How to handle timeseries data with Pandas?

Programming Language
#1

in Pandas, we can include the date and time for every record and can fetch the records of data frame. We can find out the data within a certain range of date and time by using the pandas module named Time series.

Objectives of time series analysis

  • Create the series of date
  • Work with data timestamp
  • Convert string data to timestamp
  • Slicing of data using timestamp
  • Resample your time series for different time period aggregates/summary statistics
  • Working with missing data

Example:

import pandas as pd
from datetime import datetime
import numpy as np
 
range_date = pd.date_range(start ='1/1/2019', end ='1/08/2019',
                                                   freq ='Min')
print(range_date)

Output:


DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00',
               '2019-01-01 00:02:00', '2019-01-01 00:03:00',
               '2019-01-01 00:04:00', '2019-01-01 00:05:00',
               '2019-01-01 00:06:00', '2019-01-01 00:07:00',
               '2019-01-01 00:08:00', '2019-01-01 00:09:00',
               ...
               '2019-01-07 23:51:00', '2019-01-07 23:52:00',
               '2019-01-07 23:53:00', '2019-01-07 23:54:00',
               '2019-01-07 23:55:00', '2019-01-07 23:56:00',
               '2019-01-07 23:57:00', '2019-01-07 23:58:00',
               '2019-01-07 23:59:00', '2019-01-08 00:00:00'],
              dtype='datetime64[ns]', length=10081, freq='T')