diff --git a/influxdb/_dataframe_client.py b/influxdb/_dataframe_client.py
new file mode 100644
index 00000000..7e834464
--- /dev/null
+++ b/influxdb/_dataframe_client.py
@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+"""
+DataFrame client for InfluxDB
+"""
+import math
+import warnings
+
+from .client import InfluxDBClient
+
+import pandas as pd
+
+
+class DataFrameClient(InfluxDBClient):
+ """
+ The ``DataFrameClient`` object holds information necessary to connect
+ to InfluxDB. Requests can be made to InfluxDB directly through the client.
+ The client reads and writes from pandas DataFrames.
+ """
+
+ EPOCH = pd.Timestamp('1970-01-01 00:00:00.000+00:00')
+
+ def write_points(self, data, *args, **kwargs):
+ """
+ Write to multiple time series names.
+
+ :param data: A dictionary mapping series names to pandas DataFrames
+ :param time_precision: [Optional, default 's'] Either 's', 'm', 'ms'
+ or 'u'.
+ :param batch_size: [Optional] Value to write the points in batches
+ instead of all at one time. Useful for when doing data dumps from
+ one database to another or when doing a massive write operation
+ :type batch_size: int
+ """
+
+ batch_size = kwargs.get('batch_size')
+ time_precision = kwargs.get('time_precision', 's')
+ if batch_size:
+ kwargs.pop('batch_size') # don't hand over to InfluxDBClient
+ for key, data_frame in data.items():
+ number_batches = int(math.ceil(
+ len(data_frame) / float(batch_size)))
+ for batch in range(number_batches):
+ start_index = batch * batch_size
+ end_index = (batch + 1) * batch_size
+ data = [self._convert_dataframe_to_json(
+ name=key,
+ dataframe=data_frame.ix[start_index:end_index].copy(),
+ time_precision=time_precision)]
+ super(DataFrameClient, self).write_points(data,
+ *args, **kwargs)
+ return True
+ else:
+ data = [self._convert_dataframe_to_json(
+ name=key, dataframe=dataframe, time_precision=time_precision)
+ for key, dataframe in data.items()]
+ return super(DataFrameClient, self).write_points(data,
+ *args, **kwargs)
+
+ def write_points_with_precision(self, data, time_precision='s'):
+ """
+ DEPRECATED. Write to multiple time series names
+
+ """
+ warnings.warn(
+ "write_points_with_precision is deprecated, and will be removed "
+ "in future versions. Please use "
+ "``DataFrameClient.write_points(time_precision='..')`` instead.",
+ FutureWarning)
+ return self.write_points(data, time_precision='s')
+
+ def query(self, query, time_precision='s', chunked=False):
+ """
+ Quering data into a DataFrame.
+
+ :param time_precision: [Optional, default 's'] Either 's', 'm', 'ms'
+ or 'u'.
+ :param chunked: [Optional, default=False] True if the data shall be
+ retrieved in chunks, False otherwise.
+
+ """
+ results = super(DataFrameClient, self).query(query, database=database)
+ if len(results) > 0:
+ return self._to_dataframe(results, time_precision)
+ else:
+ return results
+
+ def _to_dataframe(self, json_result, time_precision):
+ dataframe = pd.DataFrame(data=json_result['points'],
+ columns=json_result['columns'])
+ if 'sequence_number' in dataframe.keys():
+ dataframe.sort(['time', 'sequence_number'], inplace=True)
+ else:
+ dataframe.sort(['time'], inplace=True)
+ pandas_time_unit = time_precision
+ if time_precision == 'm':
+ pandas_time_unit = 'ms'
+ elif time_precision == 'u':
+ pandas_time_unit = 'us'
+ dataframe.index = pd.to_datetime(list(dataframe['time']),
+ unit=pandas_time_unit,
+ utc=True)
+ del dataframe['time']
+ return dataframe
+
+ def _convert_dataframe_to_json(self, dataframe, name, time_precision='s'):
+ if not isinstance(dataframe, pd.DataFrame):
+ raise TypeError('Must be DataFrame, but type was: {}.'
+ .format(type(dataframe)))
+ if not (isinstance(dataframe.index, pd.tseries.period.PeriodIndex) or
+ isinstance(dataframe.index, pd.tseries.index.DatetimeIndex)):
+ raise TypeError('Must be DataFrame with DatetimeIndex or \
+ PeriodIndex.')
+ dataframe.index = dataframe.index.to_datetime()
+ if dataframe.index.tzinfo is None:
+ dataframe.index = dataframe.index.tz_localize('UTC')
+ dataframe['time'] = [self._datetime_to_epoch(dt, time_precision)
+ for dt in dataframe.index]
+ data = {'name': name,
+ 'columns': [str(column) for column in dataframe.columns],
+ 'points': list([list(x) for x in dataframe.values])}
+ return data
+
+ def _datetime_to_epoch(self, datetime, time_precision='s'):
+ seconds = (datetime - self.EPOCH).total_seconds()
+ if time_precision == 's':
+ return seconds
+ elif time_precision == 'm' or time_precision == 'ms':
+ return seconds * 1000
+ elif time_precision == 'u':
+ return seconds * 1000000
diff --git a/influxdb/client.py b/influxdb/client.py
index 67d063ca..e1c75d13 100755
--- a/influxdb/client.py
+++ b/influxdb/client.py
@@ -113,10 +113,9 @@ def format_query_response(response):
if 'columns' in row.keys() and 'values' in row.keys():
for value in row['values']:
item = {}
- current_col = 0
- for field in value:
- item[row['columns'][current_col]] = field
- current_col += 1
+ for cur_col, field in enumerate(value):
+ item[row['columns'][cur_col]] = field
+ cur_col += 1
items.append(item)
return series
@@ -237,8 +236,7 @@ def write_points(self,
time_precision=None,
database=None,
retention_policy=None,
- *args,
- **kwargs):
+ ):
"""
Write to multiple time series names.
@@ -261,12 +259,12 @@ def _write_points(self,
database,
retention_policy):
if time_precision not in ['n', 'u', 'ms', 's', 'm', 'h', None]:
- raise Exception(
+ raise ValueError(
"Invalid time precision is given. "
"(use 'n', 'u', 'ms', 's', 'm' or 'h')")
if self.use_udp and time_precision and time_precision != 's':
- raise Exception(
+ raise ValueError(
"InfluxDB only supports seconds precision for udp writes"
)
@@ -325,7 +323,7 @@ def create_retention_policy(
query_string = \
"CREATE RETENTION POLICY %s ON %s " \
"DURATION %s REPLICATION %s" % \
- (name, (database or self._database), duration, replication)
+ (name, database or self._database, duration, replication)
if default is True:
query_string += " DEFAULT"
@@ -344,7 +342,7 @@ def get_list_series(self, database=None):
"""
Get the list of series
"""
- return self.query("SHOW SERIES", database=(database or self._database))
+ return self.query("SHOW SERIES", database=database)
def get_list_users(self):
"""
diff --git a/influxdb/dataframe_client.py b/influxdb/dataframe_client.py
index 8191bcba..41a6796c 100644
--- a/influxdb/dataframe_client.py
+++ b/influxdb/dataframe_client.py
@@ -2,140 +2,18 @@
"""
DataFrame client for InfluxDB
"""
-import math
-import warnings
-from .client import InfluxDBClient
+__all__ = ['DataFrameClient']
try:
- import pandas as pd
-except ImportError:
- pd = None
-
-
-class DataFrameClient(InfluxDBClient):
- """
- The ``DataFrameClient`` object holds information necessary to connect
- to InfluxDB. Requests can be made to InfluxDB directly through the client.
- The client reads and writes from pandas DataFrames.
- """
-
- def __init__(self, *args, **kwargs):
- super(DataFrameClient, self).__init__(*args, **kwargs)
- if not pd:
- raise ImportError(
- 'DataFrameClient requires Pandas'
- )
-
- self.EPOCH = pd.Timestamp('1970-01-01 00:00:00.000+00:00')
-
- def write_points(self, data, *args, **kwargs):
- """
- Write to multiple time series names.
-
- :param data: A dictionary mapping series names to pandas DataFrames
- :param time_precision: [Optional, default 's'] Either 's', 'm', 'ms'
- or 'u'.
- :param batch_size: [Optional] Value to write the points in batches
- instead of all at one time. Useful for when doing data dumps from
- one database to another or when doing a massive write operation
- :type batch_size: int
- """
-
- batch_size = kwargs.get('batch_size')
- time_precision = kwargs.get('time_precision', 's')
- if batch_size:
- kwargs.pop('batch_size') # don't hand over to InfluxDBClient
- for key, data_frame in data.items():
- number_batches = int(math.ceil(
- len(data_frame) / float(batch_size)))
- for batch in range(number_batches):
- start_index = batch * batch_size
- end_index = (batch + 1) * batch_size
- data = [self._convert_dataframe_to_json(
- name=key,
- dataframe=data_frame.ix[start_index:end_index].copy(),
- time_precision=time_precision)]
- InfluxDBClient.write_points(self, data, *args, **kwargs)
- return True
- else:
- data = [self._convert_dataframe_to_json(
- name=key, dataframe=dataframe, time_precision=time_precision)
- for key, dataframe in data.items()]
- return InfluxDBClient.write_points(self, data, *args, **kwargs)
-
- def write_points_with_precision(self, data, time_precision='s'):
- """
- DEPRECATED. Write to multiple time series names
-
- """
- warnings.warn(
- "write_points_with_precision is deprecated, and will be removed "
- "in future versions. Please use "
- "``DataFrameClient.write_points(time_precision='..')`` instead.",
- FutureWarning)
- return self.write_points(data, time_precision='s')
-
- def query(self, query, time_precision='s', chunked=False):
- """
- Quering data into a DataFrame.
-
- :param time_precision: [Optional, default 's'] Either 's', 'm', 'ms'
- or 'u'.
- :param chunked: [Optional, default=False] True if the data shall be
- retrieved in chunks, False otherwise.
-
- """
- result = InfluxDBClient.query(self,
- query=query,
- time_precision=time_precision,
- chunked=chunked)
- if len(result['results'][0]) > 0:
- return self._to_dataframe(result['results'][0], time_precision)
- else:
- return result
-
- def _to_dataframe(self, json_result, time_precision):
- dataframe = pd.DataFrame(data=json_result['points'],
- columns=json_result['columns'])
- if 'sequence_number' in dataframe.keys():
- dataframe.sort(['time', 'sequence_number'], inplace=True)
- else:
- dataframe.sort(['time'], inplace=True)
- pandas_time_unit = time_precision
- if time_precision == 'm':
- pandas_time_unit = 'ms'
- elif time_precision == 'u':
- pandas_time_unit = 'us'
- dataframe.index = pd.to_datetime(list(dataframe['time']),
- unit=pandas_time_unit,
- utc=True)
- del dataframe['time']
- return dataframe
-
- def _convert_dataframe_to_json(self, dataframe, name, time_precision='s'):
- if not isinstance(dataframe, pd.DataFrame):
- raise TypeError('Must be DataFrame, but type was: {}.'
- .format(type(dataframe)))
- if not (isinstance(dataframe.index, pd.tseries.period.PeriodIndex) or
- isinstance(dataframe.index, pd.tseries.index.DatetimeIndex)):
- raise TypeError('Must be DataFrame with DatetimeIndex or \
- PeriodIndex.')
- dataframe.index = dataframe.index.to_datetime()
- if dataframe.index.tzinfo is None:
- dataframe.index = dataframe.index.tz_localize('UTC')
- dataframe['time'] = [self._datetime_to_epoch(dt, time_precision)
- for dt in dataframe.index]
- data = {'name': name,
- 'columns': [str(column) for column in dataframe.columns],
- 'points': list([list(x) for x in dataframe.values])}
- return data
-
- def _datetime_to_epoch(self, datetime, time_precision='s'):
- seconds = (datetime - self.EPOCH).total_seconds()
- if time_precision == 's':
- return seconds
- elif time_precision == 'm' or time_precision == 'ms':
- return seconds * 1000
- elif time_precision == 'u':
- return seconds * 1000000
+ import pandas
+ del pandas
+except ImportError as err:
+ from .client import InfluxDBClient
+
+ class DataFrameClient(InfluxDBClient):
+ def __init__(self, *a, **kw):
+ raise ImportError("DataFrameClient requires Pandas "
+ "which couldn't be imported: %s" % err)
+else:
+ from ._dataframe_client import DataFrameClient
diff --git a/tests/influxdb/dataframe_client_test.py b/tests/influxdb/dataframe_client_test.py
index 135e718b..fd4af039 100644
--- a/tests/influxdb/dataframe_client_test.py
+++ b/tests/influxdb/dataframe_client_test.py
@@ -211,13 +211,24 @@ def test_query_with_empty_result(self):
assert result == []
def test_list_series(self):
- response = [
- {
- 'columns': ['time', 'name'],
- 'name': 'list_series_result',
- 'points': [[0, 'seriesA'], [0, 'seriesB']]
- }
- ]
+ response = {
+ 'results': [
+ {
+ 'series': [{
+ 'columns': ['id'],
+ 'name': 'seriesA',
+ 'values': [[0]],
+ }]
+ },
+ {
+ 'series': [{
+ 'columns': ['id'],
+ 'name': 'seriesB',
+ 'values': [[1]],
+ }]
+ },
+ ]
+ }
with _mocked_session('get', 200, response):
cli = DataFrameClient('host', 8086, 'username', 'password', 'db')
series_list = cli.get_list_series()
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