand(len(self._df))]
                     )
             else:
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
diff --git a/python/cudf/cudf/_fuzz_testing/csv.py b/python/cudf/cudf/_fuzz_testing/csv.py
index 67211a1c4bf..4ab666f1726 100644
--- a/python/cudf/cudf/_fuzz_testing/csv.py
+++ b/python/cudf/cudf/_fuzz_testing/csv.py
@@ -54,7 +54,7 @@ def generate_input(self):
             random.seed(seed)
             dtypes_list = list(cudf.utils.dtypes.ALL_TYPES)
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
             self._current_params["seed"] = seed
@@ -77,25 +77,22 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "usecols":
                     col_size = self._rand(len(self._df.columns))
-                    col_val = np.random.choice(
+                    col_val = rng.choice(
                         [
                             None,
-                            np.unique(
-                                np.random.choice(self._df.columns, col_size)
-                            ),
+                            np.unique(rng.choice(self._df.columns, col_size)),
                         ]
                     )
                     params_dict[param] = (
                         col_val if col_val is None else list(col_val)
                     )
                 elif param == "dtype":
-                    dtype_val = np.random.choice(
-                        [None, self._df.dtypes.to_dict()]
-                    )
+                    dtype_val = rng.choice([None, self._df.dtypes.to_dict()])
                     if dtype_val is not None:
                         dtype_val = {
                             col_name: "category"
@@ -105,25 +102,25 @@ def set_rand_params(self, params):
                         }
                     params_dict[param] = dtype_val
                 elif param == "header":
-                    header_val = np.random.choice(
-                        ["infer", np.random.randint(low=0, high=len(self._df))]
+                    header_val = rng.choice(
+                        ["infer", rng.integers(low=0, high=len(self._df))]
                     )
                     params_dict[param] = header_val
                 elif param == "skiprows":
-                    params_dict[param] = np.random.randint(
+                    params_dict[param] = rng.integers(
                         low=0, high=len(self._df)
                     )
                 elif param == "skipfooter":
-                    params_dict[param] = np.random.randint(
+                    params_dict[param] = rng.integers(
                         low=0, high=len(self._df)
                     )
                 elif param == "nrows":
-                    nrows_val = np.random.choice(
-                        [None, np.random.randint(low=0, high=len(self._df))]
+                    nrows_val = rng.choice(
+                        [None, rng.integers(low=0, high=len(self._df))]
                     )
                     params_dict[param] = nrows_val
             else:
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
 
 
@@ -159,7 +156,7 @@ def generate_input(self):
             random.seed(seed)
             dtypes_list = list(cudf.utils.dtypes.ALL_TYPES)
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
             self._current_params["seed"] = seed
@@ -182,26 +179,25 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "columns":
                     col_size = self._rand(len(self._current_buffer.columns))
                     params_dict[param] = list(
                         np.unique(
-                            np.random.choice(
-                                self._current_buffer.columns, col_size
-                            )
+                            rng.choice(self._current_buffer.columns, col_size)
                         )
                     )
                 elif param == "chunksize":
-                    params_dict[param] = np.random.choice(
+                    params_dict[param] = rng.choice(
                         [
                             None,
-                            np.random.randint(
+                            rng.integers(
                                 low=1, high=max(1, len(self._current_buffer))
                             ),
                         ]
                     )
             else:
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
diff --git a/python/cudf/cudf/_fuzz_testing/io.py b/python/cudf/cudf/_fuzz_testing/io.py
index ffb7171a855..f267c5bf77e 100644
--- a/python/cudf/cudf/_fuzz_testing/io.py
+++ b/python/cudf/cudf/_fuzz_testing/io.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 import copy
 import json
@@ -91,8 +91,9 @@ def get_next_regression_params(self):
         return dtypes_meta, num_rows, num_cols, seed
 
     def set_rand_params(self, params):
+        rng = np.random.default_rng()
         params_dict = {
-            param: np.random.choice(values) for param, values in params.items()
+            param: rng.choice(values) for param, values in params.items()
         }
         self._current_params["test_kwargs"] = self.process_kwargs(
             params_dict=params_dict
diff --git a/python/cudf/cudf/_fuzz_testing/json.py b/python/cudf/cudf/_fuzz_testing/json.py
index e987529c8ba..9d437dd2247 100644
--- a/python/cudf/cudf/_fuzz_testing/json.py
+++ b/python/cudf/cudf/_fuzz_testing/json.py
@@ -80,7 +80,7 @@ def generate_input(self):
             # https://github.com/rapidsai/cudf/issues/7086
             # dtypes_list.extend(["list"])
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
             self._current_params["seed"] = seed
@@ -105,14 +105,15 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if param == "dtype" and values == ALL_POSSIBLE_VALUES:
-                dtype_val = np.random.choice(
+                dtype_val = rng.choice(
                     [True, self._current_buffer.dtypes.to_dict()]
                 )
                 params_dict[param] = _get_dtype_param_value(dtype_val)
             else:
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
 
 
@@ -155,7 +156,7 @@ def generate_input(self):
             # https://github.com/rapidsai/cudf/issues/7086
             # dtypes_list.extend(["list"])
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
             self._current_params["seed"] = seed
@@ -180,12 +181,13 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if param == "dtype" and values == ALL_POSSIBLE_VALUES:
-                dtype_val = np.random.choice(
+                dtype_val = rng.choice(
                     [True, self._current_buffer.dtypes.to_dict()]
                 )
                 params_dict[param] = _get_dtype_param_value(dtype_val)
             else:
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
diff --git a/python/cudf/cudf/_fuzz_testing/orc.py b/python/cudf/cudf/_fuzz_testing/orc.py
index ecddc72fa85..bff8a061255 100644
--- a/python/cudf/cudf/_fuzz_testing/orc.py
+++ b/python/cudf/cudf/_fuzz_testing/orc.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 import copy
 import io
@@ -62,13 +62,11 @@ def generate_input(self):
                 - cudf.utils.dtypes.UNSIGNED_TYPES
                 - {"datetime64[ns]"}
             )
-
+            seed = random.randint(0, 2**32 - 1)
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
-
             self._current_params["dtypes_meta"] = dtypes_meta
-            seed = random.randint(0, 2**32 - 1)
             self._current_params["seed"] = seed
             self._current_params["num_rows"] = num_rows
             self._current_params["num_cols"] = num_cols
@@ -94,42 +92,41 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "columns":
                     col_size = self._rand(len(self._df.columns))
                     params_dict[param] = list(
-                        np.unique(np.random.choice(self._df.columns, col_size))
+                        np.unique(rng.choice(self._df.columns, col_size))
                     )
                 elif param == "stripes":
                     f = io.BytesIO(self._current_buffer)
                     orcFile = pa.orc.ORCFile(f)
                     stripes = list(range(orcFile.nstripes))
-                    params_dict[param] = np.random.choice(
+                    params_dict[param] = rng.choice(
                         [
                             None,
                             list(
                                 map(
                                     int,
                                     np.unique(
-                                        np.random.choice(
-                                            stripes, orcFile.nstripes
-                                        )
+                                        rng.choice(stripes, orcFile.nstripes)
                                     ),
                                 )
                             ),
                         ]
                     )
                 elif param == "use_index":
-                    params_dict[param] = np.random.choice([True, False])
+                    params_dict[param] = rng.choice([True, False])
                 elif param in ("skiprows", "num_rows"):
-                    params_dict[param] = np.random.choice(
+                    params_dict[param] = rng.choice(
                         [None, self._rand(len(self._df))]
                     )
             else:
                 if not isinstance(values, list):
                     raise TypeError("values must be of type list")
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
 
 
@@ -177,12 +174,11 @@ def generate_input(self):
                 # https://github.com/rapidsai/cudf/issues/7355
                 - cudf.utils.dtypes.DATETIME_TYPES
             )
-
+            seed = random.randint(0, 2**32 - 1)
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
-            seed = random.randint(0, 2**32 - 1)
             self._current_params["seed"] = seed
             self._current_params["num_rows"] = num_rows
             self._current_params["num_cols"] = num_cols
diff --git a/python/cudf/cudf/_fuzz_testing/parquet.py b/python/cudf/cudf/_fuzz_testing/parquet.py
index 2d934e4816d..0f52795e2a2 100644
--- a/python/cudf/cudf/_fuzz_testing/parquet.py
+++ b/python/cudf/cudf/_fuzz_testing/parquet.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 import logging
 import random
@@ -59,12 +59,11 @@ def generate_input(self):
                 - {"uint32"}
                 | {"list", "decimal64"}
             )
-
+            seed = random.randint(0, 2**32 - 1)
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
-            seed = random.randint(0, 2**32 - 1)
             self._current_params["seed"] = seed
             self._current_params["num_rows"] = num_rows
             self._current_params["num_cols"] = num_cols
@@ -96,14 +95,15 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
+        rng = np.random.default_rng()
         for param, values in params.items():
             if param == "columns" and values == ALL_POSSIBLE_VALUES:
                 col_size = self._rand(len(self._df.columns))
                 params_dict[param] = list(
-                    np.unique(np.random.choice(self._df.columns, col_size))
+                    np.unique(rng.choice(self._df.columns, col_size))
                 )
             else:
-                params_dict[param] = np.random.choice(values)
+                params_dict[param] = rng.choice(values)
         self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
 
 
@@ -146,7 +146,7 @@ def generate_input(self):
                 | {"list", "decimal64"}
             )
             dtypes_meta, num_rows, num_cols = _generate_rand_meta(
-                self, dtypes_list
+                self, dtypes_list, seed
             )
             self._current_params["dtypes_meta"] = dtypes_meta
             self._current_params["seed"] = seed
diff --git a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py
index 3d070576a12..bbc19dce1a4 100644
--- a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py
+++ b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 import sys
 
@@ -68,7 +68,9 @@ def parquet_writer_test(pdf):
 @pythonfuzz(
     data_handle=ParquetWriter,
     params={
-        "row_group_size": np.random.random_integers(1, 10000, 100),
+        "row_group_size": np.random.default_rng(seed=0).integers(
+            1, 10000, 100
+        ),
         "compression": ["snappy", None],
     },
 )
diff --git a/python/cudf/cudf/_fuzz_testing/utils.py b/python/cudf/cudf/_fuzz_testing/utils.py
index 8ce92e1c0f6..4cadb3a109c 100644
--- a/python/cudf/cudf/_fuzz_testing/utils.py
+++ b/python/cudf/cudf/_fuzz_testing/utils.py
@@ -40,8 +40,11 @@
 }
 
 
-def _generate_rand_meta(obj, dtypes_list, null_frequency_override=None):
+def _generate_rand_meta(
+    obj, dtypes_list, null_frequency_override=None, seed=0
+):
     obj._current_params = {}
+    rng = np.random.default_rng(seed=seed)
     num_rows = obj._rand(obj._max_rows)
     num_cols = obj._rand(obj._max_columns)
 
@@ -69,12 +72,12 @@ def _generate_rand_meta(obj, dtypes_list, null_frequency_override=None):
                 meta["max_string_length"] = obj._max_string_length
         elif dtype == "list":
             if obj._max_lists_length is None:
-                meta["lists_max_length"] = np.random.randint(0, 2000000000)
+                meta["lists_max_length"] = rng.integers(0, 2000000000)
             else:
                 meta["lists_max_length"] = obj._max_lists_length
 
             if obj._max_lists_nesting_depth is None:
-                meta["nesting_max_depth"] = np.random.randint(
+                meta["nesting_max_depth"] = rng.integers(
                     1, np.iinfo("int64").max
                 )
             else:
@@ -85,7 +88,7 @@ def _generate_rand_meta(obj, dtypes_list, null_frequency_override=None):
             )
         elif dtype == "struct":
             if obj._max_lists_nesting_depth is None:
-                meta["nesting_max_depth"] = np.random.randint(2, 10)
+                meta["nesting_max_depth"] = rng.integers(2, 10)
             else:
                 meta["nesting_max_depth"] = obj._max_lists_nesting_depth
 
@@ -95,9 +98,7 @@ def _generate_rand_meta(obj, dtypes_list, null_frequency_override=None):
                 meta["max_null_frequency"] = obj._max_struct_null_frequency
 
             if obj._max_struct_types_at_each_level is None:
-                meta["max_types_at_each_level"] = np.random.randint(
-                    low=1, high=10
-                )
+                meta["max_types_at_each_level"] = rng.integers(low=1, high=10)
             else:
                 meta["max_types_at_each_level"] = (
                     obj._max_struct_types_at_each_level
diff --git a/python/cudf/cudf/testing/dataset_generator.py b/python/cudf/cudf/testing/dataset_generator.py
index 13c194d6be0..538b01c6157 100644
--- a/python/cudf/cudf/testing/dataset_generator.py
+++ b/python/cudf/cudf/testing/dataset_generator.py
@@ -48,16 +48,19 @@ def __init__(
         self,
         cardinality=100,
         null_frequency=0.1,
-        generator=lambda: [
-            _generate_string(string.ascii_letters, random.randint(4, 8))
-            for _ in range(100)
-        ],
+        generator=None,
         is_sorted=True,
         dtype=None,
     ):
         self.cardinality = cardinality
         self.null_frequency = null_frequency
-        self.generator = generator
+        if generator is None:
+            self.generator = lambda: [
+                np.random.default_rng(seed=0).integers(0, 100)
+                for _ in range(100)
+            ]
+        else:
+            self.generator = generator
         self.is_sorted = is_sorted
         self.dtype = dtype
 
@@ -96,7 +99,7 @@ def _write(tbl, path, format):
             tbl.to_parquet(path, row_group_size=format["row_group_size"])
 
 
-def _generate_column(column_params, num_rows):
+def _generate_column(column_params, num_rows, rng):
     # If cardinality is specified, we create a set to sample from.
     # Otherwise, we simply use the given generator to generate each value.
 
@@ -115,10 +118,8 @@ def _generate_column(column_params, num_rows):
             )
             return pa.DictionaryArray.from_arrays(
                 dictionary=vals,
-                indices=np.random.randint(
-                    low=0, high=len(vals), size=num_rows
-                ),
-                mask=np.random.choice(
+                indices=rng.integers(low=0, high=len(vals), size=num_rows),
+                mask=rng.choice(
                     [True, False],
                     size=num_rows,
                     p=[
@@ -142,7 +143,7 @@ def _generate_column(column_params, num_rows):
                 column_params.generator,
                 names=column_params.dtype.fields.keys(),
                 mask=pa.array(
-                    np.random.choice(
+                    rng.choice(
                         [True, False],
                         size=num_rows,
                         p=[
@@ -163,10 +164,10 @@ def _generate_column(column_params, num_rows):
                 type=arrow_type,
             )
         vals = pa.array(
-            np.random.choice(column_params.generator, size=num_rows)
+            rng.choice(column_params.generator, size=num_rows)
             if isinstance(arrow_type, pa.lib.Decimal128Type)
-            else np.random.choice(vals, size=num_rows),
-            mask=np.random.choice(
+            else rng.choice(vals, size=num_rows),
+            mask=rng.choice(
                 [True, False],
                 size=num_rows,
                 p=[
@@ -189,7 +190,7 @@ def _generate_column(column_params, num_rows):
         # Generate data for current column
         return pa.array(
             column_params.generator,
-            mask=np.random.choice(
+            mask=rng.choice(
                 [True, False],
                 size=num_rows,
                 p=[
@@ -233,7 +234,9 @@ def generate(
 def get_dataframe(parameters, use_threads):
     # Initialize seeds
     if parameters.seed is not None:
-        np.random.seed(parameters.seed)
+        rng = np.random.default_rng(seed=parameters.seed)  # noqa: F841
+    else:
+        rng = np.random.default_rng(seed=0)  # noqa: F841
 
     # For each column, invoke the data generator
     for column_params in parameters.column_parameters:
@@ -281,14 +284,16 @@ def get_dataframe(parameters, use_threads):
     if not use_threads:
         for i, column_params in enumerate(parameters.column_parameters):
             column_data[i] = _generate_column(
-                column_params, parameters.num_rows
+                column_params,
+                parameters.num_rows,
+                rng,
             )
     else:
         pool = Pool(pa.cpu_count())
         column_data = pool.starmap(
             _generate_column,
             [
-                (column_params, parameters.num_rows)
+                (column_params, parameters.num_rows, rng)
                 for i, column_params in enumerate(parameters.column_parameters)
             ],
         )
@@ -336,7 +341,7 @@ def rand_dataframe(
     """
     # Apply seed
     random.seed(seed)
-    np.random.seed(seed)
+    rng = np.random.default_rng(seed=seed)
 
     column_params = []
     for meta in dtypes_meta:
@@ -348,7 +353,7 @@ def rand_dataframe(
             lists_max_length = meta["lists_max_length"]
             nesting_max_depth = meta["nesting_max_depth"]
             value_type = meta["value_type"]
-            nesting_depth = np.random.randint(1, nesting_max_depth)
+            nesting_depth = rng.integers(1, nesting_max_depth)
 
             dtype = cudf.core.dtypes.ListDtype(value_type)
 
@@ -368,19 +373,22 @@ def rand_dataframe(
                         size=cardinality,
                         nesting_depth=nesting_depth,
                         lists_max_length=lists_max_length,
+                        rng=rng,
                     ),
                     is_sorted=False,
                     dtype=dtype,
+                    rng=rng,
                 )
             )
         elif dtype == "struct":
             nesting_max_depth = meta["nesting_max_depth"]
             max_types_at_each_level = meta["max_types_at_each_level"]
             max_null_frequency = meta["max_null_frequency"]
-            nesting_depth = np.random.randint(1, nesting_max_depth)
+            nesting_depth = rng.integers(1, nesting_max_depth)
             structDtype = create_nested_struct_type(
                 max_types_at_each_level=max_types_at_each_level,
                 nesting_level=nesting_depth,
+                rng=rng,
             )
 
             column_params.append(
@@ -392,6 +400,7 @@ def rand_dataframe(
                         cardinality=cardinality,
                         size=rows,
                         max_null_frequency=max_null_frequency,
+                        rng=rng,
                     ),
                     is_sorted=False,
                     dtype=structDtype,
@@ -401,14 +410,16 @@ def rand_dataframe(
             max_precision = meta.get(
                 "max_precision", cudf.Decimal64Dtype.MAX_PRECISION
             )
-            precision = np.random.randint(1, max_precision)
-            scale = np.random.randint(0, precision)
+            precision = rng.integers(1, max_precision)
+            scale = rng.integers(0, precision)
             dtype = cudf.Decimal64Dtype(precision=precision, scale=scale)
             column_params.append(
                 ColumnParameters(
                     cardinality=cardinality,
                     null_frequency=null_frequency,
-                    generator=decimal_generator(dtype=dtype, size=cardinality),
+                    generator=decimal_generator(
+                        dtype=dtype, size=cardinality, rng=rng
+                    ),
                     is_sorted=False,
                     dtype=dtype,
                 )
@@ -417,14 +428,16 @@ def rand_dataframe(
             max_precision = meta.get(
                 "max_precision", cudf.Decimal32Dtype.MAX_PRECISION
             )
-            precision = np.random.randint(1, max_precision)
-            scale = np.random.randint(0, precision)
+            precision = rng.integers(1, max_precision)
+            scale = rng.integers(0, precision)
             dtype = cudf.Decimal32Dtype(precision=precision, scale=scale)
             column_params.append(
                 ColumnParameters(
                     cardinality=cardinality,
                     null_frequency=null_frequency,
-                    generator=decimal_generator(dtype=dtype, size=cardinality),
+                    generator=decimal_generator(
+                        dtype=dtype, size=cardinality, rng=rng
+                    ),
                     is_sorted=False,
                     dtype=dtype,
                 )
@@ -433,14 +446,16 @@ def rand_dataframe(
             max_precision = meta.get(
                 "max_precision", cudf.Decimal128Dtype.MAX_PRECISION
             )
-            precision = np.random.randint(1, max_precision)
-            scale = np.random.randint(0, precision)
+            precision = rng.integers(1, max_precision)
+            scale = rng.integers(0, precision)
             dtype = cudf.Decimal128Dtype(precision=precision, scale=scale)
             column_params.append(
                 ColumnParameters(
                     cardinality=cardinality,
                     null_frequency=null_frequency,
-                    generator=decimal_generator(dtype=dtype, size=cardinality),
+                    generator=decimal_generator(
+                        dtype=dtype, size=cardinality, rng=rng
+                    ),
                     is_sorted=False,
                     dtype=dtype,
                 )
@@ -469,6 +484,7 @@ def rand_dataframe(
                             size=cardinality,
                             min_bound=meta.get("min_bound", None),
                             max_bound=meta.get("max_bound", None),
+                            rng=rng,
                         ),
                         is_sorted=False,
                         dtype=dtype,
@@ -484,6 +500,7 @@ def rand_dataframe(
                             size=cardinality,
                             min_bound=meta.get("min_bound", None),
                             max_bound=meta.get("max_bound", None),
+                            rng=rng,
                         ),
                         is_sorted=False,
                         dtype=dtype,
@@ -497,7 +514,8 @@ def rand_dataframe(
                         generator=lambda cardinality=cardinality: [
                             _generate_string(
                                 string.printable,
-                                np.random.randint(
+                                rng,
+                                rng.integers(
                                     low=0,
                                     high=meta.get("max_string_length", 1000),
                                     size=1,
@@ -519,6 +537,7 @@ def rand_dataframe(
                             size=cardinality,
                             min_bound=meta.get("min_bound", None),
                             max_bound=meta.get("max_bound", None),
+                            rng=rng,
                         ),
                         is_sorted=False,
                         dtype=cudf.dtype(dtype),
@@ -534,6 +553,7 @@ def rand_dataframe(
                             size=cardinality,
                             min_bound=meta.get("min_bound", None),
                             max_bound=meta.get("max_bound", None),
+                            rng=rng,
                         ),
                         is_sorted=False,
                         dtype=cudf.dtype(dtype),
@@ -544,7 +564,7 @@ def rand_dataframe(
                     ColumnParameters(
                         cardinality=cardinality,
                         null_frequency=null_frequency,
-                        generator=boolean_generator(cardinality),
+                        generator=boolean_generator(cardinality, rng),
                         is_sorted=False,
                         dtype=cudf.dtype(dtype),
                     )
@@ -567,7 +587,7 @@ def rand_dataframe(
     return df
 
 
-def int_generator(dtype, size, min_bound=None, max_bound=None):
+def int_generator(dtype, size, rng, min_bound=None, max_bound=None):
     """
     Generator for int data
     """
@@ -577,7 +597,7 @@ def int_generator(dtype, size, min_bound=None, max_bound=None):
         iinfo = np.iinfo(dtype)
         low, high = iinfo.min, iinfo.max
 
-    return lambda: np.random.randint(
+    return lambda: rng.integers(
         low=low,
         high=high,
         size=size,
@@ -585,13 +605,13 @@ def int_generator(dtype, size, min_bound=None, max_bound=None):
     )
 
 
-def float_generator(dtype, size, min_bound=None, max_bound=None):
+def float_generator(dtype, size, rng, min_bound=None, max_bound=None):
     """
     Generator for float data
     """
     if min_bound is not None and max_bound is not None:
         low, high = min_bound, max_bound
-        return lambda: np.random.uniform(
+        return lambda: rng.uniform(
             low=low,
             high=high,
             size=size,
@@ -599,7 +619,7 @@ def float_generator(dtype, size, min_bound=None, max_bound=None):
     else:
         finfo = np.finfo(dtype)
         return (
-            lambda: np.random.uniform(
+            lambda: rng.uniform(
                 low=finfo.min / 2,
                 high=finfo.max / 2,
                 size=size,
@@ -608,7 +628,7 @@ def float_generator(dtype, size, min_bound=None, max_bound=None):
         )
 
 
-def datetime_generator(dtype, size, min_bound=None, max_bound=None):
+def datetime_generator(dtype, size, rng, min_bound=None, max_bound=None):
     """
     Generator for datetime data
     """
@@ -618,14 +638,14 @@ def datetime_generator(dtype, size, min_bound=None, max_bound=None):
         iinfo = np.iinfo("int64")
         low, high = iinfo.min + 1, iinfo.max
 
-    return lambda: np.random.randint(
+    return lambda: rng.integers(
         low=np.datetime64(low, "ns").astype(dtype).astype("int"),
         high=np.datetime64(high, "ns").astype(dtype).astype("int"),
         size=size,
     )
 
 
-def timedelta_generator(dtype, size, min_bound=None, max_bound=None):
+def timedelta_generator(dtype, size, rng, min_bound=None, max_bound=None):
     """
     Generator for timedelta data
     """
@@ -635,25 +655,25 @@ def timedelta_generator(dtype, size, min_bound=None, max_bound=None):
         iinfo = np.iinfo("int64")
         low, high = iinfo.min + 1, iinfo.max
 
-    return lambda: np.random.randint(
+    return lambda: rng.integers(
         low=np.timedelta64(low, "ns").astype(dtype).astype("int"),
         high=np.timedelta64(high, "ns").astype(dtype).astype("int"),
         size=size,
     )
 
 
-def boolean_generator(size):
+def boolean_generator(size, rng):
     """
     Generator for bool data
     """
-    return lambda: np.random.choice(a=[False, True], size=size)
+    return lambda: rng.choice(a=[False, True], size=size)
 
 
-def decimal_generator(dtype, size):
+def decimal_generator(dtype, size, rng):
     max_integral = 10 ** (dtype.precision - dtype.scale) - 1
     max_float = (10**dtype.scale - 1) if dtype.scale != 0 else 0
     return lambda: (
-        np.random.uniform(
+        rng.uniform(
             low=-max_integral,
             high=max_integral + (max_float / 10**dtype.scale),
             size=size,
@@ -661,32 +681,33 @@ def decimal_generator(dtype, size):
     )
 
 
-def get_values_for_nested_data(dtype, lists_max_length=None, size=None):
+def get_values_for_nested_data(dtype, rng, lists_max_length=None, size=None):
     """
     Returns list of values based on dtype.
     """
     if size is None:
-        cardinality = np.random.randint(0, lists_max_length)
+        cardinality = rng.integers(0, lists_max_length)
     else:
         cardinality = size
 
     dtype = cudf.dtype(dtype)
     if dtype.kind in ("i", "u"):
-        values = int_generator(dtype=dtype, size=cardinality)()
+        values = int_generator(dtype=dtype, size=cardinality, rng=rng)()
     elif dtype.kind == "f":
-        values = float_generator(dtype=dtype, size=cardinality)()
+        values = float_generator(dtype=dtype, size=cardinality, rng=rng)()
     elif dtype.kind in ("U", "O"):
         values = [
             _generate_string(
                 string.printable,
+                rng,
                 100,
             )
             for _ in range(cardinality)
         ]
     elif dtype.kind == "M":
-        values = datetime_generator(dtype=dtype, size=cardinality)().astype(
-            dtype
-        )
+        values = datetime_generator(
+            dtype=dtype, size=cardinality, rng=rng
+        )().astype(dtype)
     elif dtype.kind == "m":
         values = timedelta_generator(dtype=dtype, size=cardinality)().astype(
             dtype
@@ -699,14 +720,14 @@ def get_values_for_nested_data(dtype, lists_max_length=None, size=None):
     return values
 
 
-def make_lists(dtype, lists_max_length, nesting_depth, top_level_list):
+def make_lists(dtype, lists_max_length, nesting_depth, top_level_list, rng):
     """
     Helper to create random list of lists with `nesting_depth` and
     specified value type `dtype`.
     """
     nesting_depth -= 1
     if nesting_depth >= 0:
-        L = np.random.randint(1, lists_max_length)
+        L = rng.integers(1, lists_max_length)
         for i in range(L):
             top_level_list.append(
                 make_lists(
@@ -714,11 +735,14 @@ def make_lists(dtype, lists_max_length, nesting_depth, top_level_list):
                     lists_max_length=lists_max_length,
                     nesting_depth=nesting_depth,
                     top_level_list=[],
+                    rng=rng,
                 )
             )
     else:
         top_level_list = get_values_for_nested_data(
-            dtype=dtype, lists_max_length=lists_max_length
+            dtype=dtype,
+            lists_max_length=lists_max_length,
+            rng=rng,
         )
         # To ensure numpy arrays are not passed as input to
         # list constructor, returning a python list object here.
@@ -728,22 +752,22 @@ def make_lists(dtype, lists_max_length, nesting_depth, top_level_list):
     return top_level_list
 
 
-def make_array_for_struct(dtype, cardinality, size, max_null_frequency):
+def make_array_for_struct(dtype, cardinality, size, max_null_frequency, rng):
     """
     Helper to create a pa.array with `size` and `dtype`
     for a `StructArray`.
     """
 
-    null_frequency = np.random.uniform(low=0, high=max_null_frequency)
-    local_cardinality = max(np.random.randint(low=0, high=cardinality), 1)
+    null_frequency = rng.uniform(low=0, high=max_null_frequency)
+    local_cardinality = max(rng.integers(low=0, high=cardinality), 1)
     data = get_values_for_nested_data(
-        dtype=dtype.type.to_pandas_dtype(), size=local_cardinality
+        dtype=dtype.type.to_pandas_dtype(), size=local_cardinality, rng=rng
     )
-    vals = np.random.choice(data, size=size)
+    vals = rng.choice(data, size=size)
 
     return pa.array(
         vals,
-        mask=np.random.choice(
+        mask=rng.choice(
             [True, False],
             size=size,
             p=[null_frequency, 1 - null_frequency],
@@ -756,7 +780,7 @@ def make_array_for_struct(dtype, cardinality, size, max_null_frequency):
     )
 
 
-def get_nested_lists(dtype, size, nesting_depth, lists_max_length):
+def get_nested_lists(dtype, size, nesting_depth, lists_max_length, rng):
     """
     Returns a list of nested lists with random nesting
     depth and random nested lists length.
@@ -776,7 +800,7 @@ def get_nested_lists(dtype, size, nesting_depth, lists_max_length):
     return list_of_lists
 
 
-def get_nested_structs(dtype, cardinality, size, max_null_frequency):
+def get_nested_structs(dtype, cardinality, size, max_null_frequency, rng):
     """
     Returns a list of arrays with random data
     corresponding to the dtype provided.
@@ -787,7 +811,7 @@ def get_nested_structs(dtype, cardinality, size, max_null_frequency):
     for name, col_dtype in dtype.fields.items():
         if isinstance(col_dtype, cudf.StructDtype):
             result_arrays = get_nested_structs(
-                col_dtype, cardinality, size, max_null_frequency
+                col_dtype, cardinality, size, max_null_frequency, rng
             )
             result_arrays = pa.StructArray.from_arrays(
                 result_arrays, names=col_dtype.fields.keys()
@@ -798,13 +822,14 @@ def get_nested_structs(dtype, cardinality, size, max_null_frequency):
                 cardinality=cardinality,
                 size=size,
                 max_null_frequency=max_null_frequency,
+                rng=rng,
             )
         list_of_arrays.append(result_arrays)
 
     return list_of_arrays
 
 
-def list_generator(dtype, size, nesting_depth, lists_max_length):
+def list_generator(dtype, size, nesting_depth, lists_max_length, rng):
     """
     Generator for list data
     """
@@ -813,10 +838,11 @@ def list_generator(dtype, size, nesting_depth, lists_max_length):
         size=size,
         nesting_depth=nesting_depth,
         lists_max_length=lists_max_length,
+        rng=rng,
     )
 
 
-def struct_generator(dtype, cardinality, size, max_null_frequency):
+def struct_generator(dtype, cardinality, size, max_null_frequency, rng):
     """
     Generator for struct data
     """
@@ -825,25 +851,26 @@ def struct_generator(dtype, cardinality, size, max_null_frequency):
         cardinality=cardinality,
         size=size,
         max_null_frequency=max_null_frequency,
+        rng=rng,
     )
 
 
-def create_nested_struct_type(max_types_at_each_level, nesting_level):
+def create_nested_struct_type(max_types_at_each_level, nesting_level, rng):
     dtypes_list = cudf.utils.dtypes.ALL_TYPES
-    picked_types = np.random.choice(list(dtypes_list), max_types_at_each_level)
+    picked_types = rng.choice(list(dtypes_list), max_types_at_each_level)
     type_dict = {}
     for name, type_ in enumerate(picked_types):
         if type_ == "struct":
             type_dict[str(name)] = create_nested_struct_type(
-                max_types_at_each_level, nesting_level - 1
+                max_types_at_each_level, nesting_level - 1, rng
             )
         else:
             type_dict[str(name)] = cudf.dtype(type_)
     return cudf.StructDtype(type_dict)
 
 
-def _generate_string(str_seq: str, length: int = 10) -> str:
-    return "".join(random.choices(str_seq, k=length))
+def _generate_string(str_seq: str, rng, length: int = 10) -> str:
+    return "".join(rng.choices(str_seq, k=length))
 
 
 def _unique_string() -> str:
diff --git a/python/cudf/cudf/tests/groupby/test_ordering_pandas_compat.py b/python/cudf/cudf/tests/groupby/test_ordering_pandas_compat.py
index a009802bab0..a8c5ae3b6a3 100644
--- a/python/cudf/cudf/tests/groupby/test_ordering_pandas_compat.py
+++ b/python/cudf/cudf/tests/groupby/test_ordering_pandas_compat.py
@@ -14,9 +14,10 @@ def with_nulls(request):
 @pytest.mark.parametrize("nrows", [30, 300, 300_000])
 @pytest.mark.parametrize("nkeys", [1, 2, 4])
 def test_groupby_maintain_order_random(nrows, nkeys, with_nulls):
+    rng = np.random.default_rng(seed=0)
     key_names = [f"key{key}" for key in range(nkeys)]
-    key_values = [np.random.randint(100, size=nrows) for _ in key_names]
-    value = np.random.randint(-100, 100, size=nrows)
+    key_values = [rng.integers(100, size=nrows) for _ in key_names]
+    value = rng.integers(-100, 100, size=nrows)
     df = cudf.DataFrame(dict(zip(key_names, key_values), value=value))
     if with_nulls:
         for key in key_names:

From e100e2d4c27d221b0163b26edd3b0295f88af7d7 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Fri, 11 Oct 2024 22:38:17 +0000
Subject: [PATCH 07/21] fix issues

---
 python/cudf/cudf/testing/dataset_generator.py |  4 ++--
 python/cudf/cudf/tests/test_factorize.py      | 10 ++++++++--
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/python/cudf/cudf/testing/dataset_generator.py b/python/cudf/cudf/testing/dataset_generator.py
index 538b01c6157..4cc5b00f63e 100644
--- a/python/cudf/cudf/testing/dataset_generator.py
+++ b/python/cudf/cudf/testing/dataset_generator.py
@@ -377,7 +377,6 @@ def rand_dataframe(
                     ),
                     is_sorted=False,
                     dtype=dtype,
-                    rng=rng,
                 )
             )
         elif dtype == "struct":
@@ -794,6 +793,7 @@ def get_nested_lists(dtype, size, nesting_depth, lists_max_length, rng):
                 lists_max_length=lists_max_length,
                 nesting_depth=nesting_depth,
                 top_level_list=[],
+                rng=rng,
             )
         )
 
@@ -870,7 +870,7 @@ def create_nested_struct_type(max_types_at_each_level, nesting_level, rng):
 
 
 def _generate_string(str_seq: str, rng, length: int = 10) -> str:
-    return "".join(rng.choices(str_seq, k=length))
+    return "".join(rng.choice(list(str_seq), size=length))
 
 
 def _unique_string() -> str:
diff --git a/python/cudf/cudf/tests/test_factorize.py b/python/cudf/cudf/tests/test_factorize.py
index cd558cc1415..cfb4ae2c0f8 100644
--- a/python/cudf/cudf/tests/test_factorize.py
+++ b/python/cudf/cudf/tests/test_factorize.py
@@ -19,7 +19,10 @@ def test_factorize_series_obj(ncats, nelem):
     df["cats"] = arr = rng.integers(2, size=10, dtype=np.int32)
 
     uvals, labels = df["cats"].factorize()
-    np.testing.assert_array_equal(labels.to_numpy(), sorted(set(arr)))
+    unique_values, indices = np.unique(arr, return_index=True)
+    expected_values = unique_values[np.argsort(indices)]
+
+    np.testing.assert_array_equal(labels.to_numpy(), expected_values)
     assert isinstance(uvals, cp.ndarray)
     assert isinstance(labels, Index)
 
@@ -38,7 +41,10 @@ def test_factorize_index_obj(ncats, nelem):
     df = df.set_index("cats")
 
     uvals, labels = df.index.factorize()
-    np.testing.assert_array_equal(labels.values.get(), sorted(set(arr)))
+    unique_values, indices = np.unique(arr, return_index=True)
+    expected_values = unique_values[np.argsort(indices)]
+
+    np.testing.assert_array_equal(labels.values.get(), expected_values)
     assert isinstance(uvals, cp.ndarray)
     assert isinstance(labels, Index)
 

From 33234fc08cae6bf6a06db6c16dee18488a82b2f0 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Sat, 12 Oct 2024 12:45:30 +0000
Subject: [PATCH 08/21] fix more failures

---
 python/cudf/cudf/core/dataframe.py    |  1 +
 python/cudf/cudf/testing/_utils.py    |  2 +-
 python/cudf/cudf/tests/test_binops.py | 12 ++++++++++--
 3 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index 3b09bf2c408..f8d544e04b1 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -5118,6 +5118,7 @@ def info(
         useful for big DataFrames and fine-tune memory optimization:
 
         >>> import numpy as np
+        >>> rng = np.random.default_rng(seed=0)
         >>> random_strings_array = rng.choice(['a', 'b', 'c'], 10 ** 6)
         >>> df = cudf.DataFrame({
         ...     'column_1': rng.choice(['a', 'b', 'c'], 10 ** 6),
diff --git a/python/cudf/cudf/testing/_utils.py b/python/cudf/cudf/testing/_utils.py
index 38b46ff74c7..8a30b0ae6d6 100644
--- a/python/cudf/cudf/testing/_utils.py
+++ b/python/cudf/cudf/testing/_utils.py
@@ -217,7 +217,7 @@ def gen_rand(dtype, size, **kwargs):
         if kwargs.get("positive_only", False):
             return res
         else:
-            return 2 * res - 2
+            return res * 2 - 1
     elif dtype == np.int8 or dtype == np.int16:
         low = kwargs.get("low", -32)
         high = kwargs.get("high", 32)
diff --git a/python/cudf/cudf/tests/test_binops.py b/python/cudf/cudf/tests/test_binops.py
index 138e6e92515..206374a83cd 100644
--- a/python/cudf/cudf/tests/test_binops.py
+++ b/python/cudf/cudf/tests/test_binops.py
@@ -178,7 +178,13 @@
 
 @pytest.mark.parametrize("obj_class", ["Series", "Index"])
 @pytest.mark.parametrize("binop", _binops)
-def test_series_binop(binop, obj_class):
+def test_series_binop(request, binop, obj_class):
+    request.applymarker(
+        pytest.mark.xfail(
+            binop is operator.floordiv,
+            reason="https://github.com/rapidsai/cudf/issues/17073",
+        )
+    )
     nelem = 1000
     arr1 = utils.gen_rand("float64", nelem) * 10000
     # Keeping a low value because CUDA 'pow' has 2 full range error
@@ -186,13 +192,15 @@ def test_series_binop(binop, obj_class):
 
     sr1 = Series(arr1)
     sr2 = Series(arr2)
+    psr1 = sr1.to_pandas()
+    psr2 = sr2.to_pandas()
 
     if obj_class == "Index":
         sr1 = Index(sr1)
         sr2 = Index(sr2)
 
+    expect = binop(psr1, psr2)
     result = binop(sr1, sr2)
-    expect = binop(pd.Series(arr1), pd.Series(arr2))
 
     if obj_class == "Index":
         result = Series(result)

From 5915e5b7ad4950363ebfc2e03da4b5f1ea9c3ca1 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Sat, 12 Oct 2024 12:54:26 +0000
Subject: [PATCH 09/21] update pre-commit

---
 .pre-commit-config.yaml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 09f441b143b..88ecf4f287c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -95,6 +95,12 @@ repos:
         entry: 'pytest\.xfail'
         language: pygrep
         types: [python]
+      - id: no-unseeded-default-rng
+        name: no-unseeded-default-rng
+        description: 'Enforce that no non-seeded default_rng is used'
+        entry: 'default_rng\(\)'
+        language: pygrep
+        types: [python]
       - id: cmake-format
         name: cmake-format
         entry: ./cpp/scripts/run-cmake-format.sh cmake-format
@@ -141,7 +147,6 @@ repos:
     hooks:
       - id: ruff
         files: python/.*$
-        args: [ "--fix" ]
       - id: ruff-format
         files: python/.*$
   - repo: https://github.com/rapidsai/pre-commit-hooks

From 3d35be19744dadf52ff1aaffe2469da0408addf5 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Sat, 12 Oct 2024 12:58:41 +0000
Subject: [PATCH 10/21] fix default seed

---
 python/cudf/cudf/_fuzz_testing/avro.py    | 2 +-
 python/cudf/cudf/_fuzz_testing/csv.py     | 4 ++--
 python/cudf/cudf/_fuzz_testing/io.py      | 2 +-
 python/cudf/cudf/_fuzz_testing/json.py    | 4 ++--
 python/cudf/cudf/_fuzz_testing/orc.py     | 2 +-
 python/cudf/cudf/_fuzz_testing/parquet.py | 2 +-
 python/cudf/cudf/tests/test_column.py     | 2 +-
 python/dask_cudf/dask_cudf/tests/utils.py | 2 +-
 8 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/python/cudf/cudf/_fuzz_testing/avro.py b/python/cudf/cudf/_fuzz_testing/avro.py
index 4236ecb0219..172193aa672 100644
--- a/python/cudf/cudf/_fuzz_testing/avro.py
+++ b/python/cudf/cudf/_fuzz_testing/avro.py
@@ -100,7 +100,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "columns":
diff --git a/python/cudf/cudf/_fuzz_testing/csv.py b/python/cudf/cudf/_fuzz_testing/csv.py
index 4ab666f1726..fa3ed40ce91 100644
--- a/python/cudf/cudf/_fuzz_testing/csv.py
+++ b/python/cudf/cudf/_fuzz_testing/csv.py
@@ -77,7 +77,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "usecols":
@@ -179,7 +179,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "columns":
diff --git a/python/cudf/cudf/_fuzz_testing/io.py b/python/cudf/cudf/_fuzz_testing/io.py
index f267c5bf77e..a4b8e18d8b4 100644
--- a/python/cudf/cudf/_fuzz_testing/io.py
+++ b/python/cudf/cudf/_fuzz_testing/io.py
@@ -91,7 +91,7 @@ def get_next_regression_params(self):
         return dtypes_meta, num_rows, num_cols, seed
 
     def set_rand_params(self, params):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         params_dict = {
             param: rng.choice(values) for param, values in params.items()
         }
diff --git a/python/cudf/cudf/_fuzz_testing/json.py b/python/cudf/cudf/_fuzz_testing/json.py
index 9d437dd2247..45d2c8d8cf0 100644
--- a/python/cudf/cudf/_fuzz_testing/json.py
+++ b/python/cudf/cudf/_fuzz_testing/json.py
@@ -105,7 +105,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if param == "dtype" and values == ALL_POSSIBLE_VALUES:
                 dtype_val = rng.choice(
@@ -181,7 +181,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if param == "dtype" and values == ALL_POSSIBLE_VALUES:
                 dtype_val = rng.choice(
diff --git a/python/cudf/cudf/_fuzz_testing/orc.py b/python/cudf/cudf/_fuzz_testing/orc.py
index bff8a061255..4d9e4abb09e 100644
--- a/python/cudf/cudf/_fuzz_testing/orc.py
+++ b/python/cudf/cudf/_fuzz_testing/orc.py
@@ -92,7 +92,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if values == ALL_POSSIBLE_VALUES:
                 if param == "columns":
diff --git a/python/cudf/cudf/_fuzz_testing/parquet.py b/python/cudf/cudf/_fuzz_testing/parquet.py
index 0f52795e2a2..bd3df1b0847 100644
--- a/python/cudf/cudf/_fuzz_testing/parquet.py
+++ b/python/cudf/cudf/_fuzz_testing/parquet.py
@@ -95,7 +95,7 @@ def write_data(self, file_name):
 
     def set_rand_params(self, params):
         params_dict = {}
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(seed=None)
         for param, values in params.items():
             if param == "columns" and values == ALL_POSSIBLE_VALUES:
                 col_size = self._rand(len(self._df.columns))
diff --git a/python/cudf/cudf/tests/test_column.py b/python/cudf/cudf/tests/test_column.py
index 6073c4e6434..c5b3191edd1 100644
--- a/python/cudf/cudf/tests/test_column.py
+++ b/python/cudf/cudf/tests/test_column.py
@@ -31,7 +31,7 @@
 @pytest.fixture(params=dtypes, ids=dtypes)
 def pandas_input(request):
     dtype = request.param
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(seed=None)
     size = 100
 
     def random_ints(dtype, size):
diff --git a/python/dask_cudf/dask_cudf/tests/utils.py b/python/dask_cudf/dask_cudf/tests/utils.py
index 1744b08cd8d..9aaf6dc8420 100644
--- a/python/dask_cudf/dask_cudf/tests/utils.py
+++ b/python/dask_cudf/dask_cudf/tests/utils.py
@@ -19,7 +19,7 @@
 
 
 def _make_random_frame(nelem, npartitions=2, include_na=False):
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(seed=None)
     df = pd.DataFrame(
         {"x": rng.random(size=nelem), "y": rng.random(size=nelem)}
     )

From 8336a5f6623cda234173e0db734cfa20efa9980b Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Sat, 12 Oct 2024 12:59:48 +0000
Subject: [PATCH 11/21] style

---
 python/cudf/cudf/tests/test_parquet.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/cudf/cudf/tests/test_parquet.py b/python/cudf/cudf/tests/test_parquet.py
index 6d3ff918673..c9ce24d2a5b 100644
--- a/python/cudf/cudf/tests/test_parquet.py
+++ b/python/cudf/cudf/tests/test_parquet.py
@@ -455,7 +455,9 @@ def test_parquet_read_filtered(tmpdir, rdg_seed):
                 dg.ColumnParameters(
                     40,
                     0.2,
-                    lambda: np.random.default_rng().integers(0, 100, size=40),
+                    lambda: np.random.default_rng(seed=None).integers(
+                        0, 100, size=40
+                    ),
                     True,
                 ),
             ],

From cd7e1984f2c22ad7f770680cd874455c420c54af Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 14 Oct 2024 19:16:33 +0000
Subject: [PATCH 12/21] update files

---
 .../bert_base_cased_sampled/vocab-hash.txt    | 8580 ++++++++---------
 python/cudf/cudf/utils/hash_vocab_utils.py    |   16 +-
 2 files changed, 4297 insertions(+), 4299 deletions(-)

diff --git a/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab-hash.txt b/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab-hash.txt
index 84b13c9d946..566ac2c337d 100644
--- a/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab-hash.txt
+++ b/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab-hash.txt
@@ -1,4382 +1,4382 @@
-26899
-27424
+19535
+9039
 875
-7428432802425011718 0
-5054974408289448963 6
-18358444369622338053 9
-5716902217424485892 14
-8236612966193239043 18
-15282833726017872390 21
-15533348956988973570 27
-9001315167781089284 29
-7621090240282984451 33
-15337888141402371590 36
-16169070283077377537 42
-15615300272936709634 43
-12338784885023498756 45
-3175624061711419395 49
-9436392785812228615 52
-12978641027296058883 59
-14468815760709033991 62
-15607694490571932163 69
-53295083356623878 72
-0 78
-2230148770582976004 78
-6120456721458209796 82
-15411373208619074054 86
-10274574020114097153 92
-9000294930530661890 93
-13031557903172483076 95
-11350066664294002181 99
-6325605033787362307 104
-2909954277284188676 107
-4104562716099355138 111
-3267092979937387012 113
-17525453481571210244 117
-11532627846208440834 121
-10784672185103672321 123
-11229796758348255749 124
-4379577250247562242 129
-1041161126836283908 131
-3854383966527313413 135
-16467720483237810694 140
-14820844471735454722 146
-13111220924289178119 148
-2548683052821249538 155
-719749806464434178 157
-2121722119826170883 159
-9005614210949580292 162
-7050169108294333445 166
-17351764915062575107 171
-14644698505496219141 174
-11657834349296686081 179
-13626797927783164930 180
-14735048589438940164 182
-1078491261937017863 186
-7952761372439242754 193
-7692446865301965827 195
-4552111108816020995 198
-12455022990418032132 201
-1123962659471997957 205
-3056549312838577156 210
-1025661670765243906 214
-5397331336358247944 216
-7810366437124875782 224
-1195318972358038531 230
-7079722807026103811 233
-2524512050942986248 236
-1208593608912656389 244
-458260789232344578 249
-13194777122325112327 251
-5922704468287492 258
-11746235869336195079 262
-8611574268876189188 269
-7889840228953421829 273
-16998721522558936068 278
-6703563424903621638 282
-8885848295085850114 288
-13776273837475230211 290
-6036043703810622467 293
-2006225773287659526 296
-14202467530861800964 302
-7157057020317447684 306
-16885485872491802629 310
-12800303798361952772 315
-621325108927868418 319
-16727475898656483841 321
-6890112792805515778 322
-2421332377941126151 324
-16243404411124196356 331
-179400401794890244 335
-2630159406474274819 339
-1306609735592145925 342
-14908020842914311174 347
-1684452927247835651 353
-9400495923215416322 356
-8041860727239247878 358
-5619270496913133574 364
-2985476283152588291 370
-18150632792370312198 373
-13075355875451793410 379
-7596576612263365635 381
-7174955249282660868 384
-2272878747426984963 388
-9645618748109430277 391
-5995177571885476868 396
-16871713338758691845 400
-11801224416933808644 405
-15551192014010130949 409
-8196030292452405250 414
-4794784530053649411 416
-68047322062825475 419
-10163451915097363972 422
-4366630365820669955 426
-9174613115382159879 429
-17673253091692480002 436
-10710744348807818249 438
-6301209632168211460 447
-6557199531177304066 451
-10370980735304160259 453
-2426040420413965827 456
-18123352379522220547 459
-15891150425892429319 462
-16507447417454265351 469
-487708338428237827 476
-14107089365716616196 479
-747857609528251395 483
-17357876987202521607 486
-321005419951863300 493
-703083947315053061 497
-0 502
-17149635587492691460 502
-8277651075246678020 506
-1819886593879462403 510
-13106328552418381315 513
-17519686381941948418 516
-10696099526822671877 518
-4627984173327437314 523
-2628632462897246722 525
-3686397216490033667 527
-6617920799692924934 530
-6679301623707790339 536
-2596030458845084674 539
-13288938917088308226 541
-8348492885671808517 543
-6252009608718840325 548
-5807005916268695559 553
-15382799971167504899 560
-14954638692016032262 563
-8963684459383523331 569
-2934745887866391556 572
-8236887590303639044 576
-2016330563068923911 580
-12976290063611676164 587
-9986513189506445831 591
-780378482699725318 598
-383862355994530823 604
-7511344867307093508 611
-1435616864863593988 615
-12590979271693393411 619
-859813995721111047 622
-17910873098448224770 629
-16703366890805911553 631
-6922480979814889987 632
-8200210214462711297 635
-18382541080931060232 636
-12959023536126992897 644
-11055794376142651906 645
-8668012051305565187 647
-6795201209679524868 650
-3864186432644490244 654
-4574634299775772674 658
-2086703290536303619 660
-7145543127561014787 663
-9889572542971630085 666
-3510566585561691650 671
-10482036181312531460 673
-4296479271603189251 677
-17165580381790665732 680
-17931697598514948104 684
-5072138329769649158 692
-17857316349005986308 698
-1196313437880152072 702
-16094827446472526340 710
-6365083142954013701 714
-17639674970007880709 719
-1336948026798963208 724
-15719079816546418177 732
-453771991153695748 733
-15666021623592344581 737
-3887496731301423107 742
-16351565489992748547 745
-12913808626051103749 748
-9427161342471792643 753
-14610089064185748483 756
-11909740995340709890 759
-3386059367942955011 761
-7100313088634791944 764
-14954362273735097348 772
-5300343188950335490 776
-3306636399811602435 778
-15049176780536452612 781
-11478464585367391747 785
-4192691696663825924 788
-1724981527538165256 792
-8923121468991320579 800
-10407927314751914499 803
-4140577061391662082 806
-11024499228689010181 808
-11103397578962422789 813
-16103730809841527300 818
-2161511371026989571 822
-16905537098408481288 825
-14418359835235787780 833
-8643099440826274820 837
-15803230958149170691 841
-2270949347024239618 844
-16607521085023703556 846
-12520505897845165062 850
-10502193626894192132 856
-12350321094518214659 860
-4950437143309872131 863
-938542234576037889 866
-9547302901107668484 867
-7827404372121768966 871
-17757593377946824198 877
-13699186867246955524 883
-9859653826627356163 887
-16394835100035514883 890
-13800374264730731525 893
-16954635983094506500 898
-8015308433863798275 902
-858715644299290630 905
-4519655150699331077 911
-7134867591233050115 916
-6432786657037144579 919
-0 922
-9408341322832972291 922
-13653279902433200130 925
-1249019122170091524 927
-5444522055126761479 931
-18233734556082323457 938
-1838285473517654531 939
-10799019207790220804 942
-2448710159565130755 946
-18425837006146807297 949
-1384258267102048263 950
-6553795393861204486 957
-5022631533298058243 963
-2595435540421003780 966
-18298501952506793480 970
-17380720526409169413 978
-10291550905275666437 983
-8968303908578660869 988
-7762552109517888009 993
-12993351549860134403 1002
-13098482377540869636 1005
-17174134275815044100 1009
-2405939573849534980 1013
-11051603729345690626 1017
-2765842466801084934 1019
-13348255112383532037 1025
-4560899789258637829 1030
-17071422935680193539 1035
-11513452937230732294 1038
-1637355496640499203 1044
-14940739688966611972 1047
-8286559267538602502 1051
-6029036263825492484 1057
-6337648087046756355 1061
-12327119652833755139 1064
-7489768843341343236 1067
-17101806024406781955 1071
-1494687508867621385 1074
-915975103999953922 1083
-14731060910946571783 1085
-7993361195780195330 1092
-13688799604315935236 1094
-7328858946338903047 1098
-2913637027195678723 1105
-18189363439163655681 1108
-11261484070936291332 1109
-1244962005334571010 1113
-12618388435910808066 1115
-655187203027088898 1117
-1699259352638115337 1119
-9837815037477742085 1128
-10558465000768489987 1133
-3128326958710492164 1136
-16210393874387209731 1140
-3831602806328386054 1143
-1858477608543888899 1149
-11203849268139405826 1152
-14876215834473532933 1154
-838167957834962945 1159
-4472540425609859076 1160
-11410947109444917250 1164
-8435818218907397633 1166
-11045000766266457089 1167
-12325335880954441220 1168
-16708265953266297345 1172
-18342265362969646594 1173
-6953158344648897539 1175
-9922701673105435137 1178
-10113283973443524101 1179
-11668798096262926343 1184
-2129351334726026241 1191
-5692959118811792390 1192
-2917574127780044290 1198
-0 1200
-14420924818562740228 1200
-6098057863303978497 1204
-1252966646111680002 1205
-7111078464697947144 1207
-14144456899593720327 1215
-7367692118573781509 1222
-9319588592876439043 1227
-5212294342286609410 1230
-1600499660866511361 1232
-17579747388547180552 1233
-8365608306992954885 1241
-10307394306592963076 1246
-17092600292669807621 1250
-17030981925892977667 1255
-6929843536411176451 1258
-9908722951841282057 1261
-14685407131320535554 1270
-12861962652898171396 1272
-11958437143660911107 1276
-15904867421058229764 1279
-7283769647955500035 1283
-7872121678898447876 1286
-11726527760261815816 1290
-2316085662456682505 1298
-12840093831481137155 1307
-15574983692566917639 1310
-15176154862895929860 1317
-16186650646772958214 1321
-1965140296142659588 1327
-17362020270091437575 1331
-26356620300320263 1338
-4688323194808506371 1345
-470137109846916612 1348
-785647648524588041 1352
-686083037273571331 1361
-8705676087000994307 1364
-15985311040931325446 1367
-8848102120172622345 1373
-14900059783221505542 1382
-11611185676221023751 1388
-5823293000835959809 1395
-11173877492782561286 1396
-5985141512875075076 1402
-16607272189142469634 1406
-7000924871247012354 1408
-12796508861938638339 1410
-16352304696891085315 1413
-12654027566339262469 1416
-17652126895193709571 1421
-2059554016646703617 1424
-8824828815238545922 1425
-8026041213654553606 1427
-189105210507091461 1433
-8038465995762949635 1438
-0 1441
-4346653818095449092 1441
-13441396742193060358 1445
-5067771148519478785 1451
-210369551309682178 1452
-7856429334361659909 1454
-6456628847560069634 1459
-4777640967745320451 1461
-8983636279512822276 1464
-14568805960710332932 1468
-13817574021643753989 1472
-14625711259902278149 1477
-4632056779689710085 1482
-17613320542667293189 1487
-3172012402848437254 1492
-8040798394603101188 1498
-14064841209998140419 1502
-1914908168343121410 1505
-7368139610144548354 1507
-12868473585497306119 1509
-0 1516
-1618708134596732930 1516
-12587973098332420105 1518
-4964388169698209795 1527
-11644359715676310021 1530
-2644060095775605251 1535
-6430078223195648003 1538
-10183198452214045187 1541
-1240799682393062914 1544
-594310634075621378 1546
-2369514519273954820 1548
-10180653661786314245 1552
-954303650251543043 1557
-14430712698160791045 1560
-7362398115224322564 1565
-17170839233019868678 1569
-4334478792852912645 1575
-6976600872204725253 1580
-2757627166710815234 1585
-11581525848542896643 1587
-1902097979216049156 1590
-7092174838851165700 1594
-3776232881097953287 1598
-4956341896516184071 1605
-16560365104979398147 1612
-9985649880040289799 1615
-8870322153106933763 1622
-6905121755133908995 1625
-13368640352340902916 1628
-6681848478588709895 1632
-1825204937600832520 1639
-10492979809894170628 1647
-16021790814379410438 1651
-2537982728896871938 1657
-17110141827238231043 1659
-8972517116882764291 1662
-6878463938568223238 1665
-3653948979877717506 1671
-11414481194651397126 1673
-14522267179648162819 1679
-3098339502618796035 1682
-7079749050994126342 1685
-13571764215085394946 1691
-4748948606525397506 1693
-1577643399485818884 1695
-4080235243237779462 1699
-10874175738252140040 1705
-8407257242091918850 1713
-13208300770644489219 1715
-692428139842995202 1718
-1811883090719733762 1720
-9059362818280152070 1722
-1942856588307002885 1728
-8118332366482353665 1733
-4958069245857057284 1734
-14647311378680886789 1738
-10762024033896625670 1743
-28898254948429830 1749
-9834906317233815042 1755
-14985989359682912259 1757
-1282980713864208388 1760
-6063131598875265027 1764
-11171681444901584901 1767
-9942643440891227650 1772
-7536761905759707139 1774
-17586310513048226310 1777
-5368266791748388869 1783
-14231943828217691651 1788
-12518647321260815877 1791
-129394441281844743 1796
-2483490487411335170 1803
-654244401428041732 1805
-15646533714849457160 1809
-11807354932867949571 1817
-15902831808268765699 1820
-16275101253541722114 1823
-7489443708629377026 1825
-15395914353243975682 1827
-5617555619731661829 1829
-3134100206450675206 1834
-11607495136261988868 1840
-4974806308616426501 1844
-17446584074836170241 1849
-15686830167444742663 1850
-9706307518401206273 1857
-1668062460313515521 1858
-1175330870409010693 1859
-6316020408117881860 1864
-3926008952689808899 1868
-7412001888157663237 1871
-16350342416828571139 1876
-17722048717800707588 1879
-6638262866276511751 1883
-7428951476729761793 1890
-17816197047883941382 1891
-1346568064340942337 1897
-3701787015222295555 1898
-6659812133237486083 1901
-1828541539854978054 1904
-12379063259192634885 1910
-2611769333840765443 1915
-9618163593004828678 1918
-10135224491789939206 1924
-12979651712861326853 1930
-8882180359699969027 1935
-8839565787481092102 1938
-13328456084920556038 1944
-14232512278042323458 1950
-1868952656876792325 1952
-7567044498348088836 1957
-9878469525845452294 1961
-10877666723773861891 1967
-4437849393189355524 1970
-542122243470857732 1974
-4059190346138068994 1978
-14321675947144358916 1980
-14971180244834539009 1984
-7944574903635664900 1985
-6982417546170903047 1989
-9205813465909939715 1996
-14237044737088801799 1999
-636814072910696963 2006
-12520841226045264391 2009
-8898943418672995331 2016
-15646690259358356484 2019
-15618851112604340228 2023
-10285088843216830977 2027
-18286036510192394760 2028
-6450286360774949890 2036
-12025307250191760899 2038
-7044602746592181249 2041
-8270361223031661060 2042
-7199149542695273990 2046
-16798091800673956358 2052
-5285433079037354499 2058
-8498140496880657410 2061
-18434636390635965953 2063
-8780418579830073348 2064
-959965579978681347 2068
-2666650386212475906 2071
-4093783342266269185 2073
-7977153448080645638 2074
-3230317076849645570 2080
-2644129221999468547 2082
-7597431151331275265 2085
-6151418962808616963 2086
-16786361788616914434 2089
-9522044737514147334 2091
-15360350686533802498 2097
-4398995179394704386 2099
-4163122903470647302 2101
-18110267126768664070 2107
-17811600627481865731 2113
-11988559903619469315 2116
-5893679902922151940 2119
-3302430115655037445 2123
-2756050317441962502 2128
-7373324598575981572 2134
-15626353672087051269 2138
-9026268416534243843 2143
-5857105831257628164 2146
-11246462751297413124 2150
-7459631049065515526 2154
-2175352842263141379 2160
-9748465532031254533 2163
-12060676108130005507 2168
-8160425232164846593 2171
-1665947540125783558 2172
-10758171140537368580 2178
-5744770555727548418 2182
-15867521551313803780 2184
-11178209498970826244 2188
-2663862265833334277 2192
-646145646253570050 2197
-6886825228888300036 2199
-5219187155516171272 2203
-16142200027647465989 2211
-8727938199665870852 2216
-1200328579526163971 2220
-12449385538114001417 2223
-14632283715533800450 2232
-5295800027246062086 2234
-8827019094633400323 2240
-14543826221768176641 2243
-12388128316821831686 2244
-3087048392675298821 2250
-17669786912563615747 2255
-3879520399747123716 2258
-15648071975541157893 2262
-5580473107362200071 2267
-6895786389712974853 2274
-17709709086906012676 2279
-9627483233657542665 2283
-9602326803985618949 2292
-6748599026443758086 2297
-11488364339401397254 2303
-6716511183525677573 2309
-16003763240189186563 2314
-6003803301075291138 2317
-15800367754014516746 2319
-2817341800198731782 2329
-2110085916033252869 2335
-10353852055773781511 2340
-8745468498457416193 2347
-15197463976907486213 2348
-11844773108515011075 2353
-10745169896165544965 2356
-9502565595236673539 2361
-18340734722524717062 2364
-0 2370
-4877506240735029250 2370
-6632868101528461318 2372
-1094192348264738308 2378
-15930308455756352518 2382
-7517061312773919237 2388
-11537382714050522116 2393
-15343851421525887493 2397
-15685583084244037124 2402
-11443729733346354693 2406
-18096845502703148037 2411
-13060060807344890377 2416
-8226818503915081731 2425
-5171144332412330499 2428
-5367144440061049859 2431
-4687503341676126209 2434
-8115677569098133507 2435
-8753274682505368066 2438
-6767268893840927749 2440
-10747160183142327300 2445
-5318831768157948930 2449
-16744837601970291208 2451
-3968740997769839108 2459
-1041860322726726147 2463
-13185494599343868419 2466
-3781663100474830852 2469
-8664347289501861378 2473
-7145447006642560001 2475
-977858689003972101 2476
-188865761021926916 2481
-14781205616979726850 2485
-7514076159997088261 2487
-15227633270557658627 2492
-7486357174119883778 2495
-7899052859637422087 2497
-4312982947448530435 2504
-2484418012864310785 2507
-8450324929602980870 2508
-11374778755239228418 2514
-10780034123560756745 2516
-10313953391808102916 2525
-13836623279669341188 2529
-16297706918062760459 2533
-6404560275247226885 2544
-8323769790774729734 2549
-10061687257419431941 2555
-6724033317759518212 2560
-12265972209834273288 2564
-4748706107567735299 2572
-17588235414846031363 2575
-16029681841978911746 2578
-333014962274056196 2580
-2819861156000228870 2584
-17301319418358929926 2590
-14323022738651812355 2596
-17758251407482208260 2599
-9992216596142364674 2603
-5541911712511293955 2605
-1880849355295036931 2608
-15421034026101803523 2611
-2288503501826235907 2614
-2336333131728265731 2617
-15127408664422292997 2620
-6756061181968708102 2625
-2316367058427453443 2631
-13786932856453332482 2634
-17564157627292750852 2636
-5809790665868502019 2640
-9389430036410766853 2643
-15157257604368261123 2648
-523412383725034497 2651
-5270886391729814021 2652
-8987256414287503365 2657
-2751897370690544643 2662
-47819066577966599 2665
-9543124453318907909 2672
-15186331456703232514 2677
-9731347057535958023 2679
-6234700495105510914 2686
-17720066604242729989 2688
-611878128332703234 2693
-6029104170087404549 2695
-14612606995632327172 2700
-7357792311987945475 2704
-6074856230289873410 2707
-13368808999886628358 2709
-5918378978107988995 2715
-15624776793824203778 2718
-4241055509726121476 2720
-12687432015779367427 2724
-4003272975122620932 2727
-17483676776191982087 2731
-2701605488646040584 2738
-7387630099939362308 2746
-16331822462747681798 2750
-2197183442359868933 2756
-17624623361194542087 2761
-1749450990014992388 2768
-2888206094896619010 2772
-12985412669390948353 2774
-9843120678422464515 2775
-15590458610270713859 2778
-5950622975418741251 2781
-17607672802725530117 2784
-1225097419526011394 2789
-3758572251524375044 2791
-5891371767718009858 2795
-6843754938996156419 2797
-13418347525088883204 2800
-2887280155684756490 2804
-7867196614872225796 2814
-10992396837241625094 2818
-15526482250456426497 2824
-7582254907030848515 2825
-14309589056601523716 2828
-2843794758628944386 2832
-10106627892829635078 2834
-11117505412117820418 2840
-17559521087909430786 2842
-18410508844162253834 2844
-7796754440171003912 2854
-1826091018065355268 2862
-5568124937607335426 2866
-9164033835486570503 2868
-7917102923116225537 2875
-10708221634884163076 2876
-966446973350329348 2880
-1882776320247897092 2884
-18137433528115911172 2888
-7577505208556149252 2892
-3902521102041700356 2896
-11942362790107158020 2900
-2328713611561709573 2904
-8376513561567004165 2909
-18415012889800110091 2914
-7983446382889179652 2925
-2304166271864391689 2929
-708759182721729026 2938
-10774631175750681603 2940
-2608247964063907842 2943
-7317603117343176707 2945
-12615180422705001477 2948
-17995452459822326275 2953
-12439250137675515394 2956
-9947610136498965509 2958
-10340600516380348420 2963
-10073894039732477444 2967
-15954561361998232578 2971
-6039226287079734788 2973
-12684813664097613833 2977
-8337524429261820932 2986
-0 2990
-5738139389410570757 2990
-0 2995
-163262518049440773 2995
-11390362112332120070 3000
-7666496378417453571 3006
-17188351170280199170 3009
-14157925477049500677 3011
-16535316221715341826 3016
-701193705161007105 3018
-15417977144980853763 3019
-9623949443365348357 3022
-16537640731048440324 3027
-9880057250380779521 3031
-10507448958568448514 3032
-9901540867816521219 3034
-10882434502571251716 3037
-15939490563935542790 3041
-3818155241101528578 3047
-10810785028031231493 3049
-17268925026504538113 3054
-6000103580025957894 3055
-14492044616225970179 3061
-8964295197943843335 3064
-13244227239481936387 3071
-2072267724499101186 3074
-735562179013069826 3076
-3271477415853879302 3078
-1150251700717751812 3084
-11835839830005115393 3088
-17028480913889055238 3089
-16864969398419772420 3095
-9646252156141336066 3099
-5589333819644110342 3101
-14729039479109188098 3107
-2256025994407046148 3109
-5630416426912279555 3113
-23611161351524356 3116
-16061932977440933889 3120
-7560058124185071106 3121
-8943767870065516551 3123
-17388385529962317834 3130
-11686727589179028995 3140
-2993671307613155843 3143
-7451626547139373061 3146
-12726375988952098305 3151
-0 3152
-1735273330892205060 3152
-2746028049042776065 3156
-17093562035495421445 3157
-7598703106262353411 3162
-17526920923827930631 3165
-0 3172
-18087597149122765317 3172
-11336730259137625602 3177
-9704022087244797957 3179
-14531181144788964866 3184
-5103530438547424773 3186
-7049971328222257156 3191
-2593832991454060548 3195
-2549992206172832771 3199
-2656864556911864322 3202
-3094347590740453380 3204
-0 3208
-10556974365044028932 3208
-12597146506913681926 3212
-18243354473097630721 3218
-4168646291002030084 3219
-8893226051755120644 3223
-7904367695210051587 3227
-17247367703075879942 3230
-1338287165638264836 3236
-6734394253777139715 3240
-14645087877274778627 3243
-1841749727013933062 3246
-0 3252
-9793622484838288388 3252
-15384076833580083718 3256
-14678310837729104389 3262
-8947895455599830021 3267
-12421729442783160325 3272
-14382812703434878978 3277
-3484468606955360259 3279
-2411175954345499653 3282
-18322361710054416389 3287
-8989744845956541448 3292
-9637438279185886726 3300
-8282725403817063939 3306
-10727259769060221446 3309
-280860399088910340 3315
-3074647116268871172 3319
-9311932047626983431 3323
-2990333995786696707 3330
-11415454184475025922 3333
-8194042667332418565 3335
-11269986522125913093 3340
-10773634478079810565 3345
-0 3350
-4302235270674672643 3350
-4579270605621971460 3353
-3687011949425630213 3357
-9678333478858482691 3362
-14661606109051090440 3365
-9504123850532876291 3373
-14299233528797568008 3376
-10370491504729965060 3384
-286239823911254530 3388
-7969121812144744451 3390
-16606218867148559880 3393
-11756345184017143302 3401
-8204961944753809412 3407
-12456910480062157316 3411
-7569786299014196739 3415
-3372309516929818119 3418
-16631131943564946948 3425
-4436969913528429575 3429
-14467771002258720772 3436
-15278270405312088583 3440
-6638334178561090565 3447
-8154814430089498114 3452
-17289464348431017987 3454
-13185969354886446085 3457
-4725380864147687429 3462
-14933071000620043778 3467
-12471883028204926466 3469
-13286302152236950530 3471
-12020003522260348419 3473
-11784545509165047810 3476
-10311182359550097412 3478
-2262872037167824902 3482
-15672162207595698690 3488
-8479660175647360516 3490
-543122224331105283 3494
-8738610060644560897 3497
-15969479020845567490 3498
+0 0
+1196190418526572547 0
+3117251964976502276 3
+0 7
+3266452963994632202 7
+6701451810090115586 17
+10156473964989528067 19
+6270220596053033473 22
+8689732391113957377 23
+345423933508452359 24
+9048486634542125058 31
+13000119181766437380 33
+1008808785591799299 37
+12586249368236978177 40
+11161089178393358857 41
+0 50
+6900865085865625094 50
+2615908179610132483 56
+1617129254806601731 59
+1607892326666533378 62
+123501381755693059 64
+17180234710792039941 67
+17345742025318016002 72
+7933590365928361474 74
+16187522989672200717 76
+14893593683284454915 89
+6001767212789422083 92
+1805417936920808451 95
+8589625060174958594 98
+13148488988905702416 100
+6759231203841442819 116
+798806762886474754 119
+13949836854106156034 121
+4277844318153606661 123
+18162360468357982216 128
+17429735113921325570 136
+10428297564837543938 138
+10174389176493224450 140
+4782734429389924866 142
+16828613770926935558 144
+16924367891356487169 150
+15473269356473895940 151
+10277883249583756290 155
+7398921953351034881 157
+15672774546004063755 158
+7032338026028942337 169
+12638648541163088900 170
+11956890857542837252 174
+10813991647348979717 178
+698603259209416204 183
+104155371596876289 195
+8849883347580968451 196
+13523964487472320004 199
+12948374094552270339 203
+16624700721113753096 206
+0 214
+630014773304871940 214
+14669827911540386306 218
+16593543947487157254 220
+16189120489289924617 226
+5936869209199720450 235
+6504800368776816645 237
+17628010111075734529 242
+16073662248530872322 243
+15997624981342335497 245
+13519486007586370049 254
+469623719382726661 255
+10478598590185625089 260
+5239294057556035586 261
+17274642882001730567 263
+7924882265216651266 270
+13138720901108912133 272
+13741737182438464004 277
+14608811194009491970 281
+2489742908982890509 283
+14952279757728973318 296
+13432486964055121926 302
+15397241996877524995 308
+7400937882698838020 311
+13309132794101168654 315
+8519404085542453250 329
+2551722931538879493 331
+4492819152473235971 336
+9634175483270757380 339
+5023439465649179147 343
+2912624940235659267 354
+15615524075652075524 357
+15131856319265032196 361
+7560465986110364673 365
+16393161300057821706 366
+6737538541011470849 376
+6394493716971627523 377
+0 380
+6957953643235488257 380
+7533365794097524234 381
+11551517784611555841 391
+0 392
+14017003685401013761 392
+13868858036311946245 393
+609890416048967688 398
+15853752823436186626 406
+13008887538399190534 408
+275598997711474690 414
+612244017304434692 416
+265561555991638021 420
+0 425
+4771730300985403909 425
+14595656195986303489 430
+13010615142623560194 431
+3520044222049365512 433
+4843556531627173889 441
+9544321596489038851 442
+18097338319835691009 445
+17588488217883868161 446
+4553739803879796748 447
+12247953831639953411 459
+1685939678565356546 462
+2454121115370725890 464
+7699707784321416706 466
+2322428462912444939 468
+4251948422489921028 479
+8009626371771665409 483
+15830912148611917313 484
+15530208627603713027 485
+14550069280077337095 488
+3074860258671426050 495
+9819565310679728648 497
+0 505
+239920763215632386 505
+4479084686100589069 507
+7541436040510714881 520
+0 521
+18361828565940659201 521
+13943609537766478850 522
+1644071836581560844 524
+3325147442114083333 536
+9121949682662027269 541
+5375060563545179653 546
+11461944020052039682 551
+10205876604940857353 553
+17856338086929782276 562
+3964733248608209412 566
+15252617693956101123 570
+5198588053258159617 573
+7294352613378259976 574
+14274593384918848004 582
+12443356879762990084 586
+15967601366558600195 590
+0 593
+1596502676746638348 593
+3447763432008799745 605
+2154246728958848517 606
+1249748142575979010 611
+12802117032328183298 613
+14720455521613154825 615
+14431397366571454983 624
+8968154969419252739 631
+61922506310515202 634
+17332184019644205571 636
+1580044533016865796 639
+0 643
+16037339623732295172 643
+0 647
+6451385579602643969 647
+2249232807147062791 648
+15969372656029624833 655
+9184080755936318981 656
+10444965622910510594 661
+976846907109217284 663
+15036566770534162954 667
+2852219209756952581 677
+14428186506827194885 682
+0 687
+9583345567128655877 687
+8154021185610424842 692
+7639653587249864197 702
+284400846134645765 707
+5822594207495943172 712
+4666916656146452484 716
+10837424823999667726 720
+7662230599689246212 734
+16769958284715374596 738
+14214321919518354947 742
+7700892892210644993 745
+5647486165416790024 746
+12807160877623480835 754
+17202327424132939777 757
+5849043248643779075 758
+18232796011600235523 761
+4957062118189902859 764
+6105730765254667266 775
+8753292226633308675 777
+14066686889142136835 780
+1047708050925830148 783
+5555751253338228747 787
+8205438979066793987 798
+10100035083082646017 801
+3037731532850264067 802
+16470238215781450756 805
+15841867742103541257 809
+8087512074161331714 818
+15493250668750321668 820
+3797087601271950854 824
+2623502875154101252 830
+15159098560356506121 834
+343051006899596292 843
+16668194639613285891 847
+0 850
+9601059867653113858 850
+1570493927206813191 852
+9118300038493915138 859
+9563382677447647747 861
+5285530497249013763 864
+14598000812816350721 867
+15243372398425255435 868
+9815541045508240385 879
+408899826773384197 880
+7463961818871554565 885
+12980371725716597249 890
+15376403281856848903 891
+0 898
+5841652391326774789 898
+6476912065420260354 903
+3963854010828661252 905
+5784218172655345161 909
+15327721657175197701 918
+13180549833166182403 923
+15904501101973266436 926
+0 930
+14206180323061139974 930
+1106786522797875713 936
+17058832169116321282 937
+721828206256696835 939
+0 942
+8561789411832569355 942
+13374043249168898050 953
+15922789491870388229 955
+0 960
+16131878595889026564 960
+5509499768642979336 964
+12415614990376579585 972
+11304605070154481157 973
+7663245502729528834 978
+2692663086158549507 980
+14133757573751133701 983
+6813598296480126979 988
+13616528755765764611 991
+16303994430841145861 994
+12880492472155407874 999
+14023778603465187338 1001
+1658551813664662018 1011
+8148008758896362498 1013
+10688946549204321795 1015
+13274653424094307841 1018
+10847911221158770190 1019
+0 1033
+4643539771717744131 1033
+4169507947260962821 1036
+3126526255358650372 1041
+13449815687571241992 1045
+9421207081901200898 1053
+6898163624184020997 1055
+7290174431607841794 1060
+2741902156609523715 1062
+15499057183587255302 1065
+16461426401301993476 1071
+11278211202787295747 1075
+0 1078
+9413985875830324739 1078
+4646548733144616463 1081
+7078801759685020673 1096
+5376123263925219331 1097
+14227335667134915589 1100
+0 1105
+7295351152600562699 1105
+0 1116
+1397641409882635269 1116
+2364632016557825025 1121
+7290779788839345158 1122
+223977268476071945 1128
+13026660262516529667 1137
+17998435953459809796 1140
+8522469059272339460 1144
+16293947433309880833 1148
+4576500186674335749 1149
+0 1154
+4042247147937702403 1154
+3034443556411821057 1157
+13667368622259281923 1158
+15202537810082257934 1161
+15337640185400698372 1175
+8308041085868251649 1179
+8832030889396702722 1180
+10436989792260434949 1182
+14898581533124037641 1187
+9317528159836099585 1196
+1612938252083390982 1197
+6278485319310800898 1203
+10612805446261845508 1205
+13787162434835940874 1209
+12133705386992745478 1219
+5227473436681376774 1225
+5656787771058157057 1231
+4433258109319585794 1232
+6704526927800668169 1234
+17440456789764264962 1243
+6979104089888754689 1245
+10768049747876580866 1246
+15707303682313568257 1248
+15148244407999994380 1249
+2841265161354426373 1261
+5252307512862989316 1266
+13331565891980378113 1270
+18159416118263116290 1271
+501516395825858060 1273
+3867012501081805829 1285
+8267472486312505860 1290
+12872828689431491073 1294
+727773195231890946 1295
+7322382021491738631 1297
+5402024496579473921 1304
+6959655625064837122 1305
+10187142685062514177 1307
+3029360479097259523 1308
+3524388403479357447 1311
+5803404108302127107 1318
+3322880653425492483 1321
+14014789072627667972 1324
+0 1328
+17779075582177396743 1328
+11597164340541700097 1335
+18164718194518923266 1336
+0 1338
+3688441162538457604 1338
+12763684824056344584 1342
+6555198237040291843 1350
+8999497138912988675 1353
+9277828726380557826 1356
+1652226711750231042 1358
+6386464493042135559 1360
+11832103051565904386 1367
+7889400420599073793 1369
+5173699340624307713 1370
+9839391635984425985 1371
+9179189546563518985 1372
+8987610858276033026 1381
+14211262843725043205 1383
+9924217736728436740 1388
+4401850895204555779 1392
+5541709837691148811 1395
+10214740045672277507 1406
+14656675767246138369 1409
+5518164076312088578 1410
+8819194535554354691 1412
+1202694809888231436 1415
+9937648736864647683 1427
+4776509399304216066 1430
+3828150896429232641 1432
+9726415758235178498 1433
+15478358790166008844 1435
+0 1447
+447632828248568324 1447
+10254625284015096321 1451
+9602208154038649858 1452
+7918490636759656966 1454
+4464032935723660291 1460
+517803065456797188 1463
+11296051306811729413 1467
+9559870439106258948 1472
+18140734313948729864 1476
+5761393475703308289 1484
+5817187969532432391 1485
+7214411138154648580 1492
+8556555308704695297 1496
+5517275039512219661 1497
+155198283803470849 1510
+12028807386786979841 1511
+9402878779861331461 1512
+7529466829850301953 1517
+3700043109242268166 1518
+7889220073888590849 1524
+9698905706548099588 1525
+950350740255051780 1529
+16659267722661032455 1533
+11934825441675277832 1540
+1840952787151591937 1548
+3181706929772123141 1549
+13084360636440561667 1554
+7392348362663288323 1557
+11299566685738323463 1560
+11865504406956790788 1567
+470806909387516931 1571
+11392390055026286594 1574
+0 1576
+15250035972710306824 1576
+1841748561073501700 1584
+13959366503388518404 1588
+16383575845586120707 1592
+5993903773214649347 1595
+12927537188954086928 1598
+6310676060569643522 1614
+6823572598110530053 1616
+0 1621
+10355215107753852930 1621
+12991560131813107723 1623
+6463225875312731650 1634
+444925180768886788 1636
+8287375501749122564 1640
+8102699978355624961 1644
+3217121844483982342 1645
+0 1651
+15310893597687290371 1651
+4651888484278436356 1654
+16622466823413339137 1658
+14426029300798547465 1659
+16208338759425902084 1668
+13384891560853317123 1672
+10542264124115582467 1675
+0 1678
+13404868863569442317 1678
+8380728838811013123 1691
+2656782871938641923 1694
+5621105522992570375 1697
+16165957063051496962 1704
+17183335989224497157 1706
+0 1711
+12377944724210268163 1711
+15698714840429098497 1714
+2063306500131813891 1715
+7135499884796623879 1718
+14916197160702468612 1725
+14565364212611500547 1729
+17109666354199615491 1732
+18420265465448709122 1735
+5039636110599831051 1737
+13648715526743665665 1748
+8648155745742680580 1749
+0 1753
+4128476852805537282 1753
+12229435493123252233 1755
+18671114624524289 1764
+0 1765
+4330985506003776003 1765
+4960636854468069379 1768
+2825174586054641673 1771
+8083214972260871169 1780
+1656668836635006471 1781
+15658718806708214274 1788
+1364137667359422465 1790
+5440910769879224326 1791
+1242060995600047617 1797
+6028285323527704577 1798
+9862524515548398083 1799
+14095132043223516673 1802
+5330121798209797643 1803
+3047808178481674242 1814
+7009881287782938629 1816
+3836453927748870146 1821
+4828562734878493698 1823
+6251707885160171534 1825
+13503013357676597250 1839
+13120060435028427777 1841
+17453157023102628866 1842
+6659266074333195266 1844
+12122449770852231175 1846
+76872493233309186 1853
+10510620038219076100 1855
+3104474465142299652 1859
+15145875800387371010 1863
+14514645157364972555 1865
+5990940750853294082 1876
+9568631318395414530 1878
+13307393937882497539 1880
+0 1883
+13432428898749511691 1883
+2851874300532727813 1894
+16127254686981486084 1899
+11152828733555106817 1903
+8099684063905722369 1904
+10726727557015251463 1905
+0 1912
+16773004137299201537 1912
+0 1913
+1737396243104320517 1913
+12312810570815952904 1918
+8420117868402509825 1926
+4468099455608655362 1927
+17181412210024682497 1929
+7344171998747088899 1930
+11200240032637073926 1933
+9773885730549905922 1939
+2888420847349521921 1941
+0 1942
+3301971714535044611 1942
+6622000068430301708 1945
+14679279568503564291 1957
+15312513401406547971 1960
+11219696574507219971 1963
+15557068645919193090 1966
+14518831268196627465 1968
+11306244334020066818 1977
+445302382600591361 1979
+4798518764725378563 1980
+12833053520101596161 1983
+6569110733351726088 1984
+1133142439547627010 1992
+6020738327851480577 1994
+0 1995
+0 1995
+15123217074875560455 1995
+5146261845254048769 2002
+15577303646915962882 2003
+5068854713026915334 2005
+5662217880612308482 2011
+13584286678752042508 2013
+17647669975855288324 2025
+7092182408195844613 2029
+5243600304614296065 2034
+16379641210199802883 2035
+6541142296931350023 2038
+17648968980389751301 2045
+3633167252938199556 2050
+691728008305302531 2054
+7434042972483105284 2057
+1243474674683616271 2061
+439217426838173186 2076
+10460352595647090183 2078
+5080394082232633345 2085
+7346464481151790597 2086
+8068677175549539843 2091
+4859996294860352513 2094
+12470823893961605122 2095
+10033529424736163842 2097
+10769920382809060357 2099
+16128670331104411146 2104
+2973668094989328385 2114
+16323032859702780931 2115
+12227727930958763521 2118
+7302528030871866371 2119
+8967586997946816013 2122
+13935701471042006020 2135
+15676859696752227844 2139
+0 2143
+2397906929972799494 2143
+731429270944234509 2149
+14629591375919925252 2162
+14201687141277194244 2166
+8813493889730974725 2170
+4967156306307785221 2175
+12152782138863493635 2180
+5716269545878689795 2183
+12118250850399448070 2186
+10079764034817249795 2192
+9905170822798166018 2195
+7330246949116896272 2197
+4975588281894977539 2213
+2377967791858227715 2216
+1711948357573607427 2219
+15733402191778006532 2222
+13617127880905861132 2226
+5413022680339381252 2238
+12001217113207191043 2242
+605362804928389124 2245
+10888521749365150723 2249
+11742554576381655052 2252
+3591551764774430724 2264
+8647496912976230402 2268
+3843626828621262342 2270
+3921763517492323331 2276
+7707493410895858692 2279
+3920334550068498946 2283
+2658528064200329217 2285
+9038122947820533253 2286
+6952499746958836740 2291
+7951530266135717388 2295
+16076637508890388481 2307
+15187897527562671106 2308
+5520701509759360003 2310
+2598679891400145409 2313
+17512255026679867408 2314
+10995766946592999425 2330
+18117038245928559618 2331
+5391766950501834244 2333
+14461374868186265605 2337
+1273598128050393611 2342
+11820949665032480260 2353
+17841646829021216260 2357
+10200569215461547521 2361
+3670141860910412289 2362
+18396940417538187269 2363
+14261984156631670787 2368
+106960762513502723 2371
+16393357936187300353 2374
+7032931990465729538 2375
+15907195827890083338 2377
+16437195285078765571 2387
+17301257309241798147 2390
+8236593629924756481 2393
+1379157623727557125 2394
+14767417508072398345 2399
+16695407490005887489 2408
+1414009372711604744 2409
+499004129948061185 2417
+5775255721778604547 2418
+16754393591199635469 2421
+10568987941526160386 2434
+3311623553148127749 2436
+10255724520964794369 2441
+3121950734017230849 2442
+2129428121322164230 2443
+5233872436075409922 2449
+5115946926893418500 2451
+298818270766586369 2455
+2534391384903305218 2456
+13962240998865999372 2458
+2858192092257344002 2470
+2246014736733727747 2472
+18208224108542041605 2475
+5900635063125726209 2480
+8459478259862856201 2481
+3106812066263162882 2490
+6016756381746226178 2492
+375597697640802819 2494
+2513762961093744131 2497
+15366269329105501700 2500
+10035949288505144322 2504
+427851159373997574 2506
+4274431321888115714 2512
+5253654952100000770 2514
+16894221500064376839 2516
+14687193167626954754 2523
+13771965837935513090 2525
+8874009193925074945 2527
+4974093839237721093 2528
+741620693598341642 2533
+11991618038806280705 2543
+11116672093208526850 2544
+15807249887587362818 2546
+7323942637968351746 2548
+3660270925885407751 2550
+0 2557
+10684033640943126020 2557
+16989816981004759553 2561
+9001924880900419075 2562
+1998443310251235851 2565
+17567979874939109890 2576
+13652482471668535812 2578
+17509569230481751555 2582
+3182500785161561606 2585
+13325982159032983558 2591
+1923914978402147329 2597
+5589189981371284484 2598
+1161601912578541572 2602
+1916235467976744451 2606
+16280831412119656968 2609
+5531274414859838467 2617
+13599333592024061957 2620
+17989155199582565378 2625
+3030922814179764740 2627
+14644007464957335564 2631
+0 2643
+5497605392959732225 2643
+2032331457863458818 2644
+8100338548587463682 2646
+993329328502006794 2648
+6750921732510502913 2658
+13748899324120622595 2659
+15617703054210413571 2662
+13138109094843573761 2665
+6544485718564688390 2666
+4168731610225209858 2672
+7315066071491735044 2674
+11306658702491732995 2678
+1460741416990041090 2681
+8624484085251326469 2683
+4952143576172173826 2688
+11470130411385533445 2690
+8808161070990530055 2695
+3407659004810532870 2702
+9761503347061253645 2708
+347929962150473217 2721
+15682869073661250565 2722
+12636859761190001153 2727
+2169559175677957635 2728
+6583723534446631435 2731
+11332478688871909892 2742
+3541912969021597188 2746
+15665073567582359041 2750
+6811971824255872515 2751
+17832657550632072714 2754
+8908928359280249862 2764
+16149194899805562374 2770
+16584564148406323202 2776
+8926638669588577795 2778
+8056234806465729542 2781
+20557314279745028 2787
+1574148835258315780 2791
+0 2795
+5593745704266732037 2795
+8450014032945361420 2800
+7024373575570305540 2812
+11737655816003366406 2816
+4727037432569372673 2822
+8600949146786643459 2823
+9003058529087846919 2826
+14052664559056898 2833
+1424791599736305667 2835
+5413427196124183555 2838
+13050600684981920260 2841
+8589685071512056331 2845
+13186761374251900929 2856
+14090913721681066498 2857
+0 2859
+2742241767433926657 2859
+6309431184810384395 2860
+16867533333923942913 2871
+555261403132789763 2872
+5659601479152637444 2875
+18276768397881284098 2879
+6852010445819064844 2881
+16631838326863331329 2893
+246764640492975110 2894
+1313867708490425347 2900
+8944238870676823556 2903
+1060867472129666057 2907
+16635885715046522883 2916
+13334184179287121921 2919
+1341139991463623173 2920
+0 2925
+6310211216600221189 2925
+3521973268169620995 2930
+1462184866304097281 2933
+8359017763585949185 2934
+14138351761235446785 2935
+6817592922583008262 2936
+0 2942
+6385096150346020868 2942
+0 2946
+5484657660585723395 2946
+10615912620259059212 2949
+11956475177743584771 2961
+14617995947569946629 2964
+16460942815259223553 2969
+9814422111234662404 2970
+4608931955518876683 2974
+8617716815688349187 2985
+17740454941921826819 2988
+0 2991
+10586556775954286081 2991
+11028786367153901576 2992
+7561184979369551368 3000
+10180555287637633027 3008
+262376940139235842 3011
+1252244297117510657 3013
+17286434400127825418 3014
+11940732067173687811 3024
+9446744360256471555 3027
+583923543216445954 3030
+8153426984110241281 3032
+8998238685693393417 3033
+11022193474305971204 3042
+18018779292443289604 3046
+13782486654821986817 3050
+1031535266324627457 3051
+17367371162468022278 3052
+16063095350159409665 3058
+16006913374966627331 3059
+0 3062
+317830424679224322 3062
+14882116247225631239 3064
+9977848214775454210 3071
+15016859152309685763 3073
+1451917599200393219 3076
+14163345466838668289 3079
+7124786413716748809 3080
+8972415547684808706 3089
+17905923295565835779 3091
+11508735911159903238 3094
+1060738927182784515 3100
+3235164743035444235 3103
+7249634886133244929 3114
+13627026919527422469 3115
+804144428748921345 3120
+4260278694170215937 3121
+2554890109424057864 3122
+0 3130
+2939022249034957313 3130
+3727916159743203841 3131
+14170274700031256577 3132
+7153627445263524879 3133
+6798175517396767234 3148
+1899052595905691141 3150
+4651137331222245891 3155
+14020723224952528387 3158
+5768869715157669895 3161
+13394211108659571714 3168
+15788932119193980932 3170
+13584005658508513793 3174
+9286626632069867523 3175
+2398026920081879562 3178
+1285989134179298818 3188
+9371873775174273029 3190
+18182246561705410049 3195
+3627164815665507843 3196
+18002283031389555722 3199
+13723140536667785217 3209
+11940684153082156547 3210
+16151440538186193925 3213
+13475891972713434115 3218
+5932226594251481096 3221
+15508203776273810434 3229
+13958242421862434307 3231
+2178759546197172739 3234
+12536204645038731778 3237
+14021691565090239498 3239
+0 3249
+18424936840617633797 3249
+9515558058741110274 3254
+14427656809453646337 3256
+15295479713001905676 3257
+6924455800485778945 3269
+5547275743159208965 3270
+15965423529103676930 3275
+6276065480049782274 3277
+923852355669990415 3279
+5171389834127005698 3294
+15756927494767584258 3296
+5380717287071449607 3298
+6048706605171842052 3305
+10493631130929582093 3309
+2792686703001238018 3322
+16318095573166788102 3324
+14961739739381704706 3330
+13885085964549002242 3332
+8803999472247604229 3334
+13681809489997040642 3339
+1274343414475602434 3341
+17525390131260455942 3343
+4637625228183366658 3349
+8313154017818126861 3351
+13090076428282480132 3364
+18133227728108545 3368
+8282473413611347970 3369
+107193099920609282 3371
+8505179371271580173 3373
+11102079825957593602 3386
+10212767298703785475 3388
+5215453497761775618 3391
+3298152084179375111 3393
+1095163960428030473 3400
+16887781145875813889 3409
+14786085928210816520 3410
+8581278387803219458 3418
+6241337607249230852 3420
+9254719800476612099 3424
+2568855290428722689 3427
+1289519920250085381 3428
+14618186241114017793 3433
+9612541243912769538 3434
+13926515287424429066 3436
+11093957915681312769 3446
+12010544601346956290 3447
+11839562359654205442 3449
+6839541636025740804 3451
+6012482217637302795 3455
+0 3466
+5775335776577318914 3466
+2685494297938271233 3468
+18186802079969910787 3469
+3127521196291951624 3472
+6934893239724900866 3480
+11630798772510404609 3482
+2767762624498050052 3483
+14135084772626181124 3487
+11643008759045397001 3491
 3500
-5303047073946667464
+14107087915135404740
+3545799512027105927
+32996413518841137
+15568274631689570656
+20587511236070012
+2390363305266056430
+3863606920688567965
 210658854139
+9870724567599405
+103154228
+3007753865419557454
 493093586
-15289397349632312454
-5941764183477191834
-3477193953305167424
-236453760381
-7470284155521404014
-24445261
-16426766960960540026
-14549236
-817365937
+814220189
+538968856
+45810044
+11403474
+2625321602296383846
+3076135121411313050
+16635669954819197974
+5514354727165429372
+18413391979390173264
+3544953467117898450
+6361518319333476776
+5833854247140395797
+518849275
+2752627
+71565807
+9870724570416301
+163316374
+60096910
+817038254
+18411417877468545037
+5993603989931887912
+1873618523431177265
+14787093348585572176
+18413109988782047308
+1283692271244348427
+17461812017531651650
+13165236096819726043
+14883032307819284131
+2789363538679106064
+11161692095903435283
+62914993
+2365498112266798670
+154665586
+13726068529822894439
+5570718
+544604964
+33560368941433940
+819856323
+1873618458944931675
+1873618489039064439
+6156738032733324876
+10259573046193883986
+6208295848581203181
+5991347927496394467
+2272905061487347697
+8972557000702888938
+15289397384024950845
+4767727591019973374
+10758418391935812957
+2292825785040636736
+1545208828
+219257441372
+5569296714050766113
+2207492642904016905
+12612941966326959190
+12426051065400527122
+18331556280207363
+2785415334835848520
+6156737968247080128
+15292217517958891614
+5780604328577598853
+3188833133853148985
+4078298757842341053
+6051485356288903427
+573178715
+102957618
+91488775
+2625321602296187261
+114426460
+22675774
+11206864
+9870724567402585
+5406444726343502428
+68551110
+515834601
+2431124533
+538772246
+11065179658016983681
+8930986418384079868
+4076606646528706921
 1873618471841499416
-71893492
-10694515171064744788
-29330183088506125
-61997475
-4653200
-109445719
-8926052536804313893
-7528330190111771360
-1418462186
-5887104182899575287
-2625321597997091447
-23407864425745813
-1647838213
-6152225753094686522
-14151987057237756511
-18058417591402760409
-538510099
-17855463731522440261
-240752528220
-27920040887059601
-11078361536363433136
-12517601
-15885957841278600403
-518718202
-805438326
-2621553
-1550910461
-2411070513
-59965836
-13012951802392676509
-97518103
-2625321602295859611
-30277976
-546374457
+3701601059573925529
+16166203682344470241
+6101795981361546864
+15289397371128186695
+7569568047215545466
+18411981910273949729
 16759426304739641933
-259654328
-27356063970624739
-1873618458944931675
-6209987959894902621
-5728764444739437994
-18413109988782047308
-13885455448020813663
+48431492
+24535874148371011
+14024943
+59900299
+105775699
+10770155859627543824
+71369196
+9870724570219682
+163119765
+2530739313276357975
+5052785364214352114
+805372789
+5652457623480305518
+644809585
+816841645
+2556016
+4501477955215362649
+4502324021619918399
+2150364451440035988
+6156455943246842659
+1873618497637649718
+12309852946450942075
+3660444556051220001
+11103300151687644832
+8714520725396523830
+5461104765611607541
+27356033875641745
+5352348805862394041
+2012415014
+5151629580948802356
+5374107
+154468975
+108593749
+62718382
+16843031
+28311895
+1107456968073808590
+11490081257974859839
+16633695840000739887
+9386257335747873389
+4959080478982475006
+11408348231855703653
 13464164481390611573
-5514354709969504081
-6364097374632348674
-2676033351739376985
-1136798196293306910
-5299098874403555921
-2120987217453057458
-17306856587979066781
-1873618532028844481
-5572365145471912335
-18412263926676652075
-105382480
-5303047039553965447
-9881712940254169714
-152830562
-8610102806501591788
-15524263781940136850
-14282671233461718187
-2857298572705729021
-29330122900898936
-10554335258691243263
-8453377129057749572
-18411417864571256842
-811271050
-1873618489038604579
-4657106642463886071
-2676033356038145381
-514654951
-10757572347027851837
-4237766514325588729
-571999061
-9821766011288487605
-7230168968130792223
-2704904949959166469
-1823671323
-103350839
-46006654
-2755882956846859930
-15289397371128186695
-12662636664722033563
-16318735
-18411417894664929297
-5462796894122411284
-9950019064427710530
-6981729909914862956
-1992588707391932346
-63766972
-6422699
-23407808536904833
-15394822466617412826
-16881139139804531782
-14312300901618944289
-2625321593698061230
-9870724570679212
-5780604289886653255
-3870997034531752803
-2531021389865944442
-10908568553618343357
-1860700038481053299
-196215461
-1801847830
-24183115
-18424247431471827427
-14287090
-417019855960
-71631344
-4391052
-61735328
-18413674012989259870
-2625321597996829544
-17957750408840481687
-9870724568648556
-41943405
-2789363542978135882
-18412827950883864637
-548143940
-22151483
-17257283845880874759
-899112529018292807
-538247952
-69599701
-8510664359869943178
-27356081165698156
-27638084672359236
-12255453
-11400819049620310987
-1321272283
-16881139122607162703
-2359405
-3101815889301670444
-518456056
-9232147856523987724
-3758799212073651272
-3591160524196219107
-154600049
-17946608694533885076
-11500631658516907905
-825323275339564903
-9870724566615620
-39911783
-12318365723907459763
-546112310
-18412827980977537092
-536216330
-2676033351739114988
-11069796553860646809
-7880043043777809442
-451412296787
-18411981918872141859
-11678577273375754735
-8856014234050823647
-105120332
-1309344723
-162464400
-681145240220010584
-2626514825137096412
-6589396841525218018
-356832249381
-6156738032733324876
-11202456151687629452
-27638041680086900
-11243723090649876783
-5726358144768542273
-12498251711624252784
-13702827714901707594
-811008904
+15494005608834598990
+1407386597
 8192198
-8714520725396523830
-514392806
-9960543895307946415
-15287141235608259625
-5727354401416546168
+219257244681
+42598769
+811008904
+2573543610120276856
+5356297048398365877
+7595953279435999504
+5726226297114658480
+2723374776553770162
+1543385872365455415
+11535686880442518166
+15289397379726773461
+5565348488711963913
+504169174
+9870724567205432
+14212253575230457510
+5831598111619679502
+2625321602295990612
+572982104
+813826970
+279448324634
+538575636
+11010253
+68354499
+11243723090649876783
+18331491793766525
+15292781563660995825
+5991347884505304103
+9409295256684857617
+3759645248384009814
+5832726134240118664
+14312300901618944289
+20305615210743190
+13001845694847518363
+2652485274356286816
+6151097653090126690
+2203332276215481610
+18412545964574834746
 1808894516123993997
-3686437022462641529
+518456056
+2359405
+1321272283
+71172585
+417019398489
+18895516000586505
+162923155
+9870724570023121
+13828334
+2625321864544389907
+816645035
+8453377129057749572
+11949535972653271176
+1873618467543321286
 5249797181178709209
-2625321589399030850
-103088691
-3062219857732765097
-830399540494469985
-530117487457144076
-12454108019635062383
-197984938
-8930986418384079868
-818873277
-16056587
-11526999220155450649
-6160551
-63504826
-7621890105505615217
-11847668763332905754
-10377426660276898779
-1873618519132015281
-18092519415945890646
-15882855708139391266
-7993599274919922706
-2789363538679106064
-2150364451440035988
-9870724570416301
-2625321593697799226
-91161094
-1410073577
-23920969
-7513578521803359945
-22279798815198594
-15520597512816297356
-1023125932615797552
-540017436
-8910392170935354895
-195953314
-644809585
-14024943
-71369196
-1873618476141774348
-816841645
-10906583479868327250
-1454041666728626384
-4128904
-18413392005184749654
-108921430
-468609401971
-16204201012116260706
-99025451
-9870724568385196
-18412545943079354421
-11878630053446878902
+5567604589840172352
+3707523343842937215
+17088205463377873568
+2169005683868174908
+9568723490388248888
+6103488088376871190
+4025969582498383295
+62521771
+18276979644936029994
+154272366
+16646420
+544211744
+28766107292140894
+5177496
+509805280
+1873618519132801026
+1873618544926132491
+7676326001635166459
+7676326031729298383
+869984510486186619
+13146357072728951328
+2000487899013646903
+2449021711964768402
+6155298010574883251
+6098975770044401989
+3189961199463959445
+2676033351739376985
+7995587
+19464489
+547029825
+219257046468
+2021331689141374237
+15288269301218674108
+11705421198335413148
+2508194873
+2625321610894575340
+6097847713031849822
+16064731596255856452
+13701595356116683915
+6364097396127827248
+18413391987988365394
+16364556117061994922
+10296839827164892306
+5403008449516603011
+15858116883009440274
+5833854255738587405
+45220217
+194314911
+10813643
+68157888
+56689033
+114033243
+4287350266942457603
+987047180239768912
+813630359
+18411417886066737167
+18413109997380239438
+11548493110908749415
+6364097387529046615
+5561348123192067576
+5835546388547569431
+5246976935469649046
+13884327378110449525
 18204249488608200784
-5566476545725367766
-17951898368652543383
-7558005371879033601
-16542141154387102177
-6316393479032998553
-11694336983993944146
-11427331956784106382
-4662073785906890031
-1873618454645640429
-537985804
-12999620585941961275
-2295119206548507606
-11993306
-1597536180772867045
-5299098844309358384
-8294669686619703163
-69337553
-1873618506235448739
-518193910
-5406444726343502428
-16765215479188031591
-5460499803636172954
-3431717683755289915
-28202117477106938
-5249797172580910311
-5745384143842643344
-14065038233622153931
-14311172801615955497
-16758489844492275047
-5510538272098551989
-11065487220741573048
-9870724566353399
-5679882735784101879
-259130038
-87097857
-3491703471172619422
-545850164
-18271599167641487963
-5991347923196709309
-1873618458944406678
-7033448275620070919
-812778389
-434977997061097911
-3445982126355516078
-2676033351738852867
-3545799512027105927
-1873618484739311861
-12749251354825264418
-14836382508930370955
-2625321585100000596
-21997756618246082
-8716776809328151764
-15580874176502892132
-3332575624131774585
-4445946672738010859
-5780604328577598853
-2848264744227112681
-1873618441749072804
-257098416
-4930631980557601532
-6877319166685482198
-1005889956380019628
-820642761
-17826079
-23125779236849772
-810746758
-7930050
-8929320279979198383
-9654763076979264499
-11949535972653271176
-1873618514832984063
-514130660
-18066207382028748450
-2573543666009114673
-18613585580197092
-1427238547443354327
-2625321589398768544
-102826544
-5903884228619468800
-4279043148
-7036226112429884975
-818611132
-15794439
-3324580943442478547
-1903640920853056624
-5898403
-1873618497637649718
-1133620887485417426
-10156853965084755435
-63242678
-282723005
-13586095437453200186
-9082058141968173941
-1987794462939089941
-13237708531286474753
-5240852582657493474
-1915314009235720841
-9870724570154139
-90898949
-17090754651615726815
-492307151
-195691169
-11050161621988804687
-23658823
-11623400942792738969
-9304480456320748248
-71107048
-816579498
-23971751058934778
-17869638717220195611
-1873618476141513316
-361675971417279818
-61211034
-1873618501936418049
-3866756
-567411536
-5302201063430292982
-8486888319115725460
-12406930521299355297
-9870724568123690
-11034422950646711803
-4287350254045103750
-5566476545725106758
-1923875870
-547619651
-6366353527348595732
+70975974
+9870724569826462
+816448424
+4211213383
+2162794
+12974919760129952993
+105382480
+5459976661309982295
+21433723812579518
+32432320527074663
+1873618497637255436
+9305858029919208637
+10225919154718574351
 8597156797828894009
-13590665243542948895
-13237708561380147208
-4254959725487523541
-2907303882175415846
-1873618454645376983
-9230753948926543533
-11731158
-527827717
-5511666307614640107
-1330643932
-69075405
-28202091681942395
-4727296740454696303
-1992881785902860007
-18301216972081072101
-4076606659425995504
-9870724566091296
+12461042340477994821
+1455946274504313841
+9538952396691934382
+927164962728314711
+5782296426993943791
+9714916684781063078
+16449809
+4980885
+819266496
+2625321589399030850
+10907429529076434052
+257295025
 39387493
 154075756
-5459976644113468289
-545588016
-12461042340477994821
-223556406340
-32432337723721245
-19595563
-2573543610120276856
-24535874149025753
-5196265237615086368
+62325160
+1495925747
+288043895627
+4504298205224635444
+14835085562484362568
+16881139122607162703
+1839046019115124804
+11923578915473263059
+9388513449772451585
+5247593352907982888
+5153885686374731086
+12020808312486431384
+14848239906707278405
+5405598728725530322
+3653991426073234491
+5566476498435442740
+4333982245204396969
+17007720368052373541
+14458654042895551171
+16885259953617962521
+2676033351739180486
+6877309693745106245
+21997713627284659
+7562235540534921217
+2625321610894378836
+5458848587099997499
+1647838213
+288046714075
+1454859013759438228
+1133620887485417426
+237175467
+810615685
+1418462186
+12162857194684744950
+88080898
+19267879
+7798976
+546833214
+6206321690771522709
+21433680821684597
+1873618480439692390
+3932922014897081298
+2549492329236335496
+5249797112394286460
+12294570438877711433
+2324121364801391676
+3315661715940248009
+8971880411373045432
+5461104782808583112
+18411981918872141859
+15371922320578972378
+361675971417279818
+90898949
+13390152586296232130
+492307151
+13522668389390157414
+538182415
+10617033
+12498251711624252784
+22085946
+1987794462939089941
+425617786716
+1730937871
+5356297014005859746
+5569296739846327213
+16881139139804531782
+4196703391028741586
+1873618476141710425
+821147663836514852
+3158171969379764633
+30176223702288623
 17735566651085687884
-6204347601746593065
-1873618484739049815
-812516243
-6152225714402428442
-15291935501556190620
-15505670362359531298
-451411772583
-9484411285755463284
-161940107
-15292499508566297469
-563348302
-506004186
-11238431078799509026
-18323667541285735009
-2625321610894640833
-103179363763488430
-503001580666
-12769025487284210679
-17785259844527786731
-29612147900877606
-15290243377345399572
-17563932
-7667902
-3186488476490139978
-810484612
-1192315333980326167
-1873618514832721746
-15292499491370961900
-513868514
-5347351719937377689
-45220217
-11775490430040476325
-12240192446106372977
-35324256
-2396555433535145871
-7409502855497715015
-7888341864134085054
-4278781002
-1732546121802517809
-2374936041605498895
-21433680820701635
-12189960762281954023
-869984510486186619
-3598203394278688718
-6103488079777762245
-72876542
-16990917635978692369
-818348984
-15532291
-1146796961722731823
-17761874897365304540
-62980530
-4534407021717882867
-5636255
-32714379920409891
-12552846396214610071
-6262673798361580735
-2528483177756102046
-9870724569894177
-9297735470756268616
-5831598115918776853
-32432303331018178
-6064762127302393958
-6156455943246842659
-23396678
-13500652
-16916327697533962956
-70844900
-816317351
-18411699885273055253
-5884848047378859255
-5837238405281154301
-14311736903207619026
-5141736951422061236
-3604608
-31022281504523376
-3599049409094225259
-577045344
-2974323816123992770
-8021450341214588326
-3577503648415550265
-509805280
-9870724567861628
-11098517635487303139
-7462549834646555859
-98501157
-5779476207078475458
-219257375260
-490013379
-4222974949961697922
+1427238547443354327
+10223260478367337870
+10720606758114626648
+70779363
+105185869
+162529937
+9870724569630759
+24904017
+2681814701524780811
+1320879066
+1584661506
+644219759
+13435115
+6097847786116483627
+12477949191893683608
+6925759835249836137
+27920040887322186
+10003084053115964048
+16253198
+153879145
+2625321589398833886
+257098416
+4784274
+9103100569952650951
+12474564753552836994
+1495729137
+62128549
+9774054990929462949
+5356296971014964874
+6153353870293665804
+9568883447315500158
+1915314009235720841
+16655465042802838677
+14866462842593414402
+2676033351738984017
+546636604
+535167753
+42008942
+30540122
+6365225483234117329
+7602365
+282854078
+2625321610894182276
+13307798926833551183
+10913926882465549337
+15906307047154976446
+6104586261131037638
+8483828720841721486
+15287423226215073909
+17785259896117529586
+2785415278947600352
+9000175594581527004
+14425661002709010016
+5513226652957347114
+805679481429165719
+17859691850682797212
+9181555677596944971
+1363739614
+9870724566615620
+537985804
+572392279
+15175534989820758889
+1873618476141513316
+2152780467316001469
+12601357272775920269
+16765215479188031591
+6534429686359852912
 6366353553143235674
-3158171969379764633
-21365044
-27638058876667848
-29330140097217635
-1873618454645114642
-2703776923039566000
-68813257
-279448782049
-814285726
-12237654319976351671
-517669620
-5779476284463187670
-10375505326587315831
-18411699915366727708
-6205475624366966000
-3307734082
-39125348
-1087507565178193378
-545325868
-15986098390340470919
-223556143025
-19177592590632702
-8865366478519731984
-19333416
-32432337723461001
-812254097
-11305519054433421356
-1873618484738787248
-5105416417023100899
-572982104
-505742040
-563086155
-104333894
-8070528080642443989
-11327137566841769230
-2625321610894378836
-16377260960560187819
-15586729198848181726
-1873618441748546884
-18413109971585663048
-4825924017323379312
-5915592292141435844
+12689613402799605860
+9138963602338286574
+104989258
+644023149
+361131345578
+816055205
+9870724569433729
+70582752
+1309213649
+17634738504986593825
+5639662680184522626
+6316393479032998553
+16340493341965880015
+5344573059048999857
+34124461934314600
+5994450030541998229
+2625321589398637514
+2676819007
+15515140772745448064
+498702419026
+227855238971
+4587663
+16893851890367073119
+14264208198271043974
+555090760
+818873277
+61931938
+16056587
+8821966780582857359
+18411699885273055253
+4861149623842704773
+18413391996586557524
+18115578910873816258
 5832726151436896491
-17247780946628644032
+365262179507571896
+16896582888638318388
+4445946672738929841
+17186370630874106258
 810222466
 7405754
-11549275701007551889
-10161648502327149991
-570950482
-1873618514832459339
-313841222762
-4452458274095237609
-1445774942907271091
-6101795934071424788
-92406286
-5293539447540681024
-18331491793766525
-197198505
-11199980773228349986
-32432320526091507
-818086838
-1997667722089860216
-2524806027085153844
-1964966944
-15270143
-1370042529145686776
-5565348523104797810
-18331539082773742
-62718382
-2012415014
-18413110001679335503
-5374107
-14282027259104724924
-10375505339483621145
-9887461037680036022
-1873618544926132491
-4662355883991631380
-18412263939573940270
-157614716
-3295137431799204142
-9870724569630759
-491782859
-214958343888
-16875205763331852041
-7241607903360452069
-5408471212899110030
-23134531
-18411417877468545037
-27356081166681957
-644023149
-70582752
-816055205
-3342460
-5246976952665638015
-14212253575230457510
-576783198
-1842511416005692464
-806159226
-5566476498435574920
-15292217517958891614
-13516735047310051359
-5728764487730398405
-468608617008
-4025969582498383295
-16044698410490725659
-1519546451849645365
-9870724567599405
-5566476545724581156
-5619444426388998007
-98239009
-547095362
-27356033875641745
-219257112483
-8140646021471143544
-4713167439824750602
-16357059045845960667
-5462796881224795644
-9138963602338286574
-21102898
-10905173367761798655
-13701595356116683915
-2477484405147109478
-1880166538706292058
-11206864
-1283692271244348427
-68551110
-5885543833259674054
-18413673995792875610
-2352415791
-14947075702982868
-5299098870103476096
-681145240220994278
-163447447
-331038328206
-38863202
-96207382
-153551462
-2625321606595348609
-5461104757014004985
-10744889200825601240
-1988559907
-258343605
-6517011693716180143
-535167753
-2530175340657839273
-811991951
-15291935475760762248
-4397798264919820154
-18413674025886548065
-12109395139072755174
-475082778886408323
-104071746
-161415815
-8697110475982376165
-15584540329550678645
-13669583335851559254
-2625321610894116800
-1873618441748286746
-18412827963781152832
-819856323
-6209141854797957852
-1783548230307677653
-18411981901675757599
-637928298
-7143606
-15855332315905657597
-2625321864544389907
-12020808312486431384
-3076135121411313050
-10139438201185111279
-6152225744495577231
-33560368941368890
-210659313158
-4278256712
-27638024483702949
-24904017
-32432320525830439
-13263754581809432790
-817824692
-15007995
-359800716494834349
-18613516794268696
-9839328478246341893
-62456234
-5111959
-18411981931769430054
-16219982623696489082
-6261827792145090364
-7692717626264324682
-42664306
-13806855580317125108
-9870724569368358
-16269555352897260337
-214958081659
-11214563466575480865
-15636771529559117046
-13271165719268362246
-2652485274356286816
-538968856
-3784724792312663401
-18263821886743185772
-1986666427421953426
-5565348480114297669
-5352348827359053328
-12976359
-1873618476140725820
-421319345246
-70320604
-11703165067112811597
-21715697223994697
-3757107087862401328
-60424594
-3080312
-10697899350700788395
-1873618527730534170
-468608354196
-509280991
-50528646
-1193603335023233930
-16635669954819197974
-15426482629288462533
-5460499803637156023
-2625321602296318353
-9870724567336570
-97976862
-8818864638845060491
-14288223544298637564
-88080898
-6996745855548787140
-5566476571519223063
-546833214
-220421203678071202
-31022238513759415
-1873618458945389823
-6406389097441592980
-20840752
-813761433
-27356085465188671
-68288962
-5865888353649363875
-109394696450803010
-12213481117926952067
-18413391987988365394
-10944716
-517145329
-5723537903358642458
-21715753112570631
-7758478083289188556
-10675690836223986039
-153289315
-95945236
-11547019543992076059
-9649086479758069023
-2625321606595086582
-258081459
-544801575
-5887799994573980828
-2845029447323880298
-18809125
-8510103668314541335
-6205475701751155414
-1990332636357069057
-429916882098
-2673382969485886910
-1873618489039064439
-18413392018082037849
-10914208898869168291
-3773122177597967623
-161153669
-103809598
-14107087915135404740
-6366071515245381876
-18412545955976642616
-15289397371128645360
-5462796868327967227
-1402930148
-28202057290482949
-797695489810761887
-16777494
-18116142943679220675
-5142301044413893172
-17219576355390295334
-5249797112394286460
-13735950183222348532
-6881458
-29048192479791616
-16896582888638318388
-14517406836956661503
-5458848655886518922
-313840698753
-5197393273133271298
-3861350810962691992
-6375653898722412075
-16885380374869314205
-361129707266
-210659050964
-29048123694646491
-3017170418691476659
-1873618450347593089
-15290243360149277503
-14745847
-72090103
-14546784569801180959
-7431889721301470079
-6364097387529111599
-2435475427475262665
-1873618497636600365
-6151097734773868363
-62194086
-17083693200934636558
-32150372909516328
-4849811
-3172873313800750756
-2150364429944620611
-3862478902367620470
-9305858029919208637
-2625321597997287853
-2508194873
-491258567
-1408762855
-5015996636573993090
-2414921941537785811
-538706709
-5734260728554980678
-22610237
-12714212
-70058456
-6208295882974168451
-32714336929384395
-16643035121679272213
-20023641798084435
-4770547828131824981
-2818164
-1930668198955452820
-13726068529822894439
-468608091255
-5569296714050766113
-17490170188584258190
-8694008299851745161
-7073102484926630551
-155058804
-97714714
-40370537
-2625321602296056238
-1703347206
-15895039144349470066
-5352348805862656188
-3068049059797011246
-5880738612678821404
-12309852946450942075
-33560429128451329
-15289397384024950845
-4767727591019973374
-10682570
-10233718743719545342
-850088361543927300
-2792183694107936667
-1107456968073808590
-5759560470823897206
-162923155
-29612216687004362
-5875369269012203157
-95683088
-294416195335096411
-22279760122415532
-5639662680184522626
-17619012653768771484
-13237708544183762948
-8550520059753138843
-27356042474686002
-249849483538007723
-544539427
-13390152586296232130
-10906513561824594910
-18546980
-1873618489038801706
-2676033356038342054
-6313103561496791450
-2063139881
-6848542126596623056
-160891523
-103547450
-14101293042239958
-6151097653090126690
-1584595969
-12424382439595706534
-17698252132056434004
-4129856573689694799
-16885259953617962521
-12393440069873436875
-32432320527338097
-21433680821684597
-8617826180017097033
-1413046597527668667
-3973491001936446780
-819332033
-17305802226190387588
-1873618467542665344
-16515346
-6619310
-6206321690771522709
-4089771542585346905
-1223976962194278208
-13487493291780736605
-2487491354099451134
-8854886172739175692
-9870724570875039
-2625321593698257851
-1535116279
-6262673798362565305
-91619849
-493028049
-5352348797264856883
-8143564249694210398
-6151097683183797493
-9386257309953099582
-196412070
-3865299044899163405
-71827955
-18613366323088485
-18157949162008873831
-7562235583526800081
-817300400
-4618470194090937269
-4587663
-3932922014897081298
-61931938
-1873618497636337289
-2522831856378710008
-6364097413323754682
-6053028402293443390
-42140016
-12287601267178473523
-2625321597997025900
-538444562
-15991329612793777185
-15291089478142986477
-12452064
-2676033644081056812
-2556016
-16508579235574254010
-805372789
-59900299
-14787093348585572176
-2575517759332551933
-2412665810316625225
-7730749911729375728
-6155298010574883251
-10488220504998020326
-1311572948
-883931539946605906
-5352348805862394041
-2786543383251193103
-546308920
-3346269252
-5782296426993943791
-4469799173763958889
-6205475671656957491
-7872981661881076049
-18116424960081923281
-2676033351739311464
-516621038
-1465168459078698840
-5677488692584514734
-105316943
-4562124351240801677
-5245848874158263187
-16432982289349543214
-162661010
-3971798877726246151
-4787251587800828866
-5875369294806846690
-12217235256243064050
-95420943
-5354604868299326678
-4502324021619918399
-544277281
-5940918086979029952
-2014710471177341259
-2140013610
-1873618463243635741
-18284834
-2676033356038079832
-10531295876509927029
-5458848625792321791
-18411699898170343448
-7410231625909407077
-3478039985316562895
-6204347606046083061
-31586254122912349
-6829167320236755019
-27920101074341046
-13165236096819726043
-32432389312220424
-571933524
-5727354401416743090
-10225919154718574351
-4127600472563058730
-160629376
-103285302
-8483828720842049762
-15740334315622960494
-206359759935
-9813006656186419950
-9319686106503382840
-5515085278788979157
-232154663489
-26149204
-6208295848581203181
-3094190453106412515
-6520986101609793850
-32432320527074663
-5245848925746038203
-5942328186188203485
-1873618467542403595
-16253198
-15881445561639371975
-6357162
-63701435
-15515478115209971466
-5833854247140395797
-283181761
-19177532404009207
-16567374854657149772
-684134257893509654
-9870724570613070
-15680489859993767209
-12826571498698443033
-2625321593697995819
-10329316755526125416
-10754752208794748192
-10758418391935812957
-12105446909435186010
-3143159678306028631
-236453432350
-540214046
-14848239906707278405
-29330157293274228
-684134210602468610
-817038254
-4977791693940394179
-71565807
-1873618497636075077
-807142269
-61669791
-11287403619712895066
-4325515
-13819298136066198
-7734678113259293802
-6098975847429179176
-99222062
-18056758355458722638
-9870724568582655
-16224960573811657069
-2625321597996763849
-4078298757842341053
-17625510063045740642
-10528906628815718922
-490734276
-5412367062202975465
-22085946
-12751507524739009261
-538182415
-12189916
-18413109984482951243
-2541195915421354200
-6671860954713623381
-2893509029140760671
-69534164
-747829823970020707
-6770804071406897080
-2293868
-5566476498434524382
-6534429686359852912
-18412263922377556010
-164430493
-9870724566550039
-154534512
-10167299845199168903
-12754891682880490747
-5250413516934022944
-3315661715940248009
-451651625195343029
-32432333423379563
-5941764217869305943
-2141783083
-283748271730
-10161648493728303880
-5240846595623881868
-67502526
-15618641120352995308
-2676033351739049517
-6205475697451599682
-4023356732265137752
-14986955239351847842
-31304272112126853
-516358893
-2207492698791414354
-477207135345
-1309279186
-105054795
-17859691850682797212
-162398863
-4238330517036600601
-152502880
-18412263952471228465
-257295025
-10905173350565414454
-17498716255300421272
-8881019260503721949
-18022689
-534119176
-18411417890365833232
-6293435910568086045
-9374458755688828226
-820839372
-6153071780807051278
-5909364179964069981
-8126661
-3735453693364143828
-6155045908522469290
-745740842898098858
-2625321589398965240
-12142525752872799042
-160367231
-17958290734101235336
-9523554809025136564
-16892239439269464715
-15289397371127860096
-1736311827
-15991050
-63439289
-6095014
-12484855343804124176
-9658025172156550406
-18067928153034001057
-292345808939
-16572875051796793000
-10542598463376395267
-12772641161582545873
-18413674008690163805
-1544487931
-14737352740221028816
-282919615
-12808641794728789765
-2625321593697733840
-17128487303121020
-1706624008
-14101026494875963
-11214563466576463780
-18412827946584768572
-11966722661119888545
-6156455943247300775
-5300226909920168653
-6004915412369541960
-816776108
-4223816177647290930
-71303659
-1873618476141710425
-12477949191893683608
-417019528294
-9511403338599564690
-4063367
-61407645
-2543805385922512178
-9870724578216632
-5407707525201267705
-9870724568320021
-2564752444
-98959914
-15494005608834598990
-15140097999495498431
-21823800
-12734096628671909131
-537920267
-18412827976678441027
-11927769
-69272016
-18411981914573045794
-2571498445011814318
-10592171188278987146
-2057911839619745748
-9870724566287831
-154272366
-545784627
-17616192489740896443
-21715680027609308
-16886908734816455284
-583336804
-2246313005
-516096747
-2625321585099935141
-620888934
-162136717
-331037018572
-477206873177
-503001777494
-15592058013925444099
-1652810939277510396
-10531295803425490030
-3205882223899445065
-31304323701671300
-28484129580057898
-1873618441749006513
-16893851890367073119
-820577224
-16904712944498838074
-1394017249
-17760542
-4160689491693538063
-4047541379259827663
-7864513
-14219872676477209184
-504169174
-17244622751296785814
-2625321589398702921
-4278977611
-7239633818635733091
-5462796868326918190
-1334641629
-73073152
-7460569593843485201
-15287141188316891641
-818545595
-9339868219275806468
-15728902
-5382561551670903978
-9373330690077689939
-18413392000885653589
-5832866
-63177141
-438515402871
-2373415502940997016
-2148672322930150296
-168849237244054062
-12339564610979564477
-8327325764367420682
-7630443591734791098
-12608147700378373379
-9870724570088730
-2150364451439708714
-18412545938780258356
-13221120945827219803
-492241614
-4129856608083381232
-15740733274947783803
-15858116883009440274
-1873618476141446514
-816513961
-17564225130023161250
-13697261
-10668197763104573447
-71041511
-5357143003026951378
-31022281504720056
-1873618501936351339
-3801219
-442814170389
-5701610621477129021
-8520914754064026558
-15289397306641222853
-108593749
-98697768
-9870724568058057
-5780604294184830225
-156041850
-5192881006389626514
-32150304123324262
-219257572663
-18412545968873930811
-5249797099496672683
-11127945220196076778
-9103100569952650951
-11665621
-421318034537
-17619012718254098754
-14443179094226111164
-1873618480440216958
-69009868
-10594427319499622429
-814482337
-13968724050119231192
-28202091681875145
-27638110466671725
-16166203682344470241
-1712194570
-472907842721
-507970270
-15580874172203795679
-23689855033805297
-154010219
-17092164759424403479
-12893049762838873864
-6877309693745106245
-545522479
-5887800020369606783
-14977809576148535095
-19530026
-14105033451515939293
-6795216411027442152
-2543452128325209336
-1385890784
-114426460
-6444189713816225654
-6152225714402364510
-524384476410219715
-17953567922355439196
-17113993018971653874
-573178715
-515834601
-17090754617222956318
-161874570
-1538130937
-47186305
-30458188512103543
-2449021711964768402
-2414448843017751282
-5214737420442796133
-505938649
-2625321610894575340
-13965057806789381527
-970700105235760464
-15223822230290106035
-16285378285009240167
-16940455997476965252
-2601013084734032090
-5248157445900799208
-1580068669843704469
-15043322265989680207
-29048166685607288
-3863606942184311140
-820315079
-17045009756596405420
-29048192480512516
-11510172448171493799
-5885976160280708469
-7602365
-17785259896117529586
-8856014216854897981
-14477731067643038195
-1873618514832657292
-2578187325
-15292499491370895395
-33560368941827284
-13146357072728951328
-17353152791227993245
-159842942
-15530553734630409457
-5569296726948055802
-494159375523777824
-1812923415
-6366353518750729401
-4278715465
-17097308613030775025
-35258719
-1899651063193471062
-12103109825679658143
-6364338522051512284
-2429880031182916564
-11621189233770302317
-72811005
-15466754
-3880024017885400135
-818283447
-62914993
-4076606625033226775
-1873618497637320883
-7746405201714873917
-5570718
-10859426818132543221
-6925759835249836137
-3506237898852665380
-23407812836853915
-1873618523432225060
-17166316876055971050
-18008952305986046279
-43123062
-9870724569826462
-7410173966093388838
-33560399035500221
-511599051947
-214958540605
-13237708557081051143
-20587696099952690
-15339421027537585423
-6104586261132347910
-11103300151687644832
-1456931819
-1873618450346281005
-9181531069949872018
-14650572868605052119
-17783567759008991682
-575239712866634722
-15288269284022357372
-6206321673575138470
-644219759
-13435115
-399811749952817933
-145335345147610979
-70779363
-6366071455058494624
-7529998377695250462
-519635711
-3539071
-576979807
-9568723490388248888
-634323816
-13012951802393594980
-853643387796785445
-98435620
-28766107292140894
-9181555677596944971
-5195701200510977145
-5129024196560096606
-5831598124518278362
-4844858457232050089
-219257310372
-7569568047215545466
-5461104800004441485
-1518418407735101149
-814220189
-11403474
-18005251247539029895
-10333839787251271664
-1836516380
-8054758354584013306
-507708124
-163644058
-9001701177466488459
-2625321606595545096
-153748072
-4787251587801811388
-39059811
-545260331
-2036204584
-5356296971014964874
-19267879
-9714916684781063078
-3055188874828713383
-14576212124415364447
-2150364417046743283
-4662355849599126556
-1372824966366170355
-1318388695
-15289397293744393060
-8423108281783224429
-505676503
-104268357
-477206348880
-5831598081526006949
-4625631396377398109
-2625321610894313322
-6206321759557388696
-12237654281284815334
-17236251
-9391897711091583990
-3891732840317912522
-8856014216854636141
-5758903550139959418
-7340217
-638124907
-810156929
-6206321690772243584
-112132697
-15287987228927658628
-339636063086
-7721139320100816372
-684134305183500639
-22279768720672168
-5831598111619679502
-14814059355306855043
-4211213383
-15290243360149735302
-18411699880973959188
-15204606
-11507341268100646834
-62652845
-6365225483234117329
-5308570
-3491703531359374171
-17791918762976347730
-4127600455366674792
-11130039777759856047
-13951205954302381098
-18115578910873816258
-8659114857360722535
-6153353844499089111
-157549179
-9870724569564298
-16327183209838150989
-491717322
-214958278120
-32432303330691092
-17684252729367202593
-16965951797418331227
-23068994
-2272905061487347697
-1873618450346019367
-7515799761807542411
-815989668
-2576363817137867614
-70517215
-17763448248357489818
-13172970
-3276923
-806093689
-17621268802185464283
-60621205
-18411699911067631643
-576717661
-1685722535145180234
-23689824939607125
-17256155806064642777
-5516892801706297876
-12982659022915898414
-9870724567533791
-15515140725455259155
-547029825
-219257046468
-4180850416920431050
-21037361
-68485573
-11141327
-813958043
-189614828176542708
-1873618480439692390
-279448454880
-16253215886083360174
-572110149897422243
-9896616181508082455
-153485925
-8021450371307931626
+2507605048
+17607182983838566334
+546439994
+2679637056
+41812332
+99156525
+9140909979694467183
+11742834345080916462
+9950583114428779661
+18411417894664929297
+17160329975787685834
+1518418407735101149
+18331556279224644
+15289397293744393060
+13950077918785243724
+15287141235606883137
+2789363555875490728
+491913932
+90505732
+214958474959
+21692726
+2063139881
+9870724566418979
+339635799228
+11740202404159819072
+12769623874203027091
+7171706723174648069
+16156684754098128751
+6208295835683456476
+1873618476141316771
+5882159696614657105
+3431717683755289915
+1873618506235448739
+17166316876055971050
+1023125932615797552
+22279798815198594
+12346762090311779845
+162136717
+331037018572
+13041896
+1733362705
+643826540
+2306802734
+477206873177
+17309267256018536307
+2625321597997222413
+517669620
+620888934
+70386141
+31022281504720056
+7409502855497715015
+6155045934318095559
+18412263918078459945
+5458848625792321791
 38797665
-19177566795402134
-27356016680241600
 669582195
-2625321606595283106
+27328854
 554894151
-5512098557251945790
-9568883447315500158
-1440671446449589035
-4502324021620638916
-3249068390006196153
-15292781563660995825
-821822415
-27356063969248337
-18413109967286566983
-10911952793442192048
-6064503826171693679
-11161692095903435283
-1004761907965660269
-2207210695286917386
-6388664954993575829
-46662016
-5885976061401368013
-104006209
-5572809636517250553
-2625321610894051277
-17955470565775510239
-4661227814082512385
-6368045642960996241
-5463642874544129714
-16974104
-533070599
-809894783
-18413109997380239438
-7078069
-637862761
-6288511205539515238
+468608091255
+15859976
+4287350254045103750
+61735328
+4391052
+6520986101609793850
+153485925
+8510664359869943178
+11050161621988804687
+20869691006257762
+5196265237615086368
+3491703531359374171
+1873618489037883086
+11633356565151157114
+16633695839999756254
+23407812836853915
+1873618519132015281
+12074216556992400767
+6153071832396467338
+16120716880803858984
+5299098848608717979
+17149817495305717193
+18411981927470333989
+3308118261903721908
+5831598124518278362
+7209143
+810025856
+797977540607610221
+98959914
+7470284155521404014
+2564752444
+1727529996
+12318365723907459763
+5884848047378859255
+13222096480399396057
+6314795724398267312
+4397798316509039896
 3974700764184054454
-18613559784442970
-2791055594105669609
-4504298205224635444
-18412263935274844205
-2605266760616185153
-15287987228927396675
-339635799228
-92078603
-8501910827968825512
-5991347884504386492
-210659247559
-17284241873202253123
-16893851873170950707
-651404368114879038
-18411417873169448972
-24838480
-5726226344404977639
-10259573046193883986
-2676958769323838072
-72286714
-6886936648282539655
-14942458
-521143041
-5046422
-13980703149896829784
-1495991284
-62390697
-18199185222634702635
-8834282535679560676
-15925946803693423456
-42598769
-9870724569302153
-5459976661309982295
-11084138473134491150
-5303047078245827995
-214958016090
-12451287838412704489
-5509410202188647833
-2681814701524780811
-10628953736434486617
-9774054990929462949
+5514354709969504081
+2893509029140760671
+1873618514834032208
+5516046791188875281
+1223976962194278208
+14737352740221028816
+6368045642960996241
+3489447322753895731
+21496117
+9870724566222277
+514654951
+189614828176542708
+214958278120
+491717322
+571999061
+6367324841362000185
+10375505339483621145
+8070938101082033295
+5569296709751605280
+1316357237551401394
+12020684574736647824
+15991329612793777185
+10697899350700788395
+16739161091967945306
+3891732840317912522
+1899651063193471062
+161940107
+24314188
+2224234517276395067
+17082847207615301902
+2625321597997025900
+6152225714402364510
+12845285
+506004186
+1733166096
+70189530
+10906583445476542150
+563348302
+31022281504523376
+1873618527730601376
+2530175340657839273
+1873618497636468806
+1873618441749006513
+18412827950883864637
+6366353518750729401
+1413046597527668667
+4078298775038527628
+5565348505908348542
+4022831255438034250
+153289315
+4194441
+15663365
+11700344903086704893
+73007615
+818480058
+296644709200
+95945236
+2150364417046743283
+30740204914804024
+15290525359355331959
+4237766475632413481
+16758489844492275047
+5408700909154273182
+5153885660580611393
+1873618519131818153
+13951205954302051853
+3597357340772992368
+7432602967203907143
+1880166538706292058
+399811749952817933
+10381427610856195682
+4644563108626631009
+14665351642484132
+7012532
+5141736951422061236
+3344434243
+16330874579771459902
+1873618484739705502
+8550520059753138843
+4645667113710455153
+5885976069999102359
+15501473033754248808
+9896616181508082455
+5462796868327967227
+14585410861575638263
+214958081659
+339635406848
+11818157132458100908
+11526999220155450649
+18613533990193666
+1873618450347593089
+3861350810962691992
+684134305183500639
+18413673995792875610
+15530271666638163275
+17621561863500597513
+4238330517036600601
+22279768720672168
+4502606072414800438
+10655291933708585535
+161743496
+517276402
+2625321597996829544
+12648675
+563151692
+1308623824
+104399431
+236453432350
+4279043148
+540214046
+1744643526947965735
+2065251783916127931
+18411699893871247383
+5459976691403131036
+21715680027609308
+5726226344404977639
+15292499491370895395
+18413392005184749654
+1873618497636273356
+32432320526091507
+31304323701802109
+2576363817137867614
+1631882651478526261
+5995296157134227520
+7558005371879033601
+61342108
+95748625
+520094464
+15466754
+3997830
+5240846595623881868
+5887800020369606783
+15288833278135765839
+818283447
+72811005
+5459935770830768809
+9355193091131312182
 18411417903263121427
-3865299049198390675
-12910822
-5356297009705911966
-2421359666
-70255067
-2248112069177510680
-3493395634074945822
-60359057
-12654580528992553525
-519111421
-3808100888100343209
-3014775
-13513632858283052077
-15289397310941235057
-8861613698626554738
-9697577994188492052
-155255415
-10381427610856195682
-9870724567271440
-2625321602296252770
-14512708438227029368
-97911325
-489423554
-4022831255438034250
-30671195
-1873618458945324208
-20775215
-5459976691403654584
-813695896
-12665415616966166285
-5645056620059298667
-68223425
+8430474765433179073
+5247593352907000017
+27638110466671725
+32714414313178248
+9234597529149245860
+3229097897723824621
+7449919019336600171
+2413828615876118471
+2414448843017751282
+6101795942670075923
+7697026996393675938
+31304285008889193
+15777957523720635747
+3143159678306028631
+11065487220741573048
+6815921
+2140013610
+14282671233461718187
+9230753948926543533
+98566694
+2625321585100065781
+5382561551670903978
+259130038
+155910777
+87097857
+18284834
+282067642
+545850164
+33278352538406314
+21433680820701635
+5625593289148533238
+10512763733196344280
+3784125305237867347
+1873618514833639109
+32432337723461001
+1873618454645376983
+15292499534361593441
+5133829485925763690
+16904712944497920326
+5511666277521099409
+5622264654903379074
+571605843
+514261733
+491324104
+2625321606595414132
+21102898
+1385890784
+524384476410219715
+17257283880273643533
+5195701200510977145
+10280579133800254203
+200191596416994196
+1873618476140725820
+13117263636444153530
+15096032016463431129
+6729754102968812754
+18412263926676652075
+31304272112126853
+12118995007347033900
+1996555300
+9870724568648556
+540017436
 1319896024
-2390363305266056430
-17634738504986593825
-20305632407192782
-17462509665872383079
+4236074403011431549
 1606616067
+195953314
+23920969
+104202820
 305243098454
-163119765
-48431492
-10590197086357423689
-2787671431665157349
-6366353484357502971
-18413674021587452000
-17620986833073014515
-105775699
-20869665212206112
-4445946672738929841
-95879699
-2625321606595021110
-10906583445476542150
-18412827959482056767
-17205553309096938840
-12294570438877711433
-5461104782808583112
-544736038
-9950019055828534995
-5991347927496394467
-811664269
-5403008449516603011
-18411981897376661534
-572392279
-7677136701370927115
-6155045908523191668
-18067928196024961188
-20587511236070012
-103744061
-161088132
-335336768790
-6155045934318095559
-13322381941750499717
-15291371425760087333
-30740222110467489
-5245848925746498573
-5349308051975768286
-4548309565419816229
-255984301
-5461104787107351969
-16711957
-10906583475570214623
-6365225453139920066
-6177363118375897150
-6815921
-7032232753418799293
-5558136817694803400
-4030203865610717075
-12718336251608304605
-18411981927470333989
-1545208828
-15287141235606883137
-5837238474067478018
-11705421198335413148
-5524868651610213131
-210658985303
-6098975770044925746
-24576334
-13151687854617134836
-4662073803102881076
-72024566
-817497011
+12452064
+5248157445900799208
+31022281504130688
+1873618497636075077
+1454041666728626384
+1873618441748613384
+15289397310941170468
+12999620585941961275
+5875369294806846690
+18142877345697171235
+2789363542978135882
+18411981936068526119
+12057284352529139627
+356832576945
+17092164759424403479
+1460339693
+3801219
+256115374
+1987904546
+1964966944
+15270143
+33278386930321618
+442814170389
+818086838
+17462509665872383079
+6206321759557388696
+5408471212899110030
+1873618523432225060
+17353152791227993245
+6261827792145090364
+15223822230290106035
+15287141218411547437
+7576852735584046364
+9714916620293637762
+31586271318836879
+41025899
+2625321585099869531
+223556471268
+545653553
+4023356732265137752
+98370083
+1531970550
+6619310
+23689824939607125
+9950019064427710530
+12424382439595706534
+1873618484739311861
+18331530485106038
+23407864425745813
+797695489810761887
+15289397353931868100
 29330157293733695
-17096567568145714575
-1454859013759438228
-14680310
-4784274
-62128549
-1493907215600323645
-6364097387529046615
-12583654612056476062
-12851509922494416016
-1495729137
-15287141218411547437
-828143439367899804
-2523959969279970191
-3919394969679695174
-7595953279435999504
-2625321597997222413
-491193030
-1839046019115124804
-7241043922144659849
-18613499598604650
-18413391983689269329
-10594427319500605883
-12648675
-4861149623842704773
-5782296448490276391
-5516046782590617836
-518849275
-10015828607276288922
-15662612681012938353
-2752627
-60096910
-5133829485924779401
+6101795994259359091
+7692717626264324682
+5299098870103476096
+9813006656186419950
+3591160524196219107
+4129856608083381232
+2755882956846859930
+5352348797264856883
+812254097
+5524868651610213131
+11124082762859154482
+2857298572705729021
+19177566795402134
+18301216972081072101
+2625321606595217579
+6152225753094686522
+548471621
+5512098595943810546
+6584302816815089825
+11092808190891527547
+5941764144784016877
+18412827959482056767
+14428481252717692252
+5301355009924597043
+12284124821458521275
+3577503648415550265
+9870724568450966
+69599701
+7431889721301470079
+46662016
+35193182
+104006209
+12255453
+17877509356004052233
+11069796553860646809
+4347925833116091776
+10590197086357423689
+5570988786672405753
+9297735470756268616
+14637903957824965363
+539325825270679628
+15584540329550678645
+17247780946628644032
+15073532
+1574831104
+72417787
+152699489
+496763604
+577045344
+817890229
+3604608
+6204347606046083061
+12771845711499103316
+15290243377345399572
+11127945220196076778
+6208295882974168451
+11846037961957312985
+13106160036035691955
+1906986264008723742
+4657106642463886071
+9198943117821938833
+15270695523793439937
+5246976952665638015
 7003516464553396964
-12903069678853164419
-2625321602295990612
-97649177
-259785401
-5464488953846367762
-546505531
-30409049
-374027977988
-1396769762
-21715680028329254
-5637072609524124450
-7731877951544692100
-1873618458945062288
-6767393152337644543
-9467310877347154547
-5429433323061448040
-10617033
-1730937871
-107356700000258304
-425617786716
-451412690018
+10956724774926813288
+820708298
+545456942
+63766972
+4585257123188181630
+6422699
+17891616
+9117971362513815455
+18413674004391067740
+14597841535548131734
+9772926989806013640
+1873618458945784014
+4522983015860209939
+13806855580317125108
+15426482629288462533
+3506237898852665380
+5787083718919720300
+13322381941750499717
+13237708531286474753
+32178524
+490930885
+2625321606595021110
+20709678
+1706624008
+513868514
+245051624454
+525337347
+16483453074211931840
+12217235256243064050
+4794173760728861833
+5347351719937377689
+18411699902469439513
+30458205707308380
+10750398672578676906
+13351948495571782591
 18413392013782941784
-12020684574736647824
-105513554
-3541851256594893702
-16038494049631274933
-497025749
-4661227783988316231
-18412545951677546551
-5565348467217401524
-14428481252717692252
-544473890
-3344434243
-2169005683868174908
-5993603989931887912
-12972952285742288
-13117263636444153530
-811402123
-2676033356038276482
-1873618514833639109
-514786024
-572130134
-160825986
-1938490399
-10280579133800254203
-285938493736356261
-6425213859614951480
-103481913
-11364576519499679975
-1881294612915292853
-15739206202722094240
-4397798316509039896
-17011915733784398286
-1873618446048496233
-14383326641327005
+17088031212435737557
+5105416417023100899
+11427331956784106382
+3698377064120454404
+69403090
+1629160452
+161153669
+17153706162049649384
+103809598
+15580874133512784221
+7872981661881076049
+2544766567488424310
+8818864638845060491
+1597536180772867045
+17631161371525515669
+4977791693940394179
+29048123694646491
+15288269284022357372
+806224763
 26345813
-6156455960443095577
-14975681650483333306
-819266496
-16449809
-15288269301218674108
+72221177
+14876921
+60752278
+3407997
+152502880
+2625321593698257851
+5675796551176948530
+5337229686545450161
+10649664295314066054
+18271599167641487963
+5741055947585816645
+1873618523431831649
+9763237054719266042
+5778348175860436286
+11906248855590275274
+145335345147610979
+27356063970624739
+2676033356038407648
+6226088
+7285785859232107205
+2036204584
+109445719
+5779476228575003910
+13117159209037203716
+97976862
+545260331
+1839046014815569788
+23407778443758207
+13885455448020813663
+17091318705916150977
+14749580058850363919
+32714379920475611
+5511666307614640107
+5780604362970367638
+18412263935274844205
+4767727591020628301
+5885976160280708469
+1396769762
+811860878
+5996988225456246061
+9887461037680036022
+490734276
+295544243748734701
+30458205707109873
+9950019055828534995
+17090754617222956318
+5566476545725367766
+17648172271756969893
+15289115277341755866
+30176189305784773
+6205475701751155414
+9940375093616053431
+5728764444739437994
+15140097999495498431
+6523880655054768550
+5727354401416743090
+210659313158
+1456931819
+11862232
+527958790
+103612987
+4278256712
+9870724568058057
+69206479
+1997667722089860216
+12339564610979564477
+5243108696682924272
+23125779236849772
+1873618501936285414
+16044698410490725659
+13669583335851559254
+14425661019905001872
+13681696359428851594
+16161820259100396848
+72024566
+24535844054893958
+3211386
+817497011
+9870724570875039
+60555668
+26149204
+24535874149025753
+806028152
+232154663489
+507839197
+14680310
+2625321593698061230
+15273884660262766886
+6633286351216577743
 1873618493337504776
-5782296461386581535
-12162857194684744950
-16633695839999756254
-6553773
-6206321690771457172
-5411573444917201071
-14273081993166850387
-17297538988880889355
-9870724570810095
-339635275824
-101450287
-2625321593698192308
-91554312
-3812049113439014303
-492962512
-15289397349632182266
-342928503145892901
-9257009393629660721
-13674941621707869313
-17952462371364276975
-24314188
-7676326001635166459
-12622921449567619867
-14471968401314024391
-14418163
-71762418
-4522126
-1873618497636273356
-1873618523431177265
-31304285008889193
-2625321597996960522
-42074479
-18895601982637667
-14883032307819284131
-32178524
-490930885
-5459976661309458015
-194314911
-1873618454646032908
-9386257314251803173
-13950077918785243724
-5831598146013367591
-5882159627828332650
-69730775
-6100103913039400051
-15744000533156660854
-12386527
-518587129
-59834762
-9231865831523354279
-2490479
+3094190453106412515
+1087507565178193378
+8481290603310483360
+16885380374869314205
+5412367062202975465
+1372824966366170355
+2543805385922512178
+27356033876298083
+2676033356038210923
+820315079
+63373752
+6813843502384089093
+97780251
+258343605
+155124341
 2148672331528407961
-2908260051937332390
-16876615841046071902
-9950583114428779661
-154731123
+6029477
+17632571483632043275
+18349504802666319185
+12133908888583014197
+18412827968080248897
+6100103913039400051
+5249797202674912471
+1873618458945389823
+16271603835638319461
+605557034314828631
+5881866613803452649
+3544107379218385465
+7406761759861377295
+811664269
+3234866947851553000
+43254135
+10675690836223986039
+3346269252
+17946608694533885076
+5459976644113468289
+15290525376551717170
+5946696443085589628
+3477193953305167424
+5353476789790574588
+28202091681942395
+5944020284604679310
+8143564249694210398
+31304332299601191
+8856014216854636141
+160760449
+28766090095429261
+5727354401416546168
+421318034537
+814482337
+103416376
+210659116497
+15505670362359531298
+16893851873170950707
+15515478115209971466
+46072191
+11665621
+9870724567861628
+23134531
+69009868
+14105033451515939293
+1784112314702629632
+32714336929384395
+853643387796785445
+4713167439824750602
+3812049126336301758
+16130159995999161574
+15289397371128645360
+3910652327921846506
+519111421
+71827955
+9870724570679212
+2625321593697864817
+60359057
+326737659857
+163578521
+17219576355390295334
+197984938
+817300400
+3014775
+18413674012989259870
+27638084672359236
+6156455943247300775
+18115860927276518423
+18323667541285735009
+5572809636518234139
+2581508047683782932
 13237708539884666883
-30458205708158447
-2964529530791004471
+2524806027085153844
+2676033356038014277
+31243224015702806
+12982659022915898414
+510460641
+464308734399
+2219404100148201532
+544867112
+438515402871
+258146996
+5832866
+97583640
+63177141
+21715697223994697
+14657391675119111356
+18411699911067631643
+1873618514832657292
+15339421027537585423
+3545799490531822688
+16916327697533962956
+5299098844309358384
+4127600472562141528
+8920676095134336910
+5133829485924779401
+6151097665987347595
+6152225697206437786
+1706034183
+15897859265386515143
+43057525
+536216330
+943759021439519007
+10939233345785957508
+1746899701160150410
+1384869222252743071
+5881302580997523790
+107356700000258304
+11287403619712895066
+814285726
+68813257
+279448782049
+539034393
+22937920
+210658919829
+493159123
+91750922
+5831598081526006949
+103219765
+45875581
+516096747
+9870724567665640
+2625321602296449309
+5568582435113995179
+6154199872212897041
+5356297009705911966
+9001701177466488459
+6425213859614951480
+5129024196560096606
+3971798877726246151
+6471725260172231870
+18412545934481162291
+6155045908523191668
+27356042474686002
+11226550812567014265
+9870724570481756
+417019855960
+12512221777814685432
+2625321593697668091
+18116424960081923281
+14287090
+2818164
+71631344
+18895601982637667
+18066207382028748450
+5353476806987745228
+6100103891543657570
+6996745855548787140
+10594427319500605883
+575239712866634722
+3870997034531752803
+7239633818635733091
+29612147900877606
+8865366478519731984
+5352348805862656188
+8709732826087622782
+18412263943873036335
 40042856
-2933734509745341832
-5459976691403131036
-1730675726
-1873618484739705502
-2676033351739245930
-15215179494928287321
-14866462842593414402
-5463642917535614049
-631243623
+5245789596497153220
+819921860
+15580874176502892132
+154731123
+544670501
+62980530
+17105177
+4254959725487523541
+5636255
+30458188511970924
+3973491001936446780
+7410173966093388838
+8704274751914773568
 5885261859847867262
-11391362031143292020
-506659547
-105251406
-5778348197355914873
-16324853745603185849
-5509410163496651347
-152699489
-15292499534361856724
-496763604
-544211744
-4078298792234977417
-5461104782808057591
-14648423506775771515
-10504814416598927327
-8709732826087622782
-2544766567488424310
-811139977
-17088205463377873568
-15798241638577276499
-2676033356038014277
-2785415326238639918
-12562453432512743836
-12350988444867431112
-1873618514833377412
-16940553195690134509
-45875581
-103219765
-8854886168440079511
-5941764153383128192
-2625321589399162008
-11818157132458100908
-2785415278947600352
-15257764832492062794
-232154598652
-819004351
-16187661
-4644563108626631009
-4000515045253449269
-16872667624306444468
-1873618493337242815
-6291625
-6156737968247080128
-292346005443
-283116224
-3220426554520570467
-12356593998396393868
+13590665243542948895
+2412665810316625225
+18613516794268696
+1873618514832459339
+11623400942792738969
 684134257893444250
-17175427809786595961
-9870724570547380
+9126223058424237061
+10530167802300925091
+14267039342724252928
+7042731044489660311
+811271050
+219257507157
+16204201012116260706
+19923244
+2248112069177510680
+19741625396299098
+14311172801615955497
+313840698753
+157549179
+4662073785906890031
+9658025172156550406
+6364338522051512284
+3599049409094225259
+6177363118375897150
+1801912319444323213
+11272401
+91554312
+2625321602296252770
+68616647
+538837783
+1411918553413126104
+22741311
+492962512
+451411772583
+160367231
+9870724567468020
+814089116
+2528483177756102046
+10370417990725208293
+6829167367527204602
+10167299845199168903
+14284319595218536933
+18413109967286566983
+9881712940254169714
+13819298136066198
+10906513561824594910
+9486667438472824267
+10215782083327823122
+3685635809078676834
+518718202
 1992881803100621054
-2625321593697930351
-9450798976826149302
-16655465042802838677
-6474545510181176536
-11740202404159819072
-15289397349631921063
-9714916620293637762
-6098975770044401989
-16364556117061994922
-196084388
-540148509
-24052042
-11065179658016983681
-12480382642832672298
-71500270
-7285785859232107205
-14156017
-17632571483632043275
-61604254
-4259978
-17750109864738752812
-1873618523430913566
-9830100417878166271
-14425661002709010016
-4794173760728861833
-464308734399
-510460641
-2507605048
-41812332
-2679637056
-99156525
-16044698410491643447
-9870724568517151
-5516046735301085409
-6261263733545503259
-3759645248384009814
-538116878
-5779476232874035736
-6104586261131037638
-10531295842117158093
-12124379
-69468627
-5565348505908348542
-814941090
-5299098870104394759
-14322284629040564382
-10440328872292254866
+14090480
+59965836
+1550910461
+17063697102470777209
+71434733
+2411070513
+17639632887023929831
+805438326
+2621553
+9870724570285675
+1192315333980326167
+6928358494714596151
+5512098613140196086
+15611911946388048179
+5214737420442796133
+5778348150066318224
+27638024483702949
+18412827976678441027
+8881019260503721949
+5837238405281154301
+5461104765611674055
+4181978486829943864
+154534512
+5439644
+755605599842665887
+62783919
+292345154665
+544473890
+567411536
+15257764832492062794
+15122676476569913436
+5835546375651263675
+5516046795487905107
+10758418391935682553
+27356085465188671
+11400819049620310987
+5245848947241584851
+1728578573
+42664306
+219257310372
+8257735
+524354306
+429916226796
+4076606625033226775
+10162787574158199843
+6064503826171693679
+1873618450346019367
+5566476545724581156
+2704904932763174902
+4548309565419816229
+12484855343804124176
+879419277503368073
+6153917830015027393
+573047641
+68420036
+9870724567271440
+2625321602296056238
+2531021389865944442
+11075790
+102826544
+6064762127302393958
+5249797159683425776
+18413674021587452000
+31586340104504804
+4469799173763958889
+13237708548482859013
+31586254122912349
+9870724570088730
+331037869860
+417019463453
+48300418
+71238122
+1539245050
+644678512
+2424942
+11305519054433421356
+15739206202722094240
+18411699919665823773
+4787251587801811388
+32432320527338097
+27920126869375123
+18008952305986046279
+4661227783988316231
+2543452128325209336
+12826571498698443033
+16711957
+4160546838840674266
+5245848925746038203
+62587308
+5784522635265967958
+5243033
+544277281
+6211116016906930204
+16253215886083360174
+23407808536904833
+9821766011288487605
+2676033351739442523
+8061124
+13012951802392676509
+219257112483
+547095362
+1992881785902860007
+19530026
+810877831
+2625321610894640833
+4825924017323379312
+8854886168440079511
+3808100888100343209
+3493395634074945822
+12591757708863407913
+5349308051975768286
+13361048879788721990
+4074350485214726972
+1626046306171619904
+8617826180017097033
+13513914891881875478
+29330183088506125
+18412545943079354421
+6405261049027955879
+17939922315943150958
+1410073577
+7837634943338155161
+85348920764927349
+2625321602295859611
+813695896
+538444562
+29048192480512516
+45285754
+68223425
+91161094
+10184384893691038634
+17542946455516547053
+4180850416920431050
+1928986057181235415
+6364097387529111599
+15289397371127860096
+2522831856378710008
+5885976061401368013
+21997756618246082
+18412263952471228465
+816513961
+13678484518713821516
 2228331
+2531021385567766485
+197198505
 518324983
-16872385650894636566
-6284197438710222140
-8098722631875955846
-5727354392818878727
-9870724566484489
-154468975
-2292825785040636736
-3172873343893834792
-14418466534433295118
-2707725182771857350
-15293345523383077603
-259261111
-19988781
-15371922320578972378
-19741625396299098
-18411699893871247383
-12818875419963886521
-2676033351738984017
-14268291611706526293
-1309213649
-104989258
-6367324841362000185
-7432602967203907143
-11331649863678691999
-15292499534361593441
-1815413785
-5778348223150556659
-5572809636518234139
-11408348231855703653
-2446197814
-13001682102565734253
-17186370630874106258
-2785415274648570354
+71041511
+9870724569894177
+105448017
+5779476207078475458
+13980703149896829784
+13697261
+12769025487284210679
+2150364451439708714
+4162099616697747321
+1873618497637320883
+1873618523430651633
+12716605781588708278
+5248951136269502637
+11703165067112811597
+62390697
+5046422
+521143041
+16515346
+2625321589399096275
+154141293
+1495991284
+165610142
+10528906628815718922
+555549513
+819332033
+567018319
+1095239150174145285
+1873618458944406678
+6364097374632348674
+10811668319096800853
+1465168459078698840
+17269866578628118199
+2676033351739245930
+7864513
+19333416
+11034422950646711803
+283116224
+2625321610894444316
+546898751
+18411417864571256842
+2372569380647405649
+12754891682880490747
+18413109975884759113
+3332575624131774585
+13536993689470216
+15291935475760762248
+6153353775713551670
+15289397349632312454
+9373330690077689939
+29330183088310857
 14264783202905229777
-7171706723174648069
-820773835
-4645667113710455153
-16425638839461284611
-5353476806987745228
-1840738151924108521
-6153071806601889790
-810877831
-8061124
-5356297048398365877
-4770547841029572913
-12804866717273491655
-15580874133512784221
-514261733
-571605843
-12346762090311779845
-102957618
-10907429529076434052
+5995296139937712949
+12269921124804135698
+5885976155981547843
+4129856573689694799
+538247952
+3598203394278688718
+159777405
+22151483
+1409876968
+45089144
+10682570
+3172873343893834792
+15290243360149277503
+18412827985276633157
+28202117477106938
+2057911839619745748
+1193603335023233930
+1410790466305853323
+1873618476141774348
+16643035121679272213
+8716776878113885241
+14581169549127125939
+6375653898722412075
+8694008299851745161
+21997756618049241
+13500652
+816317351
+9870724569695611
+105251406
+70844900
+4279895118
+197001895
+24969554
+15855332315905657597
+151126621
+506659547
+5142301044413893172
+1917322269
+3137288237381257087
+14409153078548760239
+14633483086300318059
+5780604315680310424
+5572809636517250553
+15592058013925444099
+17244622751296785814
+27356063969248337
+33560399034844286
+62194086
+153944682
+4849811
+16318735
 2625321589398899121
-5354604872597767596
-4279174221
-27638024484621167
-8483828720841721486
-1459422188
+9374458755688828226
+15882855708139391266
+10225919150420460684
+15292499508566297469
+14065038233622153931
+17943317531893566468
+7031431794891230329
+16385074671182940805
+6205475624366966000
+6103488079777762245
+18411981897376661534
+9796067026192895123
+7996312456522828750
+13487493291780736605
+17245750799710423012
+2676033351739049517
+546702141
+7667902
+42074479
+282919615
+5515085278788979157
+810484612
+2625321610894247837
+1544487931
+9511403338599564690
+5192881006389626514
+5778348223150556659
+32432363517642192
+14046027923586223423
+18413674030185644130
+10771469979967884371
+2229804632046437624
+17480593072628501093
+6368045642961454306
+13237708557081051143
+13331692090551241408
+7677136701370927115
+339636063086
+538051341
+21954873
+492176077
+9870724566681132
+1398211555
+8807886331112851129
+5516892801706297876
+16546579671803956126
+13217980679449939250
+8503320935775669540
+32150372909516328
+1675756474409617568
+7721139320100816372
+2541195915421354200
+24772943
+1309279186
+105054795
+9870724569498781
+162398863
+70648289
+477207135345
+3452050537366752044
+5460499803637156023
+18199185222634702635
+5512098557251945790
+21715680028265805
+249849483538007723
+3554388240579364748
+9386257314251803173
+10594427319499622429
+15289397306641222853
+29330140097217635
+14311736903207619026
+8856014234050823647
+17855463731522440261
+15291089478142986477
+6365225461738636619
+2625321589398702921
+39059811
 23689889426704296
-17648172271756969893
-232154335723
-15925513
-10811668319096800853
-6365225478934037607
-9763237054719266042
-11633356565151157114
-63373752
-1873618493336979326
-6029477
-3580814869236944221
-5199085482290645376
-282854078
-2625321593697668091
-9870724570285675
-7449919019336600171
-1839046014815569788
-23789896
-9131616131521448314
-5779476228575003910
-5511666277521099409
-13940760354079114484
-18413109980183855178
-644678512
-71238122
-417019463453
-15131353489256221185
-447360420122266222
-520094464
-3997830
-15096032016463431129
-1873618501936549084
-61342108
-1873618523430651633
-18412263918078459945
-5344573059048999857
-5155859771100236117
-5405598659939206416
-27356033876298083
-2146416200305806198
-5303893093062347743
+2474797953316292924
+61997475
+818938814
+153748072
+4653200
+50528646
+256967343
+468608354196
+509280991
+5463642917535614049
+6209141923583494372
+16122124
+10375505326587315831
+4397798264919820154
+8054758354584013306
+1873618489038145001
+830399540494469985
+5637072609524124450
+13913370016116443160
+18412545951677546551
+2676033351738852867
+99222062
+546505531
+18940198
+5411521012994540776
+2625321610894051277
+374027977988
+282723005
+30409049
+7471291
+259785401
+821756878
+11229884474259868248
+16357059045845960667
+1873618454646032908
+10542598463376395267
+5887104182899575287
+12393440069873436875
+7730749911729375728
+15289397349631921063
+7621890105505615217
+18263821886743185772
+18058417591402760409
+1317733333
+511599051947
+214958540605
+67633599
+9870724566484489
 21758263
-3189961199463959445
-527958790
-69206479
-11862232
-6364097396127827248
-1320879066
-365262179507571896
-23689855034002659
-1473119215
-18412263948172132400
-31243224015702806
-39518566
-9870724566222277
-545719090
-5301355009924597043
-9391897706793274792
-11514789185312918199
-18411417886066737167
-5299098848607995194
-2284412389694637269
-10530167802300925091
-10427987387505837891
-14322803714593785119
-2625321585099869531
-6829167367527204602
-6013889919468112625
-4181978486829943864
-8698802578697685482
-1654120425802828663
+1873618532028844481
+8328637003859953646
+5509410163496651347
+15289397375428069113
+16436648502584675667
+6205475697451599682
+8929320279979198383
+4974759074281293673
+9870724569302153
+13107433
+815924131
+2524775482
+28484129580057898
+206359759935
+2625321597997287853
+24576334
+70451678
+16432982289349543214
+5459976661309458015
+4237766514325588729
+14322284629040564382
+9697577994188492052
+5759560470823897206
+1873618527730862181
+6153071780807051278
+15586729198848181726
+5558136817694803400
+10694515171064744788
+1988559907
+5302201063430292982
+4456589
+1713308683
+96207382
+15925513
+13650504529854072320
+5458848655886518922
+61800865
+5779476232874035736
+153551462
+38863202
+8099682237308012832
+18411417873169448972
+2787671431665157349
+651404368114879038
+17955470565775510239
+18413109984482951243
+14911447610171655366
+8858552325786961082
+13840095604897025041
+5940918086979029952
+5723537903358642458
+11238431078799509026
+7758478083289188556
+2014710471177341259
+12454108019635062383
+99025451
+3862478902367620470
+17621268784989013395
+7274680
+546308920
+27638041680086900
+5991347923197297866
+6208295874376239521
+34124418943289166
+27356081166223293
+5461104782808057591
+5357143003026951378
+113312346
+7885152524183078888
+214958343888
+309541536868
+2395604016
+9870724566287831
+491782859
+2414921941537785811
+7528330190111771360
+30458205708158447
+2707725182771857350
+23407795639356361
+5991347884504386492
+14541340640523322842
+14576212124415364447
+525512494730316455
+6795216411027442152
+1160107295621122785
+11391362031143292020
+2421359666
+162005644
+517538547
+196412070
+6152225714402428442
+447112610911
+620757861
+2625321597997091447
+12910822
+9467310877347154547
+70255067
+8926052536804313893
+1873618467542403595
+1873618441749072804
+18116142943679220675
+4787251587800828866
+18411981905974853664
+17175427809786595961
+153354852
+818545595
+5462796881224795644
+15728902
+4259978
+96010773
+1334641629
+73073152
+61604254
+7570985824906512457
+20305632407192782
+14288223544298637564
+12772641161582545873
+13237708565679243273
+15394822466617412826
+18430745393540238720
+282329787
+98828841
+809894783
+17011915733784398286
+7078069
+637862761
+18546980
+7993599274919922706
+546112310
 5569296748444387676
-1873618441748940565
-256967343
-5245848947241584851
-15862817677379702068
-14633483086300318059
-288046714075
-2203332276215481610
-7798976
-810615685
-237175467
-11340219378265033230
-313841615983
-513999587
-18413674004391067740
-2116750858326574509
-8070938101082033295
-2625321589398637514
-25099937047839912
-5245848878456439955
-12118995007347033900
-4562124381333884039
-31586327206235137
-16436648502583690678
-9181481831755875838
-5516046752497929091
+5429433323061448040
+1873618454645640429
+16425638839461284611
+32432337723721245
+2785415309041207732
+6156455960443095577
+15292499534361856724
+4727296740454696303
+15289115311734721621
+10914208898869168291
+583336804
+214958147231
+21365044
+491586249
+9870724566091296
+2246313005
+3901910077209972079
 4183106466458307862
-1991460714865167155
-17082847207615301902
-818480058
-15663365
-73007615
-3701600990787603378
-63111604
-5767329
-579208034
-1493907215601306869
-11535686880442518166
-3313969578832561394
-2704904932763174902
-6570315963541227654
-282591932
-5726226297114658480
-17160329975787685834
-8843457619279611284
-18413674034484740195
-9870724570023121
-492176077
-30740204914083091
-21433663625497129
-1629160452
-1873618450346477252
-18412827972379344962
-5243108696682924272
-7260902865540482639
-816448424
-70975974
-15287423196122254433
-1873618501936285414
-5151629580948802356
-3735682
-61079961
-18411981910273949729
-7837634943338155161
-3597357340772992368
-5133829485925763690
-51184007
-10956724774926813288
-98632231
-17309267256018536307
-9870724567992379
-29048106498198701
-3544107379218385465
-14386655907412249373
-219257507157
-21496117
-68944331
-16330874579771459902
-11600084
-11124082762859154482
-5459935770830768809
-814416800
-347984565637089693
-11923578915473263059
-575144796
-517800693
-3297856681506178941
-326737923180
-16038494049632258844
-15104099179857577674
-32996413518841137
-153944682
-2152780467316001469
-8722536002903082945
-10646954815923686447
-545456942
-14458654042895551171
-3935742187522887052
-16064731596255856452
-19464489
-17648172288953812474
-6213874949885069218
-14851060135220743194
-6471725260172231870
-4504298175131421894
-573113178
-11701191021079496730
-12314601354656483126
-13957562954616997312
-161809033
-563217229
-104464968
+2989761681675848960
+681145240220994278
+4089771542585346905
+9960543895307946415
+1801847830
 1366033375
-1133620930477295468
-6209141923583494372
-2625321610894509848
-5052785364214352114
-6155298040667702671
-5246977012853376412
-4074350485214726972
-27328854
-1873618441748677997
-2000487899013646903
-7465404271946632160
-7239351853821397993
-11742834345080916462
-6368045642961454306
-5516046795487905107
-434216307724
-3493677603186412637
-810353539
-16633695840000739887
-821147663836514852
-18413391996586557524
-7536828
-4151361015346562251
-14540810596246030644
-5995296139937712949
-159777405
-8816997369364548341
-45089144
-18412545934481162291
-9298403582666148514
-15108492788614827244
-35193182
-5568582435113995179
-5570988833963444820
-15289397375428069113
-15401217
-8430474765433179073
-10750398672578676906
-72745468
-5405598728725859379
-9250794030848869727
-62849456
-17422075106091075868
-5505181
-1873618497637255436
-578945889
-13106160036035691955
-282329787
-5570988786672405753
-9870724569761068
-7031431794891230329
-43057525
-1706034183
-491913932
-214958474959
-90505732
-18412545964574834746
-32432303330887118
-846140170598090257
-5458848587099997499
-17607182983838566334
-195297952
-539362075
-5460499872422693597
-23265605
-943759021439519007
-70713826
-816186278
-2207492642904016905
-644154222
-60817815
-806290300
-3473534
-1873618501936022824
-13307798926833551183
-1873618527730926929
-11349795265056081195
-567018319
-9388513449772451585
-165610142
-2625321576501808484
-7290339324003420579
+70058456
+2625321597996894992
+104464968
+563217229
+9391897711091583990
+12714212
+161809033
+24183115
+196215461
+4662073803102881076
+5564423899624572258
+1930668198955452820
+1873618497636337289
+2785415326238575484
+14418466534433295118
+15292499491370961900
+17305802226190387588
+1413046597527538184
+11547019543992076059
+18412545960275738681
+72876542
+6671860954713623381
+818348984
+7073102484926630551
+4844858457232050089
+4063367
+1436145094782551981
+95814162
+8697110475982376165
+15532291
+61407645
+23971751058934778
+13418544886826534789
+17616192489740896443
+8486888319115725460
+5941764217869305943
+5566476498434524382
+17083693235326683482
+12583654612056476062
 15287141244205140113
-41025899
-9870724567730368
-5569296739846327213
-98370083
-1531970550
-219257244681
-2065251783916127931
-6151097665987347595
-1407386597
-3973490993339565383
-12463417266756127924
-17631161371525515669
-21233971
-3232498753
-4767727591020628301
-8972557000702888938
-1873618458945784014
-15290525376551717170
-1559626750
-68682184
-12689613402799605860
-527434500
-517538547
-3542979343701772038
-447112610911
-163578521
-326737659857
-30458205707109873
-2625321606595479619
-498702419026
-555090760
-11846037961957312985
+10329316755526125416
+545915701
+6881458
+23689855034002659
+12734096628671909131
+11199980773228349986
+98632231
+12818875419963886521
+6848542126596623056
+5941764183477191834
+3186488476490139978
+3875793754648151904
+14319322726341872694
+17090754651615726815
+3221822110943152678
+5516046735301085409
 2286775792223980496
-2676819007
-11599686562536949325
-3968978683605551949
-5831598103022077418
-15175534989820758889
-3812049126336301758
-545194794
-12348736218027264207
-12743882002561631754
-12318365723906541324
-8882845388820581451
-12769623874203027091
-1732546160493595960
-10430737389551487761
-9512531412808567772
-21433723812579518
-812123024
-9140909979694467183
-4025048830681353606
-1873618489039455401
-18331530485106038
-5516046791188875281
-6156456003434055463
-12474564753552836994
-17621561863500597513
-104202820
-29612220986426501
-1996555300
-2625321610894247837
-17489156252859434801
-103179363763095696
-15920335005095365860
-13112992413209136128
-2034107431
-17291573824845253535
-9772926989806013640
-819987397
-17170714
-1873618467543321286
-16156684754098128751
-6925759830950740072
-7274680
-16161820259100396848
-3698377064120454404
-10296839827164892306
-13913370016116443160
-1363739614
-92275213
-210659444315
-1784112314702629632
-5461104765611674055
-507299956084
-13237708552781955078
-197067432
-4211147846
-14657391675119111356
-25035091
-1735459858
-15139069
-14426056237756189706
-12771845711499103316
-9940375093616053431
-6523880655054768550
-62587308
-10967349376607587326
-1873618497636993704
-15290807392954681807
-5243033
-1133620917580466754
-1873618523431898109
-11613165301442872555
-282067642
-9870724569498781
-2141513421469058406
-14318336791419094928
-5885976069999102359
-6153917830015027393
-214958212644
-548995910
-90243587
-16101055855214332856
-9409295256684857617
-539099930
-30458248699119542
-23003457
-252379820
-6173800107753209956
-70451678
-13107433
-815924131
-1873618476140856959
-3188833133853148985
-3211386
-60555668
-5514354727165429372
-18430745393540238720
-5566476498435442740
-8821966780582857359
-806028152
-31022281504130688
-15273884660262766886
-17153706162049649384
-15568274631689570656
-98107936
-9870724567468020
-2625321602296449309
+20587696099952690
+6886936648282539655
+6013889919468112625
+2625321606595479619
+1370042529145686776
+365428606574
+339635275824
+101450287
+17625510063045740642
+214957950334
+6213874949885069218
+812516243
+5463642874544129714
+10906583479868327250
+15744000533156660854
+5885543833259674054
+18413391983689269329
+1873618476140792146
+6177363174264606048
+827351178595273968
+10488220504998020326
+12517601
+5354604868299326678
+7734678113259293802
+3055188874828713383
+477206348880
+196018851
+517145329
+1801651221
+104268357
+505676503
+9870724568714358
+10531295842117158093
+15215179494928287321
+18411417881767641102
+11678577273375754735
+50011031689890353
+1873618441748677997
+9839328478246341893
+2704904949959166469
+18413109993081143373
+15289397310941235057
+31304323701671300
+61211034
+15335680
+164430493
+1884114794138700522
+497025749
+3866756
+4131548702199581670
+17761874897365304540
+17619012653768771484
+16542141154387102177
+4451048243670025981
+2973047420892220660
+6155298040667702671
+6684847
 5250413516934940017
-10377197347619277484
-546964288
+98435620
+2625321585099935141
+15293345523383077603
+3324580943442478547
+545719090
+23689855033805297
+6829167320236755019
+5991347923196709309
+12903069678853164419
+8098722631875955846
+17142588721119759321
+6151097721875664077
+5352348827359053328
+491193030
+812319634
+2295119206548507606
+514130660
 2429420595
-68420036
-13840095604897025041
-11075790
+2625321606595283106
+10757572347027851837
+10668197763104573447
+1873618476140594617
+18411981914573045794
+14847634415788362123
+451651625195343029
+4278715465
+746324852
+69665238
+15288551330518206781
+35258719
+23789896
+12320990
+161415815
+1491796976
+1812923415
+9870724568517151
+104071746
+5460499803636172954
+10592171188278987146
+9388513432576593267
+2704904949958969710
+16038494049632258844
+28202057290482949
+5303893093062347743
+5780604350074062990
+13674941621707869313
+15881445561639371975
+11405246090117384413
+61014424
+3670145
+1685722535145180234
+8834282535679560676
+95420943
+15139069
+3205882223899445065
+255984301
+1735459858
+1192315303887243384
+20869665212206112
+6925759830950740072
+883931539946605906
+5299098848607995194
+1445774942907271091
+4445946672738010859
+63832509
+17620986833073014515
+545522479
+98239009
+17045009756596405420
+6488236
+7536133444401891169
+820773835
+13968724050119231192
+5701610621477129021
+3068049059797011246
+20775215
+2625321606595086582
+29048166685607288
+15618641120352995308
+5831598115918776853
+812123024
+313841551493
+5880738612678821404
 1873618506234530930
-517276402
+32432303331018178
+16219982623696489082
+12258209558853782455
+16436648502583690678
+9387385384162626351
+4151361015346562251
+3542979343701772038
+18412545968873930811
+6741138607599781046
+1709507593
+12124379
+451412624470
+516752111
+161219206
+9523554809025136564
+17951898368652543383
+69468627
+814941090
+92406286
+103875135
+9870724568320021
+2523959969279970191
+5144036663261072615
+30740239306197230
+2089265852158774334
+18331517588211470
+1873618501936549084
+6364097443417820330
+1873618441748286746
+27638058876667848
+828143439367899804
 31304293607146613
-10225919150420460684
-32714392818354350
-163316374
-17480593072628501093
-3653991426073234491
-28202143271093720
-2625321606595217579
-669516658
-11075097734987253589
-544932649
-5248951136269502637
-24535874148371011
-5247593352907000017
-13750803869111880047
-821756878
-5565348488711963913
-18940198
-23407778443822783
-811860878
-3910652327921846506
-2372569380647405649
-6151097721875664077
-8481290603310483360
-15289115311734721621
-5197393238738928914
-8858552325786961082
-15270695523793439937
-103940672
+1580068669843704469
+5565348480114297669
+72286714
+164037275
+3473534
+14942458
+356832249381
+806290300
+60817815
 6206603741566403719
-151388766
-2531021385567766485
-7563081637033018620
-13044533461222491710
-6154199872212897041
-9126223058424237061
-1160107295621122785
-32714349826081871
-6152225697206437786
-4333982245204396969
-7012532
-5411521012994803182
-5249797159683425776
-570557265
-17619527108083517000
-3758799224970808644
-11069796609748044689
-210659181949
-14926165161459649868
-7570985824906512457
-3234866947851553000
-1906986264008723742
-24772943
-1873618446046923526
+12240192446106372977
+5942328186188203485
+1493907215601306869
+30740204914083091
+1873618463243635741
+1873618523431898109
+2341393787733871788
+1873618549225229533
+129168850403198609
+5728764487730398405
+10015828607276288922
+12057002331826554305
+10159392401199270768
+12142525752872799042
+2676033356038473537
+494403323033
+292346005443
+223556143025
+10911952793442192048
+820577224
+6291625
+1394017249
+98042399
+12163381674616883890
+1992588707391932346
+109394696450803010
+17760542
+545325868
+12318365723906541324
 7516607870825792868
-14876921
-72221177
-18411699906768535578
-1495925747
-62325160
-288043895627
-31304259214443724
-3685635809078676834
-4980885
-313838798363
-13951205954302051853
-464309454125
-7151957518376504179
-6153353870293665804
-365428606574
-14319322726341872694
-3493083035910933027
-214957950334
-13222096480399396057
-22741311
-538837783
-12845285
-1675756474409617568
-7676326031729298383
-1873618476140594617
-70189530
-2861086850442987769
-12590629664748537952
-15501473033754248808
-1733166096
-2949238
-5833854255738587405
-6405261049027955879
-60293520
-6364097417622914469
-50397573
-15289397310941170468
-1436145094782551981
-9870724567205432
-155189878
-7996312456522828750
-2413828615876118471
-1818166298
-97845788
-2625321602296187261
+18411699880973959188
+2396555433535145871
+4074350515307087985
+18172096623373846790
+23407778443822783
+18413391992287461459
+6284197438710222140
+13819010092172884
+15636771529559117046
+530117487457144076
+7241607903360452069
+17113993018971653874
+6155045921420412650
+17869638717220195611
+8695136395555507435
+548143940
+1992881785903908578
+15741861301866530650
+18411417890365833232
+5726358144768542273
+18413110001679335503
+16149105318148965958
+6366353527348595732
+6155045912821630127
+18067928153034001057
+7888341864134085054
+8856014216854897981
+11202456151687629452
+6152225744495577231
+69272016
+631243623
+210659378559
+3541851256594893702
+23396678
+11927769
+9870724568123690
+103678524
+92209676
+17297538988880889355
+1873618501936351339
+1519546451849645365
+5438906422044199526
+2626514825137096412
+15740334315622960494
+8912366315847026281
+5461104800004441485
+326737923180
+6388664954993575829
+14264208172476401284
+14745847
+3276923
+576717661
+806093689
+1815348248
+152371807
+2625321593698126810
+9870724570940976
+72090103
+60621205
+5569296726948055802
+4502324021620638916
+2599235317101562153
+12665415616966166285
+5197393238738928914
+3701600990787603378
+9654763076979264499
+12808641794728789765
+2676033356038276482
+18412263913779363880
+3865299044899163405
+6877319166685482198
 4451323549999957434
-3544953467117898450
+15287141188316891641
+17563932
+155189878
+292345808939
 40501610
-6364097443417820330
-1543385872365455415
-12606726616442537392
-16436379939763522008
-7562235540534921217
-546702141
-20709678
-18413109962987470918
-10939233345785957508
-1384869222252743071
+97845788
+6095014
+63439289
+2791055594105669609
+28484146776247610
 14383042897579063
-245051624454
-813630359
-5881866613803452649
-1455946274504313841
-68157888
-10813643
-4502606072414800438
-9388513432576593267
-517014256
-16739161091967945306
-6203168539198949844
-20305658202031811
-15122676476569913436
-48365955
-5941764144784016877
-12601357272775920269
-5900805793554762144
-163054228
-6155327937823509637
-95814162
-2625321606594955469
-544670501
-11092808190891527547
-6365225423046182853
-3545799490531822688
-5991347927496329957
-2676033356038473537
-6928358494714596151
-18895516000586505
-18413109993081143373
-1317798870
-3242943116712479419
-8468495303965871404
-10215782083327823122
-295544243748734701
-7536133444401891169
-13880529192106527090
-18412263930975748140
-103678524
-8816997365064994109
-5513226652957347114
-13427220419978791304
-4279895118
-2581508047683782932
-151126621
-16436648502584675667
-5245789596497153220
-18411417868870352907
-1574831104
-5512098613140196086
-16646420
-16881311723980129501
-580191075
-6750384
-460010423829
-17142588721119759321
-5411521012994540776
-13331692090551241408
-2236213724530672835
-10512763733196344280
-91750922
-493159123
-210658919829
-5353476789791099071
-2973047420892220660
-102615266471184862
-817431474
-71959029
-14614773
-29330157293667421
-18411417898964025362
-8854886129749066875
-62063012
-1631882651478526261
-1873618497636468806
-1626046306171619904
-4718737
-6971710725545264615
-15463390673086056969
-5996988225456246061
-2625321597997156982
-1258091056198584472
-2365498112266798670
-12258209558853782455
-548471621
-200191596416994196
+5199085482290645376
+1818166298
+8843457619279611284
+4076606659425995504
+10441809166173275876
+17306856587979066781
+918014392040164136
+1873618458945456185
+12237654281284815334
+1873618484738787248
+3101815889301670444
+20023641798084435
+17404805271631431757
+10554335258691243263
+7239351853821397993
+12012735189037878155
+12893049762838873864
+5565348523104797810
+4078298792234977417
+18411981923171237924
+15636380174699072146
+1440671446449589035
+112132697
+429916882098
+3822179681402096264
+1881294612915292853
+16508579235574254010
+32432389312220424
+11911353550167017696
+3493395603981665996
+15287423196122254433
+6104586261132347910
+1873618450346674286
+2848264744227112681
+681145240220010584
+6366353527348399255
+437798118096833445
+16872667624306444468
+516358893
+15515140725455259155
+527827717
+103481913
+210659181949
+1938490399
+23200068
+160825986
+69075405
+7460569593843485201
+3172873313800750756
+9870724567926898
+14383326641327005
+4452458274095237609
+1330643932
+11731158
+4025048830681353606
+4530582984922301900
+10233718743719545342
+1873618471842024407
+2487491354099451134
+15292499491369977714
+11078361536363433136
+7513578521803359945
+15662612681012938353
+2146416200305806198
+342928503145892901
+7746405201714873917
 5565348480113903112
-10159392401199270768
-538575636
+60424594
+14549236
+15897908900500866947
+33560403333875446
+3080312
+1005889956380019628
+817365937
+163644058
+507708124
+71893492
+2625321593697930351
+9870724570745030
+17750109864738752812
+17419818910382754661
+15475002888547338265
+16224960573811657069
+18412827946584768572
+7880043043777809442
+27638084672424186
+5246977012853376412
+6366353484357502971
+5407707525201267705
+359800716494834349
+447360420122266222
+2676033356038079832
+12463417266756127924
+6053028402293443390
+820184006
+97649177
+63242678
+28484176870181368
+5898403
+33278412725750974
+5293539447540681024
+4504298175131421894
+6313103561496791450
+544932649
+2597848126
+17783567759008991682
+9195012299735698785
+3491703471172619422
+8722536002903082945
+1873618514832721746
+2676958769323838072
+4000515045253449269
+9298403582666148514
+5780604337176709161
+11701191021079496730
+17943317531894613402
+5299098874403555921
 5782296448490211725
-15289115277341755866
-12583138
-4959080478982475006
-4237766475632413481
-2687090
-60031373
-11241814380293784908
-18413674017288355935
-10162787574158199843
-5625593289148533238
-605557034314828631
-2625321602295925195
-97583640
-16546579671803956126
-546439994
-13513914891881875478
-18412827955182960702
-18142877345697171235
-8716776878113885241
-5991347923197297866
-21715680028265805
-5299098848608717979
-2686971790050919863
-10551496
-2676033351739442523
-5246976935469649046
-4236074403011431549
-5561348123192067576
-516752111
-13525196865559988902
-451412624470
-6813843502384089093
-3452050537366752044
-2723374776553770162
-105448017
-14284319595218536933
-356832576945
-1987904546
+13965057806789381527
+3880024017885400135
+43123062
+811533196
+1133620917580466754
+17096567568145714575
+7462549834646555859
+4223816177647290930
+1873618450346477252
+9232147856523987724
+6517011693716180143
+6219166245997316001
+29330122900898936
+17782439637510195701
+16892239439269464715
+8072726556923202784
+15862817677379702068
 2789363555876800106
-17063697102470777209
-6584302816815089825
-5727354422913010657
-13944415416121166662
-28311895
-11906248855590275274
-3707523343842937215
-18412827985276633157
-821232589
-18415907
-2676033356038210923
-17257283880273643533
-18331556279224644
-9117971362513815455
-18411981923171237924
-309541536868
-113312346
-46072191
-103416376
-27920126869375123
-160760449
-361131345578
-9234597529149245860
-14835085562484362568
-4585257123188181630
-1413046597527538184
-6208295874376239521
-13217980679449939250
-1966081057
-6101795981361546864
-16384272
-10370417990725208293
-4196703391028741586
-6488236
-63832509
-5153885660580611393
-6155045912821630127
-5197393273132877515
-2625321593698126810
-10720606758114626648
-9870724570745030
-30740204914804024
-91488775
-7792373120121047026
-3579577413
-5458848587100981064
-755605599842665887
-17404805271631431757
+11534547
+279448847671
+210658985303
+252379820
+539099930
+814351263
+160629376
+9870724567730368
+103285302
+68878794
+14268291611706526293
+23003457
+16876615841046071902
+16269555352897260337
+1873618446048496233
+10161648493728303880
+10430737389551487761
+13735950183222348532
+1991460714865167155
+9870724570547380
 417019921504
-9386257335747873389
+572110149897422243
+2883701
 817169327
-18413391979390173264
+5240852582657493474
+518980348
+60227983
+4618470194090937269
+1459422188
+232154335723
+3773122177597967623
 71696881
-8328637003859953646
-14665059300281706
-6101796011455220816
-4456589
-13070886371126478108
-8733200714257204941
-10913926882465549337
-29330183088310857
-61800865
+2625321593697733840
+331038328206
+163447447
+18411699889572151318
+13044533461223476582
+18413392000885653589
+16436379939763522008
+12314601354656483126
+5539270545646881104
+11507341268100646834
+102615266471184862
+31304259214443724
+7731878007432940921
+7401639761433855789
+9450798976826149302
+5701792
+5245848925746498573
+15104099179857577674
+108921430
+17170714
+5411573444917201071
+544736038
+468609401971
+3758799224970808644
+63046067
+819987397
+18411417898964025362
+14322803714593785119
+3062219857732765097
+2207210695286917386
+10532987970627045461
+1873618458945062288
+684134257893509654
+14101293042239958
+5015996636573993090
+6098975770044925746
+850088361543927300
+157614716
+111739480
+219257572663
+19988781
+30740222110861163
+8618954206935976415
+6206321690772243584
+1873618450346281005
+5405598659939206416
+15872788267758849077
+5572365145471912335
+4625631396377398109
+17619527108083517000
+493028049
+9870724567533791
+1559626750
+19177592590632702
+91619849
+2625321602296318353
+515965674
+103088691
+68682184
+527434500
+1990332636357069057
+899112529018292807
+6570315963541227654
+16038494049631274933
+15920335005095365860
+28202143271093720
 14949273699027977966
+18412263922377556010
+5782296461386581535
+13001682102565734253
+12972952285742288
+2703776923039566000
+2687090
+11775490430040476325
+14156017
+2686971790050919863
+17489156252859434801
+105906772
+60031373
+11241814380293784908
+71500270
+5734260728554980678
+5197393273133271298
+17648172288953812474
+1873618493336979326
+683965395648908415
+18331539082773742
+32150304123324262
+17422075106091075868
 1873618523431110190
-3573803894998305775
-5569296709751605280
-5835546375651263675
-9870724568714358
-42008942
-1746899701160150410
-9664889374910385451
-7406761759861377295
-2625321597996894992
-365428082633
+10628953736434486617
+14512708438227029368
+18411981931769430054
+16886908734816455284
+62849456
+9131616131521448314
+5505181
+11364576519499679975
+33560368941368890
+544539427
+39911783
+33560399035500221
+533070599
+578945889
+8097653480026868144
+154600049
+16974104
+17957750408840481687
+285938493736356261
+5991347927496329957
+10377197347619277484
+15131353489256221185
+15287987228927396675
+14282027259104724924
+8070528080642443989
+17128487303121020
+219257375260
+42729843
+811139977
+6365225478934037607
+460010423829
+490013379
+13850612818404510827
+2207492698791414354
+7515799761807542411
+1873618480440216958
 11888218815508973537
-6311975551774360856
-1408369638
-6101795942670075923
-15515140772745448064
-27638058877519937
-13361048879788721990
-2430665780
-22217020
-538313489
-927164962728314711
-69665238
-27638084672424186
-2573543627316201844
-12320990
-2424942
-18413392009483845719
-3660444556051220001
-18412545947378450486
-154665586
-9870724566681132
-546177847
-2229804632046437624
-5245848917148372136
-15906307047154976446
-827351178595273968
-5780604350074062990
-6350640494756627870
-9198943117821938833
-2676033351739180486
-1192315303887243384
-67633599
-6205475723246636047
-17419818910382754661
-162529937
-17083693235326683482
-105185869
-8912366315847026281
-5249797202674912471
-2446394423
-1461650414
-257426098
-17299513133793348673
-4451048243670025981
-14597841535548131734
-14130457194541352666
-15290525359355331959
-9195012299735698785
-524354306
-429916226796
-6153353788611431303
-1728578573
-6153071806602085789
-2676033356037948725
-8257735
-2785415326238575484
-1873618489038408278
-8072726556923202784
-7731878007432940921
-16271603835638319461
-11229884474259868248
-5835546388547569431
-2704904949958969710
-103154228
-2625321589399096275
-6887529782530082437
-45810044
-16365628939578247566
-4408861808311732424
-3554388240579364748
-3431353251379022211
-4131548706499659810
-3229097897723824621
-818938814
-16122124
-10831084194895235709
-6226088
-6366071472254485645
-10441809166173275876
-9538952396691934382
-5994450030541998229
-6835382734606174906
-4397798273518472097
-2625321593697864817
-9870724570481756
-17782439637510195701
-31304332299601191
-4074350515307087985
-10758418391935682553
-11405246090117384413
-196018851
-17943317531894613402
-15289397375426759758
-1801651221
-12716605781588708278
-5353476789790574588
-1873618450346936800
-14462121002204464918
-2785415309041207732
-71434733
-10770155859627543824
-1873618476141841211
-5780604362970367638
-2530739313276357975
-14090480
-5567604589840172352
-296644709200
-11266915032714840583
-4194441
-2200512120787569683
-2549492329236335496
-6211116016906930204
-99090988
-9625506809262378259
-13237708535585570818
-490103571663
-14541340640523322842
-9870724568450966
-1793158821936040552
-9486667438472824267
-21954873
-538051341
-1398211555
-5408700909154273182
-5356297014005859746
-8444237263823374707
-69403090
-2599235317101562153
-15897859265386515143
-6097847713031849822
-2162794
-9796067026192895123
-13117159209037203716
-164299420
-17088031212435737557
-8099682237308012832
-8971880411373045432
-3099205763721988894
-9870724566418979
-545915701
-13237708565679243273
-4449074137450482853
-18115860927276518423
-5247593352907982888
+10754752208794748192
+4770547841029572913
+2236213724530672835
+12085352355710699032
+2625321602296121720
+11141327
+68485573
+22610237
+279448454880
+573113178
+9870724567336570
+17097308613030775025
+45547899
+102892081
+538706709
+813958043
+2150364429944620611
+15290243360149735302
+1004761907965660269
+13427220419978791304
+18412827955182960702
+2116750858326574509
+11847668763332905754
+15530553734630409457
+6366353492955694732
+5875369269012203157
+7528870385169533979
+71303659
+816776108
+417019528294
+48365955
+2490479
+518587129
+59834762
+163054228
+9870724570154139
+5300226909920168653
+1493907215600323645
+1873618523430913566
+6293435910568086045
+4661227814082512385
+9231865831523354279
+4562124381333884039
+5994450030542718433
+8468495303965871404
+6151097734773868363
+5308570
+51184007
+16777494
+62652845
+7410231625909407077
+8336200085500660803
+10377426660276898779
+15108492788614827244
+5405598728725859379
+5349367342193968413
+1873618489038801706
+12851509922494416016
+3812049113439014303
+14151987057237756511
+10744889200825601240
+9304480456320748248
+19595563
+219257178788
+15798241638577276499
+3017170418691476659
+8857706336766199103
+13957562954616997312
+8126661
+30740222110467489
+4662355883991631380
+18413674000091971675
+6203168539198949844
+12480382642832672298
+14814059355306855043
+10229733804179524009
+6887529782530082437
+10905173350565414454
 16533468055605152863
+3758799212073651272
+240752528220
+68288962
+1318388695
+6350640494756627870
+5462796894122411284
+813761433
+10944716
+2625321602295925195
+538510099
+18411699898170343448
+13880529192106527090
+18413392009483845719
+25099937047839912
+10440328872292254866
+6835382734606174906
+3686437022462641529
+816579498
+541328159
+2293868
+71107048
+105513554
+151388766
+9870724569957729
+5197393273132877515
+5882159627828332650
+7851156332894489575
+11510172448171493799
+9250794030848869727
+8327325764367420682
+16778219695595128445
+8733200714257204941
+16580883
+13513632858283052077
+1461650414
+2625321589399162008
+819397570
+8423108281783224429
+5111959
+1473119215
+257426098
+62456234
+39518566
+12622921449567619867
+10531295803425490030
+5566476498435574920
 1873618458944474091
-19923244
-3188833116656765520
-2676033351738918494
-4501477955215362649
-17621268784989013395
-14581169549127125939
-6206321707968234614
-33278352538406314
-516227820
+1873618489038604579
+18613366323088485
+3008657884776564221
+11621189233770302317
+5464488953846367762
+475082778886408323
+6155327937823509637
+9956242218935388443
+11065487246536017596
+2676033351739311464
+14361095630442006439
+810746758
+283181761
+2625321610894509848
+546964288
+6365225423046182853
+7930050
+6262673798362565305
+1840738151924108521
+8483828720842049762
+9013091190501541709
+8294669686619703163
+9288263741171697848
+15520597512816297356
+2792183694107936667
+9227353569080969220
+2429880031182916564
+5833854255738521265
+18412263930975748140
+2430665780
+22217020
+18301216972082383618
+11964228788262537512
+159842942
+28766150282773591
+538313489
+813564822
+7032232753418799293
+12348736218027264207
+15290243360149343057
+6406389097441592980
+2964529530791004471
+18613559784442970
+1873618476141841211
+5991347884505041337
+6101796011455220816
+6366071455058494624
+6155045908522469290
+8412057600244518110
+3478039985316562895
+12718336251608304605
+70910437
+4211147846
+197067432
+14443179094226111164
+2192639020
+9870724569761068
+105316943
+25035091
+162661010
+518193910
+5303047078245827995
+1903640920853056624
+18092519415945890646
+4127600455366674792
+6474545510181176536
+7731877951544692100
+11084138473134491150
+2625321589398965240
+1495860210
+154010219
+16384272
+15043322265989680207
+6204347601746593065
+4915348
+62259623
+468608617008
+1966081057
+1192315299587689576
+17256155806064642777
+1873618489038408278
+12662636664722033563
+1654120425802828663
+25099894056749168
+5299098874402571932
+2676033351739114988
+489423554
+30671195
+5411521012994803182
+42140016
+7733439
+2625321610894313322
+7329667560521271617
+6206321690771457172
+5967447917778832244
+2284412389694637269
+2572415553107265488
+18412827963781152832
+16904712944498838074
+15289397349632182266
+29330122899915877
+27356081166681957
+6173800107753209956
+538116878
+10551496
+3919394969679695174
+9870724578216632
+492241614
+8816997369364548341
+4662355849599126556
+16567374854657149772
+12884708026702235763
+6364097417622914469
+1873618532029106835
+8861613698626554738
 6890349946557761313
-1411918553413126104
-162267790
-2474797953316292924
-1694703987789596868
-18172096623373846790
-28766090095429261
-1223976979390989739
-3221822110943152678
-104923721
-15185362616787929146
-10003084053115964048
-2625321585100065781
-437798118096833445
-1815348248
-31304323701802109
-152371807
-14046027923586223423
-2021331689141374237
-20869691006257762
-13044533461223476582
-16778219695595128445
-12057002331826554305
-17465760298758178660
-7576852735584046364
-129168850403198609
-820708298
-17891616
-1873618489038145001
-7995587
-11911353550167017696
-4522983015860209939
-12612941966326959190
-102892081
-2625321589398833886
-45547899
-11548493110908749415
-4076606693818764590
-7851156332894489575
-12779163922391107832
-5991347884505304103
-1095239150174145285
-3863606920688567965
-10771469979967884371
-15859976
-14312864964518020808
-17245750799710423012
-5963940
-10655291933708585535
-4162099616697747321
-63308215
-1873618519131818153
-30176189305784773
+5837238474067478018
+5780604294184830225
+11214563466576463780
+29612216687004362
+5516046782590617836
+10156853965084755435
+6151097683183797493
+11613165301442872555
+1986666427421953426
+6155045882728942511
+7033448275620070919
+2907303882175415846
+1320813529
+1584595969
+105120332
+7465404271946632160
+70713826
+24838480
+162464400
+12451287838412704489
+816186278
+644154222
+3735453693364143828
+9870724569564298
+1309344723
+21715680028329254
+13044533461222491710
+1873618497636993704
+3445982126355516078
+7529998377695250462
+12237654319976351671
+4534407021717882867
+3431353251379022211
+494159375523777824
+1136798196293306910
+16426766960960540026
+819004351
+12356593998396393868
+16187661
+3307734082
+14273081993166850387
+4718737
+434977997061097911
+62063012
+2625321589398768544
+39125348
+30458248699315998
+17858552114457937219
+5903884228619468800
+16872385650894636566
+10504814416598927327
+12213481117926952067
+18413674008690163805
+14101026494875963
+4709060078846741586
+2676033351738918494
+9714916620294556051
+13237708535585570818
+810353539
+2625321610894116800
 53412232
+434216307724
+7536828
+41943405
+6770804071406897080
+821822415
 318140582948
-15611911946388048179
+6365225453139920066
+4502324038816629924
+4030203865610717075
+18411699906768535578
+15290807392954681807
+11966722661119888545
+8618954206934993224
+12189960762281954023
+32432333423379563
+18413392018082037849
+6004915412369541960
+14546784569801180959
+745740842898098858
+15289397293744523027
+5299098870104394759
+9257009393629660721
+5900805793554762144
+6155045917120857525
+21823800
+1317798870
+537920267
+1730675726
+1535706104
+9870724566550039
+14648423506775771515
+10531295876509927029
+3973490993339565383
+14312864964518020808
+14824583848163281869
+16940553195690134509
+1873618476141446514
+5778348218852443426
+5758903550139959418
+27356016680241600
+13940760354079114484
+5645056620059298667
+347984565637089693
+815989668
+9870724569368358
+5887799994573980828
+162267790
+517800693
+70517215
+15925946803693423456
+2625321597997353452
+16572875051796793000
+575144796
+104923721
+13172970
+14426056237756189706
+5909364179964069981
+5459976691403654584
+4397798273518472097
+27920040887059601
+1873618527730926929
+1873618467542665344
+18613585580197092
+32714392818354350
+18613499598604650
+5780604289886653255
+3865299049198390675
+22279760122415532
+18412545930182066226
+50397573
+153616999
+2625321589398571980
+1736311827
+15991050
+14665059300281706
+4522126
+7792373120121047026
+30458248699119542
+13951205954302381098
+17785259844527786731
+6444189713816225654
+747829823970020707
+8698802578697685482
+14477731067643038195
+18412263939573940270
+14318336791419094928
+15291371425760087333
+12109395139072755174
+30277976
+99090988
+282591932
+546374457
+490103571663
+15580874172203795679
+810156929
+7340217
+638124907
+259654328
+18809125
+18056758355458722638
+5679882735784101879
+7563081637033018620
+8520914754064026558
+283748271730
+67502526
+9870724566353399
+7242736046355514492
+572130134
+514786024
+214958409445
+29048192479791616
+2625321576501808484
+5354604872597767596
+29048106498198701
+2575517759332551933
+6311975551774360856
+14036340911856223966
+32150286927595340
+17291573824845253535
+14926165161459649868
 12640696470018459947
-30176223702288623
-9870724570219682
-33278412725750974
-1409876968
-28766150282773591
-1873618450346674286
+17498716255300421272
+3968978683605551949
+16377260960560187819
+19177532404009207
+2625321597997156982
+24445261
+5245848878456439955
+421319345246
+5510538272098551989
+70320604
+3249068390006196153
+5888081980883929307
+1836516380
+12976359
+236453760381
+2141513421469058406
+1873618497636600365
+11878630053446878902
+6156456003434055463
+27638058877519937
+18413109962987470918
+6288511205539515238
+4770547828131824981
+4160689491693538063
+14836382508930370955
+12751507524739009261
+10427987387505837891
+2605266760616185153
+2524806001290315567
+33560429128451329
+4325515
+669516658
+15794439
+807142269
+5303047104041388600
+818611132
+61669791
+12644080653952551280
+6045857707735386835
+11229983338076703492
+2845029447323880298
+18412827972379344962
+6767393152337644543
+2673382969485886910
+15185362616787929146
+17490170188584258190
+4047541379259827663
+15680489859993767209
+546177847
+7143606
+637928298
+7276444624641068235
+12287601267178473523
+31022238513759415
+17698252132056434004
+1732546160493595960
+7036226112429884975
+2676033644081056812
+548995910
+90243587
+571933524
+812778389
+9870724566156739
+214958212644
+1873618446046923526
+3493083035910933027
+15291935501556190620
+14650572868605052119
+6971710725545264615
+17302333254828493968
+6098975847429179176
+4504298213822565083
+505938649
+3579577413
+2786543383251193103
+70123993
+47186305
+2352415791
+4279174221
+2625321597996960522
+1538130937
+161874570
+17082847207615236134
+6206321707968234614
+8854886129749066875
+10908568553618343357
+2785415326238639918
+1873618527730534170
+1873618441748940565
+5745384143842643344
+18413674017288355935
+16044698410491643447
+9181531069949872018
+10905173367761798655
+13237708544183762948
+3757107087862401328
+1311572948
+2034107431
+15597828
+2538734651
+5727354392818878727
+4128904
+818414521
+95879699
+5727354422913010657
+5245848874158263187
+9664889374910385451
+18411699915366727708
+14851060135220743194
+17958290734101235336
+9319686106503382840
+89657146100418951
+11349795265056081195
+14540810596246030644
+5779476284463187670
+18415907
+156041850
+259261111
+821232589
+809763710
+98697768
+6946995
+5941764153383128192
+17684252729367202593
+10233694917695638297
+970700105235760464
+21715753112570631
+17953636526298302297
+6262673798361580735
+5847102830955857465
+3313969578832561394
+2974323816123992770
+13271165719268362246
+17083693200934636558
+6101795934071424788
+16990917635978692369
+812581780
+16327183209838150989
+21233971
+1535116279
+214958016090
+2625321606595545096
+3232498753
+1500709877
+514392806
+5831598146013367591
+4502324004423927097
+3099205763721988894
 15290243360148359553
-14036340911856223966
-6365225461738636619
-816645035
-417019398489
-6206321673575531611
-12057284352529139627
-71172585
-13828334
-7528870385169533979
-5832726134240118664
-2785415334835848520
-2572415553107265488
+1873618476140856959
+3295137431799204142
+14130457194541352666
+8910392170935354895
+3967850626592737364
+18412545938780258356
+12583138
+505742040
+4278977611
+540148509
+24052042
+196084388
+563086155
+104333894
+2625321597996763849
+16324853745603185849
+13586095437453200186
+15804734059994287439
+18005251247539029895
+13516735047310051359
+3493677603186412637
+10159956468397444373
+5249797099496672683
+17763448248357489818
+18412263948172132400
 61276571
+7630443591734791098
 3932293
-9870724568188981
+72745468
+95683088
+15401217
+4076606693818764590
+15986098390340470919
+1873618519131556994
+9386257309953099582
+8501910827968825512
+168849237244054062
+6750384
+545784627
+2625321585100000596
+1652810939277510396
+580191075
+98501157
+5198803303557629187
+3297856681506178941
+3935742187522887052
+2601013084734032090
+11500631658516907905
+8021450341214588326
+14977809576148535095
+4127600472563058730
+16965951797418331227
+27356081165698156
+491258567
+12804866717273491655
+1408762855
+2573543666009114673
+2200512120787569683
+2625321606595348609
+21037361
+14462121002204464918
+5619444426388998007
+3973491023432910866
+12103109825679658143
+7260902865540482639
+5566476571519223063
+18413109971585663048
+17791918762976347730
+16365628939578247566
+4449074137450482853
+11214563466575480865
+7239069803025663720
+17952462371364276975
+9512531412808567772
+11075097734987253589
+2373415502940997016
+16874702537456224943
+517014256
+2573543627316201844
+4278781002
+69730775
+9870724568582655
+12386527
+12743882002561631754
+10906583475570214623
+104137283
+35324256
+10167863869407233224
+18412827980977537092
+363084051790629688
+11694336983993944146
+1873618441748546884
+32432320525830439
+12654580528992553525
+7241043922144659849
+9391897706793274792
+152830562
+1402930148
+164299420
+5303047073946667464
+3735682
+61079961
+15204606
 1873618549225491555
-2360543918673038210
-98828841
-12512221777814685432
-17939922315943150958
-6045857707735386835
-21692726
-4502324038816629924
-11490081257974859839
-17639632887023929831
-1316357237551401394
-6101795994259359091
-11796695
-69140942
-18411699889572151318
-12074216556992400767
-1320813529
-8618954206934993224
-164037275
-4160546838840674266
-12591757708863407913
-555549513
-9870724566156739
-154141293
-32714414313178248
-545653553
-223556471268
-12613788024133322735
-812581780
-5778348150066318224
-1500709877
-6741138607599781046
-9227353569080969220
-515965674
-13884327378110449525
-18411699919665823773
-16340493341965880015
-162005644
-620757861
-21997756618049241
-17007720368052373541
-13001845694847518363
-227855238971
-17629469
-1737950228
-9288263741171697848
-20305615210743190
-1873618489037883086
-18613533990193666
-7733439
-313841551493
-15288551330518206781
-17302333254828493968
-6153071832396467338
+3188833116656765520
+31586327206235137
+820839372
+464309454125
+18022689
+545588016
+17205553309096938840
+313838798363
+223556406340
+98304546
+15463390673086056969
+4240022615453076686
+10831084194895235709
+11549275701007551889
+155648632
+6553773
+534119176
+4222974949961697922
+8326286517935867839
+1873618454645114642
+1146796961722731823
+5509410202188647833
+1873618514833377412
+3242943116712479419
+29330157293667421
+8882845388820581451
+12608147700378373379
+14465116522071263669
+5461104757014004985
+9649086479758069023
+2625321606595152102
+513999587
+20840752
+2148672322930150296
+10646954815923686447
+10831360821402142464
+313841615983
+10139438201185111279
+16881311723980129501
+18413674025886548065
+2785415274648570354
+5353476789791099071
 2979056014524680527
-8857706336766199103
-2625321589398571980
-45285754
-5991347884505041337
-4502324004423927097
-16874702537456224943
-14911447610171655366
+6366071515245381876
+8610102806501591788
+10333839787251271664
+13237708552781955078
+451412690018
+16101055855214332856
+9870724568385196
+12189916
+23658823
+195691169
+5155859771100236117
+69534164
+35127645
+103940672
+11069796609748044689
 13944990587222231178
-3308118261903721908
-18413109975884759113
-8412057600244518110
-15597828
-2538734651
-818414521
-17082847207615236134
-18276979644936029994
-5701792
-63046067
-5882159696614657105
-1410790466305853323
-18412263913779363880
-32714379920475611
-539325825270679628
-1873618519131556994
-13536993689470216
-9870724569957729
-43254135
-5153885686374731086
-9387385384162626351
-8336200085500660803
-5303047104041388600
-5512098595943810546
-5717788221838658971
-2324121364801391676
-12012735189037878155
-2192639020
-1873618476141316771
-70910437
-3670145
-2219404100148201532
-2544580112253650683
-61014424
-6155045921420412650
-18412263943873036335
-1873618549225229533
-9870724567926898
-98566694
+27920101074341046
+17298949057997047589
+2908260051937332390
+6364097413323754682
+12350988444867431112
+1223976979390989739
+5782296431293302176
+11098517635487303139
+13525196865559988902
+2374936041605498895
+15007995
+1574765567
+519635711
+5831598103022077418
+576979807
+817824692
+634323816
+3539071
+2446394423
+6206321673575531611
+2360543918673038210
+27638024484621167
+11340219378265033230
+6366071472254485645
+4562124351240801677
 29894215892535509
-155910777
-6366353527348399255
-9956242218935388443
-31586340104504804
-219257441372
-13522668389390157414
-18411417881767641102
-11534547
-279448847671
-7242736046355514492
-68878794
-814351263
-1192315299587689576
-2524775482
-34124461934314600
-507839197
-5539270545646881104
-4974759074281293673
-5337229686545450161
-153879145
-12644080653952551280
-30458205707308380
+6153353844499089111
+13070886371126478108
+9181481831755875838
+18067928196024961188
+6981729909914862956
+63701435
+6357162
+15288269305517836796
+17299513133793348673
 545391405
-17877509356004052233
+17826079
+820642761
+98107936
+8854886172739175692
+9082058141968173941
+1873618484739049815
+11514789185312918199
+5778348197355914873
+11130039777759856047
+294416195335096411
+846140170598090257
+2571498445011814318
+18412545947378450486
+1408369638
+2625321606594955469
+5245848947242502849
+365428082633
+5245848917148372136
+10859426818132543221
+15524263781940136850
+2578187325
+17564225130023161250
+811991951
+1694703987789596868
+1873618450346936800
+12105446909435186010
+14975681650483333306
+32432303330887118
+29612220986426501
+11644189250161151139
 17520266449292560845
-11065487246536017596
-2011949215506761725
-6155045882728942511
-812319634
-1130753852548581517
-573047641
-5299098874402571932
-18413674000091971675
-18331556280207363
-17269866578628118199
-15289397293744523027
-161743496
-10649664295314066054
-6051485356288903427
-4347925833116091776
-30458188511970924
-104399431
-10184384893691038634
-7401639761433855789
-1308623824
-563151692
-2625321610894444316
-7239069803025663720
-11434534198373320614
-1873618441748613384
-5622264654903379074
-29330122899915877
-15636380174699072146
-820184006
-2597848126
-10233694917695638297
-14585410861575638263
-7471291
-85348920764927349
-6366353492955694732
-18413674030185644130
-4127600472562141528
-35127645
-5780604337176709161
-541328159
-2524806001290315567
-13850612818404510827
-18412827968080248897
-15335680
-3493395603981665996
-17858552114457937219
-62783919
-3875793754648151904
-5564423899624572258
-292345154665
-3489447322753895731
-18411981905974853664
-5439644
-42991988
-9870724569695611
-12269921124804135698
-559088458
-33278386930321618
-15289397353931868100
-214958409445
-6219166245997316001
-15289397379726773461
-30458248699315998
-23200068
-12163381674616883890
-70648289
-9000175594581527004
-806224763
-89657146100418951
-15475002888547338265
-3407997
-60752278
-18411981936068526119
-14267039342724252928
+92275213
+335336768790
+69337553
+7290339324003420579
+17621268802185464283
+161088132
+9870724568188981
+516621038
+11993306
+507299956084
+210659444315
+103744061
+13151687854617134836
+8659114857360722535
+825323275339564903
+103179363763488430
+684134210602468610
+1873618501936418049
+6205475723246636047
+5516046752497929091
+15885957841278600403
+2477484405147109478
+16875205763331852041
+72155640
+472907842721
+14471968401314024391
+806159226
+1712194570
+576783198
+1815413785
+2446197814
+14811384
+507970270
+8929038315166239946
+3342460
+3220426554520570467
+2625321593698192308
+5677488692584514734
+21433663625497129
+2435475427475262665
+16940455997476965252
+6153071806602085789
+5865888353649363875
+17465760298758178660
+13263754581809432790
+8716776809328151764
+13112992413209136128
+6153353788611431303
+3784724792312663401
+12590629664748537952
+2676033356038342054
+14219872676477209184
+11327137566841769230
+63504826
+97911325
+9339868219275806468
 13726068525522684375
-1873618527730862181
-4504298213822565083
-155648632
-98304546
-9870724567665640
-13681696359428851594
-219257178788
-24535844054893958
-50011031689890353
-10532987940533372886
-11272401
-23407795639356361
-68616647
-814089116
-15635925519041823968
-1998521381
-163512984
-797977540607610221
-32150286927595340
-4709060078846741586
-5967447917778832244
-5885976078596834724
-2625321606595414132
-153616999
-1744643526947965735
-17461812017531651650
-987047180239768912
-30740239306197230
-15288833278135765839
-525337347
-5885976155981547843
-18413391992287461459
-10532987970627045461
-56689033
-5722409915131627177
-114033243
-10159956468397444373
-18412545930182066226
-5349367342193968413
-13819010092172884
-104137283
-17953636526298302297
-2224234517276395067
-2789363555875490728
-2625321610894182276
-12426051065400527122
-9355193091131312182
-30740222110861163
-14361095630442006439
-3137288237381257087
-17105177
-819921860
-7209143
-1727529996
-810025856
-805679481429165719
-17298949057997047589
-21997713627284659
-16120716880803858984
-33560368941433940
-1535706104
-10229733804179524009
-18412545960275738681
-9714916620294556051
-4078298775038527628
-5461104765611607541
-210659378559
-92209676
-13418544886826534789
-14264208172476401284
-1917322269
-197001895
-24969554
-5405598728725530322
-15073532
-817890229
-72417787
-1873618471842024407
-17091318705916150977
-5946696443085589628
-5177496
-5847102830955857465
-62521771
-1873618523431831649
+2011949215506761725
+1737950228
+6160551
+9830100417878166271
+155255415
+17629469
+8140646021471143544
+545194794
+8510103668314541335
+18411417868870352907
 5835546371351184527
-14824583848163281869
-42729843
-9870724569433729
-5780604315680310424
-16385074671182940805
-214958147231
-3007753865419557454
-491586249
-17943317531893566468
-1801912319444323213
-22937920
-539034393
-27356055371580547
-1873618476140792146
-5198803303557629187
-6103488088376871190
-13041896
-1733362705
-70386141
-2306802734
-643826540
+18413109980183855178
+5249797172580910311
+10532987940533372886
+32714379920409891
+1873618514832984063
+13702827714901707594
+29330157293274228
+220421203678071202
+5565348467217401524
+313841222762
+570950482
+13012951802393594980
+6209141854797957852
+5717788221838658971
+5460499872422693597
+8444237263823374707
+2544580112253650683
+32432303330691092
+14986955239351847842
+4392112055939237960
+16285378285009240167
+6205475671656957491
+11266915032714840583
+15289397375426759758
+17284241873202253123
+1783548230307677653
+195297952
+69140942
+23265605
+11796695
+210659247559
+17257283845880874759
+451412296787
+92078603
+160891523
+539362075
+103547450
+9870724567992379
+11331649863678691999
+12613788024133322735
+13944415416121166662
+15895039144349470066
+8816997365064994109
+1732546121802517809
+13221120945827219803
+3863606942184311140
+12562453432512743836
+7562235583526800081
+9870724570810095
+71959029
+232154598652
+14614773
 3145849
-14637903957824965363
 519242494
-60490131
+2625321593697995819
+1133620930477295468
+817431474
 805962615
-5784522635265967958
-1873618527730601376
-18301216972082383618
-11644189250161151139
-2625321602296383846
-9870724567402585
-98042399
-15741861301866530650
-494403323033
-6729754102968812754
-546898751
-6208295835683456476
-33560403333875446
-14409153078548760239
-15530271666638163275
-1873618458945456185
+4131548706499659810
+60490131
+503001777494
+6206321673575138470
+1258091056198584472
+3573803894998305775
+10967349376607587326
+1873618523431569790
+6153071806601889790
+12749251354825264418
+9625506809262378259
+2676033356038145381
+15635925519041823968
+5885976078596834724
+9484411285755463284
+532291916112267238
+18411981901675757599
+1703347206
+33560368941827284
+5303047039553965447
+40370537
+97714714
+155058804
+6261263733545503259
+5963940
+63308215
+1130753852548581517
+5570988833963444820
+18157949162008873831
+8021450371307931626
+2861086850442987769
+1873618489039455401
+18413674034484740195
+1873618458945324208
+32714349826081871
+18424247431471827427
+1842511416005692464
+6589396841525218018
+5782296448490276391
+13237708561380147208
+27356055371580547
+5462796868326918190
+1860700038481053299
+5458848587100981064
+3580814869236944221
+5566476545725106758
+28202091681875145
+5915592292141435844
+11434534198373320614
+15740733274947783803
+10161648502327149991
+15287141235608259625
+12779163922391107832
+68944331
+814416800
+1823671323
+23068994
+210659050964
+46006654
+516227820
+11600084
+103350839
+361129707266
+13750803869111880047
+103179363763095696
+1873618501936022824
+2933734509745341832
+7230168968130792223
+14517406836956661503
+17619012718254098754
+12406930521299355297
+4408861808311732424
+2949238
+9870724570613070
+60293520
+503001580666
+14947075702982868
+1998521381
+2625321593697799226
+14418163
+163512984
+71762418
+5722409915131627177
+11599686562536949325
+1873618493337242815
 16951650337051970851
-5144036663261072615
-813826970
-12133908888583014197
-68354499
-11010253
-279448324634
-14749580058850363919
-6633286351216577743
-2089265852158774334
-8929038315166239946
-31586271318836879
-13678484518713821516
-105906772
-96010773
-2625321606595152102
-153354852
-10831360821402142464
-5652457623480305518
-8503320935775669540
-16483453074211931840
-363084051790629688
-544867112
-258146996
-5944020284604679310
-5782296431293302176
-28484176870181368
-23407778443758207
-3973491023432910866
-5778348175860436286
-1873618514834032208
-5438906422044199526
-103875135
-7697026996393675938
-1709507593
-161219206
-13237708548482859013
-3701601059573925529
-879419277503368073
-3822179681402096264
+2676033356037948725
+18412545955976642616
 5565348445721659362
-532291916112267238
-256115374
-1460339693
-13351948495571782591
-14665351642484132
-3008657884776564221
-2341393787733871788
-16904712944497920326
-3967850626592737364
-16843031
-4131548702199581670
-6946995
-809763710
-1928986057181235415
-11964228788262537512
-2989761681675848960
-1873618519132801026
-7276444624641068235
-5994450030542718433
-12284124821458521275
-111739480
-4076606646528706921
-13650504529854072320
-15804734059994287439
-14425661019905001872
-2395604016
-14465116522071263669
-210659116497
-15290243360149343057
-15777957523720635747
-10167863869407233224
-18331517588211470
-12884708026702235763
-14811384
-72155640
-7042731044489660311
-15288269305517836796
-5675796551176948530
-14264208198271043974
-1495860210
-5787083718919720300
-25099894056749168
-683965395648908415
-62259623
-4915348
-12974919760129952993
-6155045917120857525
-1873618523431569790
-9013091190501541709
-4392112055939237960
-2625321597997353452
-15897908900500866947
-6177363174264606048
-15872788267758849077
-491324104
-33560399034844286
-22675774
-17542946455516547053
-2431124533
-538772246
-27920040887322186
-8704274751914773568
-12085352355710699032
-6153353775713551670
-70123993
-27356081166223293
-7885152524183078888
-60227983
-2883701
-11700344903086704893
-7329667560521271617
-518980348
-5833854255738521265
-8618954206935976415
-3901910077209972079
-1713308683
-1992881785903908578
-4530582984922301900
-16130159995999161574
-155124341
-2625321602296121720
-1884114794138700522
-5778348218852443426
-97780251
-4240022615453076686
-6097847786116483627
-6361518319333476776
-30540122
-28484146776247610
-546636604
-5741055947585816645
-6100103891543657570
-8807886331112851129
-813564822
-10223260478367337870
-746324852
-15287423226215073909
-11226550812567014265
-1491796976
-8097653480026868144
-5995296157134227520
-1873618532029106835
-1539245050
-48300418
-331037869860
-95748625
-6314795724398267312
-5888081980883929307
-544604964
-34124418943289166
-5245848947242502849
-32432363517642192
-2676033356038407648
-811533196
-1317733333
-8920676095134336910
-17149817495305717193
-918014392040164136
-103612987
-8695136395555507435
-18349504802666319185
-14847634415788362123
-1584661506
-4287350266942457603
-525512494730316455
-5881302580997523790
-1574765567
-3784125305237867347
-819397570
-8326286517935867839
-16149105318148965958
-16580883
-6684847
-18411699902469439513
-11229983338076703492
-15292499491369977714
-339635406848
-9870724570940976
+5767329
+5250413516934022944
+97518103
+63111604
+579208034
+544801575
+17236251
+258081459
+17953567922355439196
+30458188512103543
+15287987228927658628
+4930631980557601532
+20305658202031811
+2120987217453057458
+6209987959894902621
+7151957518376504179
+12552846396214610071
+1793158821936040552
+5461104787107351969
+559088458
+14386655907412249373
+547619651
+2141783083
+12606726616442537392
+1923875870
+811402123
+570557265
+42991988
 100
 101
 102
diff --git a/python/cudf/cudf/utils/hash_vocab_utils.py b/python/cudf/cudf/utils/hash_vocab_utils.py
index 66c9dd5135f..83b59ec53ca 100644
--- a/python/cudf/cudf/utils/hash_vocab_utils.py
+++ b/python/cudf/cudf/utils/hash_vocab_utils.py
@@ -69,8 +69,7 @@ def _get_space_util(bins, init_bins):
     return sum(_new_bin_length(len(b)) for b in bins) + 2 * init_bins
 
 
-def _pick_initial_a_b(data, max_constant, init_bins):
-    rng = np.random.default_rng(seed=0)
+def _pick_initial_a_b(data, max_constant, init_bins, rng):
     while True:
         a = rng.integers(2**12, 2**15)
         b = rng.integers(2**12, 2**15)
@@ -87,8 +86,7 @@ def _pick_initial_a_b(data, max_constant, init_bins):
     return bins, a, b
 
 
-def _find_hash_for_internal(hash_bin):
-    rng = np.random.default_rng(seed=0)
+def _find_hash_for_internal(hash_bin, rng):
     if not hash_bin:
         return [[], 0, 0]
 
@@ -110,11 +108,11 @@ def _find_hash_for_internal(hash_bin):
             return bins, a, b
 
 
-def _perfect_hash(integers, max_constant):
+def _perfect_hash(integers, rng, max_constant):
     num_top_level_bins = len(integers) // 4
 
     init_bins, init_a, init_b = _pick_initial_a_b(
-        integers, max_constant, num_top_level_bins
+        integers, max_constant, num_top_level_bins, rng
     )
     flattened_bins = []
 
@@ -129,7 +127,7 @@ def _perfect_hash(integers, max_constant):
     for i, b in enumerate(init_bins):
         if i % 500 == 0:
             print(f"Processing bin {i} / {len(init_bins)} of size = {len(b)}")
-        internal_table, coeff_a, coeff_b = _find_hash_for_internal(b)
+        internal_table, coeff_a, coeff_b = _find_hash_for_internal(b, rng)
         bin_length = len(internal_table)
         max_bin_length = max(bin_length, max_bin_length)
         internal_table_coeffs[i] = (
@@ -247,7 +245,7 @@ def hash_vocab(
     """
     Write the vocab vocabulary hashtable to the output_path
     """
-    _ = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=1243342)
     vocab = _load_vocab_dict(vocab_path)
     keys = list(map(_sdbm_hash, vocab.keys()))
 
@@ -266,7 +264,7 @@ def hash_vocab(
         hash_table,
         inner_table_coeffs,
         offsets_into_ht,
-    ) = _perfect_hash(keys, 10)
+    ) = _perfect_hash(keys, rng, 10)
 
     _pack_keys_and_values(hash_table, hashed_vocab)
     _store_func(

From bc73f31fc7c771155a7779f6c97e8c3da1fcbe2c Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 14 Oct 2024 19:18:30 +0000
Subject: [PATCH 13/21] update

---
 .pre-commit-config.yaml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 88ecf4f287c..c983e6bc51e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -101,6 +101,12 @@ repos:
         entry: 'default_rng\(\)'
         language: pygrep
         types: [python]
+      - id: no-np-random-seed
+        name: no-np-random-seed
+        description: 'Enforce that default_rng is used instead of np.random.seed'
+        entry: 'random.seed\('
+        language: pygrep
+        types: [python]
       - id: cmake-format
         name: cmake-format
         entry: ./cpp/scripts/run-cmake-format.sh cmake-format

From 0939ba1506412d8748adaaf865c7f34ad6f4b461 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 14 Oct 2024 19:22:44 +0000
Subject: [PATCH 14/21] update

---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c983e6bc51e..9dd64b0c959 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -104,7 +104,7 @@ repos:
       - id: no-np-random-seed
         name: no-np-random-seed
         description: 'Enforce that default_rng is used instead of np.random.seed'
-        entry: 'random.seed\('
+        entry: '.random.seed\('
         language: pygrep
         types: [python]
       - id: cmake-format

From 8f4efb7079d1fef01482ef2ab94f40d2358001dd Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 14 Oct 2024 19:22:56 +0000
Subject: [PATCH 15/21] update

---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9dd64b0c959..4aa196ec497 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -104,7 +104,7 @@ repos:
       - id: no-np-random-seed
         name: no-np-random-seed
         description: 'Enforce that default_rng is used instead of np.random.seed'
-        entry: '.random.seed\('
+        entry: 'np.random.seed\('
         language: pygrep
         types: [python]
       - id: cmake-format

From de0813c8571e71b9806f856619c4ad47008cc4a8 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Mon, 14 Oct 2024 19:28:27 +0000
Subject: [PATCH 16/21] update notebook

---
 python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb b/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb
index cc03574155e..94904fd83d4 100644
--- a/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb
+++ b/python/cudf/cudf_pandas_tests/data/repr_slow_down_test.ipynb
@@ -18,7 +18,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "np.random.seed(0)\n",
+    "rng = np.random.default_rng(seed=0)\n",
     "\n",
     "num_rows = 25_000_000\n",
     "num_columns = 12\n",

From 3158d9c8a42bee40a31feeb9fa94bda9fed16250 Mon Sep 17 00:00:00 2001
From: GALI PREM SAGAR <sagarprem75@gmail.com>
Date: Tue, 15 Oct 2024 15:47:31 -0500
Subject: [PATCH 17/21] Apply suggestions from code review

Co-authored-by: Charles Blackmon-Luca <20627856+charlesbluca@users.noreply.github.com>
---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 0240992fe1a..661c68ee62e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -90,6 +90,7 @@ select = [
     "UP007",
     # Import from `collections.abc` instead: `Callable`
     "UP035",
+    # usage of legacy `np.random` function calls
     "NPY002",
 ]
 ignore = [

From a02907b49c75767105a0411a85af2cf83754ae8e Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Tue, 15 Oct 2024 20:49:48 +0000
Subject: [PATCH 18/21] address reviews

---
 python/cudf/cudf/testing/_utils.py            |  2 +-
 python/cudf/cudf/testing/dataset_generator.py |  5 +-
 python/cudf/cudf/tests/test_binops.py         |  4 +-
 python/cudf/cudf/tests/test_column.py         |  2 +-
 python/cudf/cudf/tests/test_dataframe.py      | 28 ++++++++--
 python/cudf/cudf/tests/test_groupby.py        |  2 +-
 python/cudf/cudf/tests/test_index.py          | 56 +++++++++----------
 python/cudf/cudf/tests/test_orc.py            |  5 +-
 python/cudf/cudf/tests/test_rank.py           | 23 ++++----
 python/cudf/cudf/tests/test_reductions.py     |  2 +-
 python/cudf/cudf/tests/test_reshape.py        | 47 +++++++---------
 python/cudf/cudf/tests/test_series.py         | 18 +++---
 12 files changed, 101 insertions(+), 93 deletions(-)

diff --git a/python/cudf/cudf/testing/_utils.py b/python/cudf/cudf/testing/_utils.py
index 8a30b0ae6d6..a5dc8a5498c 100644
--- a/python/cudf/cudf/testing/_utils.py
+++ b/python/cudf/cudf/testing/_utils.py
@@ -210,7 +210,7 @@ def _get_args_kwars_for_assert_exceptions(func_args_and_kwargs):
 
 
 def gen_rand(dtype, size, **kwargs):
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=kwargs.get("seed", 0))
     dtype = cudf.dtype(dtype)
     if dtype.kind == "f":
         res = rng.random(size=size).astype(dtype)
diff --git a/python/cudf/cudf/testing/dataset_generator.py b/python/cudf/cudf/testing/dataset_generator.py
index 4cc5b00f63e..99b686406fb 100644
--- a/python/cudf/cudf/testing/dataset_generator.py
+++ b/python/cudf/cudf/testing/dataset_generator.py
@@ -55,8 +55,11 @@ def __init__(
         self.cardinality = cardinality
         self.null_frequency = null_frequency
         if generator is None:
+            rng = np.random.default_rng(seed=0)
             self.generator = lambda: [
-                np.random.default_rng(seed=0).integers(0, 100)
+                _generate_string(
+                    string.ascii_letters, rng, rng.integers(4, 8).item()
+                )
                 for _ in range(100)
             ]
         else:
diff --git a/python/cudf/cudf/tests/test_binops.py b/python/cudf/cudf/tests/test_binops.py
index 206374a83cd..949fa909b5b 100644
--- a/python/cudf/cudf/tests/test_binops.py
+++ b/python/cudf/cudf/tests/test_binops.py
@@ -588,7 +588,7 @@ def test_series_cmpop_mixed_dtype(cmpop, lhs_dtype, rhs_dtype, obj_class):
 )
 def test_series_reflected_ops_scalar(func, dtype, obj_class):
     # create random series
-    random_series = utils.gen_rand(dtype, 100, low=10)
+    random_series = utils.gen_rand(dtype, 100, low=10, seed=12)
 
     # gpu series
     gs = Series(random_series)
@@ -644,7 +644,7 @@ def test_series_reflected_ops_cudf_scalar(funcs, dtype, obj_class):
     cpu_func, gpu_func = funcs
 
     # create random series
-    random_series = utils.gen_rand(dtype, 100, low=10)
+    random_series = utils.gen_rand(dtype, 100, low=10, seed=12)
 
     # gpu series
     gs = Series(random_series)
diff --git a/python/cudf/cudf/tests/test_column.py b/python/cudf/cudf/tests/test_column.py
index c5b3191edd1..65947efc2df 100644
--- a/python/cudf/cudf/tests/test_column.py
+++ b/python/cudf/cudf/tests/test_column.py
@@ -31,7 +31,7 @@
 @pytest.fixture(params=dtypes, ids=dtypes)
 def pandas_input(request):
     dtype = request.param
-    rng = np.random.default_rng(seed=None)
+    rng = np.random.default_rng(seed=0)
     size = 100
 
     def random_ints(dtype, size):
diff --git a/python/cudf/cudf/tests/test_dataframe.py b/python/cudf/cudf/tests/test_dataframe.py
index b26e6b3e9b2..3b13ff569e3 100644
--- a/python/cudf/cudf/tests/test_dataframe.py
+++ b/python/cudf/cudf/tests/test_dataframe.py
@@ -63,9 +63,6 @@
     pytest_xfail = pytest.mark.skipif
 
 
-rng = np.random.default_rng(seed=0)
-
-
 @contextmanager
 def _hide_ufunc_warnings(eval_str):
     # pandas raises warnings for some inputs to the following ufuncs:
@@ -1241,6 +1238,7 @@ def test_empty_dataframe_to_cupy():
 
     df = cudf.DataFrame()
     nelem = 123
+    rng = np.random.default_rng(seed=0)
     for k in "abc":
         df[k] = rng.random(nelem)
 
@@ -1253,6 +1251,7 @@ def test_dataframe_to_cupy():
     df = cudf.DataFrame()
 
     nelem = 123
+    rng = np.random.default_rng(seed=0)
     for k in "abcd":
         df[k] = rng.random(nelem)
 
@@ -1282,6 +1281,7 @@ def test_dataframe_to_cupy_null_values():
     na = -10000
 
     refvalues = {}
+    rng = np.random.default_rng(seed=0)
     for k in "abcd":
         df[k] = data = rng.random(nelem)
         bitmask = utils.random_bitmask(nelem)
@@ -1324,6 +1324,7 @@ def test_dataframe_append_empty():
 
 
 def test_dataframe_setitem_from_masked_object():
+    rng = np.random.default_rng(seed=0)
     ary = rng.standard_normal(100)
     mask = np.zeros(100, dtype=bool)
     mask[:20] = True
@@ -2225,7 +2226,7 @@ def test_dataframe_transpose(nulls, num_cols, num_rows, dtype):
     # against pandas nullable types as they are the ones that closely
     # resemble `cudf` dtypes behavior.
     pdf = pd.DataFrame()
-
+    rng = np.random.default_rng(seed=0)
     null_rep = np.nan if dtype in ["float32", "float64"] else None
     np_dtype = dtype
     dtype = np.dtype(dtype)
@@ -2709,6 +2710,7 @@ def test_iteritems(gdf):
 def test_quantile(q, numeric_only):
     ts = pd.date_range("2018-08-24", periods=5, freq="D")
     td = pd.to_timedelta(np.arange(5), unit="h")
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(
         {"date": ts, "delta": td, "val": rng.standard_normal(len(ts))}
     )
@@ -2847,6 +2849,7 @@ def test_cuda_array_interface(dtype):
 @pytest.mark.parametrize("nchunks", [1, 2, 5, 10])
 @pytest.mark.parametrize("data_type", dtypes)
 def test_from_arrow_chunked_arrays(nelem, nchunks, data_type):
+    rng = np.random.default_rng(seed=0)
     np_list_data = [
         rng.integers(0, 100, nelem).astype(data_type) for i in range(nchunks)
     ]
@@ -4104,6 +4107,7 @@ def test_ndim():
     ],
 )
 def test_dataframe_round(decimals):
+    rng = np.random.default_rng(seed=0)
     gdf = cudf.DataFrame(
         {
             "floats": np.arange(0.5, 10.5, 1),
@@ -5817,6 +5821,7 @@ def test_memory_usage(deep, index, set_index):
 @pytest_xfail
 def test_memory_usage_string():
     rows = int(100)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
             "A": np.arange(rows, dtype="int32"),
@@ -5843,6 +5848,7 @@ def test_memory_usage_string():
 
 def test_memory_usage_cat():
     rows = int(100)
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
             "A": np.arange(rows, dtype="int32"),
@@ -5876,6 +5882,7 @@ def test_memory_usage_list():
 def test_memory_usage_multi(rows):
     # We need to sample without replacement to guarantee that the size of the
     # levels are always the same.
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         {
             "A": np.arange(rows, dtype="int32"),
@@ -6704,8 +6711,16 @@ def test_dataframe_init_1d_list(data, columns):
         (cupy.array([11, 123, -2342, 232]), ["z"], [0, 1, 1, 0]),
         (cupy.array([11, 123, -2342, 232]), ["z"], [1, 2, 3, 4]),
         (cupy.array([11, 123, -2342, 232]), ["z"], ["a", "z", "d", "e"]),
-        (rng.standard_normal(size=(2, 4)), ["a", "b", "c", "d"], ["a", "b"]),
-        (rng.standard_normal(size=(2, 4)), ["a", "b", "c", "d"], [1, 0]),
+        (
+            np.random.default_rng(seed=0).standard_normal(size=(2, 4)),
+            ["a", "b", "c", "d"],
+            ["a", "b"],
+        ),
+        (
+            np.random.default_rng(seed=0).standard_normal(size=(2, 4)),
+            ["a", "b", "c", "d"],
+            [1, 0],
+        ),
         (cupy.random.randn(2, 4), ["a", "b", "c", "d"], ["a", "b"]),
         (cupy.random.randn(2, 4), ["a", "b", "c", "d"], [1, 0]),
     ],
@@ -6879,6 +6894,7 @@ def test_dataframe_info_basic():
     memory usage: 859.0+ bytes
     """
     )
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(
         rng.standard_normal(size=(10, 10)),
         index=["a", "2", "3", "4", "5", "6", "7", "8", "100", "1111"],
diff --git a/python/cudf/cudf/tests/test_groupby.py b/python/cudf/cudf/tests/test_groupby.py
index 6b9fad8e0b7..6b222841622 100644
--- a/python/cudf/cudf/tests/test_groupby.py
+++ b/python/cudf/cudf/tests/test_groupby.py
@@ -1323,7 +1323,7 @@ def test_empty_groupby(func):
 
 
 def test_groupby_unsupported_columns():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     pd_cat = pd.Categorical(
         pd.Series(rng.choice(["a", "b", 1], 3), dtype="category")
     )
diff --git a/python/cudf/cudf/tests/test_index.py b/python/cudf/cudf/tests/test_index.py
index 40dbf7a1813..24d42d9eb4c 100644
--- a/python/cudf/cudf/tests/test_index.py
+++ b/python/cudf/cudf/tests/test_index.py
@@ -2645,23 +2645,20 @@ def test_isin_multiindex(data, values, level, err):
         )
 
 
-rng = np.random.default_rng(seed=0)
-
-range_data = [
-    range(rng.integers(0, 100)),
-    range(9, 12, 2),
-    range(20, 30),
-    range(100, 1000, 10),
-    range(0, 10, -2),
-    range(0, -10, 2),
-    range(0, -10, -2),
-]
-
-
-@pytest.fixture(params=range_data)
+@pytest.fixture(
+    params=[
+        range(np.random.default_rng(seed=0).integers(0, 100)),
+        range(9, 12, 2),
+        range(20, 30),
+        range(100, 1000, 10),
+        range(0, 10, -2),
+        range(0, -10, 2),
+        range(0, -10, -2),
+    ]
+)
 def rangeindex(request):
     """Create a cudf RangeIndex of different `nrows`"""
-    return RangeIndex(request.param)
+    return cudf.RangeIndex(request.param)
 
 
 @pytest.mark.parametrize(
@@ -2832,21 +2829,20 @@ def test_rangeindex_append_return_rangeindex():
     assert_eq(result, expected)
 
 
-index_data = [
-    range(rng.integers(0, 100)),
-    range(0, 10, -2),
-    range(0, -10, 2),
-    range(0, -10, -2),
-    range(0, 1),
-    [1, 2, 3, 1, None, None],
-    [None, None, 3.2, 1, None, None],
-    [None, "a", "3.2", "z", None, None],
-    pd.Series(["a", "b", None], dtype="category"),
-    np.array([1, 2, 3, None], dtype="datetime64[s]"),
-]
-
-
-@pytest.fixture(params=index_data)
+@pytest.fixture(
+    params=[
+        range(np.random.default_rng(seed=0).integers(0, 100)),
+        range(0, 10, -2),
+        range(0, -10, 2),
+        range(0, -10, -2),
+        range(0, 1),
+        [1, 2, 3, 1, None, None],
+        [None, None, 3.2, 1, None, None],
+        [None, "a", "3.2", "z", None, None],
+        pd.Series(["a", "b", None], dtype="category"),
+        np.array([1, 2, 3, None], dtype="datetime64[s]"),
+    ]
+)
 def index(request):
     """Create a cudf Index of different dtypes"""
     return cudf.Index(request.param)
diff --git a/python/cudf/cudf/tests/test_orc.py b/python/cudf/cudf/tests/test_orc.py
index a259209bed9..41c1c3ccb20 100644
--- a/python/cudf/cudf/tests/test_orc.py
+++ b/python/cudf/cudf/tests/test_orc.py
@@ -703,6 +703,7 @@ def test_orc_chunked_write_statistics(tmpdir, datadir, nrows, stats_freq):
                 has_nulls=True,
                 low=0,
                 high=max_char_length,
+                seed=0,
             )
             for dtype in supported_stat_types
         }
@@ -985,7 +986,7 @@ def test_orc_string_stream_offset_issue():
 
 def generate_list_struct_buff(size=100_000):
     rd = random.Random(1)
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=1)
 
     buff = BytesIO()
 
@@ -1132,7 +1133,7 @@ def gen_map_buff(size):
     from pyarrow import orc
 
     rd = random.Random(1)
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=1)
 
     buff = BytesIO()
 
diff --git a/python/cudf/cudf/tests/test_rank.py b/python/cudf/cudf/tests/test_rank.py
index 4e33f91b09b..1d9c6690f14 100644
--- a/python/cudf/cudf/tests/test_rank.py
+++ b/python/cudf/cudf/tests/test_rank.py
@@ -125,24 +125,21 @@ def test_rank_error_arguments(pdf):
     )
 
 
-rng = np.random.default_rng(seed=0)
-
-sort_group_args = [
-    np.full((3,), np.nan),
-    100 * rng.random(10),
-    np.full((3,), np.inf),
-    np.full((3,), -np.inf),
-]
-sort_dtype_args = [np.int32, np.int64, np.float32, np.float64]
-
-
 @pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
 @pytest.mark.parametrize(
     "elem,dtype",
     list(
         product(
-            combinations_with_replacement(sort_group_args, 4),
-            sort_dtype_args,
+            combinations_with_replacement(
+                [
+                    np.full((3,), np.nan),
+                    100 * np.random.default_rng(seed=0).random(10),
+                    np.full((3,), np.inf),
+                    np.full((3,), -np.inf),
+                ],
+                4,
+            ),
+            [np.int32, np.int64, np.float32, np.float64],
         )
     ),
 )
diff --git a/python/cudf/cudf/tests/test_reductions.py b/python/cudf/cudf/tests/test_reductions.py
index 7513bef69da..e0bc8f32c9b 100644
--- a/python/cudf/cudf/tests/test_reductions.py
+++ b/python/cudf/cudf/tests/test_reductions.py
@@ -62,7 +62,7 @@ def test_sum_string():
 )
 @pytest.mark.parametrize("nelem", params_sizes)
 def test_sum_decimal(dtype, nelem):
-    data = [str(x) for x in gen_rand("int64", nelem) / 100]
+    data = [str(x) for x in gen_rand("int64", nelem, seed=0) / 100]
 
     expected = pd.Series([Decimal(x) for x in data]).sum()
     got = cudf.Series(data).astype(dtype).sum()
diff --git a/python/cudf/cudf/tests/test_reshape.py b/python/cudf/cudf/tests/test_reshape.py
index 0b4e5c83569..26386abb05d 100644
--- a/python/cudf/cudf/tests/test_reshape.py
+++ b/python/cudf/cudf/tests/test_reshape.py
@@ -35,9 +35,6 @@
     pytest_xfail = pytest.mark.skipif
 
 
-rng = np.random.default_rng(seed=0)
-
-
 @pytest.mark.parametrize("num_id_vars", [0, 1, 2])
 @pytest.mark.parametrize("num_value_vars", [0, 1, 2])
 @pytest.mark.parametrize("num_rows", [1, 2, 100])
@@ -49,6 +46,7 @@ def test_melt(nulls, num_id_vars, num_value_vars, num_rows, dtype):
 
     pdf = pd.DataFrame()
     id_vars = []
+    rng = np.random.default_rng(seed=0)
     for i in range(num_id_vars):
         colname = "id" + str(i)
         data = rng.integers(0, 26, num_rows).astype(dtype)
@@ -138,6 +136,7 @@ def test_df_stack(nulls, num_cols, num_rows, dtype):
         pytest.skip(reason="nulls not supported in dtype: " + dtype)
 
     pdf = pd.DataFrame()
+    rng = np.random.default_rng(seed=0)
     for i in range(num_cols):
         colname = str(i)
         data = rng.integers(0, 26, num_rows).astype(dtype)
@@ -277,7 +276,7 @@ def test_df_stack_multiindex_column_axis_pd_example(level):
         ],
         names=["exp", "animal", "hair_length"],
     )
-
+    rng = np.random.default_rng(seed=0)
     df = pd.DataFrame(rng.standard_normal(size=(4, 4)), columns=columns)
 
     with expect_warning_if(PANDAS_GE_220, FutureWarning):
@@ -305,6 +304,7 @@ def test_interleave_columns(nulls, num_cols, num_rows, dtype):
         pytest.skip(reason="nulls not supported in dtype: " + dtype)
 
     pdf = pd.DataFrame(dtype=dtype)
+    rng = np.random.default_rng(seed=0)
     for i in range(num_cols):
         colname = str(i)
         data = pd.Series(rng.integers(0, 26, num_rows)).astype(dtype)
@@ -339,6 +339,7 @@ def test_tile(nulls, num_cols, num_rows, dtype, count):
         pytest.skip(reason="nulls not supported in dtype: " + dtype)
 
     pdf = pd.DataFrame(dtype=dtype)
+    rng = np.random.default_rng(seed=0)
     for i in range(num_cols):
         colname = str(i)
         data = pd.Series(rng.integers(num_cols, 26, num_rows)).astype(dtype)
@@ -715,8 +716,12 @@ def test_pivot_duplicate_error():
 
 
 @pytest.mark.parametrize(
-    "data",
-    [
+    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
+)
+@pytest.mark.parametrize("fill_value", [0])
+def test_pivot_table_simple(aggfunc, fill_value):
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(
         {
             "A": ["one", "one", "two", "three"] * 6,
             "B": ["A", "B", "C"] * 8,
@@ -724,14 +729,7 @@ def test_pivot_duplicate_error():
             "D": rng.standard_normal(size=24),
             "E": rng.standard_normal(size=24),
         }
-    ],
-)
-@pytest.mark.parametrize(
-    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
-)
-@pytest.mark.parametrize("fill_value", [0])
-def test_pivot_table_simple(data, aggfunc, fill_value):
-    pdf = pd.DataFrame(data)
+    )
     expected = pd.pivot_table(
         pdf,
         values=["D", "E"],
@@ -740,7 +738,7 @@ def test_pivot_table_simple(data, aggfunc, fill_value):
         aggfunc=aggfunc,
         fill_value=fill_value,
     )
-    cdf = cudf.DataFrame(data)
+    cdf = cudf.DataFrame.from_pandas(pdf)
     actual = cudf.pivot_table(
         cdf,
         values=["D", "E"],
@@ -753,8 +751,12 @@ def test_pivot_table_simple(data, aggfunc, fill_value):
 
 
 @pytest.mark.parametrize(
-    "data",
-    [
+    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
+)
+@pytest.mark.parametrize("fill_value", [0])
+def test_dataframe_pivot_table_simple(aggfunc, fill_value):
+    rng = np.random.default_rng(seed=0)
+    pdf = pd.DataFrame(
         {
             "A": ["one", "one", "two", "three"] * 6,
             "B": ["A", "B", "C"] * 8,
@@ -762,14 +764,7 @@ def test_pivot_table_simple(data, aggfunc, fill_value):
             "D": rng.standard_normal(size=24),
             "E": rng.standard_normal(size=24),
         }
-    ],
-)
-@pytest.mark.parametrize(
-    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
-)
-@pytest.mark.parametrize("fill_value", [0])
-def test_dataframe_pivot_table_simple(data, aggfunc, fill_value):
-    pdf = pd.DataFrame(data)
+    )
     expected = pdf.pivot_table(
         values=["D", "E"],
         index=["A", "B"],
@@ -777,7 +772,7 @@ def test_dataframe_pivot_table_simple(data, aggfunc, fill_value):
         aggfunc=aggfunc,
         fill_value=fill_value,
     )
-    cdf = cudf.DataFrame(data)
+    cdf = cudf.DataFrame.from_pandas(pdf)
     actual = cdf.pivot_table(
         values=["D", "E"],
         index=["A", "B"],
diff --git a/python/cudf/cudf/tests/test_series.py b/python/cudf/cudf/tests/test_series.py
index e7e45c574cc..7f0a4902ed1 100644
--- a/python/cudf/cudf/tests/test_series.py
+++ b/python/cudf/cudf/tests/test_series.py
@@ -27,8 +27,6 @@
     gen_rand,
 )
 
-rng = np.random.default_rng(seed=0)
-
 
 def _series_na_data():
     return [
@@ -521,7 +519,7 @@ def test_series_factorize_sort(data, sort):
 @pytest.mark.parametrize("nulls", ["none", "some"])
 def test_series_datetime_value_counts(data, nulls, normalize, dropna):
     psr = data.copy()
-
+    rng = np.random.default_rng(seed=0)
     if len(data) > 0:
         if nulls == "one":
             p = rng.integers(0, len(data))
@@ -548,7 +546,7 @@ def test_series_datetime_value_counts(data, nulls, normalize, dropna):
 @pytest.mark.parametrize("num_elements", [10, 100, 1000])
 def test_categorical_value_counts(dropna, normalize, num_elements):
     # create categorical series
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     pd_cat = pd.Categorical(
         pd.Series(
             rng.choice(list(ascii_letters + digits), num_elements),
@@ -588,6 +586,7 @@ def test_categorical_value_counts(dropna, normalize, num_elements):
 @pytest.mark.parametrize("dropna", [True, False])
 @pytest.mark.parametrize("normalize", [True, False])
 def test_series_value_counts(dropna, normalize):
+    rng = np.random.default_rng(seed=0)
     for size in [10**x for x in range(5)]:
         arr = rng.integers(low=-1, high=10, size=size)
         mask = arr != -1
@@ -716,8 +715,8 @@ def test_series_mode(gs, dropna):
 @pytest.mark.parametrize(
     "arr",
     [
-        rng.normal(-100, 100, 1000),
-        rng.integers(-50, 50, 1000),
+        np.random.default_rng(seed=0).normal(-100, 100, 1000),
+        np.random.default_rng(seed=0).integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat([-0.6459412758761901], 100),
         np.repeat(np.nan, 100),
@@ -733,7 +732,7 @@ def test_series_round(arr, decimals):
     expected = pser.round(decimals)
 
     assert_eq(result, expected)
-
+    rng = np.random.default_rng(seed=0)
     # with nulls, maintaining existing null mask
     arr = arr.astype("float64")  # for pandas nulls
     arr.ravel()[rng.choice(arr.shape[0], arr.shape[0] // 2, replace=False)] = (
@@ -1728,7 +1727,7 @@ def test_series_truncate_datetimeindex():
         [],
         [0, 12, 14],
         [0, 14, 12, 12, 3, 10, 12, 14],
-        rng.integers(-100, 100, 200),
+        np.random.default_rng(seed=0).integers(-100, 100, 200),
         pd.Series([0.0, 1.0, None, 10.0]),
         [None, None, None, None],
         [np.nan, None, -1, 2, 3],
@@ -1737,7 +1736,7 @@ def test_series_truncate_datetimeindex():
 @pytest.mark.parametrize(
     "values",
     [
-        rng.integers(-100, 100, 10),
+        np.random.default_rng(seed=0).integers(-100, 100, 10),
         [],
         [np.nan, None, -1, 2, 3],
         [1.0, 12.0, None, None, 120],
@@ -1748,6 +1747,7 @@ def test_series_truncate_datetimeindex():
     ],
 )
 def test_isin_numeric(data, values):
+    rng = np.random.default_rng(seed=0)
     index = rng.integers(0, 100, len(data))
     psr = pd.Series(data, index=index)
     gsr = cudf.Series.from_pandas(psr, nan_as_null=False)

From 4a9e9442d0c35ed73739db4903f4f89da66a23dd Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Tue, 15 Oct 2024 20:53:55 +0000
Subject: [PATCH 19/21] merge into one

---
 .pre-commit-config.yaml | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4aa196ec497..0f87220b089 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -95,16 +95,15 @@ repos:
         entry: 'pytest\.xfail'
         language: pygrep
         types: [python]
-      - id: no-unseeded-default-rng
-        name: no-unseeded-default-rng
-        description: 'Enforce that no non-seeded default_rng is used'
-        entry: 'default_rng\(\)'
-        language: pygrep
-        types: [python]
-      - id: no-np-random-seed
-        name: no-np-random-seed
-        description: 'Enforce that default_rng is used instead of np.random.seed'
-        entry: 'np.random.seed\('
+      - id: use-only-default-rng
+        name: use-only-default-rng
+        description: 'Enforce that `default_rng` is used instead of `np.random.seed` and it must be seeded.'
+        entry: |
+            (?x)
+            # no unseeded default_rng
+            |default_rng\(\)
+            # no np.random.seed
+            |np.random.seed\(
         language: pygrep
         types: [python]
       - id: cmake-format

From b8f964b0a304af6a84dd61ca6d3e918e47b43ecf Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Tue, 15 Oct 2024 21:12:02 +0000
Subject: [PATCH 20/21] address reviews

---
 .pre-commit-config.yaml                       | 15 ++++-----
 python/cudf/cudf/tests/test_categorical.py    |  2 +-
 python/cudf/cudf/tests/test_dataframe.py      | 10 +++---
 python/cudf/cudf/tests/test_stats.py          | 32 +++++++++----------
 python/cudf/cudf/tests/test_string.py         | 12 +++++--
 python/cudf/cudf/utils/hash_vocab_utils.py    |  4 +--
 .../cudf_pandas_tests/test_cudf_pandas.py     |  2 +-
 .../cudf/cudf_pandas_tests/test_profiler.py   |  2 +-
 .../tests/test_stumpy_distributed.py          |  4 +--
 9 files changed, 44 insertions(+), 39 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0f87220b089..174dc72bf02 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -95,15 +95,14 @@ repos:
         entry: 'pytest\.xfail'
         language: pygrep
         types: [python]
-      - id: use-only-default-rng
-        name: use-only-default-rng
-        description: 'Enforce that `default_rng` is used instead of `np.random.seed` and it must be seeded.'
+      - id: no-unseeded-default-rng
+        name: no-unseeded-default-rng
+        description: 'Enforce that no non-seeded default_rng is used and default_rng is used instead of np.random.seed'
         entry: |
-            (?x)
-            # no unseeded default_rng
-            |default_rng\(\)
-            # no np.random.seed
-            |np.random.seed\(
+          # Check for usage of default_rng without seeding
+          default_rng\(\)|
+          # Check for usage of np.random.seed
+          np.random.seed\(
         language: pygrep
         types: [python]
       - id: cmake-format
diff --git a/python/cudf/cudf/tests/test_categorical.py b/python/cudf/cudf/tests/test_categorical.py
index 7d6c6c7d7b0..db41f689255 100644
--- a/python/cudf/cudf/tests/test_categorical.py
+++ b/python/cudf/cudf/tests/test_categorical.py
@@ -252,7 +252,7 @@ def test_cat_series_binop_error():
 @pytest.mark.parametrize("num_elements", [10, 100, 1000])
 def test_categorical_unique(num_elements):
     # create categorical series
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     pd_cat = pd.Categorical(
         pd.Series(
             rng.choice(
diff --git a/python/cudf/cudf/tests/test_dataframe.py b/python/cudf/cudf/tests/test_dataframe.py
index 3b13ff569e3..7c3547c59d6 100644
--- a/python/cudf/cudf/tests/test_dataframe.py
+++ b/python/cudf/cudf/tests/test_dataframe.py
@@ -3903,7 +3903,7 @@ def test_select_dtype_datetime_with_frequency():
 
 
 def test_dataframe_describe_exclude():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3919,7 +3919,7 @@ def test_dataframe_describe_exclude():
 
 
 def test_dataframe_describe_include():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3934,7 +3934,7 @@ def test_dataframe_describe_include():
 
 
 def test_dataframe_describe_default():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3948,7 +3948,7 @@ def test_dataframe_describe_default():
 
 
 def test_series_describe_include_all():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     data_length = 10000
 
     df = cudf.DataFrame()
@@ -3971,7 +3971,7 @@ def test_series_describe_include_all():
 
 
 def test_dataframe_describe_percentiles():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=12)
     data_length = 10000
     sample_percentiles = [0.0, 0.1, 0.33, 0.84, 0.4, 0.99]
 
diff --git a/python/cudf/cudf/tests/test_stats.py b/python/cudf/cudf/tests/test_stats.py
index a4ff042e37c..27de0ed42e5 100644
--- a/python/cudf/cudf/tests/test_stats.py
+++ b/python/cudf/cudf/tests/test_stats.py
@@ -19,8 +19,6 @@
 
 interpolation_methods = ["linear", "lower", "higher", "midpoint", "nearest"]
 
-rng = np.random.default_rng(seed=0)
-
 
 @pytest.mark.parametrize("method", methods)
 @pytest.mark.parametrize("dtype", params_dtypes)
@@ -86,6 +84,7 @@ def test_series_std(ddof):
 
 
 def test_series_unique():
+    rng = np.random.default_rng(seed=0)
     for size in [10**x for x in range(5)]:
         arr = rng.integers(low=-1, high=10, size=size)
         mask = arr != -1
@@ -131,6 +130,7 @@ def test_series_nunique(nan_as_null, dropna):
 
 
 def test_series_scale():
+    rng = np.random.default_rng(seed=0)
     arr = pd.Series(rng.integers(low=-10, high=10, size=100))
     sr = cudf.Series(arr)
 
@@ -231,8 +231,8 @@ def test_misc_quantiles(data, q):
 @pytest.mark.parametrize(
     "data",
     [
-        {"data": rng.normal(-100, 100, 1000)},
-        {"data": rng.integers(-50, 50, 1000)},
+        {"data": np.random.default_rng(seed=0).normal(-100, 100, 1000)},
+        {"data": np.random.default_rng(seed=0).integers(-50, 50, 1000)},
         {"data": (np.zeros(100))},
         {"data": np.repeat(np.nan, 100)},
         {"data": np.array([1.123, 2.343, np.nan, 0.0])},
@@ -282,8 +282,8 @@ def test_kurt_skew_error(op):
 @pytest.mark.parametrize(
     "data",
     [
-        cudf.Series(rng.normal(-100, 100, 1000)),
-        cudf.Series(rng.integers(-50, 50, 1000)),
+        cudf.Series(np.random.default_rng(seed=0).normal(-100, 100, 1000)),
+        cudf.Series(np.random.default_rng(seed=0).integers(-50, 50, 1000)),
         cudf.Series(np.zeros(100)),
         cudf.Series(np.repeat(np.nan, 100)),
         cudf.Series(np.array([1.123, 2.343, np.nan, 0.0])),
@@ -346,8 +346,8 @@ def test_series_median(dtype, num_na):
 @pytest.mark.parametrize(
     "data",
     [
-        rng.normal(-100, 100, 1000),
-        rng.integers(-50, 50, 1000),
+        np.random.default_rng(seed=0).normal(-100, 100, 1000),
+        np.random.default_rng(seed=0).integers(-50, 50, 1000),
         np.zeros(100),
         np.array([1.123, 2.343, np.nan, 0.0]),
         np.array([-2, 3.75, 6, None, None, None, -8.5, None, 4.2]),
@@ -381,8 +381,8 @@ def test_series_pct_change(data, periods, fill_method):
 @pytest.mark.parametrize(
     "data1",
     [
-        rng.normal(-100, 100, 1000),
-        rng.integers(-50, 50, 1000),
+        np.random.default_rng(seed=0).normal(-100, 100, 1000),
+        np.random.default_rng(seed=0).integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
@@ -395,8 +395,8 @@ def test_series_pct_change(data, periods, fill_method):
 @pytest.mark.parametrize(
     "data2",
     [
-        rng.normal(-100, 100, 1000),
-        rng.integers(-50, 50, 1000),
+        np.random.default_rng(seed=0).normal(-100, 100, 1000),
+        np.random.default_rng(seed=0).integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
@@ -425,8 +425,8 @@ def test_cov1d(data1, data2):
 @pytest.mark.parametrize(
     "data1",
     [
-        rng.normal(-100, 100, 1000),
-        rng.integers(-50, 50, 1000),
+        np.random.default_rng(seed=0).normal(-100, 100, 1000),
+        np.random.default_rng(seed=0).integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
@@ -439,8 +439,8 @@ def test_cov1d(data1, data2):
 @pytest.mark.parametrize(
     "data2",
     [
-        rng.normal(-100, 100, 1000),
-        rng.integers(-50, 50, 1000),
+        np.random.default_rng(seed=0).normal(-100, 100, 1000),
+        np.random.default_rng(seed=0).integers(-50, 50, 1000),
         np.zeros(100),
         np.repeat(np.nan, 100),
         np.array([1.123, 2.343, np.nan, 0.0]),
diff --git a/python/cudf/cudf/tests/test_string.py b/python/cudf/cudf/tests/test_string.py
index 02e2ee96aca..e25f99d7bee 100644
--- a/python/cudf/cudf/tests/test_string.py
+++ b/python/cudf/cudf/tests/test_string.py
@@ -133,9 +133,14 @@ def test_string_get_item(ps_gs, item):
         np.array([False] * 5),
         cupy.asarray(np.array([True] * 5)),
         cupy.asarray(np.array([False] * 5)),
-        rng.integers(0, 2, 5).astype("bool").tolist(),
-        rng.integers(0, 2, 5).astype("bool"),
-        cupy.asarray(rng.integers(0, 2, 5).astype("bool")),
+        np.random.default_rng(seed=0)
+        .integers(0, 2, 5)
+        .astype("bool")
+        .tolist(),
+        np.random.default_rng(seed=0).integers(0, 2, 5).astype("bool"),
+        cupy.asarray(
+            np.random.default_rng(seed=0).integers(0, 2, 5).astype("bool")
+        ),
     ],
 )
 def test_string_bool_mask(ps_gs, item):
@@ -1079,6 +1084,7 @@ def test_string_set_scalar(scalar):
 
 
 def test_string_index():
+    rng = np.random.default_rng(seed=0)
     pdf = pd.DataFrame(rng.random(size=(5, 5)))
     gdf = cudf.DataFrame.from_pandas(pdf)
     stringIndex = ["a", "b", "c", "d", "e"]
diff --git a/python/cudf/cudf/utils/hash_vocab_utils.py b/python/cudf/cudf/utils/hash_vocab_utils.py
index 83b59ec53ca..896a3809c67 100644
--- a/python/cudf/cudf/utils/hash_vocab_utils.py
+++ b/python/cudf/cudf/utils/hash_vocab_utils.py
@@ -108,7 +108,7 @@ def _find_hash_for_internal(hash_bin, rng):
             return bins, a, b
 
 
-def _perfect_hash(integers, rng, max_constant):
+def _perfect_hash(integers, max_constant, rng):
     num_top_level_bins = len(integers) // 4
 
     init_bins, init_a, init_b = _pick_initial_a_b(
@@ -264,7 +264,7 @@ def hash_vocab(
         hash_table,
         inner_table_coeffs,
         offsets_into_ht,
-    ) = _perfect_hash(keys, rng, 10)
+    ) = _perfect_hash(keys, 10, rng)
 
     _pack_keys_and_values(hash_table, hashed_vocab)
     _store_func(
diff --git a/python/cudf/cudf_pandas_tests/test_cudf_pandas.py b/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
index 693bd0c8d22..7aefdc386bb 100644
--- a/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
+++ b/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
@@ -1142,7 +1142,7 @@ def test_private_method_result_wrapped():
 
 
 def test_numpy_var():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=42)
     data = rng.random(1000)
     psr = pd.Series(data)
     sr = xpd.Series(data)
diff --git a/python/cudf/cudf_pandas_tests/test_profiler.py b/python/cudf/cudf_pandas_tests/test_profiler.py
index b20c3fff346..a5c29bd93a2 100644
--- a/python/cudf/cudf_pandas_tests/test_profiler.py
+++ b/python/cudf/cudf_pandas_tests/test_profiler.py
@@ -23,7 +23,7 @@
     reason="function names change across versions of pandas, so making sure it only runs on latest version of pandas",
 )
 def test_profiler():
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=42)
     with Profiler() as profiler:
         df = pd.DataFrame(
             {
diff --git a/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py b/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py
index a81d1d01aa2..0777d982ac2 100644
--- a/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py
+++ b/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py
@@ -31,14 +31,14 @@ def dask_client():
 
 
 def test_1d_distributed(dask_client):
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=42)
     ts = pd.Series(rng.random(100))
     m = 10
     return stumpy.stumped(dask_client, ts, m)
 
 
 def test_multidimensional_distributed_timeseries(dask_client):
-    rng = np.random.default_rng(seed=0)
+    rng = np.random.default_rng(seed=42)
     # Each row represents data from a different dimension while each column represents
     # data from the same dimension
     your_time_series = rng.random(3, 1000)

From 2350a4663f0acb851969a2cf8cf54b784505a6d0 Mon Sep 17 00:00:00 2001
From: galipremsagar <sagarprem75@gmail.com>
Date: Wed, 16 Oct 2024 20:44:19 +0000
Subject: [PATCH 21/21] fix struct data type corruption

---
 python/cudf/cudf/core/column/struct.py |  7 +------
 python/cudf/cudf/tests/test_struct.py  | 12 ++++++++----
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/python/cudf/cudf/core/column/struct.py b/python/cudf/cudf/core/column/struct.py
index 2fda3b2c434..8f16ba4e15b 100644
--- a/python/cudf/cudf/core/column/struct.py
+++ b/python/cudf/cudf/core/column/struct.py
@@ -68,12 +68,7 @@ def base_size(self):
             return self.size + self.offset
 
     def to_arrow(self) -> pa.Array:
-        children = [
-            pa.nulls(len(child))
-            if len(child) == child.null_count
-            else child.to_arrow()
-            for child in self.children
-        ]
+        children = [child.to_arrow() for child in self.children]
 
         pa_type = pa.struct(
             {
diff --git a/python/cudf/cudf/tests/test_struct.py b/python/cudf/cudf/tests/test_struct.py
index e91edc9eec6..899d78c999b 100644
--- a/python/cudf/cudf/tests/test_struct.py
+++ b/python/cudf/cudf/tests/test_struct.py
@@ -50,10 +50,14 @@ def test_struct_for_field(key, expect):
     assert_eq(expect, got)
 
 
-@pytest.mark.parametrize("input_obj", [[{"a": 1, "b": cudf.NA, "c": 3}]])
-def test_series_construction_with_nulls(input_obj):
-    expect = pa.array(input_obj, from_pandas=True)
-    got = cudf.Series(input_obj).to_arrow()
+def test_series_construction_with_nulls():
+    fields = [
+        pa.array([1], type=pa.int64()),
+        pa.array([None], type=pa.int64()),
+        pa.array([3], type=pa.int64()),
+    ]
+    expect = pa.StructArray.from_arrays(fields, ["a", "b", "c"])
+    got = cudf.Series(expect).to_arrow()
 
     assert expect == got