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.inferred_freq attribute tries to return a string representing a frequency guess, generated by infer_freq. For those cases in which the function is not able to auto detect the frequency of the DatetimeIndex it returns None.
Syntax: DatetimeIndex.inferred_freq
Return: freq
Example #1: Use DatetimeIndex.inferred_freq attribute to auto detect the frequency of the given DatetimeIndex object.
# importing pandas as pd import pandas as pd # Create the DatetimeIndex didx = pd.date_range("2008-12-30", periods = 5, freq ='Q') # Print the DatetimeIndex print(didx) |
Output :
Now we want the function to auto detect the frequency of the given DatetimeIndex object.
# find the frequency of the object. didx.inferred_freq |
Output :
As we can see in the output, the function has tried to auto detect the frequency of the given DatetimeIndex object and has returned a quarter type frequency starting from the month of December.
Example #2: Use DatetimeIndex.inferred_freq attribute to auto detect the frequency of the given DatetimeIndex object.
# importing pandas as pd import pandas as pd # Create the DatetimeIndex didx = pd.DatetimeIndex(start ='2000-01-31 06:30', freq ='BM', periods = 5, tz ='Asia/Calcutta') # Print the DatetimeIndex print(didx) |
Output :
Now we want the function to auto detect the frequency of the given DatetimeIndex object.
# find the frequency of the object. didx.inferred_freq |
Output :
As we can see in the output, the function has tried to auto detect the frequency of the given DatetimeIndex object and has returned ‘BM’ (Business Month end) frequency.