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 DatetimeIndex.freqstr attribute returns the frequency object as a string if it is set in the DatetimeIndex object. If the frequency is not set then it returns None.
Syntax: DatetimeIndex.freqstr
Return: frequency object as string
Example #1: Use DatetimeIndex.freqstr attribute to return the frequency object as a string for the given DatetimeIndex object.
# importing pandas as pd import pandas as pd
# Create the DatetimeIndex # Here 'BQ' represents Business quarter frequency didx = pd.DatetimeIndex(start ='2014-08-01 10:05:45', freq ='BQ',
periods = 5, tz ='Asia/Calcutta')
# Print the DatetimeIndex print(didx)
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Output :
Now we want to find the string value of frequency for the given DatetimeIndex object.
# find the value of frequency object as string didx.freqstr |
Output :
As we can see in the output, the function has returned the frequency object as string for the given DatetimeIndex object. ‘BQ’ represents business quarter and ‘DEC’ means it begins from the month of December.
Example #2: Use DatetimeIndex.freqstr attribute to find the frequency object as string for the given DatetimeIndex object.
# importing pandas as pd import pandas as pd
# Create the DatetimeIndex # Here 'CBMS' represents custom business month start frequency didx = pd.DatetimeIndex(start ='2000-01-10 06:30', freq ='CBMS',
periods = 5, tz ='Asia/Calcutta')
# Print the DatetimeIndex print(didx)
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Output :
Now we want to find the string value of frequency for the given DatetimeIndex object.
# find the value of frequency object as string didx.freqstr |
Output :
As we can see in the output, the function has returned the frequency object as a string for the given DatetimeIndex object. The didx DatetimeIndex object is having custom business month start frequency.