Time series / date functionality#
pandas contains extensive capabilities and features for working with time series data for all domains.Using the NumPydatetime64 andtimedelta64 dtypes, pandas has consolidated a large number offeatures from other Python libraries likescikits.timeseries as well as createda tremendous amount of new functionality for manipulating time series data.
For example, pandas supports:
Parsing time series information from various sources and formats
In [1]:importdatetimeIn [2]:dti=pd.to_datetime( ...:["1/1/2018",np.datetime64("2018-01-01"),datetime.datetime(2018,1,1)] ...:) ...:In [3]:dtiOut[3]:DatetimeIndex(['2018-01-01', '2018-01-01', '2018-01-01'], dtype='datetime64[ns]', freq=None)
Generate sequences of fixed-frequency dates and time spans
In [4]:dti=pd.date_range("2018-01-01",periods=3,freq="h")In [5]:dtiOut[5]:DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00', '2018-01-01 02:00:00'], dtype='datetime64[ns]', freq='h')
Manipulating and converting date times with timezone information
In [6]:dti=dti.tz_localize("UTC")In [7]:dtiOut[7]:DatetimeIndex(['2018-01-01 00:00:00+00:00', '2018-01-01 01:00:00+00:00', '2018-01-01 02:00:00+00:00'], dtype='datetime64[ns, UTC]', freq='h')In [8]:dti.tz_convert("US/Pacific")Out[8]:DatetimeIndex(['2017-12-31 16:00:00-08:00', '2017-12-31 17:00:00-08:00', '2017-12-31 18:00:00-08:00'], dtype='datetime64[ns, US/Pacific]', freq='h')
Resampling or converting a time series to a particular frequency
In [9]:idx=pd.date_range("2018-01-01",periods=5,freq="h")In [10]:ts=pd.Series(range(len(idx)),index=idx)In [11]:tsOut[11]:2018-01-01 00:00:00 02018-01-01 01:00:00 12018-01-01 02:00:00 22018-01-01 03:00:00 32018-01-01 04:00:00 4Freq: h, dtype: int64In [12]:ts.resample("2h").mean()Out[12]:2018-01-01 00:00:00 0.52018-01-01 02:00:00 2.52018-01-01 04:00:00 4.0Freq: 2h, dtype: float64
Performing date and time arithmetic with absolute or relative time increments
In [13]:friday=pd.Timestamp("2018-01-05")In [14]:friday.day_name()Out[14]:'Friday'# Add 1 dayIn [15]:saturday=friday+pd.Timedelta("1 day")In [16]:saturday.day_name()Out[16]:'Saturday'# Add 1 business day (Friday --> Monday)In [17]:monday=friday+pd.offsets.BDay()In [18]:monday.day_name()Out[18]:'Monday'
pandas provides a relatively compact and self-contained set of tools forperforming the above tasks and more.
Overview#
pandas captures 4 general time related concepts:
Date times: A specific date and time with timezone support. Similar to
datetime.datetimefrom the standard library.Time deltas: An absolute time duration. Similar to
datetime.timedeltafrom the standard library.Time spans: A span of time defined by a point in time and its associated frequency.
Date offsets: A relative time duration that respects calendar arithmetic. Similar to
dateutil.relativedelta.relativedeltafrom thedateutilpackage.
Concept | Scalar Class | Array Class | pandas Data Type | Primary Creation Method |
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Date times |
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Time deltas |
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Time spans |
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Date offsets |
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For time series data, it’s conventional to represent the time component in the index of aSeries orDataFrameso manipulations can be performed with respect to the time element.
In [19]:pd.Series(range(3),index=pd.date_range("2000",freq="D",periods=3))Out[19]:2000-01-01 02000-01-02 12000-01-03 2Freq: D, dtype: int64
However,Series andDataFrame can directly also support the time component as data itself.
In [20]:pd.Series(pd.date_range("2000",freq="D",periods=3))Out[20]:0 2000-01-011 2000-01-022 2000-01-03dtype: datetime64[ns]
Series andDataFrame have extended data type support and functionality fordatetime,timedeltaandPeriod data when passed into those constructors.DateOffsetdata however will be stored asobject data.
In [21]:pd.Series(pd.period_range("1/1/2011",freq="M",periods=3))Out[21]:0 2011-011 2011-022 2011-03dtype: period[M]In [22]:pd.Series([pd.DateOffset(1),pd.DateOffset(2)])Out[22]:0 <DateOffset>1 <2 * DateOffsets>dtype: objectIn [23]:pd.Series(pd.date_range("1/1/2011",freq="ME",periods=3))Out[23]:0 2011-01-311 2011-02-282 2011-03-31dtype: datetime64[ns]
Lastly, pandas represents null date times, time deltas, and time spans asNaT whichis useful for representing missing or null date like values and behaves similarasnp.nan does for float data.
In [24]:pd.Timestamp(pd.NaT)Out[24]:NaTIn [25]:pd.Timedelta(pd.NaT)Out[25]:NaTIn [26]:pd.Period(pd.NaT)Out[26]:NaT# Equality acts as np.nan wouldIn [27]:pd.NaT==pd.NaTOut[27]:False
Timestamps vs. time spans#
Timestamped data is the most basic type of time series data that associatesvalues with points in time. For pandas objects it means using the points intime.
In [28]:importdatetimeIn [29]:pd.Timestamp(datetime.datetime(2012,5,1))Out[29]:Timestamp('2012-05-01 00:00:00')In [30]:pd.Timestamp("2012-05-01")Out[30]:Timestamp('2012-05-01 00:00:00')In [31]:pd.Timestamp(2012,5,1)Out[31]:Timestamp('2012-05-01 00:00:00')
However, in many cases it is more natural to associate things like changevariables with a time span instead. The span represented byPeriod can bespecified explicitly, or inferred from datetime string format.
For example:
In [32]:pd.Period("2011-01")Out[32]:Period('2011-01', 'M')In [33]:pd.Period("2012-05",freq="D")Out[33]:Period('2012-05-01', 'D')
Timestamp andPeriod can serve as an index. Lists ofTimestamp andPeriod are automatically coerced toDatetimeIndexandPeriodIndex respectively.
In [34]:dates=[ ....:pd.Timestamp("2012-05-01"), ....:pd.Timestamp("2012-05-02"), ....:pd.Timestamp("2012-05-03"), ....:] ....:In [35]:ts=pd.Series(np.random.randn(3),dates)In [36]:type(ts.index)Out[36]:pandas.core.indexes.datetimes.DatetimeIndexIn [37]:ts.indexOut[37]:DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)In [38]:tsOut[38]:2012-05-01 0.4691122012-05-02 -0.2828632012-05-03 -1.509059dtype: float64In [39]:periods=[pd.Period("2012-01"),pd.Period("2012-02"),pd.Period("2012-03")]In [40]:ts=pd.Series(np.random.randn(3),periods)In [41]:type(ts.index)Out[41]:pandas.core.indexes.period.PeriodIndexIn [42]:ts.indexOut[42]:PeriodIndex(['2012-01', '2012-02', '2012-03'], dtype='period[M]')In [43]:tsOut[43]:2012-01 -1.1356322012-02 1.2121122012-03 -0.173215Freq: M, dtype: float64
pandas allows you to capture both representations andconvert between them. Under the hood, pandas represents timestamps usinginstances ofTimestamp and sequences of timestamps using instances ofDatetimeIndex. For regular time spans, pandas usesPeriod objects forscalar values andPeriodIndex for sequences of spans. Better support forirregular intervals with arbitrary start and end points are forth-coming infuture releases.
Converting to timestamps#
To convert aSeries or list-like object of date-like objects e.g. strings,epochs, or a mixture, you can use theto_datetime function. When passedaSeries, this returns aSeries (with the same index), while a list-likeis converted to aDatetimeIndex:
In [44]:pd.to_datetime(pd.Series(["Jul 31, 2009","Jan 10, 2010",None]))Out[44]:0 2009-07-311 2010-01-102 NaTdtype: datetime64[ns]In [45]:pd.to_datetime(["2005/11/23","2010/12/31"])Out[45]:DatetimeIndex(['2005-11-23', '2010-12-31'], dtype='datetime64[ns]', freq=None)
If you use dates which start with the day first (i.e. European style),you can pass thedayfirst flag:
In [46]:pd.to_datetime(["04-01-2012 10:00"],dayfirst=True)Out[46]:DatetimeIndex(['2012-01-04 10:00:00'], dtype='datetime64[ns]', freq=None)In [47]:pd.to_datetime(["04-14-2012 10:00"],dayfirst=True)Out[47]:DatetimeIndex(['2012-04-14 10:00:00'], dtype='datetime64[ns]', freq=None)
Warning
You see in the above example thatdayfirst isn’t strict. If a datecan’t be parsed with the day being first it will be parsed as ifdayfirst wereFalse and a warning will also be raised.
If you pass a single string toto_datetime, it returns a singleTimestamp.Timestamp can also accept string input, but it doesn’t accept string parsingoptions likedayfirst orformat, so useto_datetime if these are required.
In [48]:pd.to_datetime("2010/11/12")Out[48]:Timestamp('2010-11-12 00:00:00')In [49]:pd.Timestamp("2010/11/12")Out[49]:Timestamp('2010-11-12 00:00:00')
You can also use theDatetimeIndex constructor directly:
In [50]:pd.DatetimeIndex(["2018-01-01","2018-01-03","2018-01-05"])Out[50]:DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], dtype='datetime64[ns]', freq=None)
The string ‘infer’ can be passed in order to set the frequency of the index as theinferred frequency upon creation:
In [51]:pd.DatetimeIndex(["2018-01-01","2018-01-03","2018-01-05"],freq="infer")Out[51]:DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], dtype='datetime64[ns]', freq='2D')
Providing a format argument#
In addition to the required datetime string, aformat argument can be passed to ensure specific parsing.This could also potentially speed up the conversion considerably.
In [52]:pd.to_datetime("2010/11/12",format="%Y/%m/%d")Out[52]:Timestamp('2010-11-12 00:00:00')In [53]:pd.to_datetime("12-11-2010 00:00",format="%d-%m-%Y %H:%M")Out[53]:Timestamp('2010-11-12 00:00:00')
For more information on the choices available when specifying theformatoption, see the Pythondatetime documentation.
Assembling datetime from multiple DataFrame columns#
You can also pass aDataFrame of integer or string columns to assemble into aSeries ofTimestamps.
In [54]:df=pd.DataFrame( ....:{"year":[2015,2016],"month":[2,3],"day":[4,5],"hour":[2,3]} ....:) ....:In [55]:pd.to_datetime(df)Out[55]:0 2015-02-04 02:00:001 2016-03-05 03:00:00dtype: datetime64[ns]
You can pass only the columns that you need to assemble.
In [56]:pd.to_datetime(df[["year","month","day"]])Out[56]:0 2015-02-041 2016-03-05dtype: datetime64[ns]
pd.to_datetime looks for standard designations of the datetime component in the column names, including:
required:
year,month,dayoptional:
hour,minute,second,millisecond,microsecond,nanosecond
Invalid data#
The default behavior,errors='raise', is to raise when unparsable:
In [57]:pd.to_datetime(['2009/07/31','asd'],errors='raise')---------------------------------------------------------------------------ValueErrorTraceback (most recent call last)CellIn[57],line1---->1pd.to_datetime(['2009/07/31','asd'],errors='raise')File ~/work/pandas/pandas/pandas/core/tools/datetimes.py:1099, into_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)1097result=_convert_and_box_cache(argc,cache_array)1098else:->1099result=convert_listlike(argc,format)1100else:1101result=convert_listlike(np.array([arg]),format)[0]File ~/work/pandas/pandas/pandas/core/tools/datetimes.py:433, in_convert_listlike_datetimes(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)431# `format` could be inferred, or user didn't ask for mixed-format parsing.432ifformatisnotNoneandformat!="mixed":-->433return_array_strptime_with_fallback(arg,name,utc,format,exact,errors)435result,tz_parsed=objects_to_datetime64(436arg,437dayfirst=dayfirst,(...)441allow_object=True,442)444iftz_parsedisnotNone:445# We can take a shortcut since the datetime64 numpy array446# is in UTCFile ~/work/pandas/pandas/pandas/core/tools/datetimes.py:467, in_array_strptime_with_fallback(arg, name, utc, fmt, exact, errors)456def_array_strptime_with_fallback(457arg,458name,(...)462errors:str,463)->Index:464"""465 Call array_strptime, with fallback behavior depending on 'errors'.466 """-->467result,tz_out=array_strptime(arg,fmt,exact=exact,errors=errors,utc=utc)468iftz_outisnotNone:469unit=np.datetime_data(result.dtype)[0]File strptime.pyx:501, inpandas._libs.tslibs.strptime.array_strptime()File strptime.pyx:451, inpandas._libs.tslibs.strptime.array_strptime()File strptime.pyx:583, inpandas._libs.tslibs.strptime._parse_with_format()ValueError: time data "asd" doesn't match format "%Y/%m/%d", at position 1. You might want to try:-passing`format`ifyourstringshaveaconsistentformat;-passing`format='ISO8601'`ifyourstringsareallISO8601butnotnecessarilyinexactlythesameformat;-passing`format='mixed'`,andtheformatwillbeinferredforeachelementindividually.Youmightwanttouse`dayfirst`alongsidethis.
Passerrors='coerce' to convert unparsable data toNaT (not a time):
In [58]:pd.to_datetime(["2009/07/31","asd"],errors="coerce")Out[58]:DatetimeIndex(['2009-07-31', 'NaT'], dtype='datetime64[ns]', freq=None)
Epoch timestamps#
pandas supports converting integer or float epoch times toTimestamp andDatetimeIndex. The default unit is nanoseconds, since that is howTimestampobjects are stored internally. However, epochs are often stored in anotherunitwhich can be specified. These are computed from the starting point specified by theorigin parameter.
In [59]:pd.to_datetime( ....:[1349720105,1349806505,1349892905,1349979305,1350065705],unit="s" ....:) ....:Out[59]:DatetimeIndex(['2012-10-08 18:15:05', '2012-10-09 18:15:05', '2012-10-10 18:15:05', '2012-10-11 18:15:05', '2012-10-12 18:15:05'], dtype='datetime64[ns]', freq=None)In [60]:pd.to_datetime( ....:[1349720105100,1349720105200,1349720105300,1349720105400,1349720105500], ....:unit="ms", ....:) ....:Out[60]:DatetimeIndex(['2012-10-08 18:15:05.100000', '2012-10-08 18:15:05.200000', '2012-10-08 18:15:05.300000', '2012-10-08 18:15:05.400000', '2012-10-08 18:15:05.500000'], dtype='datetime64[ns]', freq=None)
Note
Theunit parameter does not use the same strings as theformat parameterthat was discussedabove). Theavailable units are listed on the documentation forpandas.to_datetime().
Constructing aTimestamp orDatetimeIndex with an epoch timestampwith thetz argument specified will raise a ValueError. If you haveepochs in wall time in another timezone, you can read the epochsas timezone-naive timestamps and then localize to the appropriate timezone:
In [61]:pd.Timestamp(1262347200000000000).tz_localize("US/Pacific")Out[61]:Timestamp('2010-01-01 12:00:00-0800', tz='US/Pacific')In [62]:pd.DatetimeIndex([1262347200000000000]).tz_localize("US/Pacific")Out[62]:DatetimeIndex(['2010-01-01 12:00:00-08:00'], dtype='datetime64[ns, US/Pacific]', freq=None)
Note
Epoch times will be rounded to the nearest nanosecond.
Warning
Conversion of float epoch times can lead to inaccurate and unexpected results.Python floats have about 15 digits precision indecimal. Rounding during conversion from float to high precisionTimestamp isunavoidable. The only way to achieve exact precision is to use a fixed-widthtypes (e.g. an int64).
In [63]:pd.to_datetime([1490195805.433,1490195805.433502912],unit="s")Out[63]:DatetimeIndex(['2017-03-22 15:16:45.433000088', '2017-03-22 15:16:45.433502913'], dtype='datetime64[ns]', freq=None)In [64]:pd.to_datetime(1490195805433502912,unit="ns")Out[64]:Timestamp('2017-03-22 15:16:45.433502912')
See also
From timestamps to epoch#
To invert the operation from above, namely, to convert from aTimestamp to a ‘unix’ epoch:
In [65]:stamps=pd.date_range("2012-10-08 18:15:05",periods=4,freq="D")In [66]:stampsOut[66]:DatetimeIndex(['2012-10-08 18:15:05', '2012-10-09 18:15:05', '2012-10-10 18:15:05', '2012-10-11 18:15:05'], dtype='datetime64[ns]', freq='D')
We subtract the epoch (midnight at January 1, 1970 UTC) and then floor divide by the“unit” (1 second).
In [67]:(stamps-pd.Timestamp("1970-01-01"))//pd.Timedelta("1s")Out[67]:Index([1349720105, 1349806505, 1349892905, 1349979305], dtype='int64')
Using theorigin parameter#
Using theorigin parameter, one can specify an alternative starting point for creationof aDatetimeIndex. For example, to use 1960-01-01 as the starting date:
In [68]:pd.to_datetime([1,2,3],unit="D",origin=pd.Timestamp("1960-01-01"))Out[68]:DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)
The default is set atorigin='unix', which defaults to1970-01-0100:00:00.Commonly called ‘unix epoch’ or POSIX time.
In [69]:pd.to_datetime([1,2,3],unit="D")Out[69]:DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)
Generating ranges of timestamps#
To generate an index with timestamps, you can use either theDatetimeIndex orIndex constructor and pass in a list of datetime objects:
In [70]:dates=[ ....:datetime.datetime(2012,5,1), ....:datetime.datetime(2012,5,2), ....:datetime.datetime(2012,5,3), ....:] ....:# Note the frequency informationIn [71]:index=pd.DatetimeIndex(dates)In [72]:indexOut[72]:DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)# Automatically converted to DatetimeIndexIn [73]:index=pd.Index(dates)In [74]:indexOut[74]:DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
In practice this becomes very cumbersome because we often need a very longindex with a large number of timestamps. If we need timestamps on a regularfrequency, we can use thedate_range() andbdate_range() functionsto create aDatetimeIndex. The default frequency fordate_range is acalendar day while the default forbdate_range is abusiness day:
In [75]:start=datetime.datetime(2011,1,1)In [76]:end=datetime.datetime(2012,1,1)In [77]:index=pd.date_range(start,end)In [78]:indexOut[78]:DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-08', '2011-01-09', '2011-01-10', ... '2011-12-23', '2011-12-24', '2011-12-25', '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30', '2011-12-31', '2012-01-01'], dtype='datetime64[ns]', length=366, freq='D')In [79]:index=pd.bdate_range(start,end)In [80]:indexOut[80]:DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12', '2011-01-13', '2011-01-14', ... '2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22', '2011-12-23', '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30'], dtype='datetime64[ns]', length=260, freq='B')
Convenience functions likedate_range andbdate_range can utilize avariety offrequency aliases:
In [81]:pd.date_range(start,periods=1000,freq="ME")Out[81]:DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30', '2011-05-31', '2011-06-30', '2011-07-31', '2011-08-31', '2011-09-30', '2011-10-31', ... '2093-07-31', '2093-08-31', '2093-09-30', '2093-10-31', '2093-11-30', '2093-12-31', '2094-01-31', '2094-02-28', '2094-03-31', '2094-04-30'], dtype='datetime64[ns]', length=1000, freq='ME')In [82]:pd.bdate_range(start,periods=250,freq="BQS")Out[82]:DatetimeIndex(['2011-01-03', '2011-04-01', '2011-07-01', '2011-10-03', '2012-01-02', '2012-04-02', '2012-07-02', '2012-10-01', '2013-01-01', '2013-04-01', ... '2071-01-01', '2071-04-01', '2071-07-01', '2071-10-01', '2072-01-01', '2072-04-01', '2072-07-01', '2072-10-03', '2073-01-02', '2073-04-03'], dtype='datetime64[ns]', length=250, freq='BQS-JAN')
date_range andbdate_range make it easy to generate a range of datesusing various combinations of parameters likestart,end,periods,andfreq. The start and end dates are strictly inclusive, so dates outsideof those specified will not be generated:
In [83]:pd.date_range(start,end,freq="BME")Out[83]:DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29', '2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31', '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30'], dtype='datetime64[ns]', freq='BME')In [84]:pd.date_range(start,end,freq="W")Out[84]:DatetimeIndex(['2011-01-02', '2011-01-09', '2011-01-16', '2011-01-23', '2011-01-30', '2011-02-06', '2011-02-13', '2011-02-20', '2011-02-27', '2011-03-06', '2011-03-13', '2011-03-20', '2011-03-27', '2011-04-03', '2011-04-10', '2011-04-17', '2011-04-24', '2011-05-01', '2011-05-08', '2011-05-15', '2011-05-22', '2011-05-29', '2011-06-05', '2011-06-12', '2011-06-19', '2011-06-26', '2011-07-03', '2011-07-10', '2011-07-17', '2011-07-24', '2011-07-31', '2011-08-07', '2011-08-14', '2011-08-21', '2011-08-28', '2011-09-04', '2011-09-11', '2011-09-18', '2011-09-25', '2011-10-02', '2011-10-09', '2011-10-16', '2011-10-23', '2011-10-30', '2011-11-06', '2011-11-13', '2011-11-20', '2011-11-27', '2011-12-04', '2011-12-11', '2011-12-18', '2011-12-25', '2012-01-01'], dtype='datetime64[ns]', freq='W-SUN')In [85]:pd.bdate_range(end=end,periods=20)Out[85]:DatetimeIndex(['2011-12-05', '2011-12-06', '2011-12-07', '2011-12-08', '2011-12-09', '2011-12-12', '2011-12-13', '2011-12-14', '2011-12-15', '2011-12-16', '2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22', '2011-12-23', '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30'], dtype='datetime64[ns]', freq='B')In [86]:pd.bdate_range(start=start,periods=20)Out[86]:DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12', '2011-01-13', '2011-01-14', '2011-01-17', '2011-01-18', '2011-01-19', '2011-01-20', '2011-01-21', '2011-01-24', '2011-01-25', '2011-01-26', '2011-01-27', '2011-01-28'], dtype='datetime64[ns]', freq='B')
Specifyingstart,end, andperiods will generate a range of evenly spaceddates fromstart toend inclusively, withperiods number of elements in theresultingDatetimeIndex:
In [87]:pd.date_range("2018-01-01","2018-01-05",periods=5)Out[87]:DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05'], dtype='datetime64[ns]', freq=None)In [88]:pd.date_range("2018-01-01","2018-01-05",periods=10)Out[88]:DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 10:40:00', '2018-01-01 21:20:00', '2018-01-02 08:00:00', '2018-01-02 18:40:00', '2018-01-03 05:20:00', '2018-01-03 16:00:00', '2018-01-04 02:40:00', '2018-01-04 13:20:00', '2018-01-05 00:00:00'], dtype='datetime64[ns]', freq=None)
Custom frequency ranges#
bdate_range can also generate a range of custom frequency dates by usingtheweekmask andholidays parameters. These parameters will only beused if a custom frequency string is passed.
In [89]:weekmask="Mon Wed Fri"In [90]:holidays=[datetime.datetime(2011,1,5),datetime.datetime(2011,3,14)]In [91]:pd.bdate_range(start,end,freq="C",weekmask=weekmask,holidays=holidays)Out[91]:DatetimeIndex(['2011-01-03', '2011-01-07', '2011-01-10', '2011-01-12', '2011-01-14', '2011-01-17', '2011-01-19', '2011-01-21', '2011-01-24', '2011-01-26', ... '2011-12-09', '2011-12-12', '2011-12-14', '2011-12-16', '2011-12-19', '2011-12-21', '2011-12-23', '2011-12-26', '2011-12-28', '2011-12-30'], dtype='datetime64[ns]', length=154, freq='C')In [92]:pd.bdate_range(start,end,freq="CBMS",weekmask=weekmask)Out[92]:DatetimeIndex(['2011-01-03', '2011-02-02', '2011-03-02', '2011-04-01', '2011-05-02', '2011-06-01', '2011-07-01', '2011-08-01', '2011-09-02', '2011-10-03', '2011-11-02', '2011-12-02'], dtype='datetime64[ns]', freq='CBMS')
See also
Timestamp limitations#
The limits of timestamp representation depend on the chosen resolution. Fornanosecond resolution, the time span thatcan be represented using a 64-bit integer is limited to approximately 584 years:
In [93]:pd.Timestamp.minOut[93]:Timestamp('1677-09-21 00:12:43.145224193')In [94]:pd.Timestamp.maxOut[94]:Timestamp('2262-04-11 23:47:16.854775807')
When choosing second-resolution, the available range grows to+/-2.9e11years.Different resolutions can be converted to each other throughas_unit.
See also
Indexing#
One of the main uses forDatetimeIndex is as an index for pandas objects.TheDatetimeIndex class contains many time series related optimizations:
A large range of dates for various offsets are pre-computed and cachedunder the hood in order to make generating subsequent date ranges very fast(just have to grab a slice).
Fast shifting using the
shiftmethod on pandas objects.Unioning of overlapping
DatetimeIndexobjects with the same frequency isvery fast (important for fast data alignment).Quick access to date fields via properties such as
year,month, etc.Regularization functions like
snapand very fastasoflogic.
DatetimeIndex objects have all the basic functionality of regularIndexobjects, and a smorgasbord of advanced time series specific methods for easyfrequency processing.
See also
Note
While pandas does not force you to have a sorted date index, some of thesemethods may have unexpected or incorrect behavior if the dates are unsorted.
DatetimeIndex can be used like a regular index and offers all of itsintelligent functionality like selection, slicing, etc.
In [95]:rng=pd.date_range(start,end,freq="BME")In [96]:ts=pd.Series(np.random.randn(len(rng)),index=rng)In [97]:ts.indexOut[97]:DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29', '2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31', '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30'], dtype='datetime64[ns]', freq='BME')In [98]:ts[:5].indexOut[98]:DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29', '2011-05-31'], dtype='datetime64[ns]', freq='BME')In [99]:ts[::2].indexOut[99]:DatetimeIndex(['2011-01-31', '2011-03-31', '2011-05-31', '2011-07-29', '2011-09-30', '2011-11-30'], dtype='datetime64[ns]', freq='2BME')
Partial string indexing#
Dates and strings that parse to timestamps can be passed as indexing parameters:
In [100]:ts["1/31/2011"]Out[100]:0.11920871129693428In [101]:ts[datetime.datetime(2011,12,25):]Out[101]:2011-12-30 0.56702Freq: BME, dtype: float64In [102]:ts["10/31/2011":"12/31/2011"]Out[102]:2011-10-31 0.2718602011-11-30 -0.4249722011-12-30 0.567020Freq: BME, dtype: float64
To provide convenience for accessing longer time series, you can also pass inthe year or year and month as strings:
In [103]:ts["2011"]Out[103]:2011-01-31 0.1192092011-02-28 -1.0442362011-03-31 -0.8618492011-04-29 -2.1045692011-05-31 -0.4949292011-06-30 1.0718042011-07-29 0.7215552011-08-31 -0.7067712011-09-30 -1.0395752011-10-31 0.2718602011-11-30 -0.4249722011-12-30 0.567020Freq: BME, dtype: float64In [104]:ts["2011-6"]Out[104]:2011-06-30 1.071804Freq: BME, dtype: float64
This type of slicing will work on aDataFrame with aDatetimeIndex as well. Since thepartial string selection is a form of label slicing, the endpointswill be included. Thiswould include matching times on an included date:
Warning
IndexingDataFrame rows with asingle string with getitem (e.g.frame[dtstring])is deprecated starting with pandas 1.2.0 (given the ambiguity whether it is indexingthe rows or selecting a column) and will be removed in a future version. The equivalentwith.loc (e.g.frame.loc[dtstring]) is still supported.
In [105]:dft=pd.DataFrame( .....:np.random.randn(100000,1), .....:columns=["A"], .....:index=pd.date_range("20130101",periods=100000,freq="min"), .....:) .....:In [106]:dftOut[106]: A2013-01-01 00:00:00 0.2762322013-01-01 00:01:00 -1.0874012013-01-01 00:02:00 -0.6736902013-01-01 00:03:00 0.1136482013-01-01 00:04:00 -1.478427... ...2013-03-11 10:35:00 -0.7479672013-03-11 10:36:00 -0.0345232013-03-11 10:37:00 -0.2017542013-03-11 10:38:00 -1.5090672013-03-11 10:39:00 -1.693043[100000 rows x 1 columns]In [107]:dft.loc["2013"]Out[107]: A2013-01-01 00:00:00 0.2762322013-01-01 00:01:00 -1.0874012013-01-01 00:02:00 -0.6736902013-01-01 00:03:00 0.1136482013-01-01 00:04:00 -1.478427... ...2013-03-11 10:35:00 -0.7479672013-03-11 10:36:00 -0.0345232013-03-11 10:37:00 -0.2017542013-03-11 10:38:00 -1.5090672013-03-11 10:39:00 -1.693043[100000 rows x 1 columns]
This starts on the very first time in the month, and includes the last date andtime for the month:
In [108]:dft["2013-1":"2013-2"]Out[108]: A2013-01-01 00:00:00 0.2762322013-01-01 00:01:00 -1.0874012013-01-01 00:02:00 -0.6736902013-01-01 00:03:00 0.1136482013-01-01 00:04:00 -1.478427... ...2013-02-28 23:55:00 0.8509292013-02-28 23:56:00 0.9767122013-02-28 23:57:00 -2.6938842013-02-28 23:58:00 -1.5755352013-02-28 23:59:00 -1.573517[84960 rows x 1 columns]
This specifies a stop timethat includes all of the times on the last day:
In [109]:dft["2013-1":"2013-2-28"]Out[109]: A2013-01-01 00:00:00 0.2762322013-01-01 00:01:00 -1.0874012013-01-01 00:02:00 -0.6736902013-01-01 00:03:00 0.1136482013-01-01 00:04:00 -1.478427... ...2013-02-28 23:55:00 0.8509292013-02-28 23:56:00 0.9767122013-02-28 23:57:00 -2.6938842013-02-28 23:58:00 -1.5755352013-02-28 23:59:00 -1.573517[84960 rows x 1 columns]
This specifies anexact stop time (and is not the same as the above):
In [110]:dft["2013-1":"2013-2-28 00:00:00"]Out[110]: A2013-01-01 00:00:00 0.2762322013-01-01 00:01:00 -1.0874012013-01-01 00:02:00 -0.6736902013-01-01 00:03:00 0.1136482013-01-01 00:04:00 -1.478427... ...2013-02-27 23:56:00 1.1977492013-02-27 23:57:00 0.7205212013-02-27 23:58:00 -0.0727182013-02-27 23:59:00 -0.6811922013-02-28 00:00:00 -0.557501[83521 rows x 1 columns]
We are stopping on the included end-point as it is part of the index:
In [111]:dft["2013-1-15":"2013-1-15 12:30:00"]Out[111]: A2013-01-15 00:00:00 -0.9848102013-01-15 00:01:00 0.9414512013-01-15 00:02:00 1.5593652013-01-15 00:03:00 1.0343742013-01-15 00:04:00 -1.480656... ...2013-01-15 12:26:00 0.3714542013-01-15 12:27:00 -0.9308062013-01-15 12:28:00 -0.0691772013-01-15 12:29:00 0.0665102013-01-15 12:30:00 -0.003945[751 rows x 1 columns]
DatetimeIndex partial string indexing also works on aDataFrame with aMultiIndex:
In [112]:dft2=pd.DataFrame( .....:np.random.randn(20,1), .....:columns=["A"], .....:index=pd.MultiIndex.from_product( .....:[pd.date_range("20130101",periods=10,freq="12h"),["a","b"]] .....:), .....:) .....:In [113]:dft2Out[113]: A2013-01-01 00:00:00 a -0.298694 b 0.8235532013-01-01 12:00:00 a 0.943285 b -1.4793992013-01-02 00:00:00 a -1.643342... ...2013-01-04 12:00:00 b 0.0690362013-01-05 00:00:00 a 0.122297 b 1.4220602013-01-05 12:00:00 a 0.370079 b 1.016331[20 rows x 1 columns]In [114]:dft2.loc["2013-01-05"]Out[114]: A2013-01-05 00:00:00 a 0.122297 b 1.4220602013-01-05 12:00:00 a 0.370079 b 1.016331In [115]:idx=pd.IndexSliceIn [116]:dft2=dft2.swaplevel(0,1).sort_index()In [117]:dft2.loc[idx[:,"2013-01-05"],:]Out[117]: Aa 2013-01-05 00:00:00 0.122297 2013-01-05 12:00:00 0.370079b 2013-01-05 00:00:00 1.422060 2013-01-05 12:00:00 1.016331
Slicing with string indexing also honors UTC offset.
In [118]:df=pd.DataFrame([0],index=pd.DatetimeIndex(["2019-01-01"],tz="US/Pacific"))In [119]:dfOut[119]: 02019-01-01 00:00:00-08:00 0In [120]:df["2019-01-01 12:00:00+04:00":"2019-01-01 13:00:00+04:00"]Out[120]: 02019-01-01 00:00:00-08:00 0
Slice vs. exact match#
The same string used as an indexing parameter can be treated either as a slice or as an exact match depending on the resolution of the index. If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match.
Consider aSeries object with a minute resolution index:
In [121]:series_minute=pd.Series( .....:[1,2,3], .....:pd.DatetimeIndex( .....:["2011-12-31 23:59:00","2012-01-01 00:00:00","2012-01-01 00:02:00"] .....:), .....:) .....:In [122]:series_minute.index.resolutionOut[122]:'minute'
A timestamp string less accurate than a minute gives aSeries object.
In [123]:series_minute["2011-12-31 23"]Out[123]:2011-12-31 23:59:00 1dtype: int64
A timestamp string with minute resolution (or more accurate), gives a scalar instead, i.e. it is not casted to a slice.
In [124]:series_minute["2011-12-31 23:59"]Out[124]:1In [125]:series_minute["2011-12-31 23:59:00"]Out[125]:1
If index resolution is second, then the minute-accurate timestamp gives aSeries.
In [126]:series_second=pd.Series( .....:[1,2,3], .....:pd.DatetimeIndex( .....:["2011-12-31 23:59:59","2012-01-01 00:00:00","2012-01-01 00:00:01"] .....:), .....:) .....:In [127]:series_second.index.resolutionOut[127]:'second'In [128]:series_second["2011-12-31 23:59"]Out[128]:2011-12-31 23:59:59 1dtype: int64
If the timestamp string is treated as a slice, it can be used to indexDataFrame with.loc[] as well.
In [129]:dft_minute=pd.DataFrame( .....:{"a":[1,2,3],"b":[4,5,6]},index=series_minute.index .....:) .....:In [130]:dft_minute.loc["2011-12-31 23"]Out[130]: a b2011-12-31 23:59:00 1 4
Warning
However, if the string is treated as an exact match, the selection inDataFrame’s[] will be column-wise and not row-wise, seeIndexing Basics. For exampledft_minute['2011-12-3123:59'] will raiseKeyError as'2012-12-3123:59' has the same resolution as the index and there is no column with such name:
Toalways have unambiguous selection, whether the row is treated as a slice or a single selection, use.loc.
In [131]:dft_minute.loc["2011-12-31 23:59"]Out[131]:a 1b 4Name: 2011-12-31 23:59:00, dtype: int64
Note also thatDatetimeIndex resolution cannot be less precise than day.
In [132]:series_monthly=pd.Series( .....:[1,2,3],pd.DatetimeIndex(["2011-12","2012-01","2012-02"]) .....:) .....:In [133]:series_monthly.index.resolutionOut[133]:'day'In [134]:series_monthly["2011-12"]# returns SeriesOut[134]:2011-12-01 1dtype: int64
Exact indexing#
As discussed in previous section, indexing aDatetimeIndex with a partial string depends on the “accuracy” of the period, in other words how specific the interval is in relation to the resolution of the index. In contrast, indexing withTimestamp ordatetime objects is exact, because the objects have exact meaning. These also follow the semantics ofincluding both endpoints.
TheseTimestamp anddatetime objects have exacthours,minutes, andseconds, even though they were not explicitly specified (they are0).
In [135]:dft[datetime.datetime(2013,1,1):datetime.datetime(2013,2,28)]Out[135]: A2013-01-01 00:00:00 0.2762322013-01-01 00:01:00 -1.0874012013-01-01 00:02:00 -0.6736902013-01-01 00:03:00 0.1136482013-01-01 00:04:00 -1.478427... ...2013-02-27 23:56:00 1.1977492013-02-27 23:57:00 0.7205212013-02-27 23:58:00 -0.0727182013-02-27 23:59:00 -0.6811922013-02-28 00:00:00 -0.557501[83521 rows x 1 columns]
With no defaults.
In [136]:dft[ .....:datetime.datetime(2013,1,1,10,12,0):datetime.datetime( .....:2013,2,28,10,12,0 .....:) .....:] .....:Out[136]: A2013-01-01 10:12:00 0.5653752013-01-01 10:13:00 0.0681842013-01-01 10:14:00 0.7888712013-01-01 10:15:00 -0.2803432013-01-01 10:16:00 0.931536... ...2013-02-28 10:08:00 0.1480982013-02-28 10:09:00 -0.3881382013-02-28 10:10:00 0.1393482013-02-28 10:11:00 0.0852882013-02-28 10:12:00 0.950146[83521 rows x 1 columns]
Truncating & fancy indexing#
Atruncate() convenience function is provided that is similarto slicing. Note thattruncate assumes a 0 value for any unspecified datecomponent in aDatetimeIndex in contrast to slicing which returns anypartially matching dates:
In [137]:rng2=pd.date_range("2011-01-01","2012-01-01",freq="W")In [138]:ts2=pd.Series(np.random.randn(len(rng2)),index=rng2)In [139]:ts2.truncate(before="2011-11",after="2011-12")Out[139]:2011-11-06 0.4378232011-11-13 -0.2930832011-11-20 -0.0598812011-11-27 1.252450Freq: W-SUN, dtype: float64In [140]:ts2["2011-11":"2011-12"]Out[140]:2011-11-06 0.4378232011-11-13 -0.2930832011-11-20 -0.0598812011-11-27 1.2524502011-12-04 0.0466112011-12-11 0.0594782011-12-18 -0.2865392011-12-25 0.841669Freq: W-SUN, dtype: float64
Even complicated fancy indexing that breaks theDatetimeIndex frequencyregularity will result in aDatetimeIndex, although frequency is lost:
In [141]:ts2.iloc[[0,2,6]].indexOut[141]:DatetimeIndex(['2011-01-02', '2011-01-16', '2011-02-13'], dtype='datetime64[ns]', freq=None)
Time/date components#
There are several time/date properties that one can access fromTimestamp or a collection of timestamps like aDatetimeIndex.
Property | Description |
|---|---|
year | The year of the datetime |
month | The month of the datetime |
day | The days of the datetime |
hour | The hour of the datetime |
minute | The minutes of the datetime |
second | The seconds of the datetime |
microsecond | The microseconds of the datetime |
nanosecond | The nanoseconds of the datetime |
date | Returns datetime.date (does not contain timezone information) |
time | Returns datetime.time (does not contain timezone information) |
timetz | Returns datetime.time as local time with timezone information |
dayofyear | The ordinal day of year |
day_of_year | The ordinal day of year |
weekofyear | The week ordinal of the year |
week | The week ordinal of the year |
dayofweek | The number of the day of the week with Monday=0, Sunday=6 |
day_of_week | The number of the day of the week with Monday=0, Sunday=6 |
weekday | The number of the day of the week with Monday=0, Sunday=6 |
quarter | Quarter of the date: Jan-Mar = 1, Apr-Jun = 2, etc. |
days_in_month | The number of days in the month of the datetime |
is_month_start | Logical indicating if first day of month (defined by frequency) |
is_month_end | Logical indicating if last day of month (defined by frequency) |
is_quarter_start | Logical indicating if first day of quarter (defined by frequency) |
is_quarter_end | Logical indicating if last day of quarter (defined by frequency) |
is_year_start | Logical indicating if first day of year (defined by frequency) |
is_year_end | Logical indicating if last day of year (defined by frequency) |
is_leap_year | Logical indicating if the date belongs to a leap year |
Furthermore, if you have aSeries with datetimelike values, then you canaccess these properties via the.dt accessor, as detailed in the sectionon.dt accessors.
You may obtain the year, week and day components of the ISO year from the ISO 8601 standard:
In [142]:idx=pd.date_range(start="2019-12-29",freq="D",periods=4)In [143]:idx.isocalendar()Out[143]: year week day2019-12-29 2019 52 72019-12-30 2020 1 12019-12-31 2020 1 22020-01-01 2020 1 3In [144]:idx.to_series().dt.isocalendar()Out[144]: year week day2019-12-29 2019 52 72019-12-30 2020 1 12019-12-31 2020 1 22020-01-01 2020 1 3
DateOffset objects#
In the preceding examples, frequency strings (e.g.'D') were used to specifya frequency that defined:
how the date times in
DatetimeIndexwere spaced when usingdate_range()the frequency of a
PeriodorPeriodIndex
These frequency strings map to aDateOffset object and its subclasses. ADateOffsetis similar to aTimedelta that represents a duration of time but follows specific calendar duration rules.For example, aTimedelta day will always incrementdatetimes by 24 hours, while aDateOffset daywill incrementdatetimes to the same time the next day whether a day represents 23, 24 or 25 hours due to daylightsavings time. However, allDateOffset subclasses that are an hour or smaller(Hour,Minute,Second,Milli,Micro,Nano) behave likeTimedelta and respect absolute time.
The basicDateOffset acts similar todateutil.relativedelta (relativedelta documentation)that shifts a date time by the corresponding calendar duration specified. Thearithmetic operator (+) can be used to perform the shift.
# This particular day contains a day light savings time transitionIn [145]:ts=pd.Timestamp("2016-10-30 00:00:00",tz="Europe/Helsinki")# Respects absolute timeIn [146]:ts+pd.Timedelta(days=1)Out[146]:Timestamp('2016-10-30 23:00:00+0200', tz='Europe/Helsinki')# Respects calendar timeIn [147]:ts+pd.DateOffset(days=1)Out[147]:Timestamp('2016-10-31 00:00:00+0200', tz='Europe/Helsinki')In [148]:friday=pd.Timestamp("2018-01-05")In [149]:friday.day_name()Out[149]:'Friday'# Add 2 business days (Friday --> Tuesday)In [150]:two_business_days=2*pd.offsets.BDay()In [151]:friday+two_business_daysOut[151]:Timestamp('2018-01-09 00:00:00')In [152]:(friday+two_business_days).day_name()Out[152]:'Tuesday'
MostDateOffsets have associated frequencies strings, or offset aliases, that can be passedintofreq keyword arguments. The available date offsets and associated frequency strings can be found below:
Date Offset | Frequency String | Description |
|---|---|---|
None | Generic offset class, defaults to absolute 24 hours | |
| business day (weekday) | |
| custom business day | |
| one week, optionally anchored on a day of the week | |
| the x-th day of the y-th week of each month | |
| the x-th day of the last week of each month | |
| calendar month end | |
| calendar month begin | |
| business month end | |
| business month begin | |
| custom business month end | |
| custom business month begin | |
| 15th (or other day_of_month) and calendar month end | |
| 15th (or other day_of_month) and calendar month begin | |
| calendar quarter end | |
| calendar quarter begin | |
| business quarter end | |
| business quarter begin | |
| retail (aka 52-53 week) quarter | |
| calendar year end | |
| calendar year begin | |
| business year end | |
| business year begin | |
| retail (aka 52-53 week) year | |
None | Easter holiday | |
| business hour | |
| custom business hour | |
| one absolute day | |
| one hour | |
| one minute | |
| one second | |
| one millisecond | |
| one microsecond | |
| one nanosecond |
DateOffsets additionally haverollforward() androllback()methods for moving a date forward or backward respectively to a valid offsetdate relative to the offset. For example, business offsets will roll datesthat land on the weekends (Saturday and Sunday) forward to Monday sincebusiness offsets operate on the weekdays.
In [153]:ts=pd.Timestamp("2018-01-06 00:00:00")In [154]:ts.day_name()Out[154]:'Saturday'# BusinessHour's valid offset dates are Monday through FridayIn [155]:offset=pd.offsets.BusinessHour(start="09:00")# Bring the date to the closest offset date (Monday)In [156]:offset.rollforward(ts)Out[156]:Timestamp('2018-01-08 09:00:00')# Date is brought to the closest offset date first and then the hour is addedIn [157]:ts+offsetOut[157]:Timestamp('2018-01-08 10:00:00')
These operations preserve time (hour, minute, etc) information by default.To reset time to midnight, usenormalize() before or after applyingthe operation (depending on whether you want the time information includedin the operation).
In [158]:ts=pd.Timestamp("2014-01-01 09:00")In [159]:day=pd.offsets.Day()In [160]:day+tsOut[160]:Timestamp('2014-01-02 09:00:00')In [161]:(day+ts).normalize()Out[161]:Timestamp('2014-01-02 00:00:00')In [162]:ts=pd.Timestamp("2014-01-01 22:00")In [163]:hour=pd.offsets.Hour()In [164]:hour+tsOut[164]:Timestamp('2014-01-01 23:00:00')In [165]:(hour+ts).normalize()Out[165]:Timestamp('2014-01-01 00:00:00')In [166]:(hour+pd.Timestamp("2014-01-01 23:30")).normalize()Out[166]:Timestamp('2014-01-02 00:00:00')
Parametric offsets#
Some of the offsets can be “parameterized” when created to result in differentbehaviors. For example, theWeek offset for generating weekly data accepts aweekday parameter which results in the generated dates always lying on aparticular day of the week:
In [167]:d=datetime.datetime(2008,8,18,9,0)In [168]:dOut[168]:datetime.datetime(2008, 8, 18, 9, 0)In [169]:d+pd.offsets.Week()Out[169]:Timestamp('2008-08-25 09:00:00')In [170]:d+pd.offsets.Week(weekday=4)Out[170]:Timestamp('2008-08-22 09:00:00')In [171]:(d+pd.offsets.Week(weekday=4)).weekday()Out[171]:4In [172]:d-pd.offsets.Week()Out[172]:Timestamp('2008-08-11 09:00:00')
Thenormalize option will be effective for addition and subtraction.
In [173]:d+pd.offsets.Week(normalize=True)Out[173]:Timestamp('2008-08-25 00:00:00')In [174]:d-pd.offsets.Week(normalize=True)Out[174]:Timestamp('2008-08-11 00:00:00')
Another example is parameterizingYearEnd with the specific ending month:
In [175]:d+pd.offsets.YearEnd()Out[175]:Timestamp('2008-12-31 09:00:00')In [176]:d+pd.offsets.YearEnd(month=6)Out[176]:Timestamp('2009-06-30 09:00:00')
Using offsets withSeries /DatetimeIndex#
Offsets can be used with either aSeries orDatetimeIndex toapply the offset to each element.
In [177]:rng=pd.date_range("2012-01-01","2012-01-03")In [178]:s=pd.Series(rng)In [179]:rngOut[179]:DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03'], dtype='datetime64[ns]', freq='D')In [180]:rng+pd.DateOffset(months=2)Out[180]:DatetimeIndex(['2012-03-01', '2012-03-02', '2012-03-03'], dtype='datetime64[ns]', freq=None)In [181]:s+pd.DateOffset(months=2)Out[181]:0 2012-03-011 2012-03-022 2012-03-03dtype: datetime64[ns]In [182]:s-pd.DateOffset(months=2)Out[182]:0 2011-11-011 2011-11-022 2011-11-03dtype: datetime64[ns]
If the offset class maps directly to aTimedelta (Day,Hour,Minute,Second,Micro,Milli,Nano) it can beused exactly like aTimedelta - see theTimedelta section for more examples.
In [183]:s-pd.offsets.Day(2)Out[183]:0 2011-12-301 2011-12-312 2012-01-01dtype: datetime64[ns]In [184]:td=s-pd.Series(pd.date_range("2011-12-29","2011-12-31"))In [185]:tdOut[185]:0 3 days1 3 days2 3 daysdtype: timedelta64[ns]In [186]:td+pd.offsets.Minute(15)Out[186]:0 3 days 00:15:001 3 days 00:15:002 3 days 00:15:00dtype: timedelta64[ns]
Note that some offsets (such asBQuarterEnd) do not have avectorized implementation. They can still be used but maycalculate significantly slower and will show aPerformanceWarning
In [187]:rng+pd.offsets.BQuarterEnd()Out[187]:DatetimeIndex(['2012-03-30', '2012-03-30', '2012-03-30'], dtype='datetime64[ns]', freq=None)
Custom business days#
TheCDay orCustomBusinessDay class provides a parametricBusinessDay class which can be used to create customized business daycalendars which account for local holidays and local weekend conventions.
As an interesting example, let’s look at Egypt where a Friday-Saturday weekend is observed.
In [188]:weekmask_egypt="Sun Mon Tue Wed Thu"# They also observe International Workers' Day so let's# add that for a couple of yearsIn [189]:holidays=[ .....:"2012-05-01", .....:datetime.datetime(2013,5,1), .....:np.datetime64("2014-05-01"), .....:] .....:In [190]:bday_egypt=pd.offsets.CustomBusinessDay( .....:holidays=holidays, .....:weekmask=weekmask_egypt, .....:) .....:In [191]:dt=datetime.datetime(2013,4,30)In [192]:dt+2*bday_egyptOut[192]:Timestamp('2013-05-05 00:00:00')
Let’s map to the weekday names:
In [193]:dts=pd.date_range(dt,periods=5,freq=bday_egypt)In [194]:pd.Series(dts.weekday,dts).map(pd.Series("Mon Tue Wed Thu Fri Sat Sun".split()))Out[194]:2013-04-30 Tue2013-05-02 Thu2013-05-05 Sun2013-05-06 Mon2013-05-07 TueFreq: C, dtype: object
Holiday calendars can be used to provide the list of holidays. See theholiday calendar section for more information.
In [195]:frompandas.tseries.holidayimportUSFederalHolidayCalendarIn [196]:bday_us=pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar())# Friday before MLK DayIn [197]:dt=datetime.datetime(2014,1,17)# Tuesday after MLK Day (Monday is skipped because it's a holiday)In [198]:dt+bday_usOut[198]:Timestamp('2014-01-21 00:00:00')
Monthly offsets that respect a certain holiday calendar can be definedin the usual way.
In [199]:bmth_us=pd.offsets.CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())# Skip new yearsIn [200]:dt=datetime.datetime(2013,12,17)In [201]:dt+bmth_usOut[201]:Timestamp('2014-01-02 00:00:00')# Define date index with custom offsetIn [202]:pd.date_range(start="20100101",end="20120101",freq=bmth_us)Out[202]:DatetimeIndex(['2010-01-04', '2010-02-01', '2010-03-01', '2010-04-01', '2010-05-03', '2010-06-01', '2010-07-01', '2010-08-02', '2010-09-01', '2010-10-01', '2010-11-01', '2010-12-01', '2011-01-03', '2011-02-01', '2011-03-01', '2011-04-01', '2011-05-02', '2011-06-01', '2011-07-01', '2011-08-01', '2011-09-01', '2011-10-03', '2011-11-01', '2011-12-01'], dtype='datetime64[ns]', freq='CBMS')
Note
The frequency string ‘C’ is used to indicate that a CustomBusinessDayDateOffset is used, it is important to note that since CustomBusinessDay isa parameterised type, instances of CustomBusinessDay may differ and this isnot detectable from the ‘C’ frequency string. The user therefore needs toensure that the ‘C’ frequency string is used consistently within the user’sapplication.
Business hour#
TheBusinessHour class provides a business hour representation onBusinessDay,allowing to use specific start and end times.
By default,BusinessHour uses 9:00 - 17:00 as business hours.AddingBusinessHour will incrementTimestamp by hourly frequency.If targetTimestamp is out of business hours, move to the next business hourthen increment it. If the result exceeds the business hours end, the remaininghours are added to the next business day.
In [203]:bh=pd.offsets.BusinessHour()In [204]:bhOut[204]:<BusinessHour: bh=09:00-17:00># 2014-08-01 is FridayIn [205]:pd.Timestamp("2014-08-01 10:00").weekday()Out[205]:4In [206]:pd.Timestamp("2014-08-01 10:00")+bhOut[206]:Timestamp('2014-08-01 11:00:00')# Below example is the same as: pd.Timestamp('2014-08-01 09:00') + bhIn [207]:pd.Timestamp("2014-08-01 08:00")+bhOut[207]:Timestamp('2014-08-01 10:00:00')# If the results is on the end time, move to the next business dayIn [208]:pd.Timestamp("2014-08-01 16:00")+bhOut[208]:Timestamp('2014-08-04 09:00:00')# Remainings are added to the next dayIn [209]:pd.Timestamp("2014-08-01 16:30")+bhOut[209]:Timestamp('2014-08-04 09:30:00')# Adding 2 business hoursIn [210]:pd.Timestamp("2014-08-01 10:00")+pd.offsets.BusinessHour(2)Out[210]:Timestamp('2014-08-01 12:00:00')# Subtracting 3 business hoursIn [211]:pd.Timestamp("2014-08-01 10:00")+pd.offsets.BusinessHour(-3)Out[211]:Timestamp('2014-07-31 15:00:00')
You can also specifystart andend time by keywords. The argument mustbe astr with anhour:minute representation or adatetime.timeinstance. Specifying seconds, microseconds and nanoseconds as business hourresults inValueError.
In [212]:bh=pd.offsets.BusinessHour(start="11:00",end=datetime.time(20,0))In [213]:bhOut[213]:<BusinessHour: bh=11:00-20:00>In [214]:pd.Timestamp("2014-08-01 13:00")+bhOut[214]:Timestamp('2014-08-01 14:00:00')In [215]:pd.Timestamp("2014-08-01 09:00")+bhOut[215]:Timestamp('2014-08-01 12:00:00')In [216]:pd.Timestamp("2014-08-01 18:00")+bhOut[216]:Timestamp('2014-08-01 19:00:00')
Passingstart time later thanend represents midnight business hour.In this case, business hour exceeds midnight and overlap to the next day.Valid business hours are distinguished by whether it started from validBusinessDay.
In [217]:bh=pd.offsets.BusinessHour(start="17:00",end="09:00")In [218]:bhOut[218]:<BusinessHour: bh=17:00-09:00>In [219]:pd.Timestamp("2014-08-01 17:00")+bhOut[219]:Timestamp('2014-08-01 18:00:00')In [220]:pd.Timestamp("2014-08-01 23:00")+bhOut[220]:Timestamp('2014-08-02 00:00:00')# Although 2014-08-02 is Saturday,# it is valid because it starts from 08-01 (Friday).In [221]:pd.Timestamp("2014-08-02 04:00")+bhOut[221]:Timestamp('2014-08-02 05:00:00')# Although 2014-08-04 is Monday,# it is out of business hours because it starts from 08-03 (Sunday).In [222]:pd.Timestamp("2014-08-04 04:00")+bhOut[222]:Timestamp('2014-08-04 18:00:00')
ApplyingBusinessHour.rollforward androllback to out of business hours results inthe next business hour start or previous day’s end. Different from other offsets,BusinessHour.rollforwardmay output different results fromapply by definition.
This is because one day’s business hour end is equal to next day’s business hour start. For example,under the default business hours (9:00 - 17:00), there is no gap (0 minutes) between2014-08-0117:00 and2014-08-0409:00.
# This adjusts a Timestamp to business hour edgeIn [223]:pd.offsets.BusinessHour().rollback(pd.Timestamp("2014-08-02 15:00"))Out[223]:Timestamp('2014-08-01 17:00:00')In [224]:pd.offsets.BusinessHour().rollforward(pd.Timestamp("2014-08-02 15:00"))Out[224]:Timestamp('2014-08-04 09:00:00')# It is the same as BusinessHour() + pd.Timestamp('2014-08-01 17:00').# And it is the same as BusinessHour() + pd.Timestamp('2014-08-04 09:00')In [225]:pd.offsets.BusinessHour()+pd.Timestamp("2014-08-02 15:00")Out[225]:Timestamp('2014-08-04 10:00:00')# BusinessDay results (for reference)In [226]:pd.offsets.BusinessHour().rollforward(pd.Timestamp("2014-08-02"))Out[226]:Timestamp('2014-08-04 09:00:00')# It is the same as BusinessDay() + pd.Timestamp('2014-08-01')# The result is the same as rollworward because BusinessDay never overlap.In [227]:pd.offsets.BusinessHour()+pd.Timestamp("2014-08-02")Out[227]:Timestamp('2014-08-04 10:00:00')
BusinessHour regards Saturday and Sunday as holidays. To use arbitraryholidays, you can useCustomBusinessHour offset, as explained in thefollowing subsection.
Custom business hour#
TheCustomBusinessHour is a mixture ofBusinessHour andCustomBusinessDay whichallows you to specify arbitrary holidays.CustomBusinessHour works as the sameasBusinessHour except that it skips specified custom holidays.
In [228]:frompandas.tseries.holidayimportUSFederalHolidayCalendarIn [229]:bhour_us=pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar())# Friday before MLK DayIn [230]:dt=datetime.datetime(2014,1,17,15)In [231]:dt+bhour_usOut[231]:Timestamp('2014-01-17 16:00:00')# Tuesday after MLK Day (Monday is skipped because it's a holiday)In [232]:dt+bhour_us*2Out[232]:Timestamp('2014-01-21 09:00:00')
You can use keyword arguments supported by eitherBusinessHour andCustomBusinessDay.
In [233]:bhour_mon=pd.offsets.CustomBusinessHour(start="10:00",weekmask="Tue Wed Thu Fri")# Monday is skipped because it's a holiday, business hour starts from 10:00In [234]:dt+bhour_mon*2Out[234]:Timestamp('2014-01-21 10:00:00')
Offset aliases#
A number of string aliases are given to useful common time seriesfrequencies. We will refer to these aliases asoffset aliases.
Alias | Description |
|---|---|
B | business day frequency |
C | custom business day frequency |
D | calendar day frequency |
W | weekly frequency |
ME | month end frequency |
SME | semi-month end frequency (15th and end of month) |
BME | business month end frequency |
CBME | 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 |
QE | quarter end frequency |
BQE | business quarter end frequency |
QS | quarter start frequency |
BQS | business quarter start frequency |
YE | year end frequency |
BYE | business year end frequency |
YS | year start frequency |
BYS | business year start frequency |
h | hourly frequency |
bh | business hour frequency |
cbh | custom business hour frequency |
min | minutely frequency |
s | secondly frequency |
ms | milliseconds |
us | microseconds |
ns | nanoseconds |
Deprecated since version 2.2.0:AliasesH,BH,CBH,T,S,L,U, andNare deprecated in favour of the aliasesh,bh,cbh,min,s,ms,us, andns.
Note
When using the offset aliases above, it should be noted that functionssuch as
date_range(),bdate_range(), will only returntimestamps that are in the interval defined bystart_dateandend_date. If thestart_datedoes not correspond to the frequency,the returned timestamps will start at the next valid timestamp, same forend_date, the returned timestamps will stop at the previous validtimestamp.
For example, for the offsetMS, if thestart_date is not the firstof the month, the returned timestamps will start with the first day of thenext month. Ifend_date is not the first day of a month, the lastreturned timestamp will be the first day of the corresponding month.
In [235]:dates_lst_1=pd.date_range("2020-01-06","2020-04-03",freq="MS")In [236]:dates_lst_1Out[236]:DatetimeIndex(['2020-02-01', '2020-03-01', '2020-04-01'], dtype='datetime64[ns]', freq='MS')In [237]:dates_lst_2=pd.date_range("2020-01-01","2020-04-01",freq="MS")In [238]:dates_lst_2Out[238]:DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01'], dtype='datetime64[ns]', freq='MS')
We can see in the above exampledate_range() andbdate_range() will only return the valid timestamps between thestart_date andend_date. If these are not valid timestamps for thegiven frequency it will roll to the next value forstart_date(respectively previous for theend_date)
Period aliases#
A number of string aliases are given to useful common time seriesfrequencies. We will refer to these aliases asperiod aliases.
Alias | Description |
|---|---|
B | business day frequency |
D | calendar day frequency |
W | weekly frequency |
M | monthly frequency |
Q | quarterly frequency |
Y | yearly frequency |
h | hourly frequency |
min | minutely frequency |
s | secondly frequency |
ms | milliseconds |
us | microseconds |
ns | nanoseconds |
Deprecated since version 2.2.0:AliasesA,H,T,S,L,U, andN are deprecated in favour of the aliasesY,h,min,s,ms,us, andns.
Combining aliases#
As we have seen previously, the alias and the offset instance are fungible inmost functions:
In [239]:pd.date_range(start,periods=5,freq="B")Out[239]:DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07'], dtype='datetime64[ns]', freq='B')In [240]:pd.date_range(start,periods=5,freq=pd.offsets.BDay())Out[240]:DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07'], dtype='datetime64[ns]', freq='B')
You can combine together day and intraday offsets:
In [241]:pd.date_range(start,periods=10,freq="2h20min")Out[241]:DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 02:20:00', '2011-01-01 04:40:00', '2011-01-01 07:00:00', '2011-01-01 09:20:00', '2011-01-01 11:40:00', '2011-01-01 14:00:00', '2011-01-01 16:20:00', '2011-01-01 18:40:00', '2011-01-01 21:00:00'], dtype='datetime64[ns]', freq='140min')In [242]:pd.date_range(start,periods=10,freq="1D10us")Out[242]:DatetimeIndex([ '2011-01-01 00:00:00', '2011-01-02 00:00:00.000010', '2011-01-03 00:00:00.000020', '2011-01-04 00:00:00.000030', '2011-01-05 00:00:00.000040', '2011-01-06 00:00:00.000050', '2011-01-07 00:00:00.000060', '2011-01-08 00:00:00.000070', '2011-01-09 00:00:00.000080', '2011-01-10 00:00:00.000090'], dtype='datetime64[ns]', freq='86400000010us')
Anchored offsets#
For some frequencies you can specify an anchoring suffix:
Alias | Description |
|---|---|
W-SUN | weekly frequency (Sundays). Same as ‘W’ |
W-MON | weekly frequency (Mondays) |
W-TUE | weekly frequency (Tuesdays) |
W-WED | weekly frequency (Wednesdays) |
W-THU | weekly frequency (Thursdays) |
W-FRI | weekly frequency (Fridays) |
W-SAT | weekly frequency (Saturdays) |
(B)Q(E)(S)-DEC | quarterly frequency, year ends in December. Same as ‘QE’ |
(B)Q(E)(S)-JAN | quarterly frequency, year ends in January |
(B)Q(E)(S)-FEB | quarterly frequency, year ends in February |
(B)Q(E)(S)-MAR | quarterly frequency, year ends in March |
(B)Q(E)(S)-APR | quarterly frequency, year ends in April |
(B)Q(E)(S)-MAY | quarterly frequency, year ends in May |
(B)Q(E)(S)-JUN | quarterly frequency, year ends in June |
(B)Q(E)(S)-JUL | quarterly frequency, year ends in July |
(B)Q(E)(S)-AUG | quarterly frequency, year ends in August |
(B)Q(E)(S)-SEP | quarterly frequency, year ends in September |
(B)Q(E)(S)-OCT | quarterly frequency, year ends in October |
(B)Q(E)(S)-NOV | quarterly frequency, year ends in November |
(B)Y(E)(S)-DEC | annual frequency, anchored end of December. Same as ‘YE’ |
(B)Y(E)(S)-JAN | annual frequency, anchored end of January |
(B)Y(E)(S)-FEB | annual frequency, anchored end of February |
(B)Y(E)(S)-MAR | annual frequency, anchored end of March |
(B)Y(E)(S)-APR | annual frequency, anchored end of April |
(B)Y(E)(S)-MAY | annual frequency, anchored end of May |
(B)Y(E)(S)-JUN | annual frequency, anchored end of June |
(B)Y(E)(S)-JUL | annual frequency, anchored end of July |
(B)Y(E)(S)-AUG | annual frequency, anchored end of August |
(B)Y(E)(S)-SEP | annual frequency, anchored end of September |
(B)Y(E)(S)-OCT | annual frequency, anchored end of October |
(B)Y(E)(S)-NOV | annual frequency, anchored end of November |
These can be used as arguments todate_range,bdate_range, constructorsforDatetimeIndex, as well as various other timeseries-related functionsin pandas.
Anchored offset semantics#
For those offsets that are anchored to the start or end of specificfrequency (MonthEnd,MonthBegin,WeekEnd, etc), the followingrules apply to rolling forward and backwards.
Whenn is not 0, if the given date is not on an anchor point, it snapped to the next(previous)anchor point, and moved|n|-1 additional steps forwards or backwards.
In [243]:pd.Timestamp("2014-01-02")+pd.offsets.MonthBegin(n=1)Out[243]:Timestamp('2014-02-01 00:00:00')In [244]:pd.Timestamp("2014-01-02")+pd.offsets.MonthEnd(n=1)Out[244]:Timestamp('2014-01-31 00:00:00')In [245]:pd.Timestamp("2014-01-02")-pd.offsets.MonthBegin(n=1)Out[245]:Timestamp('2014-01-01 00:00:00')In [246]:pd.Timestamp("2014-01-02")-pd.offsets.MonthEnd(n=1)Out[246]:Timestamp('2013-12-31 00:00:00')In [247]:pd.Timestamp("2014-01-02")+pd.offsets.MonthBegin(n=4)Out[247]:Timestamp('2014-05-01 00:00:00')In [248]:pd.Timestamp("2014-01-02")-pd.offsets.MonthBegin(n=4)Out[248]:Timestamp('2013-10-01 00:00:00')
If the given dateis on an anchor point, it is moved|n| points forwardsor backwards.
In [249]:pd.Timestamp("2014-01-01")+pd.offsets.MonthBegin(n=1)Out[249]:Timestamp('2014-02-01 00:00:00')In [250]:pd.Timestamp("2014-01-31")+pd.offsets.MonthEnd(n=1)Out[250]:Timestamp('2014-02-28 00:00:00')In [251]:pd.Timestamp("2014-01-01")-pd.offsets.MonthBegin(n=1)Out[251]:Timestamp('2013-12-01 00:00:00')In [252]:pd.Timestamp("2014-01-31")-pd.offsets.MonthEnd(n=1)Out[252]:Timestamp('2013-12-31 00:00:00')In [253]:pd.Timestamp("2014-01-01")+pd.offsets.MonthBegin(n=4)Out[253]:Timestamp('2014-05-01 00:00:00')In [254]:pd.Timestamp("2014-01-31")-pd.offsets.MonthBegin(n=4)Out[254]:Timestamp('2013-10-01 00:00:00')
For the case whenn=0, the date is not moved if on an anchor point, otherwiseit is rolled forward to the next anchor point.
In [255]:pd.Timestamp("2014-01-02")+pd.offsets.MonthBegin(n=0)Out[255]:Timestamp('2014-02-01 00:00:00')In [256]:pd.Timestamp("2014-01-02")+pd.offsets.MonthEnd(n=0)Out[256]:Timestamp('2014-01-31 00:00:00')In [257]:pd.Timestamp("2014-01-01")+pd.offsets.MonthBegin(n=0)Out[257]:Timestamp('2014-01-01 00:00:00')In [258]:pd.Timestamp("2014-01-31")+pd.offsets.MonthEnd(n=0)Out[258]:Timestamp('2014-01-31 00:00:00')
Holidays / holiday calendars#
Holidays and calendars provide a simple way to define holiday rules to be usedwithCustomBusinessDay or in other analysis that requires a predefinedset of holidays. TheAbstractHolidayCalendar class provides all the necessarymethods to return a list of holidays and onlyrules need to be definedin a specific holiday calendar class. Furthermore, thestart_date andend_dateclass attributes determine over what date range holidays are generated. Theseshould be overwritten on theAbstractHolidayCalendar class to have the rangeapply to all calendar subclasses.USFederalHolidayCalendar is theonly calendar that exists and primarily serves as an example for developingother calendars.
For holidays that occur on fixed dates (e.g., US Memorial Day or July 4th) anobservance rule determines when that holiday is observed if it falls on a weekendor some other non-observed day. Defined observance rules are:
Rule | Description |
|---|---|
nearest_workday | move Saturday to Friday and Sunday to Monday |
sunday_to_monday | move Sunday to following Monday |
next_monday_or_tuesday | move Saturday to Monday and Sunday/Monday to Tuesday |
previous_friday | move Saturday and Sunday to previous Friday” |
next_monday | move Saturday and Sunday to following Monday |
An example of how holidays and holiday calendars are defined:
In [259]:frompandas.tseries.holidayimport( .....:Holiday, .....:USMemorialDay, .....:AbstractHolidayCalendar, .....:nearest_workday, .....:MO, .....:) .....:In [260]:classExampleCalendar(AbstractHolidayCalendar): .....:rules=[ .....:USMemorialDay, .....:Holiday("July 4th",month=7,day=4,observance=nearest_workday), .....:Holiday( .....:"Columbus Day", .....:month=10, .....:day=1, .....:offset=pd.DateOffset(weekday=MO(2)), .....:), .....:] .....:In [261]:cal=ExampleCalendar()In [262]:cal.holidays(datetime.datetime(2012,1,1),datetime.datetime(2012,12,31))Out[262]:DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None)
- hint:
weekday=MO(2) is same as2 * Week(weekday=2)
Using this calendar, creating an index or doing offset arithmetic skips weekendsand holidays (i.e., Memorial Day/July 4th). For example, the below definesa custom business day offset using theExampleCalendar. Like any other offset,it can be used to create aDatetimeIndex or added todatetimeorTimestamp objects.
In [263]:pd.date_range( .....:start="7/1/2012",end="7/10/2012",freq=pd.offsets.CDay(calendar=cal) .....:).to_pydatetime() .....:Out[263]:array([datetime.datetime(2012, 7, 2, 0, 0), datetime.datetime(2012, 7, 3, 0, 0), datetime.datetime(2012, 7, 5, 0, 0), datetime.datetime(2012, 7, 6, 0, 0), datetime.datetime(2012, 7, 9, 0, 0), datetime.datetime(2012, 7, 10, 0, 0)], dtype=object)In [264]:offset=pd.offsets.CustomBusinessDay(calendar=cal)In [265]:datetime.datetime(2012,5,25)+offsetOut[265]:Timestamp('2012-05-29 00:00:00')In [266]:datetime.datetime(2012,7,3)+offsetOut[266]:Timestamp('2012-07-05 00:00:00')In [267]:datetime.datetime(2012,7,3)+2*offsetOut[267]:Timestamp('2012-07-06 00:00:00')In [268]:datetime.datetime(2012,7,6)+offsetOut[268]:Timestamp('2012-07-09 00:00:00')
Ranges are defined by thestart_date andend_date class attributesofAbstractHolidayCalendar. The defaults are shown below.
In [269]:AbstractHolidayCalendar.start_dateOut[269]:Timestamp('1970-01-01 00:00:00')In [270]:AbstractHolidayCalendar.end_dateOut[270]:Timestamp('2200-12-31 00:00:00')
These dates can be overwritten by setting the attributes asdatetime/Timestamp/string.
In [271]:AbstractHolidayCalendar.start_date=datetime.datetime(2012,1,1)In [272]:AbstractHolidayCalendar.end_date=datetime.datetime(2012,12,31)In [273]:cal.holidays()Out[273]:DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None)
Every calendar class is accessible by name using theget_calendar functionwhich returns a holiday class instance. Any imported calendar class willautomatically be available by this function. Also,HolidayCalendarFactoryprovides an easy interface to create calendars that are combinations of calendarsor calendars with additional rules.
In [274]:frompandas.tseries.holidayimportget_calendar,HolidayCalendarFactory,USLaborDayIn [275]:cal=get_calendar("ExampleCalendar")In [276]:cal.rulesOut[276]:[Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>), Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7fe8b0553c70>), Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]In [277]:new_cal=HolidayCalendarFactory("NewExampleCalendar",cal,USLaborDay)In [278]:new_cal.rulesOut[278]:[Holiday: Labor Day (month=9, day=1, offset=<DateOffset: weekday=MO(+1)>), Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>), Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7fe8b0553c70>), Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]
Time Series-related instance methods#
Shifting / lagging#
One may want toshift orlag the values in a time series back and forward intime. The method for this isshift(), which is available on all ofthe pandas objects.
In [279]:ts=pd.Series(range(len(rng)),index=rng)In [280]:ts=ts[:5]In [281]:ts.shift(1)Out[281]:2012-01-01 NaN2012-01-02 0.02012-01-03 1.0Freq: D, dtype: float64
Theshift method accepts anfreq argument which can accept aDateOffset class or othertimedelta-like object or also anoffset alias.
Whenfreq is specified,shift method changes all the dates in the indexrather than changing the alignment of the data and the index:
In [282]:ts.shift(5,freq="D")Out[282]:2012-01-06 02012-01-07 12012-01-08 2Freq: D, dtype: int64In [283]:ts.shift(5,freq=pd.offsets.BDay())Out[283]:2012-01-06 02012-01-09 12012-01-10 2dtype: int64In [284]:ts.shift(5,freq="BME")Out[284]:2012-05-31 02012-05-31 12012-05-31 2dtype: int64
Note that with whenfreq is specified, the leading entry is no longer NaNbecause the data is not being realigned.
Frequency conversion#
The primary function for changing frequencies is theasfreq()method. For aDatetimeIndex, this is basically just a thin, but convenientwrapper aroundreindex() which generates adate_range andcallsreindex.
In [285]:dr=pd.date_range("1/1/2010",periods=3,freq=3*pd.offsets.BDay())In [286]:ts=pd.Series(np.random.randn(3),index=dr)In [287]:tsOut[287]:2010-01-01 1.4945222010-01-06 -0.7784252010-01-11 -0.253355Freq: 3B, dtype: float64In [288]:ts.asfreq(pd.offsets.BDay())Out[288]:2010-01-01 1.4945222010-01-04 NaN2010-01-05 NaN2010-01-06 -0.7784252010-01-07 NaN2010-01-08 NaN2010-01-11 -0.253355Freq: B, dtype: float64
asfreq provides a further convenience so you can specify an interpolationmethod for any gaps that may appear after the frequency conversion.
In [289]:ts.asfreq(pd.offsets.BDay(),method="pad")Out[289]:2010-01-01 1.4945222010-01-04 1.4945222010-01-05 1.4945222010-01-06 -0.7784252010-01-07 -0.7784252010-01-08 -0.7784252010-01-11 -0.253355Freq: B, dtype: float64
Filling forward / backward#
Related toasfreq andreindex isfillna(), which isdocumented in themissing data section.
Converting to Python datetimes#
DatetimeIndex can be converted to an array of Python nativedatetime.datetime objects using theto_pydatetime method.
Resampling#
pandas has a simple, powerful, and efficient functionality for performingresampling operations during frequency conversion (e.g., converting secondlydata into 5-minutely data). This is extremely common in, but not limited to,financial applications.
resample() is a time-based groupby, followed by a reduction methodon each of its groups. See somecookbook examples forsome advanced strategies.
Theresample() method can be used directly fromDataFrameGroupBy objects,see thegroupby docs.
Basics#
In [290]:rng=pd.date_range("1/1/2012",periods=100,freq="s")In [291]:ts=pd.Series(np.random.randint(0,500,len(rng)),index=rng)In [292]:ts.resample("5Min").sum()Out[292]:2012-01-01 25103Freq: 5min, dtype: int64
Theresample function is very flexible and allows you to specify manydifferent parameters to control the frequency conversion and resamplingoperation.
Any built-in method available viaGroupBy is available asa method of the returned object, includingsum,mean,std,sem,max,min,median,first,last,ohlc:
In [293]:ts.resample("5Min").mean()Out[293]:2012-01-01 251.03Freq: 5min, dtype: float64In [294]:ts.resample("5Min").ohlc()Out[294]: open high low close2012-01-01 308 460 9 205In [295]:ts.resample("5Min").max()Out[295]:2012-01-01 460Freq: 5min, dtype: int64
For downsampling,closed can be set to ‘left’ or ‘right’ to specify whichend of the interval is closed:
In [296]:ts.resample("5Min",closed="right").mean()Out[296]:2011-12-31 23:55:00 308.0000002012-01-01 00:00:00 250.454545Freq: 5min, dtype: float64In [297]:ts.resample("5Min",closed="left").mean()Out[297]:2012-01-01 251.03Freq: 5min, dtype: float64
Parameters likelabel are used to manipulate the resulting labels.label specifies whether the result is labeled with the beginning orthe end of the interval.
In [298]:ts.resample("5Min").mean()# by default label='left'Out[298]:2012-01-01 251.03Freq: 5min, dtype: float64In [299]:ts.resample("5Min",label="left").mean()Out[299]:2012-01-01 251.03Freq: 5min, dtype: float64
Warning
The default values forlabel andclosed is ‘left’ for allfrequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BYE’, ‘BQE’, and ‘W’which all have a default of ‘right’.
This might unintendedly lead to looking ahead, where the value for a latertime is pulled back to a previous time as in the following example withtheBusinessDay frequency:
In [300]:s=pd.date_range("2000-01-01","2000-01-05").to_series()In [301]:s.iloc[2]=pd.NaTIn [302]:s.dt.day_name()Out[302]:2000-01-01 Saturday2000-01-02 Sunday2000-01-03 NaN2000-01-04 Tuesday2000-01-05 WednesdayFreq: D, dtype: object# default: label='left', closed='left'In [303]:s.resample("B").last().dt.day_name()Out[303]:1999-12-31 Sunday2000-01-03 NaN2000-01-04 Tuesday2000-01-05 WednesdayFreq: B, dtype: object
Notice how the value for Sunday got pulled back to the previous Friday.To get the behavior where the value for Sunday is pushed to Monday, useinstead
In [304]:s.resample("B",label="right",closed="right").last().dt.day_name()Out[304]:2000-01-03 Sunday2000-01-04 Tuesday2000-01-05 Wednesday2000-01-06 NaNFreq: B, dtype: object
Theaxis parameter can be set to 0 or 1 and allows you to resample thespecified axis for aDataFrame.
kind can be set to ‘timestamp’ or ‘period’ to convert the resulting indexto/from timestamp and time span representations. By defaultresampleretains the input representation.
convention can be set to ‘start’ or ‘end’ when resampling period data(detail below). It specifies how low frequency periods are converted to higherfrequency periods.
Upsampling#
For upsampling, you can specify a way to upsample and thelimit parameter to interpolate over the gaps that are created:
# from secondly to every 250 millisecondsIn [305]:ts[:2].resample("250ms").asfreq()Out[305]:2012-01-01 00:00:00.000 308.02012-01-01 00:00:00.250 NaN2012-01-01 00:00:00.500 NaN2012-01-01 00:00:00.750 NaN2012-01-01 00:00:01.000 204.0Freq: 250ms, dtype: float64In [306]:ts[:2].resample("250ms").ffill()Out[306]:2012-01-01 00:00:00.000 3082012-01-01 00:00:00.250 3082012-01-01 00:00:00.500 3082012-01-01 00:00:00.750 3082012-01-01 00:00:01.000 204Freq: 250ms, dtype: int64In [307]:ts[:2].resample("250ms").ffill(limit=2)Out[307]:2012-01-01 00:00:00.000 308.02012-01-01 00:00:00.250 308.02012-01-01 00:00:00.500 308.02012-01-01 00:00:00.750 NaN2012-01-01 00:00:01.000 204.0Freq: 250ms, dtype: float64
Sparse resampling#
Sparse timeseries are the ones where you have a lot fewer points relativeto the amount of time you are looking to resample. Naively upsampling a sparseseries can potentially generate lots of intermediate values. When you don’t wantto use a method to fill these values, e.g.fill_method isNone, thenintermediate values will be filled withNaN.
Sinceresample is a time-based groupby, the following is a method to efficientlyresample only the groups that are not allNaN.
In [308]:rng=pd.date_range("2014-1-1",periods=100,freq="D")+pd.Timedelta("1s")In [309]:ts=pd.Series(range(100),index=rng)
If we want to resample to the full range of the series:
In [310]:ts.resample("3min").sum()Out[310]:2014-01-01 00:00:00 02014-01-01 00:03:00 02014-01-01 00:06:00 02014-01-01 00:09:00 02014-01-01 00:12:00 0 ..2014-04-09 23:48:00 02014-04-09 23:51:00 02014-04-09 23:54:00 02014-04-09 23:57:00 02014-04-10 00:00:00 99Freq: 3min, Length: 47521, dtype: int64
We can instead only resample those groups where we have points as follows:
In [311]:fromfunctoolsimportpartialIn [312]:frompandas.tseries.frequenciesimportto_offsetIn [313]:defround(t,freq): .....:freq=to_offset(freq) .....:td=pd.Timedelta(freq) .....:returnpd.Timestamp((t.value//td.value)*td.value) .....:In [314]:ts.groupby(partial(round,freq="3min")).sum()Out[314]:2014-01-01 02014-01-02 12014-01-03 22014-01-04 32014-01-05 4 ..2014-04-06 952014-04-07 962014-04-08 972014-04-09 982014-04-10 99Length: 100, dtype: int64
Aggregation#
Theresample() method returns apandas.api.typing.Resampler instance. Similar totheaggregating API,groupby API,and thewindow API, aResampler can be selectively resampled.
Resampling aDataFrame, the default will be to act on all columns with the same function.
In [315]:df=pd.DataFrame( .....:np.random.randn(1000,3), .....:index=pd.date_range("1/1/2012",freq="s",periods=1000), .....:columns=["A","B","C"], .....:) .....:In [316]:r=df.resample("3min")In [317]:r.mean()Out[317]: A B C2012-01-01 00:00:00 -0.033823 -0.121514 -0.0814472012-01-01 00:03:00 0.056909 0.146731 -0.0243202012-01-01 00:06:00 -0.058837 0.047046 -0.0520212012-01-01 00:09:00 0.063123 -0.026158 -0.0665332012-01-01 00:12:00 0.186340 -0.003144 0.0747522012-01-01 00:15:00 -0.085954 -0.016287 -0.050046
We can select a specific column or columns using standard getitem.
In [318]:r["A"].mean()Out[318]:2012-01-01 00:00:00 -0.0338232012-01-01 00:03:00 0.0569092012-01-01 00:06:00 -0.0588372012-01-01 00:09:00 0.0631232012-01-01 00:12:00 0.1863402012-01-01 00:15:00 -0.085954Freq: 3min, Name: A, dtype: float64In [319]:r[["A","B"]].mean()Out[319]: A B2012-01-01 00:00:00 -0.033823 -0.1215142012-01-01 00:03:00 0.056909 0.1467312012-01-01 00:06:00 -0.058837 0.0470462012-01-01 00:09:00 0.063123 -0.0261582012-01-01 00:12:00 0.186340 -0.0031442012-01-01 00:15:00 -0.085954 -0.016287
You can pass a list or dict of functions to do aggregation with, outputting aDataFrame:
In [320]:r["A"].agg(["sum","mean","std"])Out[320]: sum mean std2012-01-01 00:00:00 -6.088060 -0.033823 1.0432632012-01-01 00:03:00 10.243678 0.056909 1.0585342012-01-01 00:06:00 -10.590584 -0.058837 0.9492642012-01-01 00:09:00 11.362228 0.063123 1.0280962012-01-01 00:12:00 33.541257 0.186340 0.8845862012-01-01 00:15:00 -8.595393 -0.085954 1.035476
On a resampledDataFrame, you can pass a list of functions to apply to eachcolumn, which produces an aggregated result with a hierarchical index:
In [321]:r.agg(["sum","mean"])Out[321]: A ... C sum mean ... sum mean2012-01-01 00:00:00 -6.088060 -0.033823 ... -14.660515 -0.0814472012-01-01 00:03:00 10.243678 0.056909 ... -4.377642 -0.0243202012-01-01 00:06:00 -10.590584 -0.058837 ... -9.363825 -0.0520212012-01-01 00:09:00 11.362228 0.063123 ... -11.975895 -0.0665332012-01-01 00:12:00 33.541257 0.186340 ... 13.455299 0.0747522012-01-01 00:15:00 -8.595393 -0.085954 ... -5.004580 -0.050046[6 rows x 6 columns]
By passing a dict toaggregate you can apply a different aggregation to thecolumns of aDataFrame:
In [322]:r.agg({"A":"sum","B":lambdax:np.std(x,ddof=1)})Out[322]: A B2012-01-01 00:00:00 -6.088060 1.0012942012-01-01 00:03:00 10.243678 1.0745972012-01-01 00:06:00 -10.590584 0.9873092012-01-01 00:09:00 11.362228 0.9449532012-01-01 00:12:00 33.541257 1.0950252012-01-01 00:15:00 -8.595393 1.035312
The function names can also be strings. In order for a string to be valid itmust be implemented on the resampled object:
In [323]:r.agg({"A":"sum","B":"std"})Out[323]: A B2012-01-01 00:00:00 -6.088060 1.0012942012-01-01 00:03:00 10.243678 1.0745972012-01-01 00:06:00 -10.590584 0.9873092012-01-01 00:09:00 11.362228 0.9449532012-01-01 00:12:00 33.541257 1.0950252012-01-01 00:15:00 -8.595393 1.035312
Furthermore, you can also specify multiple aggregation functions for each column separately.
In [324]:r.agg({"A":["sum","std"],"B":["mean","std"]})Out[324]: A B sum std mean std2012-01-01 00:00:00 -6.088060 1.043263 -0.121514 1.0012942012-01-01 00:03:00 10.243678 1.058534 0.146731 1.0745972012-01-01 00:06:00 -10.590584 0.949264 0.047046 0.9873092012-01-01 00:09:00 11.362228 1.028096 -0.026158 0.9449532012-01-01 00:12:00 33.541257 0.884586 -0.003144 1.0950252012-01-01 00:15:00 -8.595393 1.035476 -0.016287 1.035312
If aDataFrame does not have a datetimelike index, but instead you wantto resample based on datetimelike column in the frame, it can passed to theon keyword.
In [325]:df=pd.DataFrame( .....:{"date":pd.date_range("2015-01-01",freq="W",periods=5),"a":np.arange(5)}, .....:index=pd.MultiIndex.from_arrays( .....:[[1,2,3,4,5],pd.date_range("2015-01-01",freq="W",periods=5)], .....:names=["v","d"], .....:), .....:) .....:In [326]:dfOut[326]: date av d1 2015-01-04 2015-01-04 02 2015-01-11 2015-01-11 13 2015-01-18 2015-01-18 24 2015-01-25 2015-01-25 35 2015-02-01 2015-02-01 4In [327]:df.resample("ME",on="date")[["a"]].sum()Out[327]: adate2015-01-31 62015-02-28 4
Similarly, if you instead want to resample by a datetimelikelevel ofMultiIndex, its name or location can be passed to thelevel keyword.
In [328]:df.resample("ME",level="d")[["a"]].sum()Out[328]: ad2015-01-31 62015-02-28 4
Iterating through groups#
With theResampler object in hand, iterating through the grouped data is verynatural and functions similarly toitertools.groupby():
In [329]:small=pd.Series( .....:range(6), .....:index=pd.to_datetime( .....:[ .....:"2017-01-01T00:00:00", .....:"2017-01-01T00:30:00", .....:"2017-01-01T00:31:00", .....:"2017-01-01T01:00:00", .....:"2017-01-01T03:00:00", .....:"2017-01-01T03:05:00", .....:] .....:), .....:) .....:In [330]:resampled=small.resample("h")In [331]:forname,groupinresampled: .....:print("Group: ",name) .....:print("-"*27) .....:print(group,end="\n\n") .....:Group: 2017-01-01 00:00:00---------------------------2017-01-0100:00:0002017-01-0100:30:0012017-01-0100:31:002dtype: int64Group: 2017-01-01 01:00:00---------------------------2017-01-0101:00:003dtype: int64Group: 2017-01-01 02:00:00---------------------------Series([],dtype:int64)Group: 2017-01-01 03:00:00---------------------------2017-01-0103:00:0042017-01-0103:05:005dtype: int64
SeeIterating through groups orResampler.__iter__ for more.
Useorigin oroffset to adjust the start of the bins#
The bins of the grouping are adjusted based on the beginning of the day of the time series starting point. This works well with frequencies that are multiples of a day (like30D) or that divide a day evenly (like90s or1min). This can create inconsistencies with some frequencies that do not meet this criteria. To change this behavior you can specify a fixed Timestamp with the argumentorigin.
For example:
In [332]:start,end="2000-10-01 23:30:00","2000-10-02 00:30:00"In [333]:middle="2000-10-02 00:00:00"In [334]:rng=pd.date_range(start,end,freq="7min")In [335]:ts=pd.Series(np.arange(len(rng))*3,index=rng)In [336]:tsOut[336]:2000-10-01 23:30:00 02000-10-01 23:37:00 32000-10-01 23:44:00 62000-10-01 23:51:00 92000-10-01 23:58:00 122000-10-02 00:05:00 152000-10-02 00:12:00 182000-10-02 00:19:00 212000-10-02 00:26:00 24Freq: 7min, dtype: int64
Here we can see that, when usingorigin with its default value ('start_day'), the result after'2000-10-0200:00:00' are not identical depending on the start of time series:
In [337]:ts.resample("17min",origin="start_day").sum()Out[337]:2000-10-01 23:14:00 02000-10-01 23:31:00 92000-10-01 23:48:00 212000-10-02 00:05:00 542000-10-02 00:22:00 24Freq: 17min, dtype: int64In [338]:ts[middle:end].resample("17min",origin="start_day").sum()Out[338]:2000-10-02 00:00:00 332000-10-02 00:17:00 45Freq: 17min, dtype: int64
Here we can see that, when settingorigin to'epoch', the result after'2000-10-0200:00:00' are identical depending on the start of time series:
In [339]:ts.resample("17min",origin="epoch").sum()Out[339]:2000-10-01 23:18:00 02000-10-01 23:35:00 182000-10-01 23:52:00 272000-10-02 00:09:00 392000-10-02 00:26:00 24Freq: 17min, dtype: int64In [340]:ts[middle:end].resample("17min",origin="epoch").sum()Out[340]:2000-10-01 23:52:00 152000-10-02 00:09:00 392000-10-02 00:26:00 24Freq: 17min, dtype: int64
If needed you can use a custom timestamp fororigin:
In [341]:ts.resample("17min",origin="2001-01-01").sum()Out[341]:2000-10-01 23:30:00 92000-10-01 23:47:00 212000-10-02 00:04:00 542000-10-02 00:21:00 24Freq: 17min, dtype: int64In [342]:ts[middle:end].resample("17min",origin=pd.Timestamp("2001-01-01")).sum()Out[342]:2000-10-02 00:04:00 542000-10-02 00:21:00 24Freq: 17min, dtype: int64
If needed you can just adjust the bins with anoffset Timedelta that would be added to the defaultorigin.Those two examples are equivalent for this time series:
In [343]:ts.resample("17min",origin="start").sum()Out[343]:2000-10-01 23:30:00 92000-10-01 23:47:00 212000-10-02 00:04:00 542000-10-02 00:21:00 24Freq: 17min, dtype: int64In [344]:ts.resample("17min",offset="23h30min").sum()Out[344]:2000-10-01 23:30:00 92000-10-01 23:47:00 212000-10-02 00:04:00 542000-10-02 00:21:00 24Freq: 17min, dtype: int64
Note the use of'start' fororigin on the last example. In that case,origin will be set to the first value of the timeseries.
Backward resample#
Added in version 1.3.0.
Instead of adjusting the beginning of bins, sometimes we need to fix the end of the bins to make a backward resample with a givenfreq. The backward resample setsclosed to'right' by default since the last value should be considered as the edge point for the last bin.
We can setorigin to'end'. The value for a specificTimestamp index stands for the resample result from the currentTimestamp minusfreq to the currentTimestamp with a right close.
In [345]:ts.resample('17min',origin='end').sum()Out[345]:2000-10-01 23:35:00 02000-10-01 23:52:00 182000-10-02 00:09:00 272000-10-02 00:26:00 63Freq: 17min, dtype: int64
Besides, in contrast with the'start_day' option,end_day is supported. This will set the origin as the ceiling midnight of the largestTimestamp.
In [346]:ts.resample('17min',origin='end_day').sum()Out[346]:2000-10-01 23:38:00 32000-10-01 23:55:00 152000-10-02 00:12:00 452000-10-02 00:29:00 45Freq: 17min, dtype: int64
The above result uses2000-10-0200:29:00 as the last bin’s right edge since the following computation.
In [347]:ceil_mid=rng.max().ceil('D')In [348]:freq=pd.offsets.Minute(17)In [349]:bin_res=ceil_mid-freq*((ceil_mid-rng.max())//freq)In [350]:bin_resOut[350]:Timestamp('2000-10-02 00:29:00')
Time span representation#
Regular intervals of time are represented byPeriod objects in pandas whilesequences ofPeriod objects are collected in aPeriodIndex, which canbe created with the convenience functionperiod_range.
Period#
APeriod represents a span of time (e.g., a day, a month, a quarter, etc).You can specify the span viafreq keyword using a frequency alias like below.Becausefreq represents a span ofPeriod, it cannot be negative like “-3D”.
In [351]:pd.Period("2012",freq="Y-DEC")Out[351]:Period('2012', 'Y-DEC')In [352]:pd.Period("2012-1-1",freq="D")Out[352]:Period('2012-01-01', 'D')In [353]:pd.Period("2012-1-1 19:00",freq="h")Out[353]:Period('2012-01-01 19:00', 'h')In [354]:pd.Period("2012-1-1 19:00",freq="5h")Out[354]:Period('2012-01-01 19:00', '5h')
Adding and subtracting integers from periods shifts the period by its ownfrequency. Arithmetic is not allowed betweenPeriod with differentfreq (span).
In [355]:p=pd.Period("2012",freq="Y-DEC")In [356]:p+1Out[356]:Period('2013', 'Y-DEC')In [357]:p-3Out[357]:Period('2009', 'Y-DEC')In [358]:p=pd.Period("2012-01",freq="2M")In [359]:p+2Out[359]:Period('2012-05', '2M')In [360]:p-1Out[360]:Period('2011-11', '2M')In [361]:p==pd.Period("2012-01",freq="3M")Out[361]:False
IfPeriod freq is daily or higher (D,h,min,s,ms,us, andns),offsets andtimedelta-like can be added if the result can have the same freq. Otherwise,ValueError will be raised.
In [362]:p=pd.Period("2014-07-01 09:00",freq="h")In [363]:p+pd.offsets.Hour(2)Out[363]:Period('2014-07-01 11:00', 'h')In [364]:p+datetime.timedelta(minutes=120)Out[364]:Period('2014-07-01 11:00', 'h')In [365]:p+np.timedelta64(7200,"s")Out[365]:Period('2014-07-01 11:00', 'h')
In [366]:p+pd.offsets.Minute(5)---------------------------------------------------------------------------ValueErrorTraceback (most recent call last)File period.pyx:1824, inpandas._libs.tslibs.period._Period._add_timedeltalike_scalar()File timedeltas.pyx:278, inpandas._libs.tslibs.timedeltas.delta_to_nanoseconds()File np_datetime.pyx:661, inpandas._libs.tslibs.np_datetime.convert_reso()ValueError: Cannot losslessly convert unitsTheaboveexceptionwasthedirectcauseofthefollowingexception:IncompatibleFrequencyTraceback (most recent call last)CellIn[366],line1---->1p+pd.offsets.Minute(5)File period.pyx:1845, inpandas._libs.tslibs.period._Period.__add__()File period.pyx:1826, inpandas._libs.tslibs.period._Period._add_timedeltalike_scalar()IncompatibleFrequency: Input cannot be converted to Period(freq=h)
IfPeriod has other frequencies, only the sameoffsets can be added. Otherwise,ValueError will be raised.
In [367]:p=pd.Period("2014-07",freq="M")In [368]:p+pd.offsets.MonthEnd(3)Out[368]:Period('2014-10', 'M')
In [369]:p+pd.offsets.MonthBegin(3)---------------------------------------------------------------------------IncompatibleFrequencyTraceback (most recent call last)CellIn[369],line1---->1p+pd.offsets.MonthBegin(3)File period.pyx:1847, inpandas._libs.tslibs.period._Period.__add__()File period.pyx:1837, inpandas._libs.tslibs.period._Period._add_offset()File period.pyx:1732, inpandas._libs.tslibs.period.PeriodMixin._require_matching_freq()IncompatibleFrequency: Input has different freq=3M from Period(freq=M)
Taking the difference ofPeriod instances with the same frequency willreturn the number of frequency units between them:
In [370]:pd.Period("2012",freq="Y-DEC")-pd.Period("2002",freq="Y-DEC")Out[370]:<10 * YearEnds: month=12>
PeriodIndex and period_range#
Regular sequences ofPeriod objects can be collected in aPeriodIndex,which can be constructed using theperiod_range convenience function:
In [371]:prng=pd.period_range("1/1/2011","1/1/2012",freq="M")In [372]:prngOut[372]:PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12', '2012-01'], dtype='period[M]')
ThePeriodIndex constructor can also be used directly:
In [373]:pd.PeriodIndex(["2011-1","2011-2","2011-3"],freq="M")Out[373]:PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]')
Passing multiplied frequency outputs a sequence ofPeriod whichhas multiplied span.
In [374]:pd.period_range(start="2014-01",freq="3M",periods=4)Out[374]:PeriodIndex(['2014-01', '2014-04', '2014-07', '2014-10'], dtype='period[3M]')
Ifstart orend arePeriod objects, they will be used as anchorendpoints for aPeriodIndex with frequency matching that of thePeriodIndex constructor.
In [375]:pd.period_range( .....:start=pd.Period("2017Q1",freq="Q"),end=pd.Period("2017Q2",freq="Q"),freq="M" .....:) .....:Out[375]:PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'], dtype='period[M]')
Just likeDatetimeIndex, aPeriodIndex can also be used to index pandasobjects:
In [376]:ps=pd.Series(np.random.randn(len(prng)),prng)In [377]:psOut[377]:2011-01 -2.9169012011-02 0.5144742011-03 1.3464702011-04 0.8163972011-05 2.2586482011-06 0.4947892011-07 0.3012392011-08 0.4647762011-09 -1.3935812011-10 0.0567802011-11 0.1970352011-12 2.2613852012-01 -0.329583Freq: M, dtype: float64
PeriodIndex supports addition and subtraction with the same rule asPeriod.
In [378]:idx=pd.period_range("2014-07-01 09:00",periods=5,freq="h")In [379]:idxOut[379]:PeriodIndex(['2014-07-01 09:00', '2014-07-01 10:00', '2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00'], dtype='period[h]')In [380]:idx+pd.offsets.Hour(2)Out[380]:PeriodIndex(['2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00', '2014-07-01 14:00', '2014-07-01 15:00'], dtype='period[h]')In [381]:idx=pd.period_range("2014-07",periods=5,freq="M")In [382]:idxOut[382]:PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]')In [383]:idx+pd.offsets.MonthEnd(3)Out[383]:PeriodIndex(['2014-10', '2014-11', '2014-12', '2015-01', '2015-02'], dtype='period[M]')
PeriodIndex has its own dtype namedperiod, refer toPeriod Dtypes.
Period dtypes#
PeriodIndex has a customperiod dtype. This is a pandas extensiondtype similar to thetimezone aware dtype (datetime64[ns,tz]).
Theperiod dtype holds thefreq attribute and is represented withperiod[freq] likeperiod[D] orperiod[M], usingfrequency strings.
In [384]:pi=pd.period_range("2016-01-01",periods=3,freq="M")In [385]:piOut[385]:PeriodIndex(['2016-01', '2016-02', '2016-03'], dtype='period[M]')In [386]:pi.dtypeOut[386]:period[M]
Theperiod dtype can be used in.astype(...). It allows one to change thefreq of aPeriodIndex like.asfreq() and convert aDatetimeIndex toPeriodIndex liketo_period():
# change monthly freq to daily freqIn [387]:pi.astype("period[D]")Out[387]:PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]')# convert to DatetimeIndexIn [388]:pi.astype("datetime64[ns]")Out[388]:DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='datetime64[ns]', freq='MS')# convert to PeriodIndexIn [389]:dti=pd.date_range("2011-01-01",freq="ME",periods=3)In [390]:dtiOut[390]:DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], dtype='datetime64[ns]', freq='ME')In [391]:dti.astype("period[M]")Out[391]:PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]')
PeriodIndex partial string indexing#
PeriodIndex now supports partial string slicing with non-monotonic indexes.
You can pass in dates and strings toSeries andDataFrame withPeriodIndex, in the same manner asDatetimeIndex. For details, refer toDatetimeIndex Partial String Indexing.
In [392]:ps["2011-01"]Out[392]:-2.9169013294054507In [393]:ps[datetime.datetime(2011,12,25):]Out[393]:2011-12 2.2613852012-01 -0.329583Freq: M, dtype: float64In [394]:ps["10/31/2011":"12/31/2011"]Out[394]:2011-10 0.0567802011-11 0.1970352011-12 2.261385Freq: M, dtype: float64
Passing a string representing a lower frequency thanPeriodIndex returns partial sliced data.
In [395]:ps["2011"]Out[395]:2011-01 -2.9169012011-02 0.5144742011-03 1.3464702011-04 0.8163972011-05 2.2586482011-06 0.4947892011-07 0.3012392011-08 0.4647762011-09 -1.3935812011-10 0.0567802011-11 0.1970352011-12 2.261385Freq: M, dtype: float64In [396]:dfp=pd.DataFrame( .....:np.random.randn(600,1), .....:columns=["A"], .....:index=pd.period_range("2013-01-01 9:00",periods=600,freq="min"), .....:) .....:In [397]:dfpOut[397]: A2013-01-01 09:00 -0.5384682013-01-01 09:01 -1.3658192013-01-01 09:02 -0.9690512013-01-01 09:03 -0.3311522013-01-01 09:04 -0.245334... ...2013-01-01 18:55 0.5224602013-01-01 18:56 0.1187102013-01-01 18:57 0.1675172013-01-01 18:58 0.9228832013-01-01 18:59 1.721104[600 rows x 1 columns]In [398]:dfp.loc["2013-01-01 10h"]Out[398]: A2013-01-01 10:00 -0.3089752013-01-01 10:01 0.5425202013-01-01 10:02 1.0610682013-01-01 10:03 0.7540052013-01-01 10:04 0.352933... ...2013-01-01 10:55 -0.8656212013-01-01 10:56 -1.1678182013-01-01 10:57 -2.0817482013-01-01 10:58 -0.5271462013-01-01 10:59 0.802298[60 rows x 1 columns]
As withDatetimeIndex, the endpoints will be included in the result. The example below slices data starting from 10:00 to 11:59.
In [399]:dfp["2013-01-01 10h":"2013-01-01 11h"]Out[399]: A2013-01-01 10:00 -0.3089752013-01-01 10:01 0.5425202013-01-01 10:02 1.0610682013-01-01 10:03 0.7540052013-01-01 10:04 0.352933... ...2013-01-01 11:55 -0.5902042013-01-01 11:56 1.5399902013-01-01 11:57 -1.2248262013-01-01 11:58 0.5787982013-01-01 11:59 -0.685496[120 rows x 1 columns]
Frequency conversion and resampling with PeriodIndex#
The frequency ofPeriod andPeriodIndex can be converted via theasfreqmethod. Let’s start with the fiscal year 2011, ending in December:
In [400]:p=pd.Period("2011",freq="Y-DEC")In [401]:pOut[401]:Period('2011', 'Y-DEC')
We can convert it to a monthly frequency. Using thehow parameter, we canspecify whether to return the starting or ending month:
In [402]:p.asfreq("M",how="start")Out[402]:Period('2011-01', 'M')In [403]:p.asfreq("M",how="end")Out[403]:Period('2011-12', 'M')
The shorthands ‘s’ and ‘e’ are provided for convenience:
In [404]:p.asfreq("M","s")Out[404]:Period('2011-01', 'M')In [405]:p.asfreq("M","e")Out[405]:Period('2011-12', 'M')
Converting to a “super-period” (e.g., annual frequency is a super-period ofquarterly frequency) automatically returns the super-period that includes theinput period:
In [406]:p=pd.Period("2011-12",freq="M")In [407]:p.asfreq("Y-NOV")Out[407]:Period('2012', 'Y-NOV')
Note that since we converted to an annual frequency that ends the year inNovember, the monthly period of December 2011 is actually in the 2012 Y-NOVperiod.
Period conversions with anchored frequencies are particularly useful forworking with various quarterly data common to economics, business, and otherfields. Many organizations define quarters relative to the month in which theirfiscal year starts and ends. Thus, first quarter of 2011 could start in 2010 ora few months into 2011. Via anchored frequencies, pandas works for all quarterlyfrequenciesQ-JAN throughQ-DEC.
Q-DEC define regular calendar quarters:
In [408]:p=pd.Period("2012Q1",freq="Q-DEC")In [409]:p.asfreq("D","s")Out[409]:Period('2012-01-01', 'D')In [410]:p.asfreq("D","e")Out[410]:Period('2012-03-31', 'D')
Q-MAR defines fiscal year end in March:
In [411]:p=pd.Period("2011Q4",freq="Q-MAR")In [412]:p.asfreq("D","s")Out[412]:Period('2011-01-01', 'D')In [413]:p.asfreq("D","e")Out[413]:Period('2011-03-31', 'D')
Converting between representations#
Timestamped data can be converted to PeriodIndex-ed data usingto_periodand vice-versa usingto_timestamp:
In [414]:rng=pd.date_range("1/1/2012",periods=5,freq="ME")In [415]:ts=pd.Series(np.random.randn(len(rng)),index=rng)In [416]:tsOut[416]:2012-01-31 1.9312532012-02-29 -0.1845942012-03-31 0.2496562012-04-30 -0.9781512012-05-31 -0.873389Freq: ME, dtype: float64In [417]:ps=ts.to_period()In [418]:psOut[418]:2012-01 1.9312532012-02 -0.1845942012-03 0.2496562012-04 -0.9781512012-05 -0.873389Freq: M, dtype: float64In [419]:ps.to_timestamp()Out[419]:2012-01-01 1.9312532012-02-01 -0.1845942012-03-01 0.2496562012-04-01 -0.9781512012-05-01 -0.873389Freq: MS, dtype: float64
Remember that ‘s’ and ‘e’ can be used to return the timestamps at the start orend of the period:
In [420]:ps.to_timestamp("D",how="s")Out[420]:2012-01-01 1.9312532012-02-01 -0.1845942012-03-01 0.2496562012-04-01 -0.9781512012-05-01 -0.873389Freq: MS, dtype: float64
Converting between period and timestamp enables some convenient arithmeticfunctions to be used. In the following example, we convert a quarterlyfrequency with year ending in November to 9am of the end of the month followingthe quarter end:
In [421]:prng=pd.period_range("1990Q1","2000Q4",freq="Q-NOV")In [422]:ts=pd.Series(np.random.randn(len(prng)),prng)In [423]:ts.index=(prng.asfreq("M","e")+1).asfreq("h","s")+9In [424]:ts.head()Out[424]:1990-03-01 09:00 -0.1092911990-06-01 09:00 -0.6372351990-09-01 09:00 -1.7359251990-12-01 09:00 2.0969461991-03-01 09:00 -1.039926Freq: h, dtype: float64
Representing out-of-bounds spans#
If you have data that is outside of theTimestamp bounds, seeTimestamp limitations,then you can use aPeriodIndex and/orSeries ofPeriods to do computations.
In [425]:span=pd.period_range("1215-01-01","1381-01-01",freq="D")In [426]:spanOut[426]:PeriodIndex(['1215-01-01', '1215-01-02', '1215-01-03', '1215-01-04', '1215-01-05', '1215-01-06', '1215-01-07', '1215-01-08', '1215-01-09', '1215-01-10', ... '1380-12-23', '1380-12-24', '1380-12-25', '1380-12-26', '1380-12-27', '1380-12-28', '1380-12-29', '1380-12-30', '1380-12-31', '1381-01-01'], dtype='period[D]', length=60632)
To convert from anint64 based YYYYMMDD representation.
In [427]:s=pd.Series([20121231,20141130,99991231])In [428]:sOut[428]:0 201212311 201411302 99991231dtype: int64In [429]:defconv(x): .....:returnpd.Period(year=x//10000,month=x//100%100,day=x%100,freq="D") .....:In [430]:s.apply(conv)Out[430]:0 2012-12-311 2014-11-302 9999-12-31dtype: period[D]In [431]:s.apply(conv)[2]Out[431]:Period('9999-12-31', 'D')
These can easily be converted to aPeriodIndex:
In [432]:span=pd.PeriodIndex(s.apply(conv))In [433]:spanOut[433]:PeriodIndex(['2012-12-31', '2014-11-30', '9999-12-31'], dtype='period[D]')
Time zone handling#
pandas provides rich support for working with timestamps in different timezones using thepytz anddateutil libraries ordatetime.timezoneobjects from the standard library.
Working with time zones#
By default, pandas objects are time zone unaware:
In [434]:rng=pd.date_range("3/6/2012 00:00",periods=15,freq="D")In [435]:rng.tzisNoneOut[435]:True
To localize these dates to a time zone (assign a particular time zone to a naive date),you can use thetz_localize method or thetz keyword argument indate_range(),Timestamp, orDatetimeIndex.You can either passpytz ordateutil time zone objects or Olson time zone database strings.Olson time zone strings will returnpytz time zone objects by default.To returndateutil time zone objects, appenddateutil/ before the string.
In
pytzyou can find a list of common (and less common) time zones usingfrompytzimportcommon_timezones,all_timezones.dateutiluses the OS time zones so there isn’t a fixed list available. Forcommon zones, the names are the same aspytz.
In [436]:importdateutil# pytzIn [437]:rng_pytz=pd.date_range("3/6/2012 00:00",periods=3,freq="D",tz="Europe/London")In [438]:rng_pytz.tzOut[438]:<DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD># dateutilIn [439]:rng_dateutil=pd.date_range("3/6/2012 00:00",periods=3,freq="D")In [440]:rng_dateutil=rng_dateutil.tz_localize("dateutil/Europe/London")In [441]:rng_dateutil.tzOut[441]:tzfile('/usr/share/zoneinfo/Europe/London')# dateutil - utc special caseIn [442]:rng_utc=pd.date_range( .....:"3/6/2012 00:00", .....:periods=3, .....:freq="D", .....:tz=dateutil.tz.tzutc(), .....:) .....:In [443]:rng_utc.tzOut[443]:tzutc()
# datetime.timezoneIn [444]:rng_utc=pd.date_range( .....:"3/6/2012 00:00", .....:periods=3, .....:freq="D", .....:tz=datetime.timezone.utc, .....:) .....:In [445]:rng_utc.tzOut[445]:datetime.timezone.utc
Note that theUTC time zone is a special case indateutil and should be constructed explicitlyas an instance ofdateutil.tz.tzutc. You can also construct other timezones objects explicitly first.
In [446]:importpytz# pytzIn [447]:tz_pytz=pytz.timezone("Europe/London")In [448]:rng_pytz=pd.date_range("3/6/2012 00:00",periods=3,freq="D")In [449]:rng_pytz=rng_pytz.tz_localize(tz_pytz)In [450]:rng_pytz.tz==tz_pytzOut[450]:True# dateutilIn [451]:tz_dateutil=dateutil.tz.gettz("Europe/London")In [452]:rng_dateutil=pd.date_range("3/6/2012 00:00",periods=3,freq="D",tz=tz_dateutil)In [453]:rng_dateutil.tz==tz_dateutilOut[453]:True
To convert a time zone aware pandas object from one time zone to another,you can use thetz_convert method.
In [454]:rng_pytz.tz_convert("US/Eastern")Out[454]:DatetimeIndex(['2012-03-05 19:00:00-05:00', '2012-03-06 19:00:00-05:00', '2012-03-07 19:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
Note
When usingpytz time zones,DatetimeIndex will construct a differenttime zone object than aTimestamp for the same time zone input. ADatetimeIndexcan hold a collection ofTimestamp objects that may have different UTC offsets and cannot besuccinctly represented by onepytz time zone instance while oneTimestamprepresents one point in time with a specific UTC offset.
In [455]:dti=pd.date_range("2019-01-01",periods=3,freq="D",tz="US/Pacific")In [456]:dti.tzOut[456]:<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>In [457]:ts=pd.Timestamp("2019-01-01",tz="US/Pacific")In [458]:ts.tzOut[458]:<DstTzInfo 'US/Pacific' PST-1 day, 16:00:00 STD>
Warning
Be wary of conversions between libraries. For some time zones,pytz anddateutil have differentdefinitions of the zone. This is more of a problem for unusual time zones than for‘standard’ zones likeUS/Eastern.
Warning
Be aware that a time zone definition across versions of time zone libraries may notbe considered equal. This may cause problems when working with stored data thatis localized using one version and operated on with a different version.Seehere for how to handle such a situation.
Warning
Forpytz time zones, it is incorrect to pass a time zone object directly intothedatetime.datetime constructor(e.g.,datetime.datetime(2011,1,1,tzinfo=pytz.timezone('US/Eastern')).Instead, the datetime needs to be localized using thelocalize methodon thepytz time zone object.
Warning
Be aware that for times in the future, correct conversion between time zones(and UTC) cannot be guaranteed by any time zone library because a timezone’soffset from UTC may be changed by the respective government.
Warning
If you are using dates beyond 2038-01-18, due to current deficienciesin the underlying libraries caused by the year 2038 problem, daylight saving time (DST) adjustmentsto timezone aware dates will not be applied. If and when the underlying libraries are fixed,the DST transitions will be applied.
For example, for two dates that are in British Summer Time (and so would normally be GMT+1), both the following asserts evaluate as true:
In [459]:d_2037="2037-03-31T010101"In [460]:d_2038="2038-03-31T010101"In [461]:DST="Europe/London"In [462]:assertpd.Timestamp(d_2037,tz=DST)!=pd.Timestamp(d_2037,tz="GMT")In [463]:assertpd.Timestamp(d_2038,tz=DST)==pd.Timestamp(d_2038,tz="GMT")
Under the hood, all timestamps are stored in UTC. Values from a time zone awareDatetimeIndex orTimestamp will have their fields (day, hour, minute, etc.)localized to the time zone. However, timestamps with the same UTC value arestill considered to be equal even if they are in different time zones:
In [464]:rng_eastern=rng_utc.tz_convert("US/Eastern")In [465]:rng_berlin=rng_utc.tz_convert("Europe/Berlin")In [466]:rng_eastern[2]Out[466]:Timestamp('2012-03-07 19:00:00-0500', tz='US/Eastern')In [467]:rng_berlin[2]Out[467]:Timestamp('2012-03-08 01:00:00+0100', tz='Europe/Berlin')In [468]:rng_eastern[2]==rng_berlin[2]Out[468]:True
Operations betweenSeries in different time zones will yield UTCSeries, aligning the data on the UTC timestamps:
In [469]:ts_utc=pd.Series(range(3),pd.date_range("20130101",periods=3,tz="UTC"))In [470]:eastern=ts_utc.tz_convert("US/Eastern")In [471]:berlin=ts_utc.tz_convert("Europe/Berlin")In [472]:result=eastern+berlinIn [473]:resultOut[473]:2013-01-01 00:00:00+00:00 02013-01-02 00:00:00+00:00 22013-01-03 00:00:00+00:00 4Freq: D, dtype: int64In [474]:result.indexOut[474]:DatetimeIndex(['2013-01-01 00:00:00+00:00', '2013-01-02 00:00:00+00:00', '2013-01-03 00:00:00+00:00'], dtype='datetime64[ns, UTC]', freq='D')
To remove time zone information, usetz_localize(None) ortz_convert(None).tz_localize(None) will remove the time zone yielding the local time representation.tz_convert(None) will remove the time zone after converting to UTC time.
In [475]:didx=pd.date_range(start="2014-08-01 09:00",freq="h",periods=3,tz="US/Eastern")In [476]:didxOut[476]:DatetimeIndex(['2014-08-01 09:00:00-04:00', '2014-08-01 10:00:00-04:00', '2014-08-01 11:00:00-04:00'], dtype='datetime64[ns, US/Eastern]', freq='h')In [477]:didx.tz_localize(None)Out[477]:DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00', '2014-08-01 11:00:00'], dtype='datetime64[ns]', freq=None)In [478]:didx.tz_convert(None)Out[478]:DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00', '2014-08-01 15:00:00'], dtype='datetime64[ns]', freq='h')# tz_convert(None) is identical to tz_convert('UTC').tz_localize(None)In [479]:didx.tz_convert("UTC").tz_localize(None)Out[479]:DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00', '2014-08-01 15:00:00'], dtype='datetime64[ns]', freq=None)
Fold#
For ambiguous times, pandas supports explicitly specifying the keyword-only fold argument.Due to daylight saving time, one wall clock time can occur twice when shiftingfrom summer to winter time; fold describes whether the datetime-like correspondsto the first (0) or the second time (1) the wall clock hits the ambiguous time.Fold is supported only for constructing from naivedatetime.datetime(seedatetime documentation for details) or fromTimestampor for constructing from components (see below). Onlydateutil timezones are supported(seedateutil documentationfordateutil methods that deal with ambiguous datetimes) aspytztimezones do not support fold (seepytz documentationfor details on howpytz deals with ambiguous datetimes). To localize an ambiguous datetimewithpytz, please useTimestamp.tz_localize(). In general, we recommend to relyonTimestamp.tz_localize() when localizing ambiguous datetimes if you need directcontrol over how they are handled.
In [480]:pd.Timestamp( .....:datetime.datetime(2019,10,27,1,30,0,0), .....:tz="dateutil/Europe/London", .....:fold=0, .....:) .....:Out[480]:Timestamp('2019-10-27 01:30:00+0100', tz='dateutil//usr/share/zoneinfo/Europe/London')In [481]:pd.Timestamp( .....:year=2019, .....:month=10, .....:day=27, .....:hour=1, .....:minute=30, .....:tz="dateutil/Europe/London", .....:fold=1, .....:) .....:Out[481]:Timestamp('2019-10-27 01:30:00+0000', tz='dateutil//usr/share/zoneinfo/Europe/London')
Ambiguous times when localizing#
tz_localize may not be able to determine the UTC offset of a timestampbecause daylight savings time (DST) in a local time zone causes some times to occurtwice within one day (“clocks fall back”). The following options are available:
'raise': Raises apytz.AmbiguousTimeError(the default behavior)'infer': Attempt to determine the correct offset base on the monotonicity of the timestamps'NaT': Replaces ambiguous times withNaTbool:Truerepresents a DST time,Falserepresents non-DST time. An array-like ofboolvalues is supported for a sequence of times.
In [482]:rng_hourly=pd.DatetimeIndex( .....:["11/06/2011 00:00","11/06/2011 01:00","11/06/2011 01:00","11/06/2011 02:00"] .....:) .....:
This will fail as there are ambiguous times ('11/06/201101:00')
In [483]:rng_hourly.tz_localize('US/Eastern')---------------------------------------------------------------------------AmbiguousTimeErrorTraceback (most recent call last)CellIn[483],line1---->1rng_hourly.tz_localize('US/Eastern')File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:293, inDatetimeIndex.tz_localize(self, tz, ambiguous, nonexistent)286@doc(DatetimeArray.tz_localize)287deftz_localize(288self,(...)291nonexistent:TimeNonexistent="raise",292)->Self:-->293arr=self._data.tz_localize(tz,ambiguous,nonexistent)294returntype(self)._simple_new(arr,name=self.name)File ~/work/pandas/pandas/pandas/core/arrays/_mixins.py:81, inravel_compat.<locals>.method(self, *args, **kwargs)78@wraps(meth)79defmethod(self,*args,**kwargs):80ifself.ndim==1:--->81returnmeth(self,*args,**kwargs)83flags=self._ndarray.flags84flat=self.ravel("K")File ~/work/pandas/pandas/pandas/core/arrays/datetimes.py:1088, inDatetimeArray.tz_localize(self, tz, ambiguous, nonexistent)1085tz=timezones.maybe_get_tz(tz)1086# Convert to UTC->1088new_dates=tzconversion.tz_localize_to_utc(1089self.asi8,1090tz,1091ambiguous=ambiguous,1092nonexistent=nonexistent,1093creso=self._creso,1094)1095new_dates_dt64=new_dates.view(f"M8[{self.unit}]")1096dtype=tz_to_dtype(tz,unit=self.unit)File tzconversion.pyx:371, inpandas._libs.tslibs.tzconversion.tz_localize_to_utc()AmbiguousTimeError: Cannot infer dst time from 2011-11-06 01:00:00, try using the 'ambiguous' argument
Handle these ambiguous times by specifying the following.
In [484]:rng_hourly.tz_localize("US/Eastern",ambiguous="infer")Out[484]:DatetimeIndex(['2011-11-06 00:00:00-04:00', '2011-11-06 01:00:00-04:00', '2011-11-06 01:00:00-05:00', '2011-11-06 02:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)In [485]:rng_hourly.tz_localize("US/Eastern",ambiguous="NaT")Out[485]:DatetimeIndex(['2011-11-06 00:00:00-04:00', 'NaT', 'NaT', '2011-11-06 02:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)In [486]:rng_hourly.tz_localize("US/Eastern",ambiguous=[True,True,False,False])Out[486]:DatetimeIndex(['2011-11-06 00:00:00-04:00', '2011-11-06 01:00:00-04:00', '2011-11-06 01:00:00-05:00', '2011-11-06 02:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
Nonexistent times when localizing#
A DST transition may also shift the local time ahead by 1 hour creating nonexistentlocal times (“clocks spring forward”). The behavior of localizing a timeseries with nonexistent timescan be controlled by thenonexistent argument. The following options are available:
'raise': Raises apytz.NonExistentTimeError(the default behavior)'NaT': Replaces nonexistent times withNaT'shift_forward': Shifts nonexistent times forward to the closest real time'shift_backward': Shifts nonexistent times backward to the closest real timetimedelta object: Shifts nonexistent times by the timedelta duration
In [487]:dti=pd.date_range(start="2015-03-29 02:30:00",periods=3,freq="h")# 2:30 is a nonexistent time
Localization of nonexistent times will raise an error by default.
In [488]:dti.tz_localize('Europe/Warsaw')---------------------------------------------------------------------------NonExistentTimeErrorTraceback (most recent call last)CellIn[488],line1---->1dti.tz_localize('Europe/Warsaw')File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:293, inDatetimeIndex.tz_localize(self, tz, ambiguous, nonexistent)286@doc(DatetimeArray.tz_localize)287deftz_localize(288self,(...)291nonexistent:TimeNonexistent="raise",292)->Self:-->293arr=self._data.tz_localize(tz,ambiguous,nonexistent)294returntype(self)._simple_new(arr,name=self.name)File ~/work/pandas/pandas/pandas/core/arrays/_mixins.py:81, inravel_compat.<locals>.method(self, *args, **kwargs)78@wraps(meth)79defmethod(self,*args,**kwargs):80ifself.ndim==1:--->81returnmeth(self,*args,**kwargs)83flags=self._ndarray.flags84flat=self.ravel("K")File ~/work/pandas/pandas/pandas/core/arrays/datetimes.py:1088, inDatetimeArray.tz_localize(self, tz, ambiguous, nonexistent)1085tz=timezones.maybe_get_tz(tz)1086# Convert to UTC->1088new_dates=tzconversion.tz_localize_to_utc(1089self.asi8,1090tz,1091ambiguous=ambiguous,1092nonexistent=nonexistent,1093creso=self._creso,1094)1095new_dates_dt64=new_dates.view(f"M8[{self.unit}]")1096dtype=tz_to_dtype(tz,unit=self.unit)File tzconversion.pyx:431, inpandas._libs.tslibs.tzconversion.tz_localize_to_utc()NonExistentTimeError: 2015-03-29 02:30:00
Transform nonexistent times toNaT or shift the times.
In [489]:dtiOut[489]:DatetimeIndex(['2015-03-29 02:30:00', '2015-03-29 03:30:00', '2015-03-29 04:30:00'], dtype='datetime64[ns]', freq='h')In [490]:dti.tz_localize("Europe/Warsaw",nonexistent="shift_forward")Out[490]:DatetimeIndex(['2015-03-29 03:00:00+02:00', '2015-03-29 03:30:00+02:00', '2015-03-29 04:30:00+02:00'], dtype='datetime64[ns, Europe/Warsaw]', freq=None)In [491]:dti.tz_localize("Europe/Warsaw",nonexistent="shift_backward")Out[491]:DatetimeIndex(['2015-03-29 01:59:59.999999999+01:00', '2015-03-29 03:30:00+02:00', '2015-03-29 04:30:00+02:00'], dtype='datetime64[ns, Europe/Warsaw]', freq=None)In [492]:dti.tz_localize("Europe/Warsaw",nonexistent=pd.Timedelta(1,unit="h"))Out[492]:DatetimeIndex(['2015-03-29 03:30:00+02:00', '2015-03-29 03:30:00+02:00', '2015-03-29 04:30:00+02:00'], dtype='datetime64[ns, Europe/Warsaw]', freq=None)In [493]:dti.tz_localize("Europe/Warsaw",nonexistent="NaT")Out[493]:DatetimeIndex(['NaT', '2015-03-29 03:30:00+02:00', '2015-03-29 04:30:00+02:00'], dtype='datetime64[ns, Europe/Warsaw]', freq=None)
Time zone Series operations#
ASeries with time zonenaive values isrepresented with a dtype ofdatetime64[ns].
In [494]:s_naive=pd.Series(pd.date_range("20130101",periods=3))In [495]:s_naiveOut[495]:0 2013-01-011 2013-01-022 2013-01-03dtype: datetime64[ns]
ASeries with a time zoneaware values isrepresented with a dtype ofdatetime64[ns,tz] wheretz is the time zone
In [496]:s_aware=pd.Series(pd.date_range("20130101",periods=3,tz="US/Eastern"))In [497]:s_awareOut[497]:0 2013-01-01 00:00:00-05:001 2013-01-02 00:00:00-05:002 2013-01-03 00:00:00-05:00dtype: datetime64[ns, US/Eastern]
Both of theseSeries time zone informationcan be manipulated via the.dt accessor, seethe dt accessor section.
For example, to localize and convert a naive stamp to time zone aware.
In [498]:s_naive.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")Out[498]:0 2012-12-31 19:00:00-05:001 2013-01-01 19:00:00-05:002 2013-01-02 19:00:00-05:00dtype: datetime64[ns, US/Eastern]
Time zone information can also be manipulated using theastype method.This method can convert between different timezone-aware dtypes.
# convert to a new time zoneIn [499]:s_aware.astype("datetime64[ns, CET]")Out[499]:0 2013-01-01 06:00:00+01:001 2013-01-02 06:00:00+01:002 2013-01-03 06:00:00+01:00dtype: datetime64[ns, CET]
Note
UsingSeries.to_numpy() on aSeries, returns a NumPy array of the data.NumPy does not currently support time zones (even though it isprinting in the local time zone!),therefore an object array of Timestamps is returned for time zone aware data:
In [500]:s_naive.to_numpy()Out[500]:array(['2013-01-01T00:00:00.000000000', '2013-01-02T00:00:00.000000000', '2013-01-03T00:00:00.000000000'], dtype='datetime64[ns]')In [501]:s_aware.to_numpy()Out[501]:array([Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern'), Timestamp('2013-01-02 00:00:00-0500', tz='US/Eastern'), Timestamp('2013-01-03 00:00:00-0500', tz='US/Eastern')], dtype=object)
By converting to an object array of Timestamps, it preserves the time zoneinformation. For example, when converting back to a Series:
In [502]:pd.Series(s_aware.to_numpy())Out[502]:0 2013-01-01 00:00:00-05:001 2013-01-02 00:00:00-05:002 2013-01-03 00:00:00-05:00dtype: datetime64[ns, US/Eastern]
However, if you want an actual NumPydatetime64[ns] array (with the valuesconverted to UTC) instead of an array of objects, you can specify thedtype argument:
In [503]:s_aware.to_numpy(dtype="datetime64[ns]")Out[503]:array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000', '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')