Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas PeriodIndex.asfreq() function convert each element of the given PeriodIndex object to the specified frequency.
Syntax : PeriodIndex.asfreq(freq=None, how=’E’)
Parameters :
freq : a frequency
how : ‘E’, ‘END’, or ‘FINISH’ for end, ‘S’, ‘START’, or ‘BEGIN’ for start.Return : PeriodIndex with the new frequency
Example #1: Use PeriodIndex.asfreq() function to convert the frequency of the given PeriodIndex object to the specified time series frequency.
# importing pandas as pd import pandas as pd # Create the PeriodIndex object pidx = pd.PeriodIndex(start ='2004-11-11 02:45:21 ', end ='2021-5-21 8:45:29', freq ='Y') # Print the PeriodIndex object print(pidx) |
Output :
Now we will use the PeriodIndex.asfreq() function to convert the time series frequency of the given PeriodIndex object to the specified frequency.
# convert the frequency # 'M' stands for monthly frequency pidx.asfreq('M') |
Output :
As we can see in the output, the PeriodIndex.asfreq() function has converted the frequency of the given PeriodIndex object to the specified frequency.
Example #2: Use PeriodIndex.asfreq() function to convert the frequency of the given PeriodIndex object to the specified time series frequency.
# importing pandas as pd import pandas as pd # Create the PeriodIndex object pidx = pd.PeriodIndex(start ='2016-10-12 11:12:02', end ='2020-04-12 11:32:12', freq ='Q') # Print the PeriodIndex object print(pidx) |
Output :
Now we will use the PeriodIndex.asfreq() function to convert the time series frequency of the given PeriodIndex object to the specified frequency.
# convert the frequency # 'Y' stands for yearly frequency pidx.asfreq('Y') |
Output :
As we can see in the output, the PeriodIndex.asfreq() function has converted the frequency of the given PeriodIndex object to the specified frequency.
