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In pandas you can get the count of the frequency of a value that occurs in a DataFrame column by using Series. value_counts() method, alternatively, If you have a SQL background you can also get using groupby() and count() method.
dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series. dt. freq attribute return the time series frequency applied on the given series object if any, else it return None.
Pandas is best for handling or manipulating tabular data because it has a DataFrame object which has more functions. DataFrame is a 2-dimensional data structure that stores tabular data that can be in any form (integer values, characters, floating values, categorical, and more).
Pandas provides two primary data structures: Series and DataFrame . Series is a 1-dimensional (1D) column of data with index labels. Series is value-mutable but size-immutable.
You can find it called Offset Aliases:
A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases.
Alias Description
B business day frequency
C custom business day frequency
D calendar day frequency
W weekly frequency
M month end frequency
SM semi-month end frequency (15th and end of month)
BM business month end frequency
CBM custom business month end frequency
MS month start frequency
SMS semi-month start frequency (1st and 15th)
BMS business month start frequency
CBMS custom business month start frequency
Q quarter end frequency
BQ business quarter end frequency
QS quarter start frequency
BQS business quarter start frequency
A, Y year end frequency
BA, BY business year end frequency
AS, YS year start frequency
BAS, BYS business year start frequency
BH business hour frequency
H hourly frequency
T, min minutely frequency
S secondly frequency
L, ms milliseconds
U, us microseconds
N nanoseconds
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