总结查询结果和数据分布

查询存储在InfluxDB中的数据，并使用像pandas这样的工具来总结结果的模式和分布。

查看数据信息和统计

使用 Python 和 pandas

以下示例使用InfluxDB 客户端库 for Python来查询一个 InfluxDB 集群数据库，然后使用 pandas DataFrame.info() 和 DataFrame.describe() 方法来总结数据的模式和分布。

1. 在你的编辑器中，创建一个文件（例如，pandas-example.py）并输入以下示例代码：

   # pandas-example.py
   
   import influxdb_client_3 as InfluxDBClient3
   import pandas
   
   client = InfluxDBClient3.InfluxDBClient3(token='
   DATABASE_TOKEN
   ',
                         host='cluster-host.com',
                         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. 在终端中输入以下命令以使用Python解释器执行文件：

   python pandas-example.py
   

   输出类似于以下内容：

   <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