sonnh-influxdb-clientv1 1.50.0.dev0
InfluxDB 2.0 Python client library
Requires Python: >=3.7
Keywords: InfluxDB, InfluxDB Python Client

# influxdb-client-python

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This repository contains the Python client library for use with InfluxDB 2.x and Flux. InfluxDB 3.x users should instead use the lightweight [v3 client library](https://github.com/InfluxCommunity/influxdb3-python).
InfluxDB 1.x users should use the [v1 client library](https://github.com/influxdata/influxdb-python).

For ease of migration and a consistent query and write experience, v2 users should consider using InfluxQL and the [v1 client library](https://github.com/influxdata/influxdb-python).

The API of the **influxdb-client-python** is not the backwards-compatible with the old one - **influxdb-python**.

## Documentation

This section contains links to the client library documentation.

-   [Product documentation](https://docs.influxdata.com/influxdb/v2.0/tools/client-libraries/), [Getting Started](#getting-started)
-   [Examples](https://github.com/influxdata/influxdb-client-python/tree/master/examples)
-   [API Reference](https://influxdb-client.readthedocs.io/en/stable/api.html)
-   [Changelog](https://github.com/influxdata/influxdb-client-python/blob/master/CHANGELOG.md)

## 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#L33) structure, [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)
  - [How to query](#queries)
-  Writing data using
  - [Line Protocol](https://docs.influxdata.com/influxdb/latest/reference/syntax/line-protocol)
  - [Data Point](https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py#L47)
  - [RxPY](https://rxpy.readthedocs.io/en/latest/) Observable
  - [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)
  - [How to write](#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](#influxdb-18-api-compatibility)
- Examples
  - [Connect to InfluxDB Cloud](#connect-to-influxdb-cloud)
  - [How to efficiently import large dataset](#how-to-efficiently-import-large-dataset)
  - [Efficiency write data from IOT sensor](#efficiency-write-data-from-iot-sensor)
  - [How to use Jupyter + Pandas + InfluxDB 2](#how-to-use-jupyter--pandas--influxdb-2)
- [Advanced Usage](#advanced-usage)
  - [Gzip support](#gzip-support)
  - [Proxy configuration](#proxy-configuration)
  - [Nanosecond precision](#nanosecond-precision)
  - [Delete data](#delete-data)
  - [Handling Errors](#handling-errors)
  - [Logging](#logging)

## Installation

InfluxDB python library uses [RxPY](https://github.com/ReactiveX/RxPY) - The Reactive Extensions for Python (RxPY).

**Python 3.7** or later is required.

:warning:
> It is recommended to use `ciso8601` with client 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:

``` sh
pip install 'influxdb-client[ciso]'
```

Then import the package:

``` python
import influxdb_client
```

If your application uses async/await in Python you can install with the `async` extra:

``` sh
$ pip install influxdb-client[async]
```

For more info see [How to use Asyncio](#how-to-use-asyncio).

### Setuptools

Install via [Setuptools](http://pypi.python.org/pypi/setuptools).

``` sh
python setup.py install --user
```

(or `sudo python setup.py install` to install the package for all users)

## Getting Started

Please follow the [Installation](#installation) and then run the following:

<!-- marker-query-start -->

``` python
from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS

bucket = "my-bucket"

client = InfluxDBClient(url="http://localhost:8086", 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)
-   `verify_ssl` - set this to false to skip verifying SSL certificate when calling API from https server
-   `ssl_ca_cert` - set this to customize the certificate file to verify the peer
-   `cert_file` - path to the certificate that will be used for mTLS authentication
-   `cert_key_file` - path to the file contains private key for mTLS certificate
-   `cert_key_password` - string or function which returns password for decrypting the mTLS private key
-   `connection_pool_maxsize` - set the number of connections to save that can be reused by urllib3
-   `auth_basic` - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
-   `profilers` - set the list of enabled [Flux profilers](https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/profiler/)

``` python
self.client = InfluxDBClient.from_config_file("config.ini")
```

``` ini
[influx2]
url=http://localhost:8086
org=my-org
token=my-token
timeout=6000
verify_ssl=False
```

### 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)
-   `INFLUXDB_V2_VERIFY_SSL` - set this to false to skip verifying SSL certificate when calling API from https server
-   `INFLUXDB_V2_SSL_CA_CERT` - set this to customize the certificate file to verify the peer
-   `INFLUXDB_V2_CERT_FILE` - path to the certificate that will be used for mTLS authentication
-   `INFLUXDB_V2_CERT_KEY_FILE` - path to the file contains private key for mTLS certificate
-   `INFLUXDB_V2_CERT_KEY_PASSWORD` - string or function which returns password for decrypting the mTLS private key
-   `INFLUXDB_V2_CONNECTION_POOL_MAXSIZE` - set the number of connections to save that can be reused by urllib3
-   `INFLUXDB_V2_AUTH_BASIC` - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
-   `INFLUXDB_V2_PROFILERS` - set the list of enabled [Flux profilers](https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/profiler/)

``` python
self.client = InfluxDBClient.from_env_properties()
```

### Profile query

The [Flux Profiler package](https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/profiler/) provides performance profiling tools for Flux queries and operations.

You can enable printing profiler information of the Flux query in client
library by:

-   set QueryOptions.profilers in QueryApi,
-   set `INFLUXDB_V2_PROFILERS` environment variable,
-   set `profilers` option in configuration file.

When the profiler is enabled, the result of flux query contains additional tables "profiler/". In order to have consistent behaviour with enabled/disabled profiler, `FluxCSVParser` excludes "profiler/" measurements from result.

Example how to enable profilers using API:

``` python
q = '''
    from(bucket: stringParam)
      |> range(start: -5m, stop: now())
      |> filter(fn: (r) => r._measurement == "mem")
      |> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
      |> aggregateWindow(every: 1m, fn: mean)
      |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
'''
p = {
    "stringParam": "my-bucket",
}

query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"]))
csv_result = query_api.query(query=q, params=p)
```

Example of a profiler output:

``` text
===============
Profiler: query
===============

from(bucket: stringParam)
  |> range(start: -5m, stop: now())
  |> filter(fn: (r) => r._measurement == "mem")
  |> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
  |> aggregateWindow(every: 1m, fn: mean)
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

========================
Profiler: profiler/query
========================
result              : _profiler
table               : 0
_measurement        : profiler/query
TotalDuration       : 8924700
CompileDuration     : 350900
QueueDuration       : 33800
PlanDuration        : 0
RequeueDuration     : 0
ExecuteDuration     : 8486500
Concurrency         : 0
MaxAllocated        : 2072
TotalAllocated      : 0
flux/query-plan     :

digraph {
  ReadWindowAggregateByTime11
  // every = 1m, aggregates = [mean], createEmpty = true, timeColumn = "_stop"
  pivot8
  generated_yield

  ReadWindowAggregateByTime11 -> pivot8
  pivot8 -> generated_yield
}


influxdb/scanned-bytes: 0
influxdb/scanned-values: 0

===========================
Profiler: profiler/operator
===========================
result              : _profiler
table               : 1
_measurement        : profiler/operator
Type                : *universe.pivotTransformation
Label               : pivot8
Count               : 3
MinDuration         : 32600
MaxDuration         : 126200
DurationSum         : 193400
MeanDuration        : 64466.666666666664

===========================
Profiler: profiler/operator
===========================
result              : _profiler
table               : 1
_measurement        : profiler/operator
Type                : *influxdb.readWindowAggregateSource
Label               : ReadWindowAggregateByTime11
Count               : 1
MinDuration         : 940500
MaxDuration         : 940500
DurationSum         : 940500
MeanDuration        : 940500.0
```

You can also use callback function to get profilers output. Return value of this callback is type of FluxRecord.

Example how to use profilers with callback:

``` python
class ProfilersCallback(object):
   def __init__(self):
       self.records = []

   def __call__(self, flux_record):
       self.records.append(flux_record.values)

callback = ProfilersCallback()

query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"], profiler_callback=callback))
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for profiler in callback.records:
   print(f'Custom processing of profiler result: {profiler}')
```

Example output of this callback:

``` text
Custom processing of profiler result: {'result': '_profiler', 'table': 0, '_measurement': 'profiler/query', 'TotalDuration': 18843792, 'CompileDuration': 1078666, 'QueueDuration': 93375, 'PlanDuration': 0, 'RequeueDuration': 0, 'ExecuteDuration': 17371000, 'Concurrency': 0, 'MaxAllocated': 448, 'TotalAllocated': 0, 'RuntimeErrors': None, 'flux/query-plan': 'digraph {\r\n  ReadRange2\r\n  generated_yield\r\n\r\n  ReadRange2 -> generated_yield\r\n}\r\n\r\n', 'influxdb/scanned-bytes': 0, 'influxdb/scanned-values': 0}
Custom processing of profiler result: {'result': '_profiler', 'table': 1, '_measurement': 'profiler/operator', 'Type': '*influxdb.readFilterSource', 'Label': 'ReadRange2', 'Count': 1, 'MinDuration': 3274084, 'MaxDuration': 3274084, 'DurationSum': 3274084, 'MeanDuration': 3274084.0}
```

<!-- 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/latest/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.

:warning:

> The `WriteApi` in batching mode (default mode) is supposed to run as a
singleton. To flush all your data you should wrap the execution using
`with client.write_api(...) as write_api:` statement or call
`write_api.close()` at the end of your script.

*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` or custom structure
4.  [NamedTuple](https://docs.python.org/3/library/collections.html#collections.namedtuple)
5.  [Data Classes](https://docs.python.org/3/library/dataclasses.html)
6.  [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)
7.  List of above items
8.  A `batching` type of write also supports an `Observable` that produce one of an above item

You can find write examples at GitHub: [influxdb-client-python/examples](https://github.com/influxdata/influxdb-client-python/tree/master/examples#writes).

#### Batching

The batching is configurable by `write_options`:



| Property             | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | Default Value |
|----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
| **batch_size**       | the number of data point 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 first unsuccessful write. The next retry delay is computed using exponential random backoff. The retry interval is used when the InfluxDB server does not specify \"Retry-After\" header.                                                                                                                                                                                                                                                           | `5000`        |
| **max_retry_time**   | maximum total retry timeout in milliseconds.                                                                                                                                                                                                                                                                                                                                                                                                                                            | `180_000`     |
| **max_retries**      | the number of max retries when write fails                                                                                                                                                                                                                                                                                                                                                                                                                                              | `5`           |
| **max_retry_delay**  | the maximum delay between each retry attempt in milliseconds                                                                                                                                                                                                                                                                                                                                                                                                                            | `125_000`     |
| **max_close_wait**   | the maximum amount of time to wait for batches to flush when `.close()` is called                                                                                                                                                                                                                                                                                                                                                                                                       | `300_000`     |
| **exponential_base** | the base for the exponential retry delay, the next delay is computed using random exponential backoff as a random value within the interval `retry_interval * exponential_base^(attempts-1)` and `retry_interval * exponential_base^(attempts)`. Example for `retry_interval=5_000, exponential_base=2, max_retry_delay=125_000, total=5` Retry delays are random distributed values within the ranges of `[5_000-10_000, 10_000-20_000, 20_000-40_000, 40_000-80_000, 80_000-125_000]` | `2`           |

``` python
from datetime import datetime, timedelta, timezone

import pandas as pd
import reactivex as rx
from reactivex import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as _client:

    with _client.write_api(write_options=WriteOptions(batch_size=500,
                                                      flush_interval=10_000,
                                                      jitter_interval=2_000,
                                                      retry_interval=5_000,
                                                      max_retries=5,
                                                      max_retry_delay=30_000,
                                                      max_close_wait=300_000,
                                                      exponential_base=2)) as _write_client:

        """
        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 = datetime.now(tz=timezone.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("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
                            data_frame_tag_columns=['location'])
```

#### Default Tags

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

``` 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)
```

``` 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 an [init](https://docs.python.org/3/library/configparser.html) configuration file you are able to specify default tags by `tags` segment.

``` python
self.client = InfluxDBClient.from_config_file("config.ini")
```

``` 
[influx2]
url=http://localhost:8086
org=my-org
token=my-token
timeout=6000

[tags]
id = 132-987-655
customer = California Miner
data_center = ${env.data_center}
```

You can also use a [TOML](https://toml.io/en/) or a[JSON](https://www.json.org/json-en.html) format for the configuration file.

##### 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`

``` python
self.client = InfluxDBClient.from_env_properties()
```

#### Synchronous client

Data are writes in a synchronous HTTP request.

``` python
from influxdb_client import InfluxDBClient, Point
from influxdb_client .client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", 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.close()
```
<!-- marker-writes-end -->

### 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.  `influxdb_client.client.flux_table.CSVIterator` 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:

``` python
from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", 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 Bind parameters
"""

p = {"_start": datetime.timedelta(hours=-1),
     "_location": "Prague",
     "_desc": True,
     "_floatParam": 25.1,
     "_every": datetime.timedelta(minutes=5)
     }

tables = query_api.query('''
    from(bucket:"my-bucket") |> range(start: _start)
        |> filter(fn: (r) => r["_measurement"] == "my_measurement")
        |> filter(fn: (r) => r["_field"] == "temperature")
        |> filter(fn: (r) => r["location"] == _location and r["_value"] > _floatParam)
        |> aggregateWindow(every: _every, fn: mean, createEmpty: true)
        |> sort(columns: ["_time"], desc: _desc)
''', params=p)

for table in tables:
    print(table)
    for record in table.records:
        print(str(record["_time"]) + " - " + record["location"] + ": " + str(record["_value"]))

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.close()
```

#### Pandas DataFrame

<!-- marker-pandas-start -->

:warning:

> For DataFrame querying you should install Pandas dependency via `pip install 'influxdb-client[extra]'`.

:warning:

> Note that if a query returns more then one table than 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`:

``` python
from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", 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.close()
```

Output:

``` text
result table  location  temperature
0  _result     0  New York         24.3
1  _result     1    Prague         25.3
```

<!-- marker-pandas-end -->

### Examples

<!-- marker-examples-start -->

#### How to efficiently import large dataset

The following example shows how to import dataset with a 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)

``` 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 reactivex as rx
from reactivex 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 datetime import timezone
     # import ciso8601
     # from influxdb_client.client.write.point import EPOCH
     #
     # time = (ciso8601.parse_datetime(row["Date"]).replace(tzinfo=timezone.utc) - 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:8086", 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.close()

"""
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.close()
```

#### Efficiency write data from IOT sensor

-   sources - [iot_sensor.py](https://github.com/influxdata/influxdb-client-python/blob/master/examples/iot_sensor.py)

``` 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 reactivex as rx
from reactivex 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.close()
    db_client.close()


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:8086", 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()
```

#### Connect to InfluxDB Cloud

The following example demonstrate the 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)

``` python
"""
Connect to InfluxDB 2.0 - write data and query them
"""

from datetime import datetime, timezone

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=da