Pandas

Quick Start

export from influxdb:

$ influx -host localhost -database mydb -format csv -execute "select * from temphumi where time <= 1526254976000000000" > test.csv

test.csv:

name,time,Humi,Temp,dev_id,mac
temphumi,1526197529000000000,39.3,27.0,NOD78783243232,AABBCCDDEEFF
temphumi,1526202738000000000,39.3,26.9,NOD78783243232,AABBCCDDEEFF
temphumi,1526203290000000000,39.4,26.9,NOD78783243232,AABBCCDDEEFF
temphumi,1526203298000000000,39.4,26.9,NOD78783243232,AABBCCDDEEFF
temphumi,1526203303000000000,39.4,26.9,NOD78783243232,AABBCCDDEEFF

$ python
Python 2.7.14 (default, Sep 23 2017, 22:06:14) 
>>> import pandas as pd
>>> d=pd.read_csv('test.csv', date_parser=lambda x: pd.to_datetime(float(x)), index_col='time')

>>> d=d.drop(columns=['name'])
>>> d
                     Humi  Temp          dev_id           mac
time                                                         
2018-05-13 07:45:29  39.3  27.0  NOD78783243232  AABBCCDDEEFF
2018-05-13 09:12:18  39.3  26.9  NOD78783243232  AABBCCDDEEFF
2018-05-13 09:21:30  39.4  26.9  NOD78783243232  AABBCCDDEEFF
2018-05-13 09:21:38  39.4  26.9  NOD78783243232  AABBCCDDEEFF
2018-05-13 09:21:43  39.4  26.9  NOD78783243232  AABBCCDDEEFF

>>> from influxdb import DataFrameClient
>>> client = DataFrameClient(host='127.0.0.1', port=8086, database='mydb')
>>> client.write_points(d, 'test', tag_columns=['dev_id','mac']
True