Summarize query results and data distribution

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

View data information and statistics

Using Python and pandas

The following example uses the InfluxDB client library for Python to query an InfluxDB Cloud Serverless bucket, and then uses pandas DataFrame.info() and DataFrame.describe() 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='
   API_TOKEN
   ',
                         host='cloud2.influxdata.com',
                         database='
   BUCKET_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