---
title: Summarize query results and data distribution
description: Query data stored in InfluxDB and use tools like pandas to summarize the results schema and distribution.
url: https://docs.influxdata.com/influxdb3/cloud-dedicated/process-data/summarize/
estimated_tokens: 2344
product: InfluxDB Cloud Dedicated
version: cloud-dedicated
---

# Summarize query results and data distribution

Query data stored in InfluxDB and use tools like pandas to summarize the results schema and distribution.

#### Sample data

The following examples use the sample data written in the [Get started writing data guide](/influxdb3/cloud-dedicated/get-started/write/). To run the example queries and return results, [write the sample data](/influxdb3/cloud-dedicated/get-started/write/#write-line-protocol-to-influxdb) to your InfluxDB Cloud Dedicated database before running the example queries.

### View data information and statistics

#### Using Python and pandas

The following example uses the [InfluxDB client library for Python](/influxdb3/cloud-dedicated/reference/client-libraries/v3/python/) to query an InfluxDB Cloud Dedicated database, and then uses pandas [`DataFrame.info()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html) and [`DataFrame.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html) methods to summarize the schema and distribution of the data.

1. In your editor, create a file (for example, `pandas-example.py`) and enter the following sample code:
    
    ```py
    # pandas-example.py
    
    import influxdb_client_3 as InfluxDBClient3
    import pandas
    
    client = InfluxDBClient3.InfluxDBClient3(token='DATABASE_TOKEN',
                          host='cluster-id.a.influxdb.io',
                          database='DATABASE_NAME',
                          org="",
                          write_options=SYNCHRONOUS)
    
    table = client.query("select * from home where room like '%'")
    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())
    ```
    
2. Enter the following command in your terminal to execute the file using the Python interpreter:
    
    ```sh
    python pandas-example.py
    ```
    
    The output is similar to the following:
    
    ```sh
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 411 entries, 0 to 410
    Data columns (total 8 columns):
    #   Column     Non-Null Count  Dtype         
    ---  ------     --------------  -----         
    0   co         405 non-null    float64       
    1   host       2 non-null      object        
    2   hum        406 non-null    float64       
    3   room       411 non-null    object        
    4   sensor     1 non-null      object        
    5   sensor_id  2 non-null      object        
    6   temp       411 non-null    float64       
    7   time       411 non-null    datetime64[ns]
    dtypes: datetime64[ns](1), float64(3), object(4)
    memory usage: 25.8+ KB
    
                  co         hum        temp                           time
    count  405.000000  406.000000  411.000000                            411
    mean     5.320988   35.860591   23.803893  2008-06-12 13:33:49.074302208
    min      0.000000   20.200000   18.400000     1970-01-01 00:00:01.641024
    25%      0.000000   35.900000   22.200000  1970-01-01 00:00:01.685054600
    50%      1.000000   36.000000   22.500000            2023-03-21 05:46:40
    75%      9.000000   36.300000   22.800000            2023-07-15 21:34:10
    max     26.000000   80.000000   74.000000            2023-07-17 02:07:00
    std      7.640154    3.318794    8.408807                            NaN
    ```
    

#### Related

-   [Use Python to query data](/influxdb3/cloud-dedicated/query-data/execute-queries/client-libraries/python/)