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How to get the final rows of a time series data using pandas series.last() method?
The pandas series.last() method is used to return final periods based on the date offset. By applying this series.last() method we can get the final periods of the time series object. The last method is very similar to the pandas series.first() method, here we can get the final periods instead of the initial periods.
The series.last() method has a parameter called offset which is used to mention the length of the offset data to select the rows within the given limit.
The last() method will return a now Series object with resultant rows and it will raise the TypeError if the index of the input series object doesn’t have the DatetimeIndex.
In this following example, a series “s” is created by using the pandas DateTime index with the corresponding month day names to the index.
# importing packages import pandas as pd # creating dates dates = pd.date_range('2021-02-01', periods=10, freq='20D') #creating pandas Series with date index s = pd.Series(dates.strftime('%B___%A'), index= dates) print (s) # get the rows by using last method result = s.last('1M') print('Result:') print(result)
Here, we applied the last() method with offset “1M” to get the final rows within one month.
The output is given below −
2021-02-01 February___Monday 2021-02-21 February___Sunday 2021-03-13 March___Saturday 2021-04-02 April___Friday 2021-04-22 April___Thursday 2021-05-12 May___Wednesday 2021-06-01 June___Tuesday 2021-06-21 June___Monday 2021-07-11 July___Sunday 2021-07-31 July___Saturday Freq: 20D, dtype: object Result: 2021-07-11 July___Sunday 2021-07-31 July___Saturday Freq: 20D, dtype: object
The instances 2021-07-11 and 2021-07-31 are the final month within a one-month duration. We can see both the initial series object, as well as the final series object in the above output block.
In the same way, we have created a pandas series object with pandas DateTime index of frequency 2days and 15 hours.
# importing packages import pandas as pd # creating dates dates = pd.date_range('2021-03-10', periods=10, freq='2D15H') #creating pandas Series with date index s = pd.Series(dates.strftime('%A_%b_%d'), index= dates) print (s) # get the rows by using last method result = s.last('1W') print('Result:') print(result)
Here, we are retrieving the rows by specifying offset “1W” one week to the last method.
The output is as follows −
2021-03-10 00:00:00 Wednesday_Mar_10 2021-03-12 15:00:00 Friday_Mar_12 2021-03-15 06:00:00 Monday_Mar_15 2021-03-17 21:00:00 Wednesday_Mar_17 2021-03-20 12:00:00 Saturday_Mar_20 2021-03-23 03:00:00 Tuesday_Mar_23 2021-03-25 18:00:00 Thursday_Mar_25 2021-03-28 09:00:00 Sunday_Mar_28 2021-03-31 00:00:00 Wednesday_Mar_31 2021-04-02 15:00:00 Friday_Apr_02 Freq: 63H, dtype: object Result: 2021-03-31 00:00:00 Wednesday_Mar_31 2021-04-02 15:00:00 Friday_Apr_02 Freq: 63H, dtype: object
The last method has successfully returned a series object with 2 instances. And these 2 rows have the index within a week.
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