Time series / date functionality — pandas 2.2.2 documentation (2024)

pandas contains extensive capabilities and features for working with time series data for all domains.Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number offeatures from other Python libraries like scikits.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]: import datetimeIn [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.datetime from the standard library.
Time deltas: An absolute time duration. Similar to datetime.timedelta from 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.relativedelta from the dateutil package.

Concept	Scalar Class	Array Class	pandas Data Type	Primary Creation Method
Date times	`Timestamp`	`DatetimeIndex`	`datetime64[ns]` or `datetime64[ns, tz]`	`to_datetime` or `date_range`
Time deltas	`Timedelta`	`TimedeltaIndex`	`timedelta64[ns]`	`to_timedelta` or `timedelta_range`
Time spans	`Period`	`PeriodIndex`	`period[freq]`	`Period` or `period_range`
Date offsets	`DateOffset`	`None`	`None`	`DateOffset`

For time series data, it’s conventional to represent the time component in the index of a Series or DataFrameso 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 and DataFrame 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 and DataFrame have extended data type support and functionality for datetime, timedeltaand Period data when passed into those constructors. DateOffsetdata however will be stored as object 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 as NaT whichis useful for representing missing or null date like values and behaves similaras np.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]: import datetimeIn [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 by Period 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 and Period can serve as an index. Lists ofTimestamp and Period are automatically coerced to DatetimeIndexand PeriodIndex 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 of Timestamp and sequences of timestamps using instances ofDatetimeIndex. For regular time spans, pandas uses Period objects forscalar values and PeriodIndex for sequences of spans. Better support forirregular intervals with arbitrary start and end points are forth-coming infuture releases.

Converting to timestamps#

To convert a Series or list-like object of date-like objects e.g. strings,epochs, or a mixture, you can use the to_datetime function. When passeda Series, this returns a Series (with the same index), while a list-likeis converted to a DatetimeIndex:

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 the dayfirst 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 that dayfirst isn’t strict. If a datecan’t be parsed with the day being first it will be parsed as ifdayfirst were False and a warning will also be raised.

If you pass a single string to to_datetime, it returns a single Timestamp.Timestamp can also accept string input, but it doesn’t accept string parsingoptions like dayfirst or format, so use to_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 the DatetimeIndex 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, a format 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 the formatoption, see the Python datetime documentation.

Assembling datetime from multiple DataFrame columns#

You can also pass a DataFrame of integer or string columns to assemble into a Series of Timestamps.

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, day
optional: 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')---------------------------------------------------------------------------ValueError Traceback (most recent call last)Cell In[57], line 1----> 1 pd.to_datetime(['2009/07/31', 'asd'], errors='raise')File ~/work/pandas/pandas/pandas/core/tools/datetimes.py:1099, in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache) 1097 result = _convert_and_box_cache(argc, cache_array) 1098 else:-> 1099 result = convert_listlike(argc, format) 1100 else: 1101 result = 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. 432 if format is not None and format != "mixed":--> 433 return _array_strptime_with_fallback(arg, name, utc, format, exact, errors) 435 result, tz_parsed = objects_to_datetime64( 436 arg, 437 dayfirst=dayfirst, (...) 441 allow_object=True, 442 ) 444 if tz_parsed is not None: 445 # We can take a shortcut since the datetime64 numpy array 446 # is in UTCFile ~/work/pandas/pandas/pandas/core/tools/datetimes.py:467, in _array_strptime_with_fallback(arg, name, utc, fmt, exact, errors) 456 def _array_strptime_with_fallback( 457 arg, 458 name, (...) 462 errors: str, 463 ) -> Index: 464 """ 465 Call array_strptime, with fallback behavior depending on 'errors'. 466 """--> 467 result, tz_out = array_strptime(arg, fmt, exact=exact, errors=errors, utc=utc) 468 if tz_out is not None: 469 unit = np.datetime_data(result.dtype)[0]File strptime.pyx:501, in pandas._libs.tslibs.strptime.array_strptime()File strptime.pyx:451, in pandas._libs.tslibs.strptime.array_strptime()File strptime.pyx:583, in pandas._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` if your strings have a consistent format; - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format; - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.

Pass errors='coerce' to convert unparsable data to NaT (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 to Timestamp andDatetimeIndex. The default unit is nanoseconds, since that is how Timestampobjects are stored internally. However, epochs are often stored in another unitwhich 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

The unit parameter does not use the same strings as the format parameterthat was discussed above). Theavailable units are listed on the documentation for pandas.to_datetime().

Constructing a Timestamp or DatetimeIndex with an epoch timestampwith the tz 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 precision Timestamp 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

Using the origin parameter

From timestamps to epoch#

To invert the operation from above, namely, to convert from a Timestamp 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 the `origin` parameter#

Using the origin parameter, one can specify an alternative starting point for creationof a DatetimeIndex. 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 at origin='unix', which defaults to 1970-01-01 00:00:00.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 the DatetimeIndex 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 the date_range() and bdate_range() functionsto create a DatetimeIndex. The default frequency for date_range is acalendar day while the default for bdate_range is a business 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 like date_range and bdate_range can utilize avariety of frequency 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 and bdate_range make it easy to generate a range of datesusing various combinations of parameters like start, end, periods,and freq. 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')

Specifying start, end, and periods will generate a range of evenly spaceddates from start to end inclusively, with periods number of elements in theresulting DatetimeIndex:

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 usingthe weekmask and holidays 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

Custom business days

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 +/- 2.9e11 years.Different resolutions can be converted to each other through as_unit.

See also

Representing out-of-bounds spans

Indexing#

One of the main uses for DatetimeIndex is as an index for pandas objects.The DatetimeIndex 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 shift method on pandas objects.
Unioning of overlapping DatetimeIndex objects 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 snap and very fast asof logic.

DatetimeIndex objects have all the basic functionality of regular Indexobjects, and a smorgasbord of advanced time series specific methods for easyfrequency processing.

See also

Reindexing methods

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 a DataFrame with a DatetimeIndex as well. Since thepartial string selection is a form of label slicing, the endpoints will be included. Thiswould include matching times on an included date:

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 a Series 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 a Series 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 index DataFrame 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 in DataFrame’s [] will be column-wise and not row-wise, see Indexing Basics. For example dft_minute['2011-12-31 23:59'] will raise KeyError as '2012-12-31 23:59' has the same resolution as the index and there is no column with such name:

To always 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 that DatetimeIndex 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 a DatetimeIndex 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 with Timestamp or datetime objects is exact, because the objects have exact meaning. These also follow the semantics of including both endpoints.

These Timestamp and datetime objects have exact hours, minutes, and seconds, even though they were not explicitly specified (they are 0).

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#

A truncate() convenience function is provided that is similarto slicing. Note that truncate assumes a 0 value for any unspecified datecomponent in a DatetimeIndex 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 the DatetimeIndex frequencyregularity will result in a DatetimeIndex, 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 from Timestamp or a collection of timestamps like a DatetimeIndex.

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 a Series 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 DatetimeIndex were spaced when using date_range()
the frequency of a Period or PeriodIndex

These frequency strings map to a DateOffset object and its subclasses. A DateOffsetis similar to a Timedelta that represents a duration of time but follows specific calendar duration rules.For example, a Timedelta day will always increment datetimes by 24 hours, while a DateOffset daywill increment datetimes to the same time the next day whether a day represents 23, 24 or 25 hours due to daylightsavings time. However, all DateOffset subclasses that are an hour or smaller(Hour, Minute, Second, Milli, Micro, Nano) behave likeTimedelta and respect absolute time.

The basic DateOffset acts similar to dateutil.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'

Most DateOffsets have associated frequencies strings, or offset aliases, that can be passedinto freq keyword arguments. The available date offsets and associated frequency strings can be found below:

Date Offset	Frequency String	Description
DateOffset	None	Generic offset class, defaults to absolute 24 hours
BDay or BusinessDay	`'B'`	business day (weekday)
CDay or CustomBusinessDay	`'C'`	custom business day
Week	`'W'`	one week, optionally anchored on a day of the week
WeekOfMonth	`'WOM'`	the x-th day of the y-th week of each month
LastWeekOfMonth	`'LWOM'`	the x-th day of the last week of each month
MonthEnd	`'ME'`	calendar month end
MonthBegin	`'MS'`	calendar month begin
BMonthEnd or BusinessMonthEnd	`'BME'`	business month end
BMonthBegin or BusinessMonthBegin	`'BMS'`	business month begin
CBMonthEnd or CustomBusinessMonthEnd	`'CBME'`	custom business month end
CBMonthBegin or CustomBusinessMonthBegin	`'CBMS'`	custom business month begin
SemiMonthEnd	`'SME'`	15th (or other day_of_month) and calendar month end
SemiMonthBegin	`'SMS'`	15th (or other day_of_month) and calendar month begin
QuarterEnd	`'QE'`	calendar quarter end
QuarterBegin	`'QS'`	calendar quarter begin
BQuarterEnd	`'BQE`	business quarter end
BQuarterBegin	`'BQS'`	business quarter begin
FY5253Quarter	`'REQ'`	retail (aka 52-53 week) quarter
YearEnd	`'YE'`	calendar year end
YearBegin	`'YS'` or `'BYS'`	calendar year begin
BYearEnd	`'BYE'`	business year end
BYearBegin	`'BYS'`	business year begin
FY5253	`'RE'`	retail (aka 52-53 week) year
Easter	None	Easter holiday
BusinessHour	`'bh'`	business hour
CustomBusinessHour	`'cbh'`	custom business hour
Day	`'D'`	one absolute day
Hour	`'h'`	one hour
Minute	`'min'`	one minute
Second	`'s'`	one second
Milli	`'ms'`	one millisecond
Micro	`'us'`	one microsecond
Nano	`'ns'`	one nanosecond

DateOffsets additionally have rollforward() and rollback()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, use normalize() 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, the Week 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')

The normalize 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 parameterizing YearEnd 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 with `Series` / `DatetimeIndex`#

Offsets can be used with either a Series or DatetimeIndex 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 a Timedelta (Day, Hour,Minute, Second, Micro, Milli, Nano) it can beused exactly like a Timedelta - 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 as BQuarterEnd) do not have avectorized implementation. They can still be used but maycalculate significantly slower and will show a PerformanceWarning

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#

The CDay or CustomBusinessDay 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]: from pandas.tseries.holiday import USFederalHolidayCalendarIn [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#

The BusinessHour class provides a business hour representation on BusinessDay,allowing to use specific start and end times.

By default, BusinessHour uses 9:00 - 17:00 as business hours.Adding BusinessHour will increment Timestamp by hourly frequency.If target Timestamp 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 specify start and end time by keywords. The argument mustbe a str with an hour:minute representation or a datetime.timeinstance. Specifying seconds, microseconds and nanoseconds as business hourresults in ValueError.

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')

Passing start time later than end 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 valid BusinessDay.

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')

Applying BusinessHour.rollforward and rollback 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 from apply 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) between 2014-08-01 17:00 and2014-08-04 09:00.

# 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 use CustomBusinessHour offset, as explained in thefollowing subsection.

Custom business hour#

The CustomBusinessHour is a mixture of BusinessHour and CustomBusinessDay whichallows you to specify arbitrary holidays. CustomBusinessHour works as the sameas BusinessHour except that it skips specified custom holidays.

In [228]: from pandas.tseries.holiday import USFederalHolidayCalendarIn [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 either BusinessHour and CustomBusinessDay.

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 as offset 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: Aliases H, BH, CBH, T, S, L, U, and Nare deprecated in favour of the aliases h, bh, cbh,min, s, ms, us, and ns.

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 by start_date andend_date. If the start_date does 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 offset MS, if the start_date is not the firstof the month, the returned timestamps will start with the first day of thenext month. If end_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 example date_range() andbdate_range() will only return the valid timestamps between thestart_date and end_date. If these are not valid timestamps for thegiven frequency it will roll to the next value for start_date(respectively previous for the end_date)

Period aliases#

A number of string aliases are given to useful common time seriesfrequencies. We will refer to these aliases as period 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: Aliases A, H, T, S, L, U, and N are deprecated in favour of the aliasesY, h, min, s, ms, us, and ns.

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 to date_range, bdate_range, constructorsfor DatetimeIndex, 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.

When n 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 date is 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 when n=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 usedwith CustomBusinessDay or in other analysis that requires a predefinedset of holidays. The AbstractHolidayCalendar class provides all the necessarymethods to return a list of holidays and only rules need to be definedin a specific holiday calendar class. Furthermore, the start_date and end_dateclass attributes determine over what date range holidays are generated. Theseshould be overwritten on the AbstractHolidayCalendar class to have the rangeapply to all calendar subclasses. USFederalHolidayCalendar is theonly calendar that exists and primarily serves as an example for developingother calendars.

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

Time Series-related instance methods#

Shifting / lagging#

One may want to shift or lag the values in a time series back and forward intime. The method for this is shift(), 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

The shift method accepts an freq argument which can accept aDateOffset class or other timedelta-like object or also anoffset alias.

When freq 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 when freq is specified, the leading entry is no longer NaNbecause the data is not being realigned.

Frequency conversion#

The primary function for changing frequencies is the asfreq()method. For a DatetimeIndex, this is basically just a thin, but convenientwrapper around reindex() which generates a date_range andcalls reindex.

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 to asfreq and reindex is fillna(), which isdocumented in the missing data section.

Converting to Python datetimes#

DatetimeIndex can be converted to an array of Python nativedatetime.datetime objects using the to_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 some cookbook examples forsome advanced strategies.

The resample() method can be used directly from DataFrameGroupBy objects,see the groupby 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

The resample function is very flexible and allows you to specify manydifferent parameters to control the frequency conversion and resamplingoperation.

Any built-in method available via GroupBy is available asa method of the returned object, including sum, 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 like label 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 for label and closed 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 withthe BusinessDay 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

The axis parameter can be set to 0 or 1 and allows you to resample thespecified axis for a DataFrame.

kind can be set to ‘timestamp’ or ‘period’ to convert the resulting indexto/from timestamp and time span representations. By default resampleretains 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 the limit 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 is None, thenintermediate values will be filled with NaN.

Since resample is a time-based groupby, the following is a method to efficientlyresample only the groups that are not all NaN.

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]: from functools import partialIn [312]: from pandas.tseries.frequencies import to_offsetIn [313]: def round(t, freq): .....:  freq = to_offset(freq) .....:  td = pd.Timedelta(freq) .....:  return pd.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#

The resample() method returns a pandas.api.typing.Resampler instance. Similar tothe aggregating API, groupby API,and the window API, a Resampler can be selectively resampled.

Resampling a DataFrame, 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 a DataFrame:

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 resampled DataFrame, 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 to aggregate you can apply a different aggregation to thecolumns of a DataFrame:

In [322]: r.agg({"A": "sum", "B": lambda x: 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 a DataFrame 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 d 1 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]:  adate 2015-01-31 62015-02-28 4

Similarly, if you instead want to resample by a datetimelikelevel of MultiIndex, its name or location can be passed to thelevel keyword.

In [328]: df.resample("ME", level="d")[["a"]].sum()Out[328]:  ad 2015-01-31 62015-02-28 4

Iterating through groups#

With the Resampler object in hand, iterating through the grouped data is verynatural and functions similarly to itertools.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]: for name, group in resampled: .....:  print("Group: ", name) .....:  print("-" * 27) .....:  print(group, end="\n\n") .....: Group: 2017-01-01 00:00:00---------------------------2017-01-01 00:00:00 02017-01-01 00:30:00 12017-01-01 00:31:00 2dtype: int64Group: 2017-01-01 01:00:00---------------------------2017-01-01 01:00:00 3dtype: int64Group: 2017-01-01 02:00:00---------------------------Series([], dtype: int64)Group: 2017-01-01 03:00:00---------------------------2017-01-01 03:00:00 42017-01-01 03:05:00 5dtype: int64

See Iterating through groups or Resampler.__iter__ for more.

Use `origin` or `offset` 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 (like 30D) or that divide a day evenly (like 90s or 1min). 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 argument origin.

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 using origin with its default value ('start_day'), the result after '2000-10-02 00:00:00' 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 setting origin to 'epoch', the result after '2000-10-02 00:00:00' 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 for origin:

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 an offset Timedelta that would be added to the default origin.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' for origin on the last example. In that case, origin will be set to the first value of the timeseries.

Backward resample#

New 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 given freq. The backward resample sets closed to 'right' by default since the last value should be considered as the edge point for the last bin.

We can set origin to 'end'. The value for a specific Timestamp index stands for the resample result from the current Timestamp minus freq to the current Timestamp 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 largest Timestamp.

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 uses 2000-10-02 00:29:00 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 by Period objects in pandas whilesequences of Period objects are collected in a PeriodIndex, which canbe created with the convenience function period_range.

Period#

A Period represents a span of time (e.g., a day, a month, a quarter, etc).You can specify the span via freq keyword using a frequency alias like below.Because freq represents a span of Period, 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 between Period with different freq (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

If Period freq is daily or higher (D, h, min, s, ms, us, and ns), offsets and timedelta-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)---------------------------------------------------------------------------ValueError Traceback (most recent call last)File period.pyx:1824, in pandas._libs.tslibs.period._Period._add_timedeltalike_scalar()File timedeltas.pyx:278, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds()File np_datetime.pyx:661, in pandas._libs.tslibs.np_datetime.convert_reso()ValueError: Cannot losslessly convert unitsThe above exception was the direct cause of the following exception:IncompatibleFrequency Traceback (most recent call last)Cell In[366], line 1----> 1 p + pd.offsets.Minute(5)File period.pyx:1845, in pandas._libs.tslibs.period._Period.__add__()File period.pyx:1826, in pandas._libs.tslibs.period._Period._add_timedeltalike_scalar()IncompatibleFrequency: Input cannot be converted to Period(freq=h)

If Period has other frequencies, only the same offsets 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)---------------------------------------------------------------------------IncompatibleFrequency Traceback (most recent call last)Cell In[369], line 1----> 1 p + pd.offsets.MonthBegin(3)File period.pyx:1847, in pandas._libs.tslibs.period._Period.__add__()File period.pyx:1837, in pandas._libs.tslibs.period._Period._add_offset()File period.pyx:1732, in pandas._libs.tslibs.period.PeriodMixin._require_matching_freq()IncompatibleFrequency: Input has different freq=3M from Period(freq=M)

Taking the difference of Period 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 of Period objects can be collected in a PeriodIndex,which can be constructed using the period_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]')

The PeriodIndex 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 of Period 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]')

If start or end are Period objects, they will be used as anchorendpoints for a PeriodIndex 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 like DatetimeIndex, a PeriodIndex 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 as Period.

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 named period, refer to Period Dtypes.

Period dtypes#

PeriodIndex has a custom period dtype. This is a pandas extensiondtype similar to the timezone aware dtype (datetime64[ns, tz]).

The period dtype holds the freq attribute and is represented withperiod[freq] like period[D] or period[M], using frequency 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]

The period dtype can be used in .astype(...). It allows one to change thefreq of a PeriodIndex like .asfreq() and convert aDatetimeIndex to PeriodIndex like to_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 to Series and DataFrame with PeriodIndex, in the same manner as DatetimeIndex. For details, refer to DatetimeIndex 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 than PeriodIndex 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 with DatetimeIndex, 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 of Period and PeriodIndex can be converted via the asfreqmethod. 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 the how 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 quarterlyfrequencies Q-JAN through Q-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 using to_periodand vice-versa using to_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 the Timestamp bounds, see Timestamp limitations,then you can use a PeriodIndex and/or Series of Periods 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 an int64 based YYYYMMDD representation.

In [427]: s = pd.Series([20121231, 20141130, 99991231])In [428]: sOut[428]: 0 201212311 201411302 99991231dtype: int64In [429]: def conv(x): .....:  return pd.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 a PeriodIndex:

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 the pytz and dateutil libraries or datetime.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.tz is NoneOut[435]: True

To localize these dates to a time zone (assign a particular time zone to a naive date),you can use the tz_localize method or the tz keyword argument indate_range(), Timestamp, or DatetimeIndex.You can either pass pytz or dateutil time zone objects or Olson time zone database strings.Olson time zone strings will return pytz time zone objects by default.To return dateutil time zone objects, append dateutil/ before the string.

In pytz you can find a list of common (and less common) time zones usingfrom pytz import common_timezones, all_timezones.
dateutil uses the OS time zones so there isn’t a fixed list available. Forcommon zones, the names are the same as pytz.

In [436]: import dateutil# 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 the UTC time zone is a special case in dateutil and should be constructed explicitlyas an instance of dateutil.tz.tzutc. You can also construct other timezones objects explicitly first.

In [446]: import pytz# 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 the tz_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 using pytz time zones, DatetimeIndex will construct a differenttime zone object than a Timestamp for the same time zone input. A DatetimeIndexcan hold a collection of Timestamp objects that may have different UTC offsets and cannot besuccinctly represented by one pytz time zone instance while one Timestamprepresents 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 and dateutil have differentdefinitions of the zone. This is more of a problem for unusual time zones than for‘standard’ zones like US/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.See here for how to handle such a situation.

Warning

For pytz time zones, it is incorrect to pass a time zone object directly intothe datetime.datetime constructor(e.g., datetime.datetime(2011, 1, 1, tzinfo=pytz.timezone('US/Eastern')).Instead, the datetime needs to be localized using the localize methodon the pytz 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]: assert pd.Timestamp(d_2037, tz=DST) != pd.Timestamp(d_2037, tz="GMT")In [463]: assert pd.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 or Timestamp 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 between Series 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, use tz_localize(None) or tz_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 naive datetime.datetime(see datetime documentation for details) or from Timestampor for constructing from components (see below). Only dateutil timezones are supported(see dateutil documentationfor dateutil methods that deal with ambiguous datetimes) as pytztimezones do not support fold (see pytz documentationfor details on how pytz deals with ambiguous datetimes). To localize an ambiguous datetimewith pytz, please use Timestamp.tz_localize(). In general, we recommend to relyon Timestamp.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 a pytz.AmbiguousTimeError (the default behavior)
'infer': Attempt to determine the correct offset base on the monotonicity of the timestamps
'NaT': Replaces ambiguous times with NaT
bool: True represents a DST time, False represents non-DST time. An array-like of bool values 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 ('11/06/2011 01:00')

In [483]: rng_hourly.tz_localize('US/Eastern')---------------------------------------------------------------------------AmbiguousTimeError Traceback (most recent call last)Cell In[483], line 1----> 1 rng_hourly.tz_localize('US/Eastern')File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:293, in DatetimeIndex.tz_localize(self, tz, ambiguous, nonexistent) 286 @doc(DatetimeArray.tz_localize) 287 def tz_localize( 288 self, (...) 291 nonexistent: TimeNonexistent = "raise", 292 ) -> Self:--> 293 arr = self._data.tz_localize(tz, ambiguous, nonexistent) 294 return type(self)._simple_new(arr, name=self.name)File ~/work/pandas/pandas/pandas/core/arrays/_mixins.py:81, in ravel_compat.<locals>.method(self, *args, **kwargs) 78 @wraps(meth) 79 def method(self, *args, **kwargs): 80 if self.ndim == 1:---> 81 return meth(self, *args, **kwargs) 83 flags = self._ndarray.flags 84 flat = self.ravel("K")File ~/work/pandas/pandas/pandas/core/arrays/datetimes.py:1088, in DatetimeArray.tz_localize(self, tz, ambiguous, nonexistent) 1085 tz = timezones.maybe_get_tz(tz) 1086 # Convert to UTC-> 1088 new_dates = tzconversion.tz_localize_to_utc( 1089 self.asi8, 1090 tz, 1091 ambiguous=ambiguous, 1092 nonexistent=nonexistent, 1093 creso=self._creso, 1094 ) 1095 new_dates_dt64 = new_dates.view(f"M8[{self.unit}]") 1096 dtype = tz_to_dtype(tz, unit=self.unit)File tzconversion.pyx:371, in pandas._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 the nonexistent argument. The following options are available:

'raise': Raises a pytz.NonExistentTimeError (the default behavior)
'NaT': Replaces nonexistent times with NaT
'shift_forward': Shifts nonexistent times forward to the closest real time
'shift_backward': Shifts nonexistent times backward to the closest real time
timedelta 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')---------------------------------------------------------------------------NonExistentTimeError Traceback (most recent call last)Cell In[488], line 1----> 1 dti.tz_localize('Europe/Warsaw')File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:293, in DatetimeIndex.tz_localize(self, tz, ambiguous, nonexistent) 286 @doc(DatetimeArray.tz_localize) 287 def tz_localize( 288 self, (...) 291 nonexistent: TimeNonexistent = "raise", 292 ) -> Self:--> 293 arr = self._data.tz_localize(tz, ambiguous, nonexistent) 294 return type(self)._simple_new(arr, name=self.name)File ~/work/pandas/pandas/pandas/core/arrays/_mixins.py:81, in ravel_compat.<locals>.method(self, *args, **kwargs) 78 @wraps(meth) 79 def method(self, *args, **kwargs): 80 if self.ndim == 1:---> 81 return meth(self, *args, **kwargs) 83 flags = self._ndarray.flags 84 flat = self.ravel("K")File ~/work/pandas/pandas/pandas/core/arrays/datetimes.py:1088, in DatetimeArray.tz_localize(self, tz, ambiguous, nonexistent) 1085 tz = timezones.maybe_get_tz(tz) 1086 # Convert to UTC-> 1088 new_dates = tzconversion.tz_localize_to_utc( 1089 self.asi8, 1090 tz, 1091 ambiguous=ambiguous, 1092 nonexistent=nonexistent, 1093 creso=self._creso, 1094 ) 1095 new_dates_dt64 = new_dates.view(f"M8[{self.unit}]") 1096 dtype = tz_to_dtype(tz, unit=self.unit)File tzconversion.pyx:431, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc()NonExistentTimeError: 2015-03-29 02:30:00

Transform nonexistent times to NaT 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#

A Series with time zone naive values isrepresented with a dtype of datetime64[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]

A Series with a time zone aware values isrepresented with a dtype of datetime64[ns, tz] where tz 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 these Series time zone informationcan be manipulated via the .dt accessor, see the 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 the astype 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

Using Series.to_numpy() on a Series, returns a NumPy array of the data.NumPy does not currently support time zones (even though it is printing 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 NumPy datetime64[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]')

Time series / date functionality — pandas 2.2.2 documentation (2024)

Overview#

Timestamps vs. time spans#

Converting to timestamps#

Providing a format argument#

Assembling datetime from multiple DataFrame columns#

Invalid data#

Epoch timestamps#

From timestamps to epoch#

Using the origin parameter#

Generating ranges of timestamps#

Custom frequency ranges#

Timestamp limitations#

Indexing#

Partial string indexing#

Slice vs. exact match#

Exact indexing#

Truncating & fancy indexing#

Time/date components#

DateOffset objects#

Parametric offsets#

Using offsets with Series / DatetimeIndex#

Custom business days#

Business hour#

Custom business hour#

Offset aliases#

Period aliases#

Combining aliases#

Anchored offsets#

Anchored offset semantics#

Holidays / holiday calendars#

Time Series-related instance methods#

Shifting / lagging#

Frequency conversion#

Filling forward / backward#

Converting to Python datetimes#

Resampling#

Basics#

Upsampling#

Sparse resampling#

Aggregation#

Iterating through groups#

Use origin or offset to adjust the start of the bins#

Backward resample#

Time span representation#

Period#

PeriodIndex and period_range#

Period dtypes#

PeriodIndex partial string indexing#

Frequency conversion and resampling with PeriodIndex#

Converting between representations#

Representing out-of-bounds spans#

Time zone handling#

Working with time zones#

Fold#

Ambiguous times when localizing#

Nonexistent times when localizing#

Time zone Series operations#

Using the `origin` parameter#

Using offsets with `Series` / `DatetimeIndex`#

Use `origin` or `offset` to adjust the start of the bins#