Implements accessor for categorical types

docs/source/conf.py

@@ -183,13 +183,13 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "dateutil": ("https://dateutil.readthedocs.io/en/latest/", None),
-    "matplotlib": ("https://matplotlib.org/", None),
+    "matplotlib": ("https://matplotlib.org/stable/", None),
     "numpy": ("https://numpy.org/doc/stable/", None),
     "pandas": ("https://pandas.pydata.org/docs/", None),
     "pandas-gbq": ("https://pandas-gbq.readthedocs.io/en/latest/", None),
     "py": ("https://pylib.readthedocs.io/en/latest/", None),
     "python": ("https://docs.python.org/3/", None),
-    "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
     "statsmodels": ("https://www.statsmodels.org/devel/", None),
     "pyarrow": ("https://arrow.apache.org/docs/", None),
 }

docs/source/reference/dataframe/series.rst

@@ -386,6 +386,31 @@ strings and apply several methods to it. These can be accessed like
        Series.str
        Series.dt
 
+
+.. _generated.series.cat:
+
+Categorical accessor
+~~~~~~~~~~~~~~~
+
+Categorical-dtype specific methods and attributes are available under
+the ``Series.cat`` accessor.
+
+.. autosummary::
+   :toctree: generated/
+   :template: accessor_method.rst
+
+       Series.cat.categories
+       Series.cat.ordered
+       Series.cat.codes
+       Series.cat.rename_categories
+       Series.cat.reorder_categories
+       Series.cat.add_categories
+       Series.cat.remove_categories
+       Series.cat.set_categories
+       Series.cat.as_ordered
+       Series.cat.as_unordered
+
+
 Plotting
 --------
 ``Series.plot`` is both a callable method and a namespace attribute for

mars/core/__init__.py

@@ -15,6 +15,7 @@
 # noinspection PyUnresolvedReferences
 from ..typing import ChunkType, TileableType, EntityType, OperandType
 from .base import ExecutionError
+from .context import get_context
 from .entity import (
     Entity,
     EntityData,

mars/dataframe/__init__.py

@@ -45,32 +45,32 @@
 
 from . import arithmetic
 from . import base
+from . import datastore
+from . import groupby
 from . import indexing
 from . import merge as merge_
 from . import missing
+from . import plotting
 from . import reduction
-from . import statistics
 from . import sort
-from . import groupby
+from . import statistics
 from . import ufunc
-from . import datastore
 from . import window
-from . import plotting
 
 del (
-    reduction,
-    statistics,
     arithmetic,
-    indexing,
-    merge_,
     base,
+    datastore,
     groupby,
+    indexing,
+    merge_,
     missing,
-    ufunc,
-    datastore,
+    plotting,
+    reduction,
     sort,
+    statistics,
+    ufunc,
     window,
-    plotting,
 )
 del DataFrameFetch, DataFrameFetchShuffle
 

mars/dataframe/base/__init__.py

@@ -55,9 +55,15 @@
 def _install():
     from ..core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE
     from .standardize_range_index import ChunkStandardizeRangeIndex
+    from .categorical import _categorical_method_to_handlers
     from .string_ import _string_method_to_handlers
     from .datetimes import _datetime_method_to_handlers
-    from .accessor import StringAccessor, DatetimeAccessor, CachedAccessor
+    from .accessor import (
+        CachedAccessor,
+        CategoricalAccessor,
+        DatetimeAccessor,
+        StringAccessor,
+    )
 
     for t in DATAFRAME_TYPE:
         setattr(t, "to_gpu", to_gpu)
@@ -134,6 +140,10 @@ def _install():
         setattr(t, "is_monotonic_increasing", property(fget=is_monotonic_increasing))
         setattr(t, "is_monotonic_decreasing", property(fget=is_monotonic_decreasing))
 
+    for method in _categorical_method_to_handlers:
+        if not hasattr(CategoricalAccessor, method):
+            CategoricalAccessor._register(method)
+
     for method in _string_method_to_handlers:
         if not hasattr(StringAccessor, method):
             StringAccessor._register(method)
@@ -143,8 +153,9 @@ def _install():
             DatetimeAccessor._register(method)
 
     for series in SERIES_TYPE:
-        series.str = CachedAccessor("str", StringAccessor)
+        series.cat = CachedAccessor("cat", CategoricalAccessor)
         series.dt = CachedAccessor("dt", DatetimeAccessor)
+        series.str = CachedAccessor("str", StringAccessor)
 
 
 _install()

mars/dataframe/base/accessor.py

@@ -17,15 +17,17 @@
 
 import pandas as pd
 from pandas.api.types import (
+    is_categorical_dtype,
     is_datetime64_dtype,
     is_datetime64tz_dtype,
-    is_timedelta64_dtype,
     is_period_dtype,
+    is_timedelta64_dtype,
 )
 
 from ...utils import adapt_mars_docstring
-from .string_ import _string_method_to_handlers, SeriesStringMethod
+from .categorical import _categorical_method_to_handlers, SeriesCategoricalMethod
 from .datetimes import _datetime_method_to_handlers, SeriesDatetimeMethod
+from .string_ import _string_method_to_handlers, SeriesStringMethod
 
 
 class StringAccessor:
@@ -262,6 +264,53 @@ def __dir__(self) -> Iterable[str]:
         return list(s)
 
 
+class CategoricalAccessor:
+    def __init__(self, series):
+        if not is_categorical_dtype(series.dtype):
+            raise AttributeError("Can only use .cat accessor with categorical values")
+        self._series = series
+
+    @property
+    def ordered(self):
+        return self._series.dtype.ordered
+
+    @property
+    def categories(self):
+        return getattr(self, "_get_categories")()
+
+    @classmethod
+    def _gen_func(cls, method, is_property):
+        def _inner(self, *args, **kwargs):
+            op = SeriesCategoricalMethod(
+                method=method,
+                is_property=is_property,
+                method_args=args,
+                method_kwargs=kwargs,
+            )
+            return op(self._series)
+
+        if hasattr(pd.Series.cat, method):
+            _inner = wraps(getattr(pd.Series.cat, method))(_inner)
+            _inner.__doc__ = adapt_mars_docstring(
+                getattr(pd.Series.cat, method).__doc__
+            )
+        return _inner
+
+    @classmethod
+    def _register(cls, method):
+        # non-existing members are considered methods by default
+        is_property = not callable(getattr(pd.Series.cat, method, lambda: None))
+        func = cls._gen_func(method, is_property)
+        if is_property:
+            func = property(func)
+        setattr(cls, method, func)
+
+    def __dir__(self) -> Iterable[str]:
+        s = set(super().__dir__())
+        s.update(_categorical_method_to_handlers.keys())
+        return list(s)
+
+
 class CachedAccessor:
     def __init__(self, name: str, accessor) -> None:
         self._name = name

mars/dataframe/base/astype.py

@@ -16,9 +16,9 @@
 import pandas as pd
 from pandas.api.types import CategoricalDtype
 
-from ... import opcodes as OperandDef
+from ... import opcodes
 from ...core import recursive_tile
-from ...serialization.serializables import AnyField, StringField, ListField
+from ...serialization.serializables import AnyField, ListField, StringField
 from ...tensor.base import sort
 from ...utils import pd_release_version
 from ..core import DATAFRAME_TYPE, SERIES_TYPE
@@ -29,42 +29,23 @@
 
 
 class DataFrameAstype(DataFrameOperand, DataFrameOperandMixin):
-    _op_type_ = OperandDef.ASTYPE
-
-    _dtype_values = AnyField("dtype_values")
-    _errors = StringField("errors")
-    _category_cols = ListField("category_cols")
-
-    def __init__(
-        self,
-        dtype_values=None,
-        errors=None,
-        category_cols=None,
-        output_types=None,
-        **kw
-    ):
-        super().__init__(
-            _dtype_values=dtype_values,
-            _errors=errors,
-            _category_cols=category_cols,
-            _output_types=output_types,
-            **kw
-        )
+    _op_type_ = opcodes.ASTYPE
 
-    @property
-    def dtype_values(self):
-        return self._dtype_values
+    dtype_values = AnyField("dtype_values", default=None)
+    errors = StringField("errors", default=None)
+    category_cols = ListField("category_cols", default=None)
 
-    @property
-    def errors(self):
-        return self._errors
+    def __init__(self, output_types=None, **kw):
+        super().__init__(_output_types=output_types, **kw)
 
-    @property
-    def category_cols(self):
-        return self._category_cols
+    @classmethod
+    def _is_categorical(cls, dtype):
+        return (isinstance(dtype, str) and dtype == "category") or (
+            isinstance(dtype, pd.CategoricalDtype) and dtype.categories is None
+        )
 
     @classmethod
-    def _tile_one_chunk(cls, op):
+    def _tile_one_chunk(cls, op: "DataFrameAstype"):
         c = op.inputs[0].chunks[0]
         chunk_op = op.copy().reset_key()
         chunk_params = op.outputs[0].params.copy()
@@ -80,23 +61,22 @@ def _tile_one_chunk(cls, op):
         )
 
     @classmethod
-    def _tile_series_index(cls, op):
+    def _tile_series_index(cls, op: "DataFrameAstype"):
         in_series = op.inputs[0]
         out = op.outputs[0]
 
         unique_chunk = None
-        if op.dtype_values == "category" and isinstance(op.dtype_values, str):
-            unique_chunk = (yield from recursive_tile(sort(in_series.unique()))).chunks[
-                0
-            ]
+        if cls._is_categorical(op.dtype_values):
+            unique = yield from recursive_tile(sort(in_series.unique()))
+            unique_chunk = unique.chunks[0]
 
         chunks = []
         for c in in_series.chunks:
             chunk_op = op.copy().reset_key()
             params = c.params.copy()
             params["dtype"] = out.dtype
             if unique_chunk is not None:
-                chunk_op._category_cols = [in_series.name]
+                chunk_op.category_cols = [in_series.name]
                 new_chunk = chunk_op.new_chunk([c, unique_chunk], **params)
             else:
                 new_chunk = chunk_op.new_chunk([c], **params)
@@ -108,13 +88,13 @@ def _tile_series_index(cls, op):
         )
 
     @classmethod
-    def _tile_dataframe(cls, op):
+    def _tile_dataframe(cls, op: "DataFrameAstype"):
         in_df = op.inputs[0]
         out = op.outputs[0]
         cum_nsplits = np.cumsum((0,) + in_df.nsplits[1])
         out_chunks = []
 
-        if op.dtype_values == "category":
+        if cls._is_categorical(op.dtype_values):
             # all columns need unique values
             for c in in_df.chunks:
                 chunk_op = op.copy().reset_key()
@@ -123,21 +103,19 @@ def _tile_dataframe(cls, op):
                     cum_nsplits[c.index[1]] : cum_nsplits[c.index[1] + 1]
                 ]
                 params["dtypes"] = dtypes
-                chunk_op._category_cols = list(c.columns_value.to_pandas())
+                chunk_op.category_cols = list(c.columns_value.to_pandas())
                 unique_chunks = []
                 for col in c.columns_value.to_pandas():
                     unique = yield from recursive_tile(sort(in_df[col].unique()))
                     unique_chunks.append(unique.chunks[0])
                 new_chunk = chunk_op.new_chunk([c] + unique_chunks, **params)
                 out_chunks.append(new_chunk)
-        elif (
-            isinstance(op.dtype_values, dict) and "category" in op.dtype_values.values()
+        elif isinstance(op.dtype_values, dict) and any(
+            cls._is_categorical(t) for t in op.dtype_values.values()
         ):
             # some columns' types are category
             category_cols = [
-                c
-                for c, v in op.dtype_values.items()
-                if isinstance(v, str) and v == "category"
+                c for c, v in op.dtype_values.items() if cls._is_categorical(v)
             ]
             unique_chunks = dict()
             for col in category_cols:
@@ -156,7 +134,7 @@ def _tile_dataframe(cls, op):
                     if col in category_cols:
                         chunk_category_cols.append(col)
                         chunk_unique_chunks.append(unique_chunks[col])
-                chunk_op._category_cols = chunk_category_cols
+                chunk_op.category_cols = chunk_category_cols
                 new_chunk = chunk_op.new_chunk([c] + chunk_unique_chunks, **params)
                 out_chunks.append(new_chunk)
         else:
@@ -176,7 +154,7 @@ def _tile_dataframe(cls, op):
         )
 
     @classmethod
-    def tile(cls, op):
+    def tile(cls, op: "DataFrameAstype"):
         if len(op.inputs[0].chunks) == 1:
             return cls._tile_one_chunk(op)
         elif isinstance(op.inputs[0], DATAFRAME_TYPE):
@@ -185,13 +163,14 @@ def tile(cls, op):
             return (yield from cls._tile_series_index(op))
 
     @classmethod
-    def execute(cls, ctx, op):
+    def execute(cls, ctx, op: "DataFrameAstype"):
         in_data = ctx[op.inputs[0].key]
         if not isinstance(op.dtype_values, dict):
             if op.category_cols is not None:
                 uniques = [ctx[c.key] for c in op.inputs[1:]]
+                ordered = getattr(op.dtype_values, "ordered", None)
                 dtype = dict(
-                    (col, CategoricalDtype(unique_values))
+                    (col, CategoricalDtype(unique_values, ordered=ordered))
                     for col, unique_values in zip(op.category_cols, uniques)
                 )
                 ctx[op.outputs[0].key] = in_data.astype(dtype, errors=op.errors)
@@ -212,7 +191,10 @@ def execute(cls, ctx, op):
             if op.category_cols is not None:
                 uniques = [ctx[c.key] for c in op.inputs[1:]]
                 for col, unique_values in zip(op.category_cols, uniques):
-                    selected_dtype[col] = CategoricalDtype(unique_values)
+                    ordered = getattr(selected_dtype[col], "ordered", None)
+                    selected_dtype[col] = CategoricalDtype(
+                        unique_values, ordered=ordered
+                    )
             ctx[op.outputs[0].key] = in_data.astype(selected_dtype, errors=op.errors)
 
     def __call__(self, df):