---
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: 890
publisher: InfluxData
canonical: https://docs.influxdata.com/influxdb3/cloud-dedicated/process-data/summarize/
date: '2026-05-15T15:46:14-06:00'
lastmod: '2026-05-15T15:46:14-06:00'
---

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:

   ```
   # 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:

   ```
   python pandas-example.py
   ```

   The output is similar to the following:

   ```
   <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/)