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')