--- title: Use pandas to analyze data description: Use the pandas Python data analysis library to analyze and visualize time series data stored in InfluxDB Clustered. url: https://docs.influxdata.com/influxdb3/clustered/process-data/tools/pandas/ estimated_tokens: 2034 publisher: InfluxData canonical: https://docs.influxdata.com/influxdb3/clustered/process-data/tools/pandas/ date: '2026-05-15T15:46:14-06:00' lastmod: '2026-05-15T15:46:14-06:00' --- Use [pandas](https://pandas.pydata.org/), the Python data analysis library, to process, analyze, and visualize data stored in an InfluxDB Clustered database. > > > **pandas** is an open source, BSD-licensed library providing high-performance, > easy-to-use data structures and data analysis tools for the Python programming language. > > > > [pandas documentation](https://pandas.pydata.org/docs/) > > * [Install prerequisites](#install-prerequisites) * [Install pandas](#install-pandas) * [Use PyArrow to convert query results to pandas](#use-pyarrow-to-convert-query-results-to-pandas) * [Use pandas to analyze data](#use-pandas-to-analyze-data) * [View data information and statistics](#view-data-information-and-statistics) * [Downsample time series](#downsample-time-series) ## Install prerequisites The examples in this guide assume using a Python virtual environment and the InfluxDB 3 [`influxdb3-python` Python client library](/influxdb3/clustered/reference/client-libraries/v3/python/). For more information, see how to [get started using Python to query InfluxDB](/influxdb3/clustered/query-data/execute-queries/client-libraries/python/). Installing `influxdb3-python` also installs the [`pyarrow`](https://arrow.apache.org/docs/python/index.html) library that provides Python bindings for Apache Arrow. ## Install pandas To use pandas, you need to install and import the `pandas` library. In your terminal, use `pip` to install `pandas` in your active [Python virtual environment](/influxdb3/clustered/query-data/execute-queries/client-libraries/python/#create-a-project-virtual-environment): ```sh pip install pandas ``` ## Use PyArrow to convert query results to pandas The following steps use Python, `influxdb3-python`, and `pyarrow` to query InfluxDB and stream Arrow data to a pandas `DataFrame`. 1. In your editor, copy and paste the following code to a new file–for example, `pandas-example.py`: ``` # pandas-example.py from influxdb_client_3 import InfluxDBClient3 import pandas # Instantiate an InfluxDB client configured for a database client = InfluxDBClient3( "https://cluster-host.com", database="DATABASE_NAME", token="DATABASE_TOKEN") # Execute the query to retrieve all record batches in the stream # formatted as a PyArrow Table. table = client.query( '''SELECT * FROM home WHERE time >= now() - INTERVAL '90 days' ORDER BY time''' ) client.close() # Convert the PyArrow Table to a pandas DataFrame. dataframe = table.to_pandas() print(dataframe) ``` 2. Replace the following configuration values: * `DATABASE_NAME`: the name of the [database](/influxdb3/clustered/admin/databases/) to query * `DATABASE_TOKEN`: a [database token](/influxdb3/clustered/admin/tokens/#database-tokens)with *read* permission on the specified database 3. In your terminal, use the Python interpreter to run the file: ``` python pandas-example.py ``` The example calls the following methods: * [`InfluxDBClient3.query()`](/influxdb3/clustered/reference/client-libraries/v3/python/#influxdbclient3query): sends the query request and returns a [`pyarrow.Table`](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html) that contains all the Arrow record batches from the response stream. * [`pyarrow.Table.to_pandas()`](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_pandas): Creates a [`pandas.DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame) from the data in the PyArrow `Table`. [](#view-example-results) View example results ``` co hum room temp time 0 0 35.9 Living Room 21.1 2022-01-02 11:46:40 1 0 35.9 Kitchen 21.0 2022-01-02 11:46:40 2 0 36.2 Kitchen 23.0 2022-01-02 12:46:40 3 0 35.9 Living Room 21.4 2022-01-02 12:46:40 4 0 36.1 Kitchen 22.7 2022-01-02 13:46:40 5 0 36.0 Living Room 21.8 2022-01-02 13:46:40 6 0 36.0 Kitchen 22.4 2022-01-02 14:46:40 7 0 36.0 Living Room 22.2 2022-01-02 14:46:40 8 0 36.0 Kitchen 22.5 2022-01-02 15:46:40 9 0 35.9 Living Room 22.2 2022-01-02 15:46:40 10 1 36.5 Kitchen 22.8 2022-01-02 16:46:40 11 0 36.0 Living Room 22.4 2022-01-02 16:46:40 12 1 36.3 Kitchen 22.8 2022-01-02 17:46:40 13 0 36.1 Living Room 22.3 2022-01-02 17:46:40 14 3 36.2 Kitchen 22.7 2022-01-02 18:46:40 15 1 36.1 Living Room 22.3 2022-01-02 18:46:40 16 7 36.0 Kitchen 22.4 2022-01-02 19:46:40 17 4 36.0 Living Room 22.4 2022-01-02 19:46:40 18 9 36.0 Kitchen 22.7 2022-01-02 20:46:40 19 5 35.9 Living Room 22.6 2022-01-02 20:46:40 20 18 36.9 Kitchen 23.3 2022-01-02 21:46:40 21 9 36.2 Living Room 22.8 2022-01-02 21:46:40 22 22 36.6 Kitchen 23.1 2022-01-02 22:46:40 23 14 36.3 Living Room 22.5 2022-01-02 22:46:40 24 26 36.5 Kitchen 22.7 2022-01-02 23:46:40 25 17 36.4 Living Room 22.2 2022-01-02 23:46:40 ``` Next, [use pandas to analyze data](#use-pandas-to-analyze-data). ## Use pandas to analyze data * [View data information and statistics](#view-data-information-and-statistics) * [Downsample time series](#downsample-time-series) ### View data information and statistics The following example shows how to use pandas `DataFrame` methods to transform and summarize data stored in InfluxDB Clustered. ```py # pandas-example.py from influxdb_client_3 import InfluxDBClient3 import pandas # Instantiate an InfluxDB client configured for a database client = InfluxDBClient3( "https://cluster-host.com", database="DATABASE_NAME", token="DATABASE_TOKEN") # Execute the query to retrieve all record batches in the stream # formatted as a PyArrow Table. table = client.query( '''SELECT * FROM home WHERE time >= now() - INTERVAL '90 days' ORDER BY time''' ) client.close() # Convert the PyArrow Table to a pandas DataFrame. dataframe = table.to_pandas() # Print information about the results DataFrame, # including the index dtype and columns, non-null values, and memory usage. dataframe.info() # Calculate descriptive statistics that summarize the distribution of the results. print(dataframe.describe()) # Extract a DataFrame column. print(dataframe['temp']) # Print the DataFrame in Markdown format. print(dataframe.to_markdown()) ``` Replace the following configuration values: * `DATABASE_NAME`: the name of the InfluxDB [database](/influxdb3/clustered/admin/databases/) to query * `DATABASE_TOKEN`: a [database token](/influxdb3/clustered/admin/tokens/#database-tokens)with read permission on the specified database ### Downsample time series The pandas library provides extensive features for working with time series data. The [`pandas.DataFrame.resample()` method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html) downsamples and upsamples data to time-based groups–for example: ```py # pandas-example.py ... # Use the `time` column to generate a DatetimeIndex for the DataFrame dataframe = dataframe.set_index('time') # Print information about the index print(dataframe.index) # Downsample data into 1-hour groups based on the DatetimeIndex resample = dataframe.resample("1H") # Print a summary that shows the start time and average temp for each group print(resample['temp'].mean()) ``` [](#view-example-results) View example results ```sh time 2023-07-16 22:00:00 NaN 2023-07-16 23:00:00 22.600000 2023-07-17 00:00:00 22.513889 2023-07-17 01:00:00 22.208333 2023-07-17 02:00:00 22.300000 ... Freq: H, Name: temp, Length: 469323, dtype: float64 ``` For more detail and examples, see the [pandas documentation](https://pandas.pydata.org/docs/index.html). #### Related * [Use Python to query data](/influxdb3/clustered/query-data/execute-queries/client-libraries/python/) [analysis](/influxdb3/clustered/tags/analysis/)[pandas](/influxdb3/clustered/tags/pandas/)[pyarrow](/influxdb3/clustered/tags/pyarrow/)[python](/influxdb3/clustered/tags/python/)