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.snap() function is used to snap time stamps to nearest occurring frequency. The function takes a single parameter which is the frequency that we want to be applied while snapping the timestamp values of the DatetimeIndex object.
Syntax: DatetimeIndex.snap(freq)
Parameters :
freq : frequencyReturn : DatetimeIndex
Example #1: Use DatetimeIndex.snap() function to convert the given DatetimeIndex object to the nearest occurring frequency based on the input frequency.
# importing pandas as pd import pandas as pd   # Create the DatetimeIndex # Here 'Q' represents quarter end frequency didx = pd.DatetimeIndex(start ='2000-01-15 08:00', freq ='Q',                           periods = 4, tz ='Asia/Calcutta')   # Print the DatetimeIndex print(didx) |
Output :
Now we want to convert the given DatetimeIndex object timestamp values to the nearest frequency based on the input.
# snap the timestamp to the nearest frequency didx.snap('MS') |
Output :
As we can see in the output, the function has snapped each timestamp value in the given DatetimeIndex object.
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Example #2: Use DatetimeIndex.snap() function to convert the given DatetimeIndex object to the nearest occurring frequency based on the input frequency.
# importing pandas as pd import pandas as pd   # Create the DatetimeIndex # Here 'MS' represents month start frequency didx = pd.date_range(pd.Timestamp("2000-01-15 08:00"),                               periods = 5, freq ='MS')   # Print the DatetimeIndex print(didx) |
Output :
Now we want to convert the given DatetimeIndex object timestamp values to the nearest frequency based on the input.
# snap the timestamp to the nearest frequency didx.snap('Q') |
Output :
As we can see in the output, the function has snapped each timestamp value in the given DatetimeIndex object.
