Time series / date functionality — pandas 2.2.3 documentation (original) (raw)

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 of features from other Python libraries like scikits.timeseries as well as created a 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 datetime

In [2]: dti = pd.to_datetime( ...: ["1/1/2018", np.datetime64("2018-01-01"), datetime.datetime(2018, 1, 1)] ...: ) ...:

In [3]: dti Out[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]: dti Out[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]: dti Out[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]: ts Out[11]: 2018-01-01 00:00:00 0 2018-01-01 01:00:00 1 2018-01-01 02:00:00 2 2018-01-01 03:00:00 3 2018-01-01 04:00:00 4 Freq: h, dtype: int64

In [12]: ts.resample("2h").mean() Out[12]: 2018-01-01 00:00:00 0.5 2018-01-01 02:00:00 2.5 2018-01-01 04:00:00 4.0 Freq: 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 day

In [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 for performing the above tasks and more.

Overview#

pandas captures 4 general time related concepts:

1. Date times: A specific date and time with timezone support. Similar to datetime.datetime from the standard library.
2. Time deltas: An absolute time duration. Similar to datetime.timedelta from the standard library.
3. Time spans: A span of time defined by a point in time and its associated frequency.
4. Date offsets: A relative time duration that respects calendar arithmetic. Similar to dateutil.relativedelta.relativedelta from the dateutil package.

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 0 2000-01-02 1 2000-01-03 2 Freq: 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 2000-01-01 1 2000-01-02 2 2000-01-03 dtype: 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 2011-01 1 2011-02 2 2011-03 dtype: period[M]

In [22]: pd.Series([pd.DateOffset(1), pd.DateOffset(2)]) Out[22]: 0 1 <2 * DateOffsets> dtype: object

In [23]: pd.Series(pd.date_range("1/1/2011", freq="ME", periods=3)) Out[23]: 0 2011-01-31 1 2011-02-28 2 2011-03-31 dtype: datetime64[ns]

Lastly, pandas represents null date times, time deltas, and time spans as NaT which is useful for representing missing or null date like values and behaves similar as np.nan does for float data.

In [24]: pd.Timestamp(pd.NaT) Out[24]: NaT

In [25]: pd.Timedelta(pd.NaT) Out[25]: NaT

In [26]: pd.Period(pd.NaT) Out[26]: NaT

Equality acts as np.nan would

In [27]: pd.NaT == pd.NaT Out[27]: False

Timestamps vs. time spans#

Timestamped data is the most basic type of time series data that associates values with points in time. For pandas objects it means using the points in time.

In [28]: import datetime

In [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 change variables with a time span instead. The span represented by Period can be specified 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.DatetimeIndex

In [37]: ts.index Out[37]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)

In [38]: ts Out[38]: 2012-05-01 0.469112 2012-05-02 -0.282863 2012-05-03 -1.509059 dtype: float64

In [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.PeriodIndex

In [42]: ts.index Out[42]: PeriodIndex(['2012-01', '2012-02', '2012-03'], dtype='period[M]')

In [43]: ts Out[43]: 2012-01 -1.135632 2012-02 1.212112 2012-03 -0.173215 Freq: M, dtype: float64

pandas allows you to capture both representations and convert between them. Under the hood, pandas represents timestamps using instances of Timestamp and sequences of timestamps using instances ofDatetimeIndex. For regular time spans, pandas uses Period objects for scalar values and PeriodIndex for sequences of spans. Better support for irregular intervals with arbitrary start and end points are forth-coming in future 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 passed a Series, this returns a Series (with the same index), while a list-like is converted to a DatetimeIndex:

In [44]: pd.to_datetime(pd.Series(["Jul 31, 2009", "Jan 10, 2010", None])) Out[44]: 0 2009-07-31 1 2010-01-10 2 NaT dtype: 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 date can’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 parsing options 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 the inferred 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 02:00:00 1 2016-03-05 03:00:00 dtype: 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 2015-02-04 1 2016-03-05 dtype: 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 UTC

File ~/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 parameter that was discussed above). The available units are listed on the documentation for pandas.to_datetime().

Constructing a Timestamp or DatetimeIndex with an epoch timestamp with the tz argument specified will raise a ValueError. If you have epochs in wall time in another timezone, you can read the epochs as 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 in decimal. Rounding during conversion from float to high precision Timestamp is unavoidable. The only way to achieve exact precision is to use a fixed-width types (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')

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]: stamps Out[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 creation of 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 information

In [71]: index = pd.DatetimeIndex(dates)

In [72]: index Out[72]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)

Automatically converted to DatetimeIndex

In [73]: index = pd.Index(dates)

In [74]: index Out[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 long index with a large number of timestamps. If we need timestamps on a regular frequency, we can use the date_range() and bdate_range() functions to 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]: index Out[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]: index Out[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 a variety 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 dates using various combinations of parameters like start, end, periods, and freq. The start and end dates are strictly inclusive, so dates outside of 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 spaced dates from start to end inclusively, with periods number of elements in the resulting 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 using the weekmask and holidays parameters. These parameters will only be used 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')

Timestamp limitations#

The limits of timestamp representation depend on the chosen resolution. For nanosecond resolution, the time span that can be represented using a 64-bit integer is limited to approximately 584 years:

In [93]: pd.Timestamp.min Out[93]: Timestamp('1677-09-21 00:12:43.145224193')

In [94]: pd.Timestamp.max Out[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.

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 cached under 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 is very 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 easy frequency processing.

Note

While pandas does not force you to have a sorted date index, some of these methods may have unexpected or incorrect behavior if the dates are unsorted.

DatetimeIndex can be used like a regular index and offers all of its intelligent 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.index Out[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].index Out[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].index Out[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]: 0.11920871129693428

In [101]: ts[datetime.datetime(2011, 12, 25):] Out[101]: 2011-12-30 0.56702 Freq: BME, dtype: float64

In [102]: ts["10/31/2011":"12/31/2011"] Out[102]: 2011-10-31 0.271860 2011-11-30 -0.424972 2011-12-30 0.567020 Freq: BME, dtype: float64

To provide convenience for accessing longer time series, you can also pass in the year or year and month as strings:

In [103]: ts["2011"] Out[103]: 2011-01-31 0.119209 2011-02-28 -1.044236 2011-03-31 -0.861849 2011-04-29 -2.104569 2011-05-31 -0.494929 2011-06-30 1.071804 2011-07-29 0.721555 2011-08-31 -0.706771 2011-09-30 -1.039575 2011-10-31 0.271860 2011-11-30 -0.424972 2011-12-30 0.567020 Freq: BME, dtype: float64

In [104]: ts["2011-6"] Out[104]: 2011-06-30 1.071804 Freq: BME, dtype: float64

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

Warning

Indexing DataFrame rows with a single string with getitem (e.g. frame[dtstring]) is deprecated starting with pandas 1.2.0 (given the ambiguity whether it is indexing the rows or selecting a column) and will be removed in a future version. The equivalent with .loc (e.g. frame.loc[dtstring]) is still supported.

In [105]: dft = pd.DataFrame( .....: np.random.randn(100000, 1), .....: columns=["A"], .....: index=pd.date_range("20130101", periods=100000, freq="min"), .....: ) .....:

In [106]: dft Out[106]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-03-11 10:35:00 -0.747967 2013-03-11 10:36:00 -0.034523 2013-03-11 10:37:00 -0.201754 2013-03-11 10:38:00 -1.509067 2013-03-11 10:39:00 -1.693043

[100000 rows x 1 columns]

In [107]: dft.loc["2013"] Out[107]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-03-11 10:35:00 -0.747967 2013-03-11 10:36:00 -0.034523 2013-03-11 10:37:00 -0.201754 2013-03-11 10:38:00 -1.509067 2013-03-11 10:39:00 -1.693043

[100000 rows x 1 columns]

This starts on the very first time in the month, and includes the last date and time for the month:

In [108]: dft["2013-1":"2013-2"] Out[108]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-02-28 23:55:00 0.850929 2013-02-28 23:56:00 0.976712 2013-02-28 23:57:00 -2.693884 2013-02-28 23:58:00 -1.575535 2013-02-28 23:59:00 -1.573517

[84960 rows x 1 columns]

This specifies a stop time that includes all of the times on the last day:

In [109]: dft["2013-1":"2013-2-28"] Out[109]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-02-28 23:55:00 0.850929 2013-02-28 23:56:00 0.976712 2013-02-28 23:57:00 -2.693884 2013-02-28 23:58:00 -1.575535 2013-02-28 23:59:00 -1.573517

[84960 rows x 1 columns]

This specifies an exact stop time (and is not the same as the above):

In [110]: dft["2013-1":"2013-2-28 00:00:00"] Out[110]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-02-27 23:56:00 1.197749 2013-02-27 23:57:00 0.720521 2013-02-27 23:58:00 -0.072718 2013-02-27 23:59:00 -0.681192 2013-02-28 00:00:00 -0.557501

[83521 rows x 1 columns]

We are stopping on the included end-point as it is part of the index:

In [111]: dft["2013-1-15":"2013-1-15 12:30:00"] Out[111]: A 2013-01-15 00:00:00 -0.984810 2013-01-15 00:01:00 0.941451 2013-01-15 00:02:00 1.559365 2013-01-15 00:03:00 1.034374 2013-01-15 00:04:00 -1.480656 ... ... 2013-01-15 12:26:00 0.371454 2013-01-15 12:27:00 -0.930806 2013-01-15 12:28:00 -0.069177 2013-01-15 12:29:00 0.066510 2013-01-15 12:30:00 -0.003945

[751 rows x 1 columns]

DatetimeIndex partial string indexing also works on a DataFrame with a MultiIndex:

In [112]: dft2 = pd.DataFrame( .....: np.random.randn(20, 1), .....: columns=["A"], .....: index=pd.MultiIndex.from_product( .....: [pd.date_range("20130101", periods=10, freq="12h"), ["a", "b"]] .....: ), .....: ) .....:

In [113]: dft2 Out[113]: A 2013-01-01 00:00:00 a -0.298694 b 0.823553 2013-01-01 12:00:00 a 0.943285 b -1.479399 2013-01-02 00:00:00 a -1.643342 ... ... 2013-01-04 12:00:00 b 0.069036 2013-01-05 00:00:00 a 0.122297 b 1.422060 2013-01-05 12:00:00 a 0.370079 b 1.016331

[20 rows x 1 columns]

In [114]: dft2.loc["2013-01-05"] Out[114]: A 2013-01-05 00:00:00 a 0.122297 b 1.422060 2013-01-05 12:00:00 a 0.370079 b 1.016331

In [115]: idx = pd.IndexSlice

In [116]: dft2 = dft2.swaplevel(0, 1).sort_index()

In [117]: dft2.loc[idx[:, "2013-01-05"], :] Out[117]: A a 2013-01-05 00:00:00 0.122297 2013-01-05 12:00:00 0.370079 b 2013-01-05 00:00:00 1.422060 2013-01-05 12:00:00 1.016331

Slicing with string indexing also honors UTC offset.

In [118]: df = pd.DataFrame([0], index=pd.DatetimeIndex(["2019-01-01"], tz="US/Pacific"))

In [119]: df Out[119]: 0 2019-01-01 00:00:00-08:00 0

In [120]: df["2019-01-01 12:00:00+04:00":"2019-01-01 13:00:00+04:00"] Out[120]: 0 2019-01-01 00:00:00-08:00 0

Slice vs. exact match#

The same string used as an indexing parameter can be treated either as a slice or as an exact match depending on the resolution of the index. If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match.

Consider 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.resolution Out[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 1 dtype: 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]: 1

In [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.resolution Out[127]: 'second'

In [128]: series_second["2011-12-31 23:59"] Out[128]: 2011-12-31 23:59:59 1 dtype: 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 b 2011-12-31 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 1 b 4 Name: 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.resolution Out[133]: 'day'

In [134]: series_monthly["2011-12"] # returns Series Out[134]: 2011-12-01 1 dtype: 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]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-02-27 23:56:00 1.197749 2013-02-27 23:57:00 0.720521 2013-02-27 23:58:00 -0.072718 2013-02-27 23:59:00 -0.681192 2013-02-28 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]: A 2013-01-01 10:12:00 0.565375 2013-01-01 10:13:00 0.068184 2013-01-01 10:14:00 0.788871 2013-01-01 10:15:00 -0.280343 2013-01-01 10:16:00 0.931536 ... ... 2013-02-28 10:08:00 0.148098 2013-02-28 10:09:00 -0.388138 2013-02-28 10:10:00 0.139348 2013-02-28 10:11:00 0.085288 2013-02-28 10:12:00 0.950146

[83521 rows x 1 columns]

Truncating & fancy indexing#

A truncate() convenience function is provided that is similar to slicing. Note that truncate assumes a 0 value for any unspecified date component in a DatetimeIndex in contrast to slicing which returns any partially 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 0.437823 2011-11-13 -0.293083 2011-11-20 -0.059881 2011-11-27 1.252450 Freq: W-SUN, dtype: float64

In [140]: ts2["2011-11":"2011-12"] Out[140]: 2011-11-06 0.437823 2011-11-13 -0.293083 2011-11-20 -0.059881 2011-11-27 1.252450 2011-12-04 0.046611 2011-12-11 0.059478 2011-12-18 -0.286539 2011-12-25 0.841669 Freq: W-SUN, dtype: float64

Even complicated fancy indexing that breaks the DatetimeIndex frequency regularity will result in a DatetimeIndex, although frequency is lost:

In [141]: ts2.iloc[[0, 2, 6]].index Out[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.

Furthermore, if you have a Series with datetimelike values, then you can access these properties via the .dt accessor, as detailed in the section on .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 day 2019-12-29 2019 52 7 2019-12-30 2020 1 1 2019-12-31 2020 1 2 2020-01-01 2020 1 3

In [144]: idx.to_series().dt.isocalendar() Out[144]: year week day 2019-12-29 2019 52 7 2019-12-30 2020 1 1 2019-12-31 2020 1 2 2020-01-01 2020 1 3

DateOffset objects#

In the preceding examples, frequency strings (e.g. 'D') were used to specify a 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 day will increment datetimes to the same time the next day whether a day represents 23, 24 or 25 hours due to daylight savings 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. The arithmetic operator (+) can be used to perform the shift.

This particular day contains a day light savings time transition

In [145]: ts = pd.Timestamp("2016-10-30 00:00:00", tz="Europe/Helsinki")

Respects absolute time

In [146]: ts + pd.Timedelta(days=1) Out[146]: Timestamp('2016-10-30 23:00:00+0200', tz='Europe/Helsinki')

Respects calendar time

In [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_days Out[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 passed into freq keyword arguments. The available date offsets and associated frequency strings can be found below:

DateOffsets additionally have rollforward() and rollback()methods for moving a date forward or backward respectively to a valid offset date relative to the offset. For example, business offsets will roll dates that land on the weekends (Saturday and Sunday) forward to Monday since business 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 Friday

In [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 added

In [157]: ts + offset Out[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 applying the operation (depending on whether you want the time information included in the operation).

In [158]: ts = pd.Timestamp("2014-01-01 09:00")

In [159]: day = pd.offsets.Day()

In [160]: day + ts Out[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 + ts Out[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 different behaviors. For example, the Week offset for generating weekly data accepts aweekday parameter which results in the generated dates always lying on a particular day of the week:

In [167]: d = datetime.datetime(2008, 8, 18, 9, 0)

In [168]: d Out[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]: 4

In [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 to apply 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]: rng Out[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 2012-03-01 1 2012-03-02 2 2012-03-03 dtype: datetime64[ns]

In [182]: s - pd.DateOffset(months=2) Out[182]: 0 2011-11-01 1 2011-11-02 2 2011-11-03 dtype: datetime64[ns]

If the offset class maps directly to a Timedelta (Day, Hour,Minute, Second, Micro, Milli, Nano) it can be used exactly like a Timedelta - see theTimedelta section for more examples.

In [183]: s - pd.offsets.Day(2) Out[183]: 0 2011-12-30 1 2011-12-31 2 2012-01-01 dtype: datetime64[ns]

In [184]: td = s - pd.Series(pd.date_range("2011-12-29", "2011-12-31"))

In [185]: td Out[185]: 0 3 days 1 3 days 2 3 days dtype: timedelta64[ns]

In [186]: td + pd.offsets.Minute(15) Out[186]: 0 3 days 00:15:00 1 3 days 00:15:00 2 3 days 00:15:00 dtype: timedelta64[ns]

Note that some offsets (such as BQuarterEnd) do not have a vectorized implementation. They can still be used but may calculate 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 day calendars 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 years

In [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_egypt Out[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 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue Freq: 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 USFederalHolidayCalendar

In [196]: bday_us = pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar())

Friday before MLK Day

In [197]: dt = datetime.datetime(2014, 1, 17)

Tuesday after MLK Day (Monday is skipped because it's a holiday)

In [198]: dt + bday_us Out[198]: Timestamp('2014-01-21 00:00:00')

Monthly offsets that respect a certain holiday calendar can be defined in the usual way.

In [199]: bmth_us = pd.offsets.CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())

Skip new years

In [200]: dt = datetime.datetime(2013, 12, 17)

In [201]: dt + bmth_us Out[201]: Timestamp('2014-01-02 00:00:00')

Define date index with custom offset

In [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 CustomBusinessDay DateOffset is used, it is important to note that since CustomBusinessDay is a parameterised type, instances of CustomBusinessDay may differ and this is not detectable from the ‘C’ frequency string. The user therefore needs to ensure that the ‘C’ frequency string is used consistently within the user’s application.

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 hour then increment it. If the result exceeds the business hours end, the remaining hours are added to the next business day.

In [203]: bh = pd.offsets.BusinessHour()

In [204]: bh Out[204]: <BusinessHour: bh=09:00-17:00>

2014-08-01 is Friday

In [205]: pd.Timestamp("2014-08-01 10:00").weekday() Out[205]: 4

In [206]: pd.Timestamp("2014-08-01 10:00") + bh Out[206]: Timestamp('2014-08-01 11:00:00')

Below example is the same as: pd.Timestamp('2014-08-01 09:00') + bh

In [207]: pd.Timestamp("2014-08-01 08:00") + bh Out[207]: Timestamp('2014-08-01 10:00:00')

If the results is on the end time, move to the next business day

In [208]: pd.Timestamp("2014-08-01 16:00") + bh Out[208]: Timestamp('2014-08-04 09:00:00')

Remainings are added to the next day

In [209]: pd.Timestamp("2014-08-01 16:30") + bh Out[209]: Timestamp('2014-08-04 09:30:00')

Adding 2 business hours

In [210]: pd.Timestamp("2014-08-01 10:00") + pd.offsets.BusinessHour(2) Out[210]: Timestamp('2014-08-01 12:00:00')

Subtracting 3 business hours

In [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 must be a str with an hour:minute representation or a datetime.timeinstance. Specifying seconds, microseconds and nanoseconds as business hour results in ValueError.

In [212]: bh = pd.offsets.BusinessHour(start="11:00", end=datetime.time(20, 0))

In [213]: bh Out[213]: <BusinessHour: bh=11:00-20:00>

In [214]: pd.Timestamp("2014-08-01 13:00") + bh Out[214]: Timestamp('2014-08-01 14:00:00')

In [215]: pd.Timestamp("2014-08-01 09:00") + bh Out[215]: Timestamp('2014-08-01 12:00:00')

In [216]: pd.Timestamp("2014-08-01 18:00") + bh Out[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]: bh Out[218]: <BusinessHour: bh=17:00-09:00>

In [219]: pd.Timestamp("2014-08-01 17:00") + bh Out[219]: Timestamp('2014-08-01 18:00:00')

In [220]: pd.Timestamp("2014-08-01 23:00") + bh Out[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") + bh Out[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") + bh Out[222]: Timestamp('2014-08-04 18:00:00')

Applying BusinessHour.rollforward and rollback to out of business hours results in the 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 edge

In [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 arbitrary holidays, you can use CustomBusinessHour offset, as explained in the following subsection.

Custom business hour#

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

In [228]: from pandas.tseries.holiday import USFederalHolidayCalendar

In [229]: bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar())

Friday before MLK Day

In [230]: dt = datetime.datetime(2014, 1, 17, 15)

In [231]: dt + bhour_us Out[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 * 2 Out[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 10:00

In [234]: dt + bhour_mon * 2 Out[234]: Timestamp('2014-01-21 10:00:00')

Offset aliases#

A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases.

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 functions such as date_range(), bdate_range(), will only return timestamps 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 valid timestamp.

For example, for the offset MS, if the start_date is not the first of the month, the returned timestamps will start with the first day of the next month. If end_date is not the first day of a month, the last returned 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_1 Out[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_2 Out[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 the given 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 series frequencies. We will refer to these aliases as period aliases.

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 in most 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:

These can be used as arguments to date_range, bdate_range, constructors for DatetimeIndex, as well as various other timeseries-related functions in pandas.

Anchored offset semantics#

For those offsets that are anchored to the start or end of specific frequency (MonthEnd, MonthBegin, WeekEnd, etc), the following rules 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 forwards or 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, otherwise it 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 used with CustomBusinessDay or in other analysis that requires a predefined set of holidays. The AbstractHolidayCalendar class provides all the necessary methods to return a list of holidays and only rules need to be defined in a specific holiday calendar class. Furthermore, the start_date and end_dateclass attributes determine over what date range holidays are generated. These should be overwritten on the AbstractHolidayCalendar class to have the range apply to all calendar subclasses. USFederalHolidayCalendar is the only calendar that exists and primarily serves as an example for developing other calendars.

For holidays that occur on fixed dates (e.g., US Memorial Day or July 4th) an observance rule determines when that holiday is observed if it falls on a weekend or some other non-observed day. Defined observance rules are:

An example of how holidays and holiday calendars are defined:

In [259]: from pandas.tseries.holiday import ( .....: Holiday, .....: USMemorialDay, .....: AbstractHolidayCalendar, .....: nearest_workday, .....: MO, .....: ) .....:

In [260]: class ExampleCalendar(AbstractHolidayCalendar): .....: rules = [ .....: USMemorialDay, .....: Holiday("July 4th", month=7, day=4, observance=nearest_workday), .....: Holiday( .....: "Columbus Day", .....: month=10, .....: day=1, .....: offset=pd.DateOffset(weekday=MO(2)), .....: ), .....: ] .....:

In [261]: cal = ExampleCalendar()

In [262]: cal.holidays(datetime.datetime(2012, 1, 1), datetime.datetime(2012, 12, 31)) Out[262]: DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None)

hint:

weekday=MO(2) is same as 2 * Week(weekday=2)

Using this calendar, creating an index or doing offset arithmetic skips weekends and holidays (i.e., Memorial Day/July 4th). For example, the below defines a custom business day offset using the ExampleCalendar. Like any other offset, it can be used to create a DatetimeIndex or added to datetimeor Timestamp objects.

In [263]: pd.date_range( .....: start="7/1/2012", end="7/10/2012", freq=pd.offsets.CDay(calendar=cal) .....: ).to_pydatetime() .....: Out[263]: array([datetime.datetime(2012, 7, 2, 0, 0), datetime.datetime(2012, 7, 3, 0, 0), datetime.datetime(2012, 7, 5, 0, 0), datetime.datetime(2012, 7, 6, 0, 0), datetime.datetime(2012, 7, 9, 0, 0), datetime.datetime(2012, 7, 10, 0, 0)], dtype=object)

In [264]: offset = pd.offsets.CustomBusinessDay(calendar=cal)

In [265]: datetime.datetime(2012, 5, 25) + offset Out[265]: Timestamp('2012-05-29 00:00:00')

In [266]: datetime.datetime(2012, 7, 3) + offset Out[266]: Timestamp('2012-07-05 00:00:00')

In [267]: datetime.datetime(2012, 7, 3) + 2 * offset Out[267]: Timestamp('2012-07-06 00:00:00')

In [268]: datetime.datetime(2012, 7, 6) + offset Out[268]: Timestamp('2012-07-09 00:00:00')

Ranges are defined by the start_date and end_date class attributes of AbstractHolidayCalendar. The defaults are shown below.

In [269]: AbstractHolidayCalendar.start_date Out[269]: Timestamp('1970-01-01 00:00:00')

In [270]: AbstractHolidayCalendar.end_date Out[270]: Timestamp('2200-12-31 00:00:00')

These dates can be overwritten by setting the attributes as datetime/Timestamp/string.

In [271]: AbstractHolidayCalendar.start_date = datetime.datetime(2012, 1, 1)

In [272]: AbstractHolidayCalendar.end_date = datetime.datetime(2012, 12, 31)

In [273]: cal.holidays() Out[273]: DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None)

Every calendar class is accessible by name using the get_calendar function which returns a holiday class instance. Any imported calendar class will automatically be available by this function. Also, HolidayCalendarFactoryprovides an easy interface to create calendars that are combinations of calendars or calendars with additional rules.

In [274]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory, USLaborDay

In [275]: cal = get_calendar("ExampleCalendar")

In [276]: cal.rules Out[276]: [Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>), Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7fe8b0553c70>), Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]

In [277]: new_cal = HolidayCalendarFactory("NewExampleCalendar", cal, USLaborDay)

In [278]: new_cal.rules Out[278]: [Holiday: Labor Day (month=9, day=1, offset=<DateOffset: weekday=MO(+1)>), Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>), Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7fe8b0553c70>), Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]

Resampling#

pandas has a simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data 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 method on each of its groups. See some cookbook examples for some 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 25103 Freq: 5min, dtype: int64

The resample function is very flexible and allows you to specify many different parameters to control the frequency conversion and resampling operation.

Any built-in method available via GroupBy is available as a method of the returned object, including sum, mean, std, sem,max, min, median, first, last, ohlc:

In [293]: ts.resample("5Min").mean() Out[293]: 2012-01-01 251.03 Freq: 5min, dtype: float64

In [294]: ts.resample("5Min").ohlc() Out[294]: open high low close 2012-01-01 308 460 9 205

In [295]: ts.resample("5Min").max() Out[295]: 2012-01-01 460 Freq: 5min, dtype: int64

For downsampling, closed can be set to ‘left’ or ‘right’ to specify which end of the interval is closed:

In [296]: ts.resample("5Min", closed="right").mean() Out[296]: 2011-12-31 23:55:00 308.000000 2012-01-01 00:00:00 250.454545 Freq: 5min, dtype: float64

In [297]: ts.resample("5Min", closed="left").mean() Out[297]: 2012-01-01 251.03 Freq: 5min, dtype: float64

Parameters like label are used to manipulate the resulting labels.label specifies whether the result is labeled with the beginning or the end of the interval.

In [298]: ts.resample("5Min").mean() # by default label='left' Out[298]: 2012-01-01 251.03 Freq: 5min, dtype: float64

In [299]: ts.resample("5Min", label="left").mean() Out[299]: 2012-01-01 251.03 Freq: 5min, dtype: float64

Warning

The default values for label and closed is ‘left’ for all frequency 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 later time is pulled back to a previous time as in the following example with the BusinessDay frequency:

In [300]: s = pd.date_range("2000-01-01", "2000-01-05").to_series()

In [301]: s.iloc[2] = pd.NaT

In [302]: s.dt.day_name() Out[302]: 2000-01-01 Saturday 2000-01-02 Sunday 2000-01-03 NaN 2000-01-04 Tuesday 2000-01-05 Wednesday Freq: D, dtype: object

default: label='left', closed='left'

In [303]: s.resample("B").last().dt.day_name() Out[303]: 1999-12-31 Sunday 2000-01-03 NaN 2000-01-04 Tuesday 2000-01-05 Wednesday Freq: 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, use instead

In [304]: s.resample("B", label="right", closed="right").last().dt.day_name() Out[304]: 2000-01-03 Sunday 2000-01-04 Tuesday 2000-01-05 Wednesday 2000-01-06 NaN Freq: B, dtype: object

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

kind can be set to ‘timestamp’ or ‘period’ to convert the resulting index to/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 higher frequency 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 milliseconds

In [305]: ts[:2].resample("250ms").asfreq() Out[305]: 2012-01-01 00:00:00.000 308.0 2012-01-01 00:00:00.250 NaN 2012-01-01 00:00:00.500 NaN 2012-01-01 00:00:00.750 NaN 2012-01-01 00:00:01.000 204.0 Freq: 250ms, dtype: float64

In [306]: ts[:2].resample("250ms").ffill() Out[306]: 2012-01-01 00:00:00.000 308 2012-01-01 00:00:00.250 308 2012-01-01 00:00:00.500 308 2012-01-01 00:00:00.750 308 2012-01-01 00:00:01.000 204 Freq: 250ms, dtype: int64

In [307]: ts[:2].resample("250ms").ffill(limit=2) Out[307]: 2012-01-01 00:00:00.000 308.0 2012-01-01 00:00:00.250 308.0 2012-01-01 00:00:00.500 308.0 2012-01-01 00:00:00.750 NaN 2012-01-01 00:00:01.000 204.0 Freq: 250ms, dtype: float64

Sparse resampling#

Sparse timeseries are the ones where you have a lot fewer points relative to the amount of time you are looking to resample. Naively upsampling a sparse series can potentially generate lots of intermediate values. When you don’t want to use a method to fill these values, e.g. fill_method is None, then intermediate values will be filled with NaN.

Since resample is a time-based groupby, the following is a method to efficiently resample 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 0 2014-01-01 00:03:00 0 2014-01-01 00:06:00 0 2014-01-01 00:09:00 0 2014-01-01 00:12:00 0 .. 2014-04-09 23:48:00 0 2014-04-09 23:51:00 0 2014-04-09 23:54:00 0 2014-04-09 23:57:00 0 2014-04-10 00:00:00 99 Freq: 3min, Length: 47521, dtype: int64

We can instead only resample those groups where we have points as follows:

In [311]: from functools import partial

In [312]: from pandas.tseries.frequencies import to_offset

In [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 0 2014-01-02 1 2014-01-03 2 2014-01-04 3 2014-01-05 4 .. 2014-04-06 95 2014-04-07 96 2014-04-08 97 2014-04-09 98 2014-04-10 99 Length: 100, dtype: int64

Aggregation#

The resample() method returns a pandas.api.typing.Resampler instance. Similar to the 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 C 2012-01-01 00:00:00 -0.033823 -0.121514 -0.081447 2012-01-01 00:03:00 0.056909 0.146731 -0.024320 2012-01-01 00:06:00 -0.058837 0.047046 -0.052021 2012-01-01 00:09:00 0.063123 -0.026158 -0.066533 2012-01-01 00:12:00 0.186340 -0.003144 0.074752 2012-01-01 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 -0.033823 2012-01-01 00:03:00 0.056909 2012-01-01 00:06:00 -0.058837 2012-01-01 00:09:00 0.063123 2012-01-01 00:12:00 0.186340 2012-01-01 00:15:00 -0.085954 Freq: 3min, Name: A, dtype: float64

In [319]: r[["A", "B"]].mean() Out[319]: A B 2012-01-01 00:00:00 -0.033823 -0.121514 2012-01-01 00:03:00 0.056909 0.146731 2012-01-01 00:06:00 -0.058837 0.047046 2012-01-01 00:09:00 0.063123 -0.026158 2012-01-01 00:12:00 0.186340 -0.003144 2012-01-01 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 std 2012-01-01 00:00:00 -6.088060 -0.033823 1.043263 2012-01-01 00:03:00 10.243678 0.056909 1.058534 2012-01-01 00:06:00 -10.590584 -0.058837 0.949264 2012-01-01 00:09:00 11.362228 0.063123 1.028096 2012-01-01 00:12:00 33.541257 0.186340 0.884586 2012-01-01 00:15:00 -8.595393 -0.085954 1.035476

On a resampled DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index:

In [321]: r.agg(["sum", "mean"]) Out[321]: A ... C
sum mean ... sum mean 2012-01-01 00:00:00 -6.088060 -0.033823 ... -14.660515 -0.081447 2012-01-01 00:03:00 10.243678 0.056909 ... -4.377642 -0.024320 2012-01-01 00:06:00 -10.590584 -0.058837 ... -9.363825 -0.052021 2012-01-01 00:09:00 11.362228 0.063123 ... -11.975895 -0.066533 2012-01-01 00:12:00 33.541257 0.186340 ... 13.455299 0.074752 2012-01-01 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 the columns of a DataFrame:

In [322]: r.agg({"A": "sum", "B": lambda x: np.std(x, ddof=1)}) Out[322]: A B 2012-01-01 00:00:00 -6.088060 1.001294 2012-01-01 00:03:00 10.243678 1.074597 2012-01-01 00:06:00 -10.590584 0.987309 2012-01-01 00:09:00 11.362228 0.944953 2012-01-01 00:12:00 33.541257 1.095025 2012-01-01 00:15:00 -8.595393 1.035312

The function names can also be strings. In order for a string to be valid it must be implemented on the resampled object:

In [323]: r.agg({"A": "sum", "B": "std"}) Out[323]: A B 2012-01-01 00:00:00 -6.088060 1.001294 2012-01-01 00:03:00 10.243678 1.074597 2012-01-01 00:06:00 -10.590584 0.987309 2012-01-01 00:09:00 11.362228 0.944953 2012-01-01 00:12:00 33.541257 1.095025 2012-01-01 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 std 2012-01-01 00:00:00 -6.088060 1.043263 -0.121514 1.001294 2012-01-01 00:03:00 10.243678 1.058534 0.146731 1.074597 2012-01-01 00:06:00 -10.590584 0.949264 0.047046 0.987309 2012-01-01 00:09:00 11.362228 1.028096 -0.026158 0.944953 2012-01-01 00:12:00 33.541257 0.884586 -0.003144 1.095025 2012-01-01 00:15:00 -8.595393 1.035476 -0.016287 1.035312

If a DataFrame does not have a datetimelike index, but instead you want to 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]: df Out[326]: date a v d
1 2015-01-04 2015-01-04 0 2 2015-01-11 2015-01-11 1 3 2015-01-18 2015-01-18 2 4 2015-01-25 2015-01-25 3 5 2015-02-01 2015-02-01 4

In [327]: df.resample("ME", on="date")[["a"]].sum() Out[327]: a date
2015-01-31 6 2015-02-28 4

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

In [328]: df.resample("ME", level="d")[["a"]].sum() Out[328]: a d
2015-01-31 6 2015-02-28 4

Iterating through groups#

With the Resampler object in hand, iterating through the grouped data is very natural 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 0 2017-01-01 00:30:00 1 2017-01-01 00:31:00 2 dtype: int64

Group: 2017-01-01 01:00:00

2017-01-01 01:00:00 3 dtype: int64

Group: 2017-01-01 02:00:00

Series([], dtype: int64)

Group: 2017-01-01 03:00:00

2017-01-01 03:00:00 4 2017-01-01 03:05:00 5 dtype: 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]: ts Out[336]: 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 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 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17min, dtype: int64

In [338]: ts[middle:end].resample("17min", origin="start_day").sum() Out[338]: 2000-10-02 00:00:00 33 2000-10-02 00:17:00 45 Freq: 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🔞00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17min, dtype: int64

In [340]: ts[middle:end].resample("17min", origin="epoch").sum() Out[340]: 2000-10-01 23:52:00 15 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 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 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64

In [342]: ts[middle:end].resample("17min", origin=pd.Timestamp("2001-01-01")).sum() Out[342]: 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 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 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64

In [344]: ts.resample("17min", offset="23h30min").sum() Out[344]: 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 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#

Added in version 1.3.0.

Instead of adjusting the beginning of bins, sometimes we need to fix the end of the bins to make a backward resample with a 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 0 2000-10-01 23:52:00 18 2000-10-02 00:09:00 27 2000-10-02 00:26:00 63 Freq: 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 3 2000-10-01 23:55:00 15 2000-10-02 00:12:00 45 2000-10-02 00:29:00 45 Freq: 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_res Out[350]: Timestamp('2000-10-02 00:29:00')

Time span representation#

Regular intervals of time are represented by Period objects in pandas while sequences of Period objects are collected in a PeriodIndex, which can be 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 own frequency. Arithmetic is not allowed between Period with different freq (span).

In [355]: p = pd.Period("2012", freq="Y-DEC")

In [356]: p + 1 Out[356]: Period('2013', 'Y-DEC')

In [357]: p - 3 Out[357]: Period('2009', 'Y-DEC')

In [358]: p = pd.Period("2012-01", freq="2M")

In [359]: p + 2 Out[359]: Period('2012-05', '2M')

In [360]: p - 1 Out[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 units

The 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 will return 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]: prng Out[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 which has 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 anchor endpoints 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 pandas objects:

In [376]: ps = pd.Series(np.random.randn(len(prng)), prng)

In [377]: ps Out[377]: 2011-01 -2.916901 2011-02 0.514474 2011-03 1.346470 2011-04 0.816397 2011-05 2.258648 2011-06 0.494789 2011-07 0.301239 2011-08 0.464776 2011-09 -1.393581 2011-10 0.056780 2011-11 0.197035 2011-12 2.261385 2012-01 -0.329583 Freq: 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]: idx Out[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]: idx Out[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 extension dtype 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]: pi Out[385]: PeriodIndex(['2016-01', '2016-02', '2016-03'], dtype='period[M]')

In [386]: pi.dtype Out[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 freq

In [387]: pi.astype("period[D]") Out[387]: PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]')

convert to DatetimeIndex

In [388]: pi.astype("datetime64[ns]") Out[388]: DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='datetime64[ns]', freq='MS')

convert to PeriodIndex

In [389]: dti = pd.date_range("2011-01-01", freq="ME", periods=3)

In [390]: dti Out[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]: -2.9169013294054507

In [393]: ps[datetime.datetime(2011, 12, 25):] Out[393]: 2011-12 2.261385 2012-01 -0.329583 Freq: M, dtype: float64

In [394]: ps["10/31/2011":"12/31/2011"] Out[394]: 2011-10 0.056780 2011-11 0.197035 2011-12 2.261385 Freq: M, dtype: float64

Passing a string representing a lower frequency than PeriodIndex returns partial sliced data.

In [395]: ps["2011"] Out[395]: 2011-01 -2.916901 2011-02 0.514474 2011-03 1.346470 2011-04 0.816397 2011-05 2.258648 2011-06 0.494789 2011-07 0.301239 2011-08 0.464776 2011-09 -1.393581 2011-10 0.056780 2011-11 0.197035 2011-12 2.261385 Freq: M, dtype: float64

In [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]: dfp Out[397]: A 2013-01-01 09:00 -0.538468 2013-01-01 09:01 -1.365819 2013-01-01 09:02 -0.969051 2013-01-01 09:03 -0.331152 2013-01-01 09:04 -0.245334 ... ... 2013-01-01 18:55 0.522460 2013-01-01 18:56 0.118710 2013-01-01 18:57 0.167517 2013-01-01 18:58 0.922883 2013-01-01 18:59 1.721104

[600 rows x 1 columns]

In [398]: dfp.loc["2013-01-01 10h"] Out[398]: A 2013-01-01 10:00 -0.308975 2013-01-01 10:01 0.542520 2013-01-01 10:02 1.061068 2013-01-01 10:03 0.754005 2013-01-01 10:04 0.352933 ... ... 2013-01-01 10:55 -0.865621 2013-01-01 10:56 -1.167818 2013-01-01 10:57 -2.081748 2013-01-01 10:58 -0.527146 2013-01-01 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]: A 2013-01-01 10:00 -0.308975 2013-01-01 10:01 0.542520 2013-01-01 10:02 1.061068 2013-01-01 10:03 0.754005 2013-01-01 10:04 0.352933 ... ... 2013-01-01 11:55 -0.590204 2013-01-01 11:56 1.539990 2013-01-01 11:57 -1.224826 2013-01-01 11:58 0.578798 2013-01-01 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]: p Out[401]: Period('2011', 'Y-DEC')

We can convert it to a monthly frequency. Using the how parameter, we can specify 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 of quarterly frequency) automatically returns the super-period that includes the input 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 in November, the monthly period of December 2011 is actually in the 2012 Y-NOV period.

Period conversions with anchored frequencies are particularly useful for working with various quarterly data common to economics, business, and other fields. Many organizations define quarters relative to the month in which their fiscal year starts and ends. Thus, first quarter of 2011 could start in 2010 or a few months into 2011. Via anchored frequencies, pandas works for all quarterly frequencies 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]: ts Out[416]: 2012-01-31 1.931253 2012-02-29 -0.184594 2012-03-31 0.249656 2012-04-30 -0.978151 2012-05-31 -0.873389 Freq: ME, dtype: float64

In [417]: ps = ts.to_period()

In [418]: ps Out[418]: 2012-01 1.931253 2012-02 -0.184594 2012-03 0.249656 2012-04 -0.978151 2012-05 -0.873389 Freq: M, dtype: float64

In [419]: ps.to_timestamp() Out[419]: 2012-01-01 1.931253 2012-02-01 -0.184594 2012-03-01 0.249656 2012-04-01 -0.978151 2012-05-01 -0.873389 Freq: MS, dtype: float64

Remember that ‘s’ and ‘e’ can be used to return the timestamps at the start or end of the period:

In [420]: ps.to_timestamp("D", how="s") Out[420]: 2012-01-01 1.931253 2012-02-01 -0.184594 2012-03-01 0.249656 2012-04-01 -0.978151 2012-05-01 -0.873389 Freq: MS, dtype: float64

Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the 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") + 9

In [424]: ts.head() Out[424]: 1990-03-01 09:00 -0.109291 1990-06-01 09:00 -0.637235 1990-09-01 09:00 -1.735925 1990-12-01 09:00 2.096946 1991-03-01 09:00 -1.039926 Freq: 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]: span Out[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]: s Out[428]: 0 20121231 1 20141130 2 99991231 dtype: int64

In [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 2012-12-31 1 2014-11-30 2 9999-12-31 dtype: 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]: span Out[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 time zones 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 None Out[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. For common zones, the names are the same as pytz.

In [436]: import dateutil

pytz

In [437]: rng_pytz = pd.date_range("3/6/2012 00:00", periods=3, freq="D", tz="Europe/London")

In [438]: rng_pytz.tz Out[438]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>

dateutil

In [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.tz Out[441]: tzfile('/usr/share/zoneinfo/Europe/London')

dateutil - utc special case

In [442]: rng_utc = pd.date_range( .....: "3/6/2012 00:00", .....: periods=3, .....: freq="D", .....: tz=dateutil.tz.tzutc(), .....: ) .....:

In [443]: rng_utc.tz Out[443]: tzutc()

datetime.timezone

In [444]: rng_utc = pd.date_range( .....: "3/6/2012 00:00", .....: periods=3, .....: freq="D", .....: tz=datetime.timezone.utc, .....: ) .....:

In [445]: rng_utc.tz Out[445]: datetime.timezone.utc

Note that the UTC time zone is a special case in dateutil and should be constructed explicitly as an instance of dateutil.tz.tzutc. You can also construct other time zones objects explicitly first.

In [446]: import pytz

pytz

In [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_pytz Out[450]: True

dateutil

In [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_dateutil Out[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 different time 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 be succinctly 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.tz Out[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.tz Out[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 different definitions 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 not be considered equal. This may cause problems when working with stored data that is 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 into the 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 method on 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’s offset from UTC may be changed by the respective government.

Warning

If you are using dates beyond 2038-01-18, due to current deficiencies in the underlying libraries caused by the year 2038 problem, daylight saving time (DST) adjustments to 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 are still 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 + berlin

In [473]: result Out[473]: 2013-01-01 00:00:00+00:00 0 2013-01-02 00:00:00+00:00 2 2013-01-03 00:00:00+00:00 4 Freq: D, dtype: int64

In [474]: result.index Out[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]: didx Out[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 shifting from summer to winter time; fold describes whether the datetime-like corresponds to 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 datetime with pytz, please use Timestamp.tz_localize(). In general, we recommend to rely on Timestamp.tz_localize() when localizing ambiguous datetimes if you need direct control 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 timestamp because daylight savings time (DST) in a local time zone causes some times to occur twice 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..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 nonexistent local times (“clocks spring forward”). The behavior of localizing a timeseries with nonexistent times can 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..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]: dti Out[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 is represented with a dtype of datetime64[ns].

In [494]: s_naive = pd.Series(pd.date_range("20130101", periods=3))

In [495]: s_naive Out[495]: 0 2013-01-01 1 2013-01-02 2 2013-01-03 dtype: datetime64[ns]

A Series with a time zone aware values is represented 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_aware Out[497]: 0 2013-01-01 00:00:00-05:00 1 2013-01-02 00:00:00-05:00 2 2013-01-03 00:00:00-05:00 dtype: datetime64[ns, US/Eastern]

Both of these Series time zone information can 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 19:00:00-05:00 1 2013-01-01 19:00:00-05:00 2 2013-01-02 19:00:00-05:00 dtype: 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 zone

In [499]: s_aware.astype("datetime64[ns, CET]") Out[499]: 0 2013-01-01 06:00:00+01:00 1 2013-01-02 06:00:00+01:00 2 2013-01-03 06:00:00+01:00 dtype: 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 zone information. For example, when converting back to a Series:

In [502]: pd.Series(s_aware.to_numpy()) Out[502]: 0 2013-01-01 00:00:00-05:00 1 2013-01-02 00:00:00-05:00 2 2013-01-03 00:00:00-05:00 dtype: datetime64[ns, US/Eastern]

However, if you want an actual NumPy datetime64[ns] array (with the values converted 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]')