fix: fixed serialization of DataFrame with empty (NaN) values, fixed …

…escaping whitespaces, fixed order of tags (#123)

CHANGELOG.md

@@ -8,6 +8,7 @@
 ### Bug Fixes
 1. [#117](https://github.com/influxdata/influxdb-client-python/pull/117): Fixed appending default tags for single Point 
 1. [#115](https://github.com/influxdata/influxdb-client-python/pull/115): Fixed serialization of `\n`, `\r` and `\t` to Line Protocol, `=` is valid sign for measurement name 
+1. [#118](https://github.com/influxdata/influxdb-client-python/issues/118): Fixed serialization of DataFrame with empty (NaN) values
 
 ## 1.8.0 [2020-06-19]
 

influxdb_client/client/write/dataframe_serializer.py

@@ -0,0 +1,103 @@
+import re
+from functools import reduce
+from itertools import chain
+
+from influxdb_client.client.write.point import _ESCAPE_KEY, _ESCAPE_MEASUREMENT
+
+"""
+Functions for serialize Pandas DataFrame.
+Much of the code here is inspired by that in the aioinflux packet found here: https://github.com/gusutabopb/aioinflux
+"""
+
+
+def _replace(data_frame):
+    from ...extras import np
+
+    # string columns
+    obj_cols = {k for k, v in dict(data_frame.dtypes).items() if v is np.dtype('O')}
+
+    # number columns
+    other_cols = set(data_frame.columns) - obj_cols
+
+    obj_nans = (f'{k}=nan' for k in obj_cols)
+    other_nans = (f'{k}=nani?' for k in other_cols)
+
+    replacements = [
+        ('|'.join(chain(obj_nans, other_nans)), ''),
+        (',{2,}', ','),
+        ('|'.join([', ,', ', ', ' ,']), ' '),
+    ]
+
+    return replacements
+
+
+def _itertuples(data_frame):
+    cols = [data_frame.iloc[:, k] for k in range(len(data_frame.columns))]
+    return zip(data_frame.index, *cols)
+
+
+def data_frame_to_list_of_points(data_frame, point_settings, **kwargs):
+    from ...extras import pd, np
+    if not isinstance(data_frame, pd.DataFrame):
+        raise TypeError('Must be DataFrame, but type was: {0}.'
+                        .format(type(data_frame)))
+
+    if 'data_frame_measurement_name' not in kwargs:
+        raise TypeError('"data_frame_measurement_name" is a Required Argument')
+
+    if isinstance(data_frame.index, pd.PeriodIndex):
+        data_frame.index = data_frame.index.to_timestamp()
+    else:
+        data_frame.index = pd.to_datetime(data_frame.index)
+
+    if data_frame.index.tzinfo is None:
+        data_frame.index = data_frame.index.tz_localize('UTC')
+
+    measurement_name = str(kwargs.get('data_frame_measurement_name')).translate(_ESCAPE_MEASUREMENT)
+    data_frame_tag_columns = kwargs.get('data_frame_tag_columns')
+    data_frame_tag_columns = set(data_frame_tag_columns or [])
+
+    tags = []
+    fields = []
+    keys = []
+
+    if point_settings.defaultTags:
+        for key, value in point_settings.defaultTags.items():
+            data_frame[key] = value
+            data_frame_tag_columns.add(key)
+
+    for index, (key, value) in enumerate(data_frame.dtypes.items()):
+        key = str(key)
+        keys.append(key.translate(_ESCAPE_KEY))
+        key_format = f'{{keys[{index}]}}'
+
+        if key in data_frame_tag_columns:
+            tags.append({'key': key, 'value': f"{key_format}={{str(p[{index + 1}]).translate(_ESCAPE_KEY)}}"})
+        elif issubclass(value.type, np.integer):
+            fields.append(f"{key_format}={{p[{index + 1}]}}i")
+        elif issubclass(value.type, (np.float, np.bool_)):
+            fields.append(f"{key_format}={{p[{index + 1}]}}")
+        else:
+            fields.append(f"{key_format}=\"{{str(p[{index + 1}]).translate(_ESCAPE_KEY)}}\"")
+
+    tags.sort(key=lambda x: x['key'])
+    tags = ','.join(map(lambda y: y['value'], tags))
+
+    fmt = ('{measurement_name}', f'{"," if tags else ""}', tags,
+           ' ', ','.join(fields), ' {p[0].value}')
+    f = eval("lambda p: f'{}'".format(''.join(fmt)),
+             {'measurement_name': measurement_name, '_ESCAPE_KEY': _ESCAPE_KEY, 'keys': keys})
+
+    for k, v in dict(data_frame.dtypes).items():
+        if k in data_frame_tag_columns:
+            data_frame[k].replace('', np.nan, inplace=True)
+
+    isnull = data_frame.isnull().any(axis=1)
+
+    if isnull.any():
+        rep = _replace(data_frame)
+        lp = (reduce(lambda a, b: re.sub(*b, a), rep, f(p))
+              for p in _itertuples(data_frame))
+        return list(lp)
+    else:
+        return list(map(f, _itertuples(data_frame)))

influxdb_client/client/write_api.py

@@ -14,7 +14,8 @@
 from rx.subject import Subject
 
 from influxdb_client import WritePrecision, WriteService
-from influxdb_client.client.write.point import Point, DEFAULT_WRITE_PRECISION, _ESCAPE_KEY
+from influxdb_client.client.write.dataframe_serializer import data_frame_to_list_of_points
+from influxdb_client.client.write.point import Point, DEFAULT_WRITE_PRECISION
 from influxdb_client.rest import ApiException
 
 logger = logging.getLogger(__name__)
@@ -258,7 +259,7 @@ def _serialize(self, record, write_precision, payload, **kwargs):
             self._serialize(Point.from_dict(record, write_precision=write_precision),
                             write_precision, payload, **kwargs)
         elif 'DataFrame' in type(record).__name__:
-            _data = self._data_frame_to_list_of_points(record, precision=write_precision, **kwargs)
+            _data = data_frame_to_list_of_points(record, self._point_settings, **kwargs)
             self._serialize(_data, write_precision, payload, **kwargs)
 
         elif isinstance(record, list):
@@ -284,7 +285,7 @@ def _write_batching(self, bucket, org, data,
                                  precision, **kwargs)
 
         elif 'DataFrame' in type(data).__name__:
-            self._write_batching(bucket, org, self._data_frame_to_list_of_points(data, precision, **kwargs),
+            self._write_batching(bucket, org, data_frame_to_list_of_points(data, self._point_settings, **kwargs),
                                  precision, **kwargs)
 
         elif isinstance(data, list):
@@ -306,57 +307,6 @@ def _append_default_tag(self, key, val, record):
             for item in record:
                 self._append_default_tag(key, val, item)
 
-    def _itertuples(self, data_frame):
-        cols = [data_frame.iloc[:, k] for k in range(len(data_frame.columns))]
-        return zip(data_frame.index, *cols)
-
-    def _data_frame_to_list_of_points(self, data_frame, precision, **kwargs):
-        from ..extras import pd, np
-        if not isinstance(data_frame, pd.DataFrame):
-            raise TypeError('Must be DataFrame, but type was: {0}.'
-                            .format(type(data_frame)))
-
-        if 'data_frame_measurement_name' not in kwargs:
-            raise TypeError('"data_frame_measurement_name" is a Required Argument')
-
-        if isinstance(data_frame.index, pd.PeriodIndex):
-            data_frame.index = data_frame.index.to_timestamp()
-        else:
-            data_frame.index = pd.to_datetime(data_frame.index)
-
-        if data_frame.index.tzinfo is None:
-            data_frame.index = data_frame.index.tz_localize('UTC')
-
-        measurement_name = kwargs.get('data_frame_measurement_name')
-        data_frame_tag_columns = kwargs.get('data_frame_tag_columns')
-        data_frame_tag_columns = set(data_frame_tag_columns or [])
-
-        tags = []
-        fields = []
-
-        if self._point_settings.defaultTags:
-            for key, value in self._point_settings.defaultTags.items():
-                data_frame[key] = value
-                data_frame_tag_columns.add(key)
-
-        for index, (key, value) in enumerate(data_frame.dtypes.items()):
-            key = str(key).translate(_ESCAPE_KEY)
-
-            if key in data_frame_tag_columns:
-                tags.append(f"{key}={{p[{index + 1}].translate(_ESCAPE_KEY)}}")
-            elif issubclass(value.type, np.integer):
-                fields.append(f"{key}={{p[{index + 1}]}}i")
-            elif issubclass(value.type, (np.float, np.bool_)):
-                fields.append(f"{key}={{p[{index + 1}]}}")
-            else:
-                fields.append(f"{key}=\"{{p[{index + 1}].translate(_ESCAPE_KEY)}}\"")
-
-        fmt = (f'{measurement_name}', f'{"," if tags else ""}', ','.join(tags),
-               ' ', ','.join(fields), ' {p[0].value}')
-        f = eval("lambda p: f'{}'".format(''.join(fmt)))
-
-        return list(map(f, self._itertuples(data_frame)))
-
     def _http(self, batch_item: _BatchItem):
 
         logger.debug("Write time series data into InfluxDB: %s", batch_item)

tests/test_WriteApiDataFrame.py

@@ -5,7 +5,8 @@
 from datetime import timedelta
 
 from influxdb_client import InfluxDBClient, WriteOptions, WriteApi
-from influxdb_client.client.write_api import SYNCHRONOUS
+from influxdb_client.client.write.dataframe_serializer import data_frame_to_list_of_points
+from influxdb_client.client.write_api import SYNCHRONOUS, PointSettings
 from tests.base_test import BaseTest
 
 
@@ -86,3 +87,130 @@ def test_write_num_py(self):
         self.assertEqual(result[0].records[1].get_value(), 200.0)
 
         pass
+
+    def test_write_nan(self):
+        from influxdb_client.extras import pd, np
+
+        now = pd.Timestamp('2020-04-05 00:00+00:00')
+
+        data_frame = pd.DataFrame(data=[[3.1955, np.nan, 20.514305, np.nan],
+                                        [5.7310, np.nan, 23.328710, np.nan],
+                                        [np.nan, 3.138664, np.nan, 20.755026],
+                                        [5.7310, 5.139563, 23.328710, 19.791240]],
+                                  index=[now, now + timedelta(minutes=30), now + timedelta(minutes=60),
+                                         now + timedelta(minutes=90)],
+                                  columns=["actual_kw_price", "forecast_kw_price", "actual_general_use",
+                                           "forecast_general_use"])
+
+        points = data_frame_to_list_of_points(data_frame=data_frame, point_settings=PointSettings(),
+                                              data_frame_measurement_name='measurement')
+
+        self.assertEqual(4, len(points))
+        self.assertEqual("measurement actual_kw_price=3.1955,actual_general_use=20.514305 1586044800000000000",
+                         points[0])
+        self.assertEqual("measurement actual_kw_price=5.731,actual_general_use=23.32871 1586046600000000000",
+                         points[1])
+        self.assertEqual("measurement forecast_kw_price=3.138664,forecast_general_use=20.755026 1586048400000000000",
+                         points[2])
+        self.assertEqual("measurement actual_kw_price=5.731,forecast_kw_price=5.139563,actual_general_use=23.32871,"
+                         "forecast_general_use=19.79124 1586050200000000000",
+                         points[3])
+
+    def test_write_tag_nan(self):
+        from influxdb_client.extras import pd, np
+
+        now = pd.Timestamp('2020-04-05 00:00+00:00')
+
+        data_frame = pd.DataFrame(data=[["", 3.1955, 20.514305],
+                                        ['', 5.7310, 23.328710],
+                                        [np.nan, 5.7310, 23.328710],
+                                        ["tag", 3.138664, 20.755026]],
+                                  index=[now, now + timedelta(minutes=30),
+                                         now + timedelta(minutes=60), now + timedelta(minutes=90)],
+                                  columns=["tag", "actual_kw_price", "forecast_kw_price"])
+
+        write_api = self.client.write_api(write_options=SYNCHRONOUS, point_settings=PointSettings())
+
+        points = data_frame_to_list_of_points(data_frame=data_frame,
+                                              point_settings=PointSettings(),
+                                              data_frame_measurement_name='measurement',
+                                              data_frame_tag_columns={"tag"})
+
+        self.assertEqual(4, len(points))
+        self.assertEqual("measurement actual_kw_price=3.1955,forecast_kw_price=20.514305 1586044800000000000",
+                         points[0])
+        self.assertEqual("measurement actual_kw_price=5.731,forecast_kw_price=23.32871 1586046600000000000",
+                         points[1])
+        self.assertEqual("measurement actual_kw_price=5.731,forecast_kw_price=23.32871 1586048400000000000",
+                         points[2])
+        self.assertEqual("measurement,tag=tag actual_kw_price=3.138664,forecast_kw_price=20.755026 1586050200000000000",
+                         points[3])
+
+        write_api.__del__()
+
+    def test_escaping_measurement(self):
+        from influxdb_client.extras import pd, np
+
+        now = pd.Timestamp('2020-04-05 00:00+00:00')
+
+        data_frame = pd.DataFrame(data=[["coyote_creek", np.int64(100.5)], ["coyote_creek", np.int64(200)]],
+                                  index=[now + timedelta(hours=1), now + timedelta(hours=2)],
+                                  columns=["location", "water_level"])
+
+        points = data_frame_to_list_of_points(data_frame=data_frame,
+                                              point_settings=PointSettings(),
+                                              data_frame_measurement_name='measu rement',
+                                              data_frame_tag_columns={"tag"})
+
+        self.assertEqual(2, len(points))
+        self.assertEqual("measu\\ rement location=\"coyote_creek\",water_level=100i 1586048400000000000",
+                         points[0])
+        self.assertEqual("measu\\ rement location=\"coyote_creek\",water_level=200i 1586052000000000000",
+                         points[1])
+
+        points = data_frame_to_list_of_points(data_frame=data_frame,
+                                              point_settings=PointSettings(),
+                                              data_frame_measurement_name='measu\nrement2',
+                                              data_frame_tag_columns={"tag"})
+
+        self.assertEqual(2, len(points))
+        self.assertEqual("measu\\nrement2 location=\"coyote_creek\",water_level=100i 1586048400000000000",
+                         points[0])
+        self.assertEqual("measu\\nrement2 location=\"coyote_creek\",water_level=200i 1586052000000000000",
+                         points[1])
+
+    def test_tag_escaping_key_and_value(self):
+        from influxdb_client.extras import pd, np
+
+        now = pd.Timestamp('2020-04-05 00:00+00:00')
+
+        data_frame = pd.DataFrame(data=[["carriage\nreturn", "new\nline", "t\tab", np.int64(2)], ],
+                                  index=[now + timedelta(hours=1), ],
+                                  columns=["carriage\rreturn", "new\nline", "t\tab", "l\ne\rv\tel"])
+
+        points = data_frame_to_list_of_points(data_frame=data_frame,
+                                              point_settings=PointSettings(),
+                                              data_frame_measurement_name='h\n2\ro\t_data',
+                                              data_frame_tag_columns={"new\nline", "carriage\rreturn", "t\tab"})
+
+        self.assertEqual(1, len(points))
+        self.assertEqual(
+            "h\\n2\\ro\\t_data,carriage\\rreturn=carriage\\nreturn,new\\nline=new\\nline,t\\tab=t\\tab l\\ne\\rv\\tel=2i 1586048400000000000",
+            points[0])
+
+    def test_tags_order(self):
+        from influxdb_client.extras import pd, np
+
+        now = pd.Timestamp('2020-04-05 00:00+00:00')
+
+        data_frame = pd.DataFrame(data=[["c", "a", "b", np.int64(2)], ],
+                                  index=[now + timedelta(hours=1), ],
+                                  columns=["c", "a", "b", "level"])
+
+        points = data_frame_to_list_of_points(data_frame=data_frame,
+                                              point_settings=PointSettings(),
+                                              data_frame_measurement_name='h2o',
+                                              data_frame_tag_columns={"c", "a", "b"})
+
+        self.assertEqual(1, len(points))
+        self.assertEqual("h2o,a=a,b=b,c=c level=2i 1586048400000000000", points[0])