Assume, you have time series and the result for the first and last three days from the given series as,
first three days: 2020-01-01 Chennai 2020-01-03 Delhi Freq: 2D, dtype: object last three days: 2020-01-07 Pune 2020-01-09 Kolkata Freq: 2D, dtype: object
To solve this, we will follow the steps given below −
Solution
Define a series and store it as data.
Apply pd.date_range() function inside start date as ‘2020-01-01’ and periods = 5, freq =’2D’ and save it as time_series
time_series = pd.date_range('2020-01-01', periods = 5, freq ='2D')Set date.index = time_series
Print the first three days using data.first(’3D’) and save it as first_day
first_day = data.first('3D')Print the last three days using data.last(’3D’) and save it as last_day
last_day = data.last('3D')Example
Let’s check the following code to get a better understanding −
import pandas as pd
data = pd.Series(['Chennai', 'Delhi', 'Mumbai', 'Pune', 'Kolkata'])
time_series = pd.date_range('2020-01-01', periods = 5, freq ='2D')
data.index = time_series
print("time series:\n",data)
first_day = data.first('3D')
print("first three days:\n",first_day)
last_day = data.last('3D')
print("last three days:\n",last_day)Output
time series: 2020-01-01 Chennai 2020-01-03 Delhi 2020-01-05 Mumbai 2020-01-07 Pune 2020-01-09 Kolkata Freq: 2D, dtype: object first three days: 2020-01-01 Chennai 2020-01-03 Delhi Freq: 2D, dtype: object last three days: 2020-01-07 Pune 2020-01-09 Kolkata Freq: 2D, dtype: object