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================ by Jawad Haider

Chpt 2 - Data Manipulation with Pandas

12 - Frequency Offset

Frequencies and Offsets

Fundamental to these Pandas time series tools is the concept of a frequency or date offset. Just as we saw the D (day) and H (hour) codes previously, we can use such codes to specify any desired frequency spacing.

Code Description
MS Month start
BMS Business month start
QS Quarter start
BQS Business quarter start
AS Year start
BAS Business year start
Code Description Code Description
D Calendar day B Business day
W Weekly
M Month end BM Business month end
Q Quarter end BQ Business quarter end
A Year end BA Business year end
H Hours BH Business hours
T Minutes
S Seconds
L Milliseonds
U Microseconds
N Nanoseconds

Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:
• Q-JAN, BQ-FEB, QS-MAR, BQS-APR, etc.
• A-JAN, BA-FEB, AS-MAR, BAS-APR, etc.
In the same way, you can modify the split-point of the weekly frequency by adding a three-letter weekday code:
• W-SUN, W-MON, W-TUE, W-WED, etc.

On top of this, codes can be combined with numbers to specify other frequencies. For example, for a frequency of 2 hours 30 minutes, we can combine the hour (H) and minute (T) codes as follows:

import pandas as pd

pd.timedelta_range(0,periods=9, freq='2H30T')

TimedeltaIndex(['0 days 00:00:00', '0 days 02:30:00', '0 days 05:00:00',
                '0 days 07:30:00', '0 days 10:00:00', '0 days 12:30:00',
                '0 days 15:00:00', '0 days 17:30:00', '0 days 20:00:00'],
               dtype='timedelta64[ns]', freq='150T')

All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the pd.tseries.offsets module. For example, we can create a busi‐ ness day offset directly as follows:

from pandas.tseries.offsets  import BDay
pd.date_range('2022-07-28', periods=5, freq=BDay())

DatetimeIndex(['2022-07-28', '2022-07-29', '2022-08-01', '2022-08-02',
               '2022-08-03'],
              dtype='datetime64[ns]', freq='B')