PKG-INFO

Metadata-Version: 2.1
Name: influxdb_client
Version: 1.7.0
Summary: InfluxDB 2.0 Python client library
Home-page: https://github.com/influxdata/influxdb-client-python
License: UNKNOWN
Description: influxdb-client-python
        ======================
        
        .. marker-index-start
        
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        .. _documentation: https://influxdb-client.readthedocs.io
        
        InfluxDB 2.0 python client library.
        
        **Note: Use this client library with InfluxDB 2.x and InfluxDB 1.8+. For connecting to InfluxDB 1.7 or earlier instances, use the `influxdb-python <https://github.com/influxdata/influxdb-python>`_ client library.**
        
        InfluxDB 2.0 client features
        ----------------------------
        
        - Querying data
            - using the Flux language
            - into csv, raw data, `flux_table <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L5>`_ structure, `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
            - `How to queries <#queries>`_
        - Writing data using
            - `Line Protocol <https://docs.influxdata.com/influxdb/v1.6/write_protocols/line_protocol_tutorial>`_
            - `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py#L16>`__
            - `RxPY <https://rxpy.readthedocs.io/en/latest/>`__ Observable
            - `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
            - `How to writes <#writes>`_
        - `InfluxDB 2.0 API <https://github.com/influxdata/influxdb/blob/master/http/swagger.yml>`_ client for management
            - the client is generated from the `swagger <https://github.com/influxdata/influxdb/blob/master/http/swagger.yml>`_ by using the `openapi-generator <https://github.com/OpenAPITools/openapi-generator>`_
            - organizations & users management
            - buckets management
            - tasks management
            - authorizations
            - health check
            - ...
        - `InfluxDB 1.8 API compatibility`_
        - Examples
            - `Connect to InfluxDB Cloud`_
            - `How to efficiently import large dataset`_
            - `Efficiency write data from IOT sensor`_
            - `How to use Jupyter + Pandas + InfluxDB 2`_
        
        Installation
        ------------
        .. marker-install-start
        
        InfluxDB python library uses `RxPY <https://github.com/ReactiveX/RxPY>`__ - The Reactive Extensions for Python (RxPY).
        
        **Python 3.6** or later is required.
        
        .. note::
        
            The client uses ``ciso8601`` for parsing dates. ``ciso8601`` is much faster than built-in Python datetime. Since it's written as a ``C`` module the best way is build it from sources:
        
            **Windows**:
        
            You have to install `Visual C++ Build Tools 2015 <http://go.microsoft.com/fwlink/?LinkId=691126&fixForIE=.exe>`_ to build ``ciso8601`` by ``pip``.
        
            **conda**:
        
            Install from sources: ``conda install -c conda-forge/label/cf202003 ciso8601``.
        
        pip install
        ^^^^^^^^^^^
        
        The python package is hosted on `PyPI <https://pypi.org/project/influxdb-client/>`_, you can install latest version directly:
        
        .. code-block:: sh
        
           pip install influxdb-client
        
        Then import the package:
        
        .. code-block:: python
        
           import influxdb_client
        
        Setuptools
        ^^^^^^^^^^
        
        Install via `Setuptools <http://pypi.python.org/pypi/setuptools>`_.
        
        .. code-block:: sh
        
           python setup.py install --user
        
        (or ``sudo python setup.py install`` to install the package for all users)
        
        .. marker-install-end
        
        Getting Started
        ---------------
        
        Please follow the `Installation`_ and then run the following:
        
        .. marker-query-start
        
        .. code-block:: python
        
           from influxdb_client import InfluxDBClient, Point
           from influxdb_client.client.write_api import SYNCHRONOUS
        
           bucket = "my-bucket"
        
           client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
        
           write_api = client.write_api(write_options=SYNCHRONOUS)
           query_api = client.query_api()
        
           p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
        
           write_api.write(bucket=bucket, record=p)
        
           ## using Table structure
           tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
        
           for table in tables:
               print(table)
               for row in table.records:
                   print (row.values)
        
        
           ## using csv library
           csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
           val_count = 0
           for row in csv_result:
               for cell in row:
                   val_count += 1
        
        
        .. marker-query-end
        
        Client configuration
        --------------------
        
        Via File
        ^^^^^^^^
        A client can be configured via ``*.ini`` file in segment ``influx2``.
        
        The following options are supported:
        
        - ``url`` - the url to connect to InfluxDB
        - ``org`` - default destination organization for writes and queries
        - ``token`` - the token to use for the authorization
        - ``timeout`` - socket timeout in ms (default value is 10000)
        
        .. code-block:: python
        
            self.client = InfluxDBClient.from_config_file("config.ini")
        
        .. code-block::
        
            [influx2]
            url=http://localhost:9999
            org=my-org
            token=my-token
            timeout=6000
        
        Via Environment Properties
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        A client can be configured via environment properties.
        
        Supported properties are:
        
        - ``INFLUXDB_V2_URL`` - the url to connect to InfluxDB
        - ``INFLUXDB_V2_ORG`` - default destination organization for writes and queries
        - ``INFLUXDB_V2_TOKEN`` - the token to use for the authorization
        - ``INFLUXDB_V2_TIMEOUT`` - socket timeout in ms (default value is 10000)
        
        .. code-block:: python
        
            self.client = InfluxDBClient.from_env_properties()
        
        .. marker-index-end
        
        
        How to use
        ----------
        
        Writes
        ^^^^^^
        .. marker-writes-start
        
        The `WriteApi <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write_api.py>`_ supports synchronous, asynchronous and batching writes into InfluxDB 2.0.
        The data should be passed as a `InfluxDB Line Protocol <https://docs.influxdata.com/influxdb/v1.6/write_protocols/line_protocol_tutorial/>`_\ , `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py>`_ or Observable stream.
        
        *The default instance of WriteApi use batching.*
        
        The data could be written as
        """"""""""""""""""""""""""""
        
        1. ``string`` or ``bytes`` that is formatted as a InfluxDB's line protocol
        2. `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py#L16>`__ structure
        3. Dictionary style mapping with keys: ``measurement``, ``tags``, ``fields`` and ``time``
        4. List of above items
        5. A ``batching`` type of write also supports an ``Observable`` that produce one of an above item
        6. `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
        
        
        Batching
        """"""""
        
        .. marker-batching-start
        
        The batching is configurable by ``write_options``\ :
        
        .. list-table::
           :header-rows: 1
        
           * - Property
             - Description
             - Default Value
           * - **batch_size**
             - the number of data pointx to collect in a batch
             - ``1000``
           * - **flush_interval**
             - the number of milliseconds before the batch is written
             - ``1000``
           * - **jitter_interval**
             - the number of milliseconds to increase the batch flush interval by a random amount
             - ``0``
           * - **retry_interval**
             - the number of milliseconds to retry unsuccessful write. The retry interval is used when the InfluxDB server does not specify "Retry-After" header.
             - ``1000``
        
        
        .. code-block:: python
        
           import rx
           from rx import operators as ops
        
           from influxdb_client import InfluxDBClient, Point, WriteOptions
           from influxdb_client.client.write_api import SYNCHRONOUS
        
           _client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
           _write_client = _client.write_api(write_options=WriteOptions(batch_size=500,
                                                                        flush_interval=10_000,
                                                                        jitter_interval=2_000,
                                                                        retry_interval=5_000))
        
           """
           Write Line Protocol formatted as string
           """
           _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
           _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
                                                       "h2o_feet,location=coyote_creek water_level=3.0 3"])
        
           """
           Write Line Protocol formatted as byte array
           """
           _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
           _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
                                                       "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])
        
           """
           Write Dictionary-style object
           """
           _write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                       "fields": {"water_level": 1.0}, "time": 1})
           _write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                       "fields": {"water_level": 2.0}, "time": 2},
                                                       {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                       "fields": {"water_level": 3.0}, "time": 3}])
        
           """
           Write Data Point
           """
           _write_client.write("my-bucket", "my-org", Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
           _write_client.write("my-bucket", "my-org", [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
                                                       Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])
        
           """
           Write Observable stream
           """
           _data = rx \
               .range(7, 11) \
               .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))
        
           _write_client.write("my-bucket", "my-org", _data)
        
           """
           Write Pandas DataFrame
           """
           _now = pd.Timestamp().now('UTC')
           _data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
                                      index=[now, now + timedelta(hours=1)],
                                      columns=["location", "water_level"])
        
           _write_client.write(bucket.name, record=data_frame, data_frame_measurement_name='h2o_feet',
                               data_frame_tag_columns=['location'])
        
           """
           Close client
           """
           _write_client.__del__()
           _client.__del__()
        
        .. marker-batching-end
        
        Default Tags
        """"""""""""
        .. marker-default-tags-start
        
        Sometimes is useful to store same information in every measurement e.g. ``hostname``, ``location``, ``customer``.
        The client is able to use static value or env property as a tag value.
        
        The expressions:
        
        - ``California Miner`` - static value
        - ``${env.hostname}`` - environment property
        
        Via API
        _______
        
        .. code-block:: python
        
            point_settings = PointSettings()
            point_settings.add_default_tag("id", "132-987-655")
            point_settings.add_default_tag("customer", "California Miner")
            point_settings.add_default_tag("data_center", "${env.data_center}")
        
            self.write_client = self.client.write_api(write_options=SYNCHRONOUS, point_settings=point_settings)
        
        .. code-block:: python
        
            self.write_client = self.client.write_api(write_options=SYNCHRONOUS,
                                                          point_settings=PointSettings(**{"id": "132-987-655",
                                                                                          "customer": "California Miner"}))
        
        Via Configuration file
        ______________________
        
        In a ini configuration file you are able to specify default tags by ``tags`` segment.
        
        .. code-block:: python
        
            self.client = InfluxDBClient.from_config_file("config.ini")
        
        .. code-block::
        
            [influx2]
            url=http://localhost:9999
            org=my-org
            token=my-token
            timeout=6000
        
            [tags]
            id = 132-987-655
            customer = California Miner
            data_center = ${env.data_center}
        
        Via Environment Properties
        __________________________
        You are able to specify default tags by environment properties with prefix ``INFLUXDB_V2_TAG_``.
        
        Examples:
        
        - ``INFLUXDB_V2_TAG_ID``
        - ``INFLUXDB_V2_TAG_HOSTNAME``
        
        .. code-block:: python
        
            self.client = InfluxDBClient.from_env_properties()
        
        .. marker-default-tags-end
        
        Asynchronous client
        """""""""""""""""""
        
        Data are writes in an asynchronous HTTP request.
        
        .. code-block:: python
        
           from influxdb_client import InfluxDBClient, Point
           from influxdb_client.client.write_api import ASYNCHRONOUS
        
           client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
           write_api = client.write_api(write_options=ASYNCHRONOUS)
        
           _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
           _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
        
           async_result = write_api.write(bucket="my-bucket", record=[_point1, _point2])
           async_result.get()
        
           client.__del__()
        
        Synchronous client
        """"""""""""""""""
        
        Data are writes in a synchronous HTTP request.
        
        .. code-block:: python
        
           from influxdb_client import InfluxDBClient, Point
           from influxdb_client .client.write_api import SYNCHRONOUS
        
           client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
           write_api = client.write_api(write_options=SYNCHRONOUS)
        
           _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
           _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
        
           write_api.write(bucket="my-bucket", record=[_point1, _point2])
        
           client.__del__()
        
        Queries
        ^^^^^^^
        
        The result retrieved by `QueryApi <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/query_api.py>`_  could be formatted as a:
        
        1. Flux data structure: `FluxTable <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L5>`_, `FluxColumn <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L22>`_ and `FluxRecord <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L31>`_
        2. `csv.reader <https://docs.python.org/3.4/library/csv.html#reader-objects>`__ which will iterate over CSV lines
        3. Raw unprocessed results as a ``str`` iterator
        4. `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
        
        The API also support streaming ``FluxRecord`` via `query_stream <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/query_api.py#L77>`_, see example below:
        
        .. code-block:: python
        
            from influxdb_client import InfluxDBClient, Point, Dialect
            from influxdb_client.client.write_api import SYNCHRONOUS
        
            client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
        
            write_api = client.write_api(write_options=SYNCHRONOUS)
            query_api = client.query_api()
        
            """
            Prepare data
            """
        
            _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
            _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
        
            write_api.write(bucket="my-bucket", record=[_point1, _point2])
        
            """
            Query: using Table structure
            """
            tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
        
            for table in tables:
                print(table)
                for record in table.records:
                    print(record.values)
        
            print()
            print()
        
            """
            Query: using Stream
            """
            records = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -10m)')
        
            for record in records:
                print(f'Temperature in {record["location"]} is {record["_value"]}')
        
            """
            Interrupt a stream after retrieve a required data
            """
            large_stream = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -100d)')
            for record in large_stream:
                if record["location"] == "New York":
                    print(f'New York temperature: {record["_value"]}')
                    break
        
            large_stream.close()
        
            print()
            print()
        
            """
            Query: using csv library
            """
            csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
                                             dialect=Dialect(header=False, delimiter=",", comment_prefix="#", annotations=[],
                                                             date_time_format="RFC3339"))
            for csv_line in csv_result:
                if not len(csv_line) == 0:
                    print(f'Temperature in {csv_line[9]} is {csv_line[6]}')
        
            """
            Close client
            """
            client.__del__()
        
        Pandas DataFrame
        """"""""""""""""
        .. marker-pandas-start
        
        .. note:: For DataFrame querying you should install Pandas dependency via ``pip install influxdb-client[extra]``.
        
        .. note:: Note that if a query returns more then one table then the client generates a ``DataFrame`` for each of them.
        
        The ``client`` is able to retrieve data in `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_ format thought ``query_data_frame``:
        
        .. code-block:: python
        
            from influxdb_client import InfluxDBClient, Point, Dialect
            from influxdb_client.client.write_api import SYNCHRONOUS
        
            client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
        
            write_api = client.write_api(write_options=SYNCHRONOUS)
            query_api = client.query_api()
        
            """
            Prepare data
            """
        
            _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
            _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
        
            write_api.write(bucket="my-bucket", record=[_point1, _point2])
        
            """
            Query: using Pandas DataFrame
            """
            data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
                                                    '|> range(start: -10m) '
                                                    '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
                                                    '|> keep(columns: ["location", "temperature"])')
            print(data_frame.to_string())
        
            """
            Close client
            """
            client.__del__()
        
        Output:
        
        .. code-block::
        
                result table  location  temperature
            0  _result     0  New York         24.3
            1  _result     1    Prague         25.3
        
        .. marker-pandas-end
        
        Examples
        ^^^^^^^^
        
        How to efficiently import large dataset
        """""""""""""""""""""""""""""""""""""""
        
        The following example shows how to import dataset with dozen megabytes.
        If you would like to import gigabytes of data then use our multiprocessing example: `import_data_set_multiprocessing.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/import_data_set_multiprocessing.py>`_ for use a full capability of your hardware.
        
        * sources - `import_data_set.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/import_data_set.py>`_
        
        .. code-block:: python
        
           """
           Import VIX - CBOE Volatility Index - from "vix-daily.csv" file into InfluxDB 2.0
        
           https://datahub.io/core/finance-vix#data
           """
        
           from collections import OrderedDict
           from csv import DictReader
        
           import rx
           from rx import operators as ops
        
           from influxdb_client import InfluxDBClient, Point, WriteOptions
        
           def parse_row(row: OrderedDict):
               """Parse row of CSV file into Point with structure:
        
                   financial-analysis,type=ily close=18.47,high=19.82,low=18.28,open=19.82 1198195200000000000
        
               CSV format:
                   Date,VIX Open,VIX High,VIX Low,VIX Close\n
                   2004-01-02,17.96,18.68,17.54,18.22\n
                   2004-01-05,18.45,18.49,17.44,17.49\n
                   2004-01-06,17.66,17.67,16.19,16.73\n
                   2004-01-07,16.72,16.75,15.5,15.5\n
                   2004-01-08,15.42,15.68,15.32,15.61\n
                   2004-01-09,16.15,16.88,15.57,16.75\n
                   ...
        
               :param row: the row of CSV file
               :return: Parsed csv row to [Point]
               """
        
               """
                For better performance is sometimes useful directly create a LineProtocol to avoid unnecessary escaping overhead:
                """
                # from pytz import UTC
                # import ciso8601
                # from influxdb_client.client.write.point import EPOCH
                #
                # time = (UTC.localize(ciso8601.parse_datetime(row["Date"])) - EPOCH).total_seconds() * 1e9
                # return f"financial-analysis,type=vix-daily" \
                #        f" close={float(row['VIX Close'])},high={float(row['VIX High'])},low={float(row['VIX Low'])},open={float(row['VIX Open'])} " \
                #        f" {int(time)}"
        
               return Point("financial-analysis") \
                   .tag("type", "vix-daily") \
                   .field("open", float(row['VIX Open'])) \
                   .field("high", float(row['VIX High'])) \
                   .field("low", float(row['VIX Low'])) \
                   .field("close", float(row['VIX Close'])) \
                   .time(row['Date'])
        
        
           """
           Converts vix-daily.csv into sequence of datad point
           """
           data = rx \
               .from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
               .pipe(ops.map(lambda row: parse_row(row)))
        
           client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org", debug=True)
        
           """
           Create client that writes data in batches with 50_000 items.
           """
           write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))
        
           """
           Write data into InfluxDB
           """
           write_api.write(bucket="my-bucket", record=data)
           write_api.__del__()
        
           """
           Querying max value of CBOE Volatility Index
           """
           query = 'from(bucket:"my-bucket")' \
                   ' |> range(start: 0, stop: now())' \
                   ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
                   ' |> max()'
           result = client.query_api().query(query=query)
        
           """
           Processing results
           """
           print()
           print("=== results ===")
           print()
           for table in result:
               for record in table.records:
                   print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))
        
           """
           Close client
           """
           client.__del__()
        
        .. marker-writes-end
        
        
        Efficiency write data from IOT sensor
        """""""""""""""""""""""""""""""""""""
        .. marker-iot-start
        
        * sources - `iot_sensor.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/iot_sensor.py>`_
        
        .. code-block:: python
        
           """
           Efficiency write data from IOT sensor - write changed temperature every minute
           """
           import atexit
           import platform
           from datetime import timedelta
        
           import psutil as psutil
           import rx
           from rx import operators as ops
        
           from influxdb_client import InfluxDBClient, WriteApi, WriteOptions
        
           def on_exit(db_client: InfluxDBClient, write_api: WriteApi):
               """Close clients after terminate a script.
        
               :param db_client: InfluxDB client
               :param write_api: WriteApi
               :return: nothing
               """
               write_api.__del__()
               db_client.__del__()
        
        
           def sensor_temperature():
               """Read a CPU temperature. The [psutil] doesn't support MacOS so we use [sysctl].
        
               :return: actual CPU temperature
               """
               os_name = platform.system()
               if os_name == 'Darwin':
                   from subprocess import check_output
                   output = check_output(["sysctl", "machdep.xcpm.cpu_thermal_level"])
                   import re
                   return re.findall(r'\d+', str(output))[0]
               else:
                   return psutil.sensors_temperatures()["coretemp"][0]
        
        
           def line_protocol(temperature):
               """Create a InfluxDB line protocol with structure:
        
                   iot_sensor,hostname=mine_sensor_12,type=temperature value=68
        
               :param temperature: the sensor temperature
               :return: Line protocol to write into InfluxDB
               """
        
               import socket
               return 'iot_sensor,hostname={},type=temperature value={}'.format(socket.gethostname(), temperature)
        
        
           """
           Read temperature every minute; distinct_until_changed - produce only if temperature change
           """
           data = rx\
               .interval(period=timedelta(seconds=60))\
               .pipe(ops.map(lambda t: sensor_temperature()),
                     ops.distinct_until_changed(),
                     ops.map(lambda temperature: line_protocol(temperature)))
        
           _db_client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org", debug=True)
        
           """
           Create client that writes data into InfluxDB
           """
           _write_api = _db_client.write_api(write_options=WriteOptions(batch_size=1))
           _write_api.write(bucket="my-bucket", record=data)
        
        
           """
           Call after terminate a script
           """
           atexit.register(on_exit, _db_client, _write_api)
        
           input()
        
        .. marker-iot-end
        
        Connect to InfluxDB Cloud
        """""""""""""""""""""""""
        The following example demonstrate a simplest way how to write and query date with the InfluxDB Cloud.
        
        At first point you should create an authentication token as is described `here <https://v2.docs.influxdata.com/v2.0/security/tokens/create-token/>`_.
        
        After that you should configure properties: ``influx_cloud_url``, ``influx_cloud_token``, ``bucket`` and ``org`` in a ``influx_cloud.py`` example.
        
        The last step is run a python script via: ``python3 influx_cloud.py``.
        
        * sources - `influx_cloud.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/influx_cloud.py>`_
        
        .. code-block:: python
        
            """
            Connect to InfluxDB 2.0 - write data and query them
            """
        
            from datetime import datetime
        
            from influxdb_client import Point, InfluxDBClient
            from influxdb_client.client.write_api import SYNCHRONOUS
        
            """
            Configure credentials
            """
            influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
            influx_cloud_token = '...'
            bucket = '...'
            org = '...'
        
            client = InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token)
            try:
                kind = 'temperature'
                host = 'host1'
                device = 'opt-123'
        
                """
                Write data by Point structure
                """
                point = Point(kind).tag('host', host).tag('device', device).field('value', 25.3).time(time=datetime.utcnow())
        
                print(f'Writing to InfluxDB cloud: {point.to_line_protocol()} ...')
        
                write_api = client.write_api(write_options=SYNCHRONOUS)
                write_api.write(bucket=bucket, org=org, record=point)
        
                print()
                print('success')
                print()
                print()
        
                """
                Query written data
                """
                query = f'from(bucket: "{bucket}") |> range(start: -1d) |> filter(fn: (r) => r._measurement == "{kind}")'
                print(f'Querying from InfluxDB cloud: "{query}" ...')
                print()
        
                query_api = client.query_api()
                tables = query_api.query(query=query, org=org)
        
                for table in tables:
                    for row in table.records:
                        print(f'{row.values["_time"]}: host={row.values["host"]},device={row.values["device"]} '
                              f'{row.values["_value"]} °C')
        
                print()
                print('success')
        
            except Exception as e:
                print(e)
            finally:
                client.close()
        
        How to use Jupyter + Pandas + InfluxDB 2
        """"""""""""""""""""""""""""""""""""""""
        The first example shows how to use client capabilities to predict stock price via `Keras <https://keras.io>`_, `TensorFlow <https://www.tensorflow.org>`_, `sklearn <https://scikit-learn.org/stable/>`_:
        
        The example is taken from `Kaggle <https://www.kaggle.com/chaitanyacc4/predicting-stock-prices-of-apple-inc>`_.
        
        * sources - `stock-predictions.ipynb <notebooks/stock-predictions.ipynb>`_
        
        .. image:: https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/stock-price-prediction.gif
        
        Result:
        
        .. image:: https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/stock-price-prediction-results.png
        
        The second example shows how to use client capabilities to realtime visualization via `hvPlot <https://hvplot.pyviz.org>`_, `Streamz <https://streamz.readthedocs.io/en/latest/>`_, `RxPY <https://rxpy.readthedocs.io/en/latest/>`_:
        
        * sources - `realtime-stream.ipynb <notebooks/realtime-stream.ipynb>`_
        
        .. image:: https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/realtime-result.gif
        
        
        Advanced Usage
        --------------
        
        Gzip support
        ^^^^^^^^^^^^
        .. marker-gzip-start
        
        ``InfluxDBClient`` does not enable gzip compression for http requests by default. If you want to enable gzip to reduce transfer data's size, you can call:
        
        .. code-block:: python
        
           from influxdb_client import InfluxDBClient
        
           _db_client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org", enable_gzip=True)
        
        .. marker-gzip-end
        
        InfluxDB 1.8 API compatibility
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        `InfluxDB 1.8.0 introduced forward compatibility APIs <https://docs.influxdata.com/influxdb/latest/tools/api/#influxdb-2-0-api-compatibility-endpoints>`_ for InfluxDB 2.0. This allow you to easily move from InfluxDB 1.x to InfluxDB 2.0 Cloud or open source.
        
        The following forward compatible APIs are available:
        
        =======================================================  ====================================================================================================  =======
         API                                                     Endpoint                                                                                              Description
        =======================================================  ====================================================================================================  =======
        `query_api.py <influxdb_client/client/query_api.py>`_    `/api/v2/query <https://docs.influxdata.com/influxdb/latest/tools/api/#api-v2-query-http-endpoint>`_  Query data in InfluxDB 1.8.0+ using the InfluxDB 2.0 API and `Flux <https://docs.influxdata.com/flux/latest/>`_ (endpoint should be enabled by `flux-enabled option <https://docs.influxdata.com/influxdb/latest/administration/config/#flux-enabled-false>`_)
        `write_api.py <influxdb_client/client/write_api.py>`_    `/api/v2/write <https://docs.influxdata.com/influxdb/latest/tools/api/#api-v2-write-http-endpoint>`_  Write data to InfluxDB 1.8.0+ using the InfluxDB 2.0 API
        `health() <influxdb_client/client/influxdb_client.py>`_  `/health <https://docs.influxdata.com/influxdb/latest/tools/api/#health-http-endpointt>`_             Check the health of your InfluxDB instance
        =======================================================  ====================================================================================================  =======
        
        For detail info see `InfluxDB 1.8 example <examples/influxdb_18_example.py>`_.
        
        Local tests
        -----------
        
        .. code-block:: python
        
            # start/restart InfluxDB2 on local machine using docker
            ./scripts/influxdb-restart.sh
        
            # install requirements
            pip install -r requirements.txt --user
            pip install -r extra-requirements.txt --user
            pip install -r test-requirements.txt --user
        
            # run unit & integration tests
            pytest tests
        
        
        Contributing
        ------------
        
        Bug reports and pull requests are welcome on GitHub at `https://github.com/influxdata/influxdb-client-python <https://github.com/influxdata/influxdb-client-python>`_.
        
        License
        -------
        
        The gem is available as open source under the terms of the `MIT License <https://opensource.org/licenses/MIT>`_.
        
Keywords: InfluxDB,InfluxDB Python Client
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Database
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: extra