fix: Optimize serializing data into Pandas DataFrame

CHANGELOG.md

@@ -1,5 +1,8 @@
 ## 1.6.0 [unreleased]
 
+### Bugs
+1. [#72](https://github.com/influxdata/influxdb-client-python/issues/72): Optimize serializing data into Pandas DataFrame
+
 ## 1.5.0 [2020-03-13]
 
 ### Features

influxdb_client/client/flux_csv_parser.py

@@ -34,6 +34,7 @@ def __init__(self, response: HTTPResponse, serialization_mode: FluxSerialization
         self.tables = []
         self._serialization_mode = serialization_mode
         self._data_frame_index = data_frame_index
+        self._data_frame_values = []
         pass
 
     def __enter__(self):
@@ -135,20 +136,27 @@ def _parse_flux_response(self):
                     yield flux_record
 
                 if self._serialization_mode is FluxSerializationMode.dataFrame:
-                    self._data_frame.loc[len(self._data_frame.index)] = flux_record.values
+                    self._data_frame_values.append(flux_record.values)
                     pass
 
-                # debug
-                # print(flux_record)
-
         # Return latest DataFrame
         if (self._serialization_mode is FluxSerializationMode.dataFrame) & hasattr(self, '_data_frame'):
             yield self._prepare_data_frame()
 
     def _prepare_data_frame(self):
+        from ..extras import pd
+
+        # We have to create temporary DataFrame because we want to preserve default column values
+        _temp_df = pd.DataFrame(self._data_frame_values)
+        self._data_frame_values = []
+
+        # Custom DataFrame index
         if self._data_frame_index:
             self._data_frame = self._data_frame.set_index(self._data_frame_index)
-        return self._data_frame
+            _temp_df = _temp_df.set_index(self._data_frame_index)
+
+        # Append data
+        return self._data_frame.append(_temp_df)
 
     def parse_record(self, table_index, table, csv):
         record = FluxRecord(table_index)

tests/test_QueryApiDataFrame.py

@@ -1,9 +1,13 @@
+import random
+
 import httpretty
+import rx
 from pandas import DataFrame
 from pandas._libs.tslibs.timestamps import Timestamp
+from rx import operators as ops
 
-from influxdb_client import InfluxDBClient
-from tests.base_test import BaseTest
+from influxdb_client import InfluxDBClient, Point, WritePrecision, WriteOptions
+from tests.base_test import BaseTest, current_milli_time
 
 
 class QueryDataFrameApi(BaseTest):
@@ -250,3 +254,37 @@ def test_more_table_custom_index(self):
                               Timestamp('2019-11-12 08:09:07+0000'), Timestamp('2019-11-12 08:09:08+0000'),
                               Timestamp('2019-11-12 08:09:09+0000')], list(_dataFrames[2].index))
         self.assertEqual(5, len(_dataFrames[2]))
+
+
+class QueryDataFrameIntegrationApi(BaseTest):
+
+    def test_large_amount_of_data(self):
+        _measurement_name = "data_frame_" + str(current_milli_time())
+
+        def _create_point(index) -> Point:
+            return Point(_measurement_name) \
+                .tag("deviceType", str(random.choice(['A', 'B']))) \
+                .tag("name", random.choice(['A', 'B'])) \
+                .field("uuid", random.randint(0, 10_000)) \
+                .field("co2", random.randint(0, 10_000)) \
+                .field("humid", random.randint(0, 10_000)) \
+                .field("lux", random.randint(0, 10_000)) \
+                .field("water", random.randint(0, 10_000)) \
+                .field("shine", random.randint(0, 10_000)) \
+                .field("temp", random.randint(0, 10_000)) \
+                .field("voc", random.randint(0, 10_000)) \
+                .time(time=(1583828781 + index), write_precision=WritePrecision.S)
+
+        data = rx.range(0, 2_000).pipe(ops.map(lambda index: _create_point(index)))
+
+        write_api = self.client.write_api(write_options=WriteOptions(batch_size=500))
+        write_api.write(org="my-org", bucket="my-bucket", record=data, write_precision=WritePrecision.S)
+        write_api.__del__()
+
+        query = 'from(bucket: "my-bucket")' \
+                '|> range(start: 2020-02-19T23:30:00Z, stop: now())' \
+                f'|> filter(fn: (r) => r._measurement == "{_measurement_name}")'
+
+        result = self.client.query_api().query_data_frame(org="my-org", query=query)
+
+        self.assertGreater(len(result), 1)