chore: optimize appending new columns to Pandas DataFrame (#348)

Author: bednar

CHANGELOG.md

@@ -5,6 +5,9 @@
 1. [#335](https://github.com/influxdata/influxdb-client-python/pull/335): Add support for custom precision for index specified as number [DataFrame] 
 1. [#341](https://github.com/influxdata/influxdb-client-python/pull/341): Add support for handling batch events
 
+### Bug Fixes
+1. [#348](https://github.com/influxdata/influxdb-client-python/pull/348): Optimize appending new columns to Pandas DataFrame [DataFrame]
+
 ### Documentation
 1. [#331](https://github.com/influxdata/influxdb-client-python/pull/331): Add [Migration Guide](MIGRATION_GUIDE.rst)
 1. [#341](https://github.com/influxdata/influxdb-client-python/pull/341): How to handle client errors

influxdb_client/client/flux_csv_parser.py

@@ -133,9 +133,8 @@ def _parse_flux_response(self):
                     # Create DataFrame with default values
                     if self._serialization_mode is FluxSerializationMode.dataFrame:
                         from ..extras import pd
-                        self._data_frame = pd.DataFrame(data=[], columns=[], index=None)
-                        for column in table.columns:
-                            self._data_frame[column.label] = column.default_value
+                        labels = list(map(lambda it: it.label, table.columns))
+                        self._data_frame = pd.DataFrame(data=[], columns=labels, index=None)
                         pass
                     continue
 

tests/test_FluxCSVParser.py

@@ -212,11 +212,38 @@ def test_to_json(self):
             import json
             self.assertEqual(query_output, json.dumps(tables, cls=FluxStructureEncoder, indent=2))
 
+    def test_pandas_lot_of_columns(self):
+        data_types = ""
+        groups = ""
+        defaults = ""
+        columns = ""
+        values = ""
+        for i in range(0, 200):
+            data_types += f",long"
+            groups += f",false"
+            defaults += f","
+            columns += f",column_{i}"
+            values += f",{i}"
+
+        data = f"#datatype,string,long,string,string,dateTime:RFC3339,dateTime:RFC3339,dateTime:RFC3339,double,string{data_types}\n" \
+               f"#group,false,false,true,true,true,true,false,false,true{groups}\n" \
+               f"#default,_result,,,,,,,,{defaults}\n" \
+               f",result,table,_field,_measurement,_start,_stop,_time,_value,tag{columns}\n" \
+               f",,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:20:00Z,2,test1{values}\n" \
+
+        parser = self._parse(data=data, serialization_mode=FluxSerializationMode.dataFrame)
+        _dataFrames = list(parser.generator())
+        self.assertEqual(1, _dataFrames.__len__())
+
     @staticmethod
-    def _parse_to_tables(data: str):
-        fp = BytesIO(str.encode(data))
-        _parser = FluxCsvParser(response=HTTPResponse(fp, preload_content=False),
-                                serialization_mode=FluxSerializationMode.tables)
+    def _parse_to_tables(data: str, serialization_mode=FluxSerializationMode.tables):
+        _parser = FluxCsvParserTest._parse(data, serialization_mode)
         list(_parser.generator())
         tables = _parser.tables
         return tables
+
+    @staticmethod
+    def _parse(data, serialization_mode):
+        fp = BytesIO(str.encode(data))
+        return FluxCsvParser(response=HTTPResponse(fp, preload_content=False),
+                             serialization_mode=serialization_mode)