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.month attribute outputs an Index object containing numeric values corresponding to each entry in the DatetimeIndex object. It outputs the month as January=1, December=12 and corresponding numeric values for each month in between.
Syntax: DatetimeIndex.month
Return: Index containing months.
Example #1: Use DatetimeIndex.month attribute to find the months present in the DatetimeIndex.
# importing pandas as pd import pandas as pd
# Create the DatetimeIndex # Here the 'B' represents Business day frequency didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='B',
periods = 5, tz ='Asia/Calcutta')
# Print the DatetimeIndex print(didx)
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Output :
Now we want to find all the month values present in the DatetimeIndex object.
# find all the months in the object didx.month |
Output :
As we can see in the output, the function has returned an Index object containing the numeric value corresponding to months of each entry in the DatetimeIndex object.
Example #2: Use DatetimeIndex.month attribute to find the months present in the DatetimeIndex.
# importing pandas as pd import pandas as pd
# Create the DatetimeIndex # Here the 'M' represents Month end frequency didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='M',
periods = 5, tz ='Asia/Calcutta')
# Print the DatetimeIndex print(didx)
|
Output :
Now we want to find all the month values present in the DatetimeIndex object.
# find all the months in the object didx.month |
Output :
As we can see in the output, the function has returned an Index object containing the numeric value corresponding to months of each entry in the DatetimeIndex object.