Truncate DataFrame Time Series Data Based on Index Value

Assume you have a dataframe with time series data and the result for truncated data is,

before truncate:
 Id time_series
0 1 2020-01-05
1 2 2020-01-12
2 3 2020-01-19
3 4 2020-01-26
4 5 2020-02-02
5 6 2020-02-09
6 7 2020-02-16
7 8 2020-02-23
8 9 2020-03-01
9 10 2020-03-08
after truncate:
 Id time_series
1 2 2020-01-12

Solution

To solve this, we will follow the steps given below −

• Define a dataframe.

• Create date_range function inside start=’01/01/2020’, periods = 10 and assign freq = ‘W’. It will generate ten dates from given start date to next weekly start dates and store it as df[‘time_series’].

df['time_series'] = pd.date_range('01/01/2020',
                                    periods=10,
                                    freq='W')

• Apply df.truncate() function inside some index values as before=’01/01/2020’, after=’10/02/2020’ and store it as result,

result = df.truncate(before='01/01/2020',after='10/02/2020')

Example

Let’s see the below implementation to get a better understanding −

import pandas as pd
d = {'Id': [1,2,3,4,5,6,7,8,9,10]}
df = pd.DataFrame(d)
df['time_series'] = pd.date_range('01/01/2020',
                                    periods=10,
                                    freq='W')

print(df)
result = df.truncate(before='01/01/2020',after='10/02/2020')
print(result)

Output

before truncate:
 Id time_series
0 1 2020-01-05
1 2 2020-01-12
2 3 2020-01-19
3 4 2020-01-26
4 5 2020-02-02
5 6 2020-02-09
6 7 2020-02-16
7 8 2020-02-23
8 9 2020-03-01
9 10 2020-03-08
after truncate:
  Id time_series
1 2 2020-01-12