rules. Rule name Description WEEKDAY business day frequency EOM business month end frequency W@MON weekly frequency (mondays) W@TUE weekly frequency (tuesdays) W@WED weekly frequency (wednesdays) number of observations in window required to have a value time_rule : {None, ‘WEEKDAY’, ‘EOM’, ‘W@MON‘, ...}, default=None Name of time rule to conform to before computing statistic Returns y : type number of observations in window required to have a value time_rule : {None, ‘WEEKDAY’, ‘EOM’, ‘W@MON‘, ...}, default=None Name of time rule to conform to before computing statistic Returns y : type

rules. Rule name Description WEEKDAY business day frequency EOM business month end frequency W@MON weekly frequency (mondays) W@TUE weekly frequency (tuesdays) W@WED weekly frequency (wednesdays) number of observations in window required to have a value time_rule : {None, ‘WEEKDAY’, ‘EOM’, ‘W@MON‘, ...}, default=None Name of time rule to conform to before computing statistic Returns y : type number of observations in window required to have a value time_rule : {None, ‘WEEKDAY’, ‘EOM’, ‘W@MON‘, ...}, default=None Name of time rule to conform to before computing statistic Returns y : type

rules. Rule name Description WEEKDAY business day frequency EOM business month end frequency W@MON weekly frequency (mondays) W@TUE weekly frequency (tuesdays) W@WED weekly frequency (wednesdays) number of observations in window required to have a value time_rule : {None, ‘WEEKDAY’, ‘EOM’, ‘W@MON‘, ...}, default=None Name of time rule to conform to before computing statistic Returns y : type number of observations in window required to have a value time_rule : {None, ‘WEEKDAY’, ‘EOM’, ‘W@MON‘, ...}, default=None Name of time rule to conform to before computing statistic Returns y : type

let’s look at Egypt where # a Friday-Saturday weekend is observed. In [42]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years [48]: print Series(dts.weekday, dts).map(Series(’Mon Tue Wed Thu Fri Sat Sun’.split())) 2013-04-30 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue dtype: object 10 Chapter 1. What’s let’s look at Egypt where # a Friday-Saturday weekend is observed. In [82]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years

These parameters will only be used if a custom frequency string is passed. In [88]: weekmask = 'Mon Wed Fri' In [89]: holidays = [datetime.datetime(2011, 1, 5), datetime.datetime(2011, 3, 14)] In [90]: let’s look at Egypt where a Friday-Saturday weekend is observed. In [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years Series(dts.weekday, dts).map( .....: pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split())) .....: Out[191]: 2013-04-30 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue Freq: C, dtype: object

These parameters will only be used if a custom frequency string is passed. In [88]: weekmask = 'Mon Wed Fri' In [89]: holidays = [datetime.datetime(2011, 1, 5), datetime.datetime(2011, 3, 14)] In [90]: let’s look at Egypt where a Friday-Saturday weekend is observed. In [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years date_range(dt, periods=5, freq=bday_egypt) In [191]: pd.Series(dts.weekday, dts).map( .....: pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split())) .....: Out[191]: (continues on next page) 4.13. Time series

These parameters will only be used if a custom frequency string is passed. In [88]: weekmask = 'Mon Wed Fri' In [89]: holidays = [datetime.datetime(2011, 1, 5), datetime.datetime(2011, 3, 14)] In [90]: let’s look at Egypt where a Friday-Saturday weekend is observed. In [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years date_range(dt, periods=5, freq=bday_egypt) In [191]: pd.Series(dts.weekday, dts).map( .....: pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split())) .....: Out[191]: (continues on next page) 4.13. Time series

Improve performance of DataFrame construction with certain offsets, by removing faulty caching (e.g. Mon- thEnd,BusinessMonthEnd), (GH6479) • Improve performance of CustomBusinessDay (GH6584) • improve let’s look at Egypt where # a Friday-Saturday weekend is observed. In [43]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years [49]: print(Series(dts.weekday, dts).map(Series(’Mon Tue Wed Thu Fri Sat Sun’.split()))) 2013-04-30 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue Freq: C, dtype: object 68 Chapter

let’s look at Egypt where # a Friday-Saturday weekend is observed. In [42]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years [48]: print(Series(dts.weekday, dts).map(Series(’Mon Tue Wed Thu Fri Sat Sun’.split()))) 2013-04-30 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue dtype: object 1.3.5 Bug Fixes let’s look at Egypt where # a Friday-Saturday weekend is observed. In [84]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years

These parameters will only be used if a custom frequency string is passed. In [88]: weekmask = 'Mon Wed Fri' In [89]: holidays = [datetime.datetime(2011, 1, 5), datetime.datetime(2011, 3, 14)] In [90]: let’s look at Egypt where a Friday-Saturday weekend is observed. In [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu' # They also observe International Workers' Day so let's # add that for a couple of years Series(dts.weekday, dts).map( .....: pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split())) .....: Out[191]: 2013-04-30 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue Freq: C, dtype: object