Time Series / Date functionality

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.

Concept

Scalar Class

Array Class

pandas Data Type

Primary Creation Method

Date times

Timestamp

DatetimeIndex

datetime64[ns] or datetime64[ns, tz]

to_datetime or date_range

Time deltas

Timedelta

TimedeltaIndex

timedelta64[ns]

to_timedelta or timedelta_range

Time spans

Period

PeriodIndex

period[freq]

Period or period_range

Date offsets

DateOffset

None

None

DateOffset

For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so 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, timedelta and Period data when passed into those constructors. DateOffset data 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         <DateOffset>
1    <2 * DateOffsets>
dtype: object

In [23]: pd.Series(pd.date_range('1/1/2011', freq='M', 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]: pd.Timestamp(datetime.datetime(2012, 5, 1))
Out[28]: Timestamp('2012-05-01 00:00:00')

In [29]: pd.Timestamp('2012-05-01')
Out[29]: Timestamp('2012-05-01 00:00:00')

In [30]: pd.Timestamp(2012, 5, 1)
Out[30]: 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 [31]: pd.Period('2011-01')
Out[31]: Period('2011-01', 'M')

In [32]: pd.Period('2012-05', freq='D')
Out[32]: Period('2012-05-01', 'D')

Timestamp and Period can serve as an index. Lists of Timestamp and Period are automatically coerced to DatetimeIndex and PeriodIndex respectively.

In [33]: dates = [pd.Timestamp('2012-05-01'),
   ....:          pd.Timestamp('2012-05-02'),
   ....:          pd.Timestamp('2012-05-03')]
   ....: 

In [34]: ts = pd.Series(np.random.randn(3), dates)

In [35]: type(ts.index)
Out[35]: pandas.core.indexes.datetimes.DatetimeIndex

In [36]: ts.index
Out[36]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)

In [37]: ts
Out[37]: 
2012-05-01    0.469112
2012-05-02   -0.282863
2012-05-03   -1.509059
dtype: float64

In [38]: periods = [pd.Period('2012-01'), pd.Period('2012-02'), pd.Period('2012-03')]

In [39]: ts = pd.Series(np.random.randn(3), periods)

In [40]: type(ts.index)
Out[40]: pandas.core.indexes.period.PeriodIndex

In [41]: ts.index
Out[41]: PeriodIndex(['2012-01', '2012-02', '2012-03'], dtype='period[M]', freq='M')

In [42]: ts
Out[42]: 
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 of DatetimeIndex. 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 [43]: pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None]))
Out[43]: 
0   2009-07-31
1   2010-01-10
2          NaT
dtype: datetime64[ns]

In [44]: pd.to_datetime(['2005/11/23', '2010.12.31'])
Out[44]: 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 [45]: pd.to_datetime(['04-01-2012 10:00'], dayfirst=True)
Out[45]: DatetimeIndex(['2012-01-04 10:00:00'], dtype='datetime64[ns]', freq=None)

In [46]: pd.to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
Out[46]: DatetimeIndex(['2012-01-14', '2012-01-14'], dtype='datetime64[ns]', freq=None)

Warning

You see in the above example that dayfirst isn’t strict, so if a date can’t be parsed with the day being first it will be parsed as if dayfirst were False.

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 [47]: pd.to_datetime('2010/11/12')
Out[47]: Timestamp('2010-11-12 00:00:00')

In [48]: pd.Timestamp('2010/11/12')
Out[48]: Timestamp('2010-11-12 00:00:00')

You can also use the DatetimeIndex constructor directly:

In [49]: pd.DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'])
Out[49]: 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 [50]: pd.DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], freq='infer')
Out[50]: 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 [51]: pd.to_datetime('2010/11/12', format='%Y/%m/%d')
Out[51]: Timestamp('2010-11-12 00:00:00')

In [52]: pd.to_datetime('12-11-2010 00:00', format='%d-%m-%Y %H:%M')
Out[52]: Timestamp('2010-11-12 00:00:00')

For more information on the choices available when specifying the format option, see the Python datetime documentation.

Assembling Datetime from Multiple DataFrame Columns

New in version 0.18.1.

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

In [53]: df = pd.DataFrame({'year': [2015, 2016],
   ....:                    'month': [2, 3],
   ....:                    'day': [4, 5],
   ....:                    'hour': [2, 3]})
   ....: 

In [54]: pd.to_datetime(df)
Out[54]: 
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 [55]: pd.to_datetime(df[['year', 'month', 'day']])
Out[55]: 
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 unparseable:

In [2]: pd.to_datetime(['2009/07/31', 'asd'], errors='raise')
ValueError: Unknown string format

Pass errors='ignore' to return the original input when unparseable:

In [56]: pd.to_datetime(['2009/07/31', 'asd'], errors='ignore')
Out[56]: Index(['2009/07/31', 'asd'], dtype='object')

Pass errors='coerce' to convert unparseable data to NaT (not a time):

In [57]: pd.to_datetime(['2009/07/31', 'asd'], errors='coerce')
Out[57]: DatetimeIndex(['2009-07-31', 'NaT'], dtype='datetime64[ns]', freq=None)

Epoch Timestamps

pandas supports converting integer or float epoch times to Timestamp and DatetimeIndex. The default unit is nanoseconds, since that is how Timestamp objects are stored internally. However, epochs are often stored in another unit which can be specified. These are computed from the starting point specified by the origin parameter.

In [58]: pd.to_datetime([1349720105, 1349806505, 1349892905,
   ....:                 1349979305, 1350065705], unit='s')
   ....: 
Out[58]: 
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 [59]: pd.to_datetime([1349720105100, 1349720105200, 1349720105300,
   ....:                 1349720105400, 1349720105500], unit='ms')
   ....: 
Out[59]: 
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)

Constructing a Timestamp or DatetimeIndex with an epoch timestamp with the tz argument specified will currently localize the epoch timestamps to UTC first then convert the result to the specified time zone. However, this behavior is deprecated, and if you have epochs in wall time in another timezone, it is recommended to read the epochs as timezone-naive timestamps and then localize to the appropriate timezone:

In [60]: pd.Timestamp(1262347200000000000).tz_localize('US/Pacific')
Out[60]: Timestamp('2010-01-01 12:00:00-0800', tz='US/Pacific')

In [61]: pd.DatetimeIndex([1262347200000000000]).tz_localize('US/Pacific')
Out[61]: 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 [62]: pd.to_datetime([1490195805.433, 1490195805.433502912], unit='s')
Out[62]: DatetimeIndex(['2017-03-22 15:16:45.433000088', '2017-03-22 15:16:45.433502913'], dtype='datetime64[ns]', freq=None)

In [63]: pd.to_datetime(1490195805433502912, unit='ns')
Out[63]: 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 [64]: stamps = pd.date_range('2012-10-08 18:15:05', periods=4, freq='D')

In [65]: stamps
Out[65]: 
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 [66]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
Out[66]: Int64Index([1349720105, 1349806505, 1349892905, 1349979305], dtype='int64')

Using the origin Parameter

New in version 0.20.0.

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 [67]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
Out[67]: 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 [68]: pd.to_datetime([1, 2, 3], unit='D')
Out[68]: 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 or Index constructor and pass in a list of datetime objects:

In [69]: dates = [datetime.datetime(2012, 5, 1),
   ....:          datetime.datetime(2012, 5, 2),
   ....:          datetime.datetime(2012, 5, 3)]
   ....: 

# Note the frequency information
In [70]: index = pd.DatetimeIndex(dates)

In [71]: index
Out[71]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)

# Automatically converted to DatetimeIndex
In [72]: index = pd.Index(dates)

In [73]: index
Out[73]: 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 a calendar day while the default for bdate_range is a business day:

In [74]: start = datetime.datetime(2011, 1, 1)

In [75]: end = datetime.datetime(2012, 1, 1)

In [76]: index = pd.date_range(start, end)

In [77]: index
Out[77]: 
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 [78]: index = pd.bdate_range(start, end)

In [79]: index
Out[79]: 
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 [80]: pd.date_range(start, periods=1000, freq='M')
Out[80]: 
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='M')

In [81]: pd.bdate_range(start, periods=250, freq='BQS')
Out[81]: 
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 [82]: pd.date_range(start, end, freq='BM')
Out[82]: 
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='BM')

In [83]: pd.date_range(start, end, freq='W')
Out[83]: 
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 [84]: pd.bdate_range(end=end, periods=20)
Out[84]: 
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 [85]: pd.bdate_range(start=start, periods=20)
Out[85]: 
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')

New in version 0.23.0.

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 [86]: pd.date_range('2018-01-01', '2018-01-05', periods=5)
Out[86]: 
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05'],
              dtype='datetime64[ns]', freq=None)

In [87]: pd.date_range('2018-01-01', '2018-01-05', periods=10)
Out[87]: 
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

Warning

This functionality was originally exclusive to cdate_range, which is deprecated as of version 0.21.0 in favor of bdate_range. Note that cdate_range only utilizes the weekmask and holidays parameters when custom business day, ‘C’, is passed as the frequency string. Support has been expanded with bdate_range to work with any custom frequency string.

New in version 0.21.0.

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 [88]: weekmask = 'Mon Wed Fri'

In [89]: holidays = [datetime.datetime(2011, 1, 5), datetime.datetime(2011, 3, 14)]

In [90]: pd.bdate_range(start, end, freq='C', weekmask=weekmask, holidays=holidays)
Out[90]: 
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 [91]: pd.bdate_range(start, end, freq='CBMS', weekmask=weekmask)
Out[91]: 
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

Since pandas represents timestamps in nanosecond resolution, the time span that can be represented using a 64-bit integer is limited to approximately 584 years:

In [92]: pd.Timestamp.min
Out[92]: Timestamp('1677-09-21 00:12:43.145225')

In [93]: pd.Timestamp.max
Out[93]: Timestamp('2262-04-11 23:47:16.854775807')

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 and tshift 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 Index objects, 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 [94]: rng = pd.date_range(start, end, freq='BM')

In [95]: ts = pd.Series(np.random.randn(len(rng)), index=rng)

In [96]: ts.index
Out[96]: 
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='BM')

In [97]: ts[:5].index
Out[97]: 
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
               '2011-05-31'],
              dtype='datetime64[ns]', freq='BM')

In [98]: ts[::2].index
Out[98]: 
DatetimeIndex(['2011-01-31', '2011-03-31', '2011-05-31', '2011-07-29',
               '2011-09-30', '2011-11-30'],
              dtype='datetime64[ns]', freq='2BM')

Partial String Indexing

Dates and strings that parse to timestamps can be passed as indexing parameters:

In [99]: ts['1/31/2011']
Out[99]: 0.11920871129693428

In [100]: ts[datetime.datetime(2011, 12, 25):]
Out[100]: 
2011-12-30    0.56702
Freq: BM, dtype: float64

In [101]: ts['10/31/2011':'12/31/2011']
Out[101]: 
2011-10-31    0.271860
2011-11-30   -0.424972
2011-12-30    0.567020
Freq: BM, dtype: float64

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

In [102]: ts['2011']
Out[102]: 
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: BM, dtype: float64

In [103]: ts['2011-6']
Out[103]: 
2011-06-30    1.071804
Freq: BM, 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:

In [104]: dft = pd.DataFrame(np.random.randn(100000, 1), columns=['A'],
   .....:                    index=pd.date_range('20130101', periods=100000, freq='T'))
   .....: 

In [105]: dft
Out[105]: 
                            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
2013-01-01 00:05:00  0.524988
2013-01-01 00:06:00  0.404705
...                       ...
2013-03-11 10:33:00 -1.562855
2013-03-11 10:34:00 -0.776320
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 [106]: dft['2013']
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
2013-01-01 00:05:00  0.524988
2013-01-01 00:06:00  0.404705
...                       ...
2013-03-11 10:33:00 -1.562855
2013-03-11 10:34:00 -0.776320
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 [107]: dft['2013-1':'2013-2']
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
2013-01-01 00:05:00  0.524988
2013-01-01 00:06:00  0.404705
...                       ...
2013-02-28 23:53:00  0.096317
2013-02-28 23:54:00  1.069352
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 [108]: dft['2013-1':'2013-2-28']
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
2013-01-01 00:05:00  0.524988
2013-01-01 00:06:00  0.404705
...                       ...
2013-02-28 23:53:00  0.096317
2013-02-28 23:54:00  1.069352
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 [109]: dft['2013-1':'2013-2-28 00:00:00']
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
2013-01-01 00:05:00  0.524988
2013-01-01 00:06:00  0.404705
...                       ...
2013-02-27 23:54:00  1.604295
2013-02-27 23:55:00  0.885350
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 [110]: dft['2013-1-15':'2013-1-15 12:30:00']
Out[110]: 
                            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 00:05:00  0.212765
2013-01-15 00:06:00  0.294784
...                       ...
2013-01-15 12:24:00 -0.850073
2013-01-15 12:25:00 -0.526367
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]

New in version 0.18.0.

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

In [111]: 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 [112]: dft2
Out[112]: 
                              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
                    b  1.005292
2013-01-02 12:00:00 a -1.562237
...                         ...
2013-01-04 00:00:00 b  0.054993
2013-01-04 12:00:00 a -0.784071
                    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 [113]: dft2.loc['2013-01-05']
Out[113]: 
                              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 [114]: idx = pd.IndexSlice

In [115]: dft2 = dft2.swaplevel(0, 1).sort_index()

In [116]: dft2.loc[idx[:, '2013-01-05'], :]
Out[116]: 
                              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

New in version 0.25.0.

Slicing with string indexing also honors UTC offset.

In [117]: df = pd.DataFrame([0], index=pd.DatetimeIndex(['2019-01-01'], tz='US/Pacific'))

In [118]: df
Out[118]: 
                           0
2019-01-01 00:00:00-08:00  0

In [119]: df['2019-01-01 12:00:00+04:00':'2019-01-01 13:00:00+04:00']
Out[119]: 
                           0
2019-01-01 00:00:00-08:00  0

Slice vs. Exact Match

Changed in version 0.20.0.

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 [120]: 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 [121]: series_minute.index.resolution
Out[121]: 'minute'

A timestamp string less accurate than a minute gives a Series object.

In [122]: series_minute['2011-12-31 23']
Out[122]: 
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 [123]: series_minute['2011-12-31 23:59']
Out[123]: 1

In [124]: series_minute['2011-12-31 23:59:00']
Out[124]: 1

If index resolution is second, then the minute-accurate timestamp gives a Series.

In [125]: 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 [126]: series_second.index.resolution
Out[126]: 'second'

In [127]: series_second['2011-12-31 23:59']
Out[127]: 
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 [] as well.

In [128]: dft_minute = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
   .....:                           index=series_minute.index)
   .....: 

In [129]: dft_minute['2011-12-31 23']
Out[129]: 
                     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 [130]: dft_minute.loc['2011-12-31 23:59']
Out[130]: 
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 [131]: series_monthly = pd.Series([1, 2, 3],
   .....:                            pd.DatetimeIndex(['2011-12', '2012-01', '2012-02']))
   .....: 

In [132]: series_monthly.index.resolution
Out[132]: 'day'

In [133]: series_monthly['2011-12']  # returns Series
Out[133]: 
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 [134]: dft[datetime.datetime(2013, 1, 1):datetime.datetime(2013, 2, 28)]
Out[134]: 
                            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
2013-01-01 00:05:00  0.524988
2013-01-01 00:06:00  0.404705
...                       ...
2013-02-27 23:54:00  1.604295
2013-02-27 23:55:00  0.885350
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 [135]: dft[datetime.datetime(2013, 1, 1, 10, 12, 0):
   .....:     datetime.datetime(2013, 2, 28, 10, 12, 0)]
   .....: 
Out[135]: 
                            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
2013-01-01 10:17:00 -0.700648
2013-01-01 10:18:00 -1.166607
...                       ...
2013-02-28 10:06:00 -1.393114
2013-02-28 10:07:00  2.321893
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 [136]: rng2 = pd.date_range('2011-01-01', '2012-01-01', freq='W')

In [137]: ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2)

In [138]: ts2.truncate(before='2011-11', after='2011-12')
Out[138]: 
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 [139]: ts2['2011-11':'2011-12']
Out[139]: 
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 [140]: ts2[[0, 2, 6]].index
Out[140]: DatetimeIndex(['2011-01-02', '2011-01-16', '2011-02-13'], dtype='datetime64[ns]', freq=None)

Iterating through groups

With the Resampler object in hand, iterating through the grouped data is very natural and functions similarly to itertools.groupby():

In [141]: 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 [142]: resampled = small.resample('H')

In [143]: 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.

Time/Date Components

There are several time/date properties that one can access from Timestamp or a collection of timestamps like a DatetimeIndex.

Property

Description

year

The year of the datetime

month

The month of the datetime

day

The days of the datetime

hour

The hour of the datetime

minute

The minutes of the datetime

second

The seconds of the datetime

microsecond

The microseconds of the datetime

nanosecond

The nanoseconds of the datetime

date

Returns datetime.date (does not contain timezone information)

time

Returns datetime.time (does not contain timezone information)

timetz

Returns datetime.time as local time with timezone information

dayofyear

The ordinal day of year

weekofyear

The week ordinal of the year

week

The week ordinal of the year

dayofweek

The number of the day of the week with Monday=0, Sunday=6

weekday

The number of the day of the week with Monday=0, Sunday=6

weekday_name

The name of the day in a week (ex: Friday)

quarter

Quarter of the date: Jan-Mar = 1, Apr-Jun = 2, etc.

days_in_month

The number of days in the month of the datetime

is_month_start

Logical indicating if first day of month (defined by frequency)

is_month_end

Logical indicating if last day of month (defined by frequency)

is_quarter_start

Logical indicating if first day of quarter (defined by frequency)

is_quarter_end

Logical indicating if last day of quarter (defined by frequency)

is_year_start

Logical indicating if first day of year (defined by frequency)

is_year_end

Logical indicating if last day of year (defined by frequency)

is_leap_year

Logical indicating if the date belongs to a leap year

Furthermore, if you have a Series with datetimelike values, then you can access these properties via the .dt accessor, as detailed in the section on .dt accessors.

DateOffset Objects

In the preceding examples, frequency strings (e.g. 'D') were used to specify a frequency that defined:

These frequency strings map to a DateOffset object and its subclasses. A DateOffset is 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 like Timedelta 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 (+) or the apply method can be used to perform the shift.

# This particular day contains a day light savings time transition
In [144]: ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')

# Respects absolute time
In [145]: ts + pd.Timedelta(days=1)
Out[145]: Timestamp('2016-10-30 23:00:00+0200', tz='Europe/Helsinki')

# Respects calendar time
In [146]: ts + pd.DateOffset(days=1)
Out[146]: Timestamp('2016-10-31 00:00:00+0200', tz='Europe/Helsinki')

In [147]: friday = pd.Timestamp('2018-01-05')

In [148]: friday.day_name()
Out[148]: 'Friday'

# Add 2 business days (Friday --> Tuesday)
In [149]: two_business_days = 2 * pd.offsets.BDay()

In [150]: two_business_days.apply(friday)
Out[150]: Timestamp('2018-01-09 00:00:00')

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:

Date Offset

Frequency String

Description

DateOffset

None

Generic offset class, defaults to 1 calendar day

BDay or BusinessDay

'B'

business day (weekday)

CDay or CustomBusinessDay

'C'

custom business day

Week

'W'

one week, optionally anchored on a day of the week

WeekOfMonth

'WOM'

the x-th day of the y-th week of each month

LastWeekOfMonth

'LWOM'

the x-th day of the last week of each month

MonthEnd

'M'

calendar month end

MonthBegin

'MS'

calendar month begin

BMonthEnd or BusinessMonthEnd

'BM'

business month end

BMonthBegin or BusinessMonthBegin

'BMS'

business month begin

CBMonthEnd or CustomBusinessMonthEnd

'CBM'

custom business month end

CBMonthBegin or CustomBusinessMonthBegin

'CBMS'

custom business month begin

SemiMonthEnd

'SM'

15th (or other day_of_month) and calendar month end

SemiMonthBegin

'SMS'

15th (or other day_of_month) and calendar month begin

QuarterEnd

'Q'

calendar quarter end

QuarterBegin

'QS'

calendar quarter begin

BQuarterEnd

'BQ

business quarter end

BQuarterBegin

'BQS'

business quarter begin

FY5253Quarter

'REQ'

retail (aka 52-53 week) quarter

YearEnd

'A'

calendar year end

YearBegin

'AS' or 'BYS'

calendar year begin

BYearEnd

'BA'

business year end

BYearBegin

'BAS'

business year begin

FY5253

'RE'

retail (aka 52-53 week) year

Easter

None

Easter holiday

BusinessHour

'BH'

business hour

CustomBusinessHour

'CBH'

custom business hour

Day

'D'

one absolute day

Hour

'H'

one hour

Minute

'T' or 'min'

one minute

Second

'S'

one second

Milli

'L' or 'ms'

one millisecond

Micro

'U' or 'us'

one microsecond

Nano

'N'

one nanosecond

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.apply(ts)
Out[160]: Timestamp('2014-01-02 09:00:00')

In [161]: day.apply(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.apply(ts)
Out[164]: Timestamp('2014-01-01 23:00:00')

In [165]: hour.apply(ts).normalize()
Out[165]: Timestamp('2014-01-01 00:00:00')

In [166]: hour.apply(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 a weekday 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='D')

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 the Timedelta 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='D')

Custom Business Days

The CDay or CustomBusinessDay class provides a parametric BusinessDay 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 the holiday 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.time instance. 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 Satuaday,
# 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.rollforward may 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 and 2014-08-04 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().apply(pd.Timestamp('2014-08-01 17:00')).
# And it is the same as BusinessHour().apply(pd.Timestamp('2014-08-04 09:00'))
In [225]: pd.offsets.BusinessHour().apply(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().apply(pd.Timestamp('2014-08-01'))
# The result is the same as rollworward because BusinessDay never overlap.
In [227]: pd.offsets.BusinessHour().apply(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

New in version 0.18.1.

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.

Alias

Description

B

business day frequency

C

custom business day frequency

D

calendar day frequency

W

weekly frequency

M

month end frequency

SM

semi-month end frequency (15th and end of month)

BM

business month end frequency

CBM

custom business month end frequency

MS

month start frequency

SMS

semi-month start frequency (1st and 15th)

BMS

business month start frequency

CBMS

custom business month start frequency

Q

quarter end frequency

BQ

business quarter end frequency

QS

quarter start frequency

BQS

business quarter start frequency

A, Y

year end frequency

BA, BY

business year end frequency

AS, YS

year start frequency

BAS, BYS

business year start frequency

BH

business hour frequency

H

hourly frequency

T, min

minutely frequency

S

secondly frequency

L, ms

milliseconds

U, us

microseconds

N

nanoseconds

Combining Aliases

As we have seen previously, the alias and the offset instance are fungible in most functions:

In [235]: pd.date_range(start, periods=5, freq='B')
Out[235]: 
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
               '2011-01-07'],
              dtype='datetime64[ns]', freq='B')

In [236]: pd.date_range(start, periods=5, freq=pd.offsets.BDay())
Out[236]: 
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 [237]: pd.date_range(start, periods=10, freq='2h20min')
Out[237]: 
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='140T')

In [238]: pd.date_range(start, periods=10, freq='1D10U')
Out[238]: 
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='86400000010U')

Anchored Offsets

For some frequencies you can specify an anchoring suffix:

Alias

Description

W-SUN

weekly frequency (Sundays). Same as ‘W’

W-MON

weekly frequency (Mondays)

W-TUE

weekly frequency (Tuesdays)

W-WED

weekly frequency (Wednesdays)

W-THU

weekly frequency (Thursdays)

W-FRI

weekly frequency (Fridays)

W-SAT

weekly frequency (Saturdays)

(B)Q(S)-DEC

quarterly frequency, year ends in December. Same as ‘Q’

(B)Q(S)-JAN

quarterly frequency, year ends in January

(B)Q(S)-FEB

quarterly frequency, year ends in February

(B)Q(S)-MAR

quarterly frequency, year ends in March

(B)Q(S)-APR

quarterly frequency, year ends in April

(B)Q(S)-MAY

quarterly frequency, year ends in May

(B)Q(S)-JUN

quarterly frequency, year ends in June

(B)Q(S)-JUL

quarterly frequency, year ends in July

(B)Q(S)-AUG

quarterly frequency, year ends in August

(B)Q(S)-SEP

quarterly frequency, year ends in September

(B)Q(S)-OCT

quarterly frequency, year ends in October

(B)Q(S)-NOV

quarterly frequency, year ends in November

(B)A(S)-DEC

annual frequency, anchored end of December. Same as ‘A’

(B)A(S)-JAN

annual frequency, anchored end of January

(B)A(S)-FEB

annual frequency, anchored end of February

(B)A(S)-MAR

annual frequency, anchored end of March

(B)A(S)-APR

annual frequency, anchored end of April

(B)A(S)-MAY

annual frequency, anchored end of May

(B)A(S)-JUN

annual frequency, anchored end of June

(B)A(S)-JUL

annual frequency, anchored end of July

(B)A(S)-AUG

annual frequency, anchored end of August

(B)A(S)-SEP

annual frequency, anchored end of September

(B)A(S)-OCT

annual frequency, anchored end of October

(B)A(S)-NOV

annual frequency, anchored end of November

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 [239]: pd.Timestamp('2014-01-02') + pd.offsets.MonthBegin(n=1)
Out[239]: Timestamp('2014-02-01 00:00:00')

In [240]: pd.Timestamp('2014-01-02') + pd.offsets.MonthEnd(n=1)
Out[240]: Timestamp('2014-01-31 00:00:00')

In [241]: pd.Timestamp('2014-01-02') - pd.offsets.MonthBegin(n=1)
Out[241]: Timestamp('2014-01-01 00:00:00')

In [242]: pd.Timestamp('2014-01-02') - pd.offsets.MonthEnd(n=1)
Out[242]: Timestamp('2013-12-31 00:00:00')

In [243]: pd.Timestamp('2014-01-02') + pd.offsets.MonthBegin(n=4)
Out[243]: Timestamp('2014-05-01 00:00:00')

In [244]: pd.Timestamp('2014-01-02') - pd.offsets.MonthBegin(n=4)
Out[244]: 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 [245]: pd.Timestamp('2014-01-01') + pd.offsets.MonthBegin(n=1)
Out[245]: Timestamp('2014-02-01 00:00:00')

In [246]: pd.Timestamp('2014-01-31') + pd.offsets.MonthEnd(n=1)
Out[246]: Timestamp('2014-02-28 00:00:00')

In [247]: pd.Timestamp('2014-01-01') - pd.offsets.MonthBegin(n=1)
Out[247]: Timestamp('2013-12-01 00:00:00')

In [248]: pd.Timestamp('2014-01-31') - pd.offsets.MonthEnd(n=1)
Out[248]: Timestamp('2013-12-31 00:00:00')

In [249]: pd.Timestamp('2014-01-01') + pd.offsets.MonthBegin(n=4)
Out[249]: Timestamp('2014-05-01 00:00:00')

In [250]: pd.Timestamp('2014-01-31') - pd.offsets.MonthBegin(n=4)
Out[250]: 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 [251]: pd.Timestamp('2014-01-02') + pd.offsets.MonthBegin(n=0)
Out[251]: Timestamp('2014-02-01 00:00:00')

In [252]: pd.Timestamp('2014-01-02') + pd.offsets.MonthEnd(n=0)
Out[252]: Timestamp('2014-01-31 00:00:00')

In [253]: pd.Timestamp('2014-01-01') + pd.offsets.MonthBegin(n=0)
Out[253]: Timestamp('2014-01-01 00:00:00')

In [254]: pd.Timestamp('2014-01-31') + pd.offsets.MonthEnd(n=0)
Out[254]: 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_date class 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:

Rule

Description

nearest_workday

move Saturday to Friday and Sunday to Monday

sunday_to_monday

move Sunday to following Monday

next_monday_or_tuesday

move Saturday to Monday and Sunday/Monday to Tuesday

previous_friday

move Saturday and Sunday to previous Friday”

next_monday

move Saturday and Sunday to following Monday

An example of how holidays and holiday calendars are defined:

In [255]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
   .....:     AbstractHolidayCalendar, nearest_workday, MO
   .....: 

In [256]: 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 [257]: cal = ExampleCalendar()

In [258]: cal.holidays(datetime.datetime(2012, 1, 1), datetime.datetime(2012, 12, 31))
Out[258]: 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 datetime or Timestamp objects.

In [259]: pd.date_range(start='7/1/2012', end='7/10/2012',
   .....:               freq=pd.offsets.CDay(calendar=cal)).to_pydatetime()
   .....: 
Out[259]: 
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 [260]: offset = pd.offsets.CustomBusinessDay(calendar=cal)

In [261]: datetime.datetime(2012, 5, 25) + offset
Out[261]: Timestamp('2012-05-29 00:00:00')

In [262]: datetime.datetime(2012, 7, 3) + offset
Out[262]: Timestamp('2012-07-05 00:00:00')

In [263]: datetime.datetime(2012, 7, 3) + 2 * offset
Out[263]: Timestamp('2012-07-06 00:00:00')

In [264]: datetime.datetime(2012, 7, 6) + offset
Out[264]: 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 [265]: AbstractHolidayCalendar.start_date
Out[265]: Timestamp('1970-01-01 00:00:00')

In [266]: AbstractHolidayCalendar.end_date
Out[266]: Timestamp('2030-12-31 00:00:00')

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

In [267]: AbstractHolidayCalendar.start_date = datetime.datetime(2012, 1, 1)

In [268]: AbstractHolidayCalendar.end_date = datetime.datetime(2012, 12, 31)

In [269]: cal.holidays()
Out[269]: 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, HolidayCalendarFactory provides an easy interface to create calendars that are combinations of calendars or calendars with additional rules.

In [270]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
   .....:     USLaborDay
   .....: 

In [271]: cal = get_calendar('ExampleCalendar')

In [272]: cal.rules
Out[272]: 
[Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>),
 Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7ff52a576598>),
 Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]

In [273]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)

In [274]: new_cal.rules
Out[274]: 
[Holiday: Labor Day (month=9, day=1, offset=<DateOffset: weekday=MO(+1)>),
 Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>),
 Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7ff52a576598>),
 Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]

Resampling

Warning

The interface to .resample has changed in 0.18.0 to be more groupby-like and hence more flexible. See the whatsnew docs for a comparison with prior versions.

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.

Starting in version 0.18.1, the resample() function can be used directly from DataFrameGroupBy objects, see the groupby docs.

Note

.resample() is similar to using a rolling() operation with a time-based offset, see a discussion here.

Basics

In [286]: rng = pd.date_range('1/1/2012', periods=100, freq='S')

In [287]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)

In [288]: ts.resample('5Min').sum()
Out[288]: 
2012-01-01    25103
Freq: 5T, 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 function available via dispatching is available as a method of the returned object, including sum, mean, std, sem, max, min, median, first, last, ohlc:

In [289]: ts.resample('5Min').mean()
Out[289]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

In [290]: ts.resample('5Min').ohlc()
Out[290]: 
            open  high  low  close
2012-01-01   308   460    9    205

In [291]: ts.resample('5Min').max()
Out[291]: 
2012-01-01    460
Freq: 5T, dtype: int64

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

In [292]: ts.resample('5Min', closed='right').mean()
Out[292]: 
2011-12-31 23:55:00    308.000000
2012-01-01 00:00:00    250.454545
Freq: 5T, dtype: float64

In [293]: ts.resample('5Min', closed='left').mean()
Out[293]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

Parameters like label and loffset are used to manipulate the resulting labels. label specifies whether the result is labeled with the beginning or the end of the interval. loffset performs a time adjustment on the output labels.

In [294]: ts.resample('5Min').mean()  # by default label='left'
Out[294]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

In [295]: ts.resample('5Min', label='left').mean()
Out[295]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

In [296]: ts.resample('5Min', label='left', loffset='1s').mean()
Out[296]: 
2012-01-01 00:00:01    251.03
dtype: float64

Note

The default values for label and closed is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

In [297]: rng2 = pd.date_range('1/1/2012', end='3/31/2012', freq='D')

In [298]: ts2 = pd.Series(range(len(rng2)), index=rng2)

# default: label='right', closed='right'
In [299]: ts2.resample('M').max()
Out[299]: 
2012-01-31    30
2012-02-29    59
2012-03-31    90
Freq: M, dtype: int64

# default: label='left', closed='left'
In [300]: ts2.resample('SM').max()
Out[300]: 
2011-12-31    13
2012-01-15    29
2012-01-31    44
2012-02-15    58
2012-02-29    73
2012-03-15    89
2012-03-31    90
Freq: SM-15, dtype: int64

In [301]: ts2.resample('SM', label='right', closed='right').max()
Out[301]: 
2012-01-15    14.0
2012-01-31    30.0
2012-02-15    45.0
2012-02-29    59.0
2012-03-15    74.0
2012-03-31    90.0
2012-04-15     NaN
Freq: SM-15, dtype: float64

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 resample retains 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 [302]: ts[:2].resample('250L').asfreq()
Out[302]: 
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: 250L, dtype: float64

In [303]: ts[:2].resample('250L').ffill()
Out[303]: 
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: 250L, dtype: int64

In [304]: ts[:2].resample('250L').ffill(limit=2)
Out[304]: 
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: 250L, 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 [305]: rng = pd.date_range('2014-1-1', periods=100, freq='D') + pd.Timedelta('1s')

In [306]: ts = pd.Series(range(100), index=rng)

If we want to resample to the full range of the series:

In [307]: ts.resample('3T').sum()
Out[307]: 
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
2014-01-01 00:15:00     0
2014-01-01 00:18:00     0
                       ..
2014-04-09 23:42:00     0
2014-04-09 23:45:00     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: 3T, Length: 47521, dtype: int64

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

In [308]: from functools import partial

In [309]: from pandas.tseries.frequencies import to_offset

In [310]: def round(t, freq):
   .....:     freq = to_offset(freq)
   .....:     return pd.Timestamp((t.value // freq.delta.value) * freq.delta.value)
   .....: 

In [311]: ts.groupby(partial(round, freq='3T')).sum()
Out[311]: 
2014-01-01     0
2014-01-02     1
2014-01-03     2
2014-01-04     3
2014-01-05     4
2014-01-06     5
2014-01-07     6
              ..
2014-04-04    93
2014-04-05    94
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

Similar to the aggregating API, groupby API, and the window functions API, a Resampler can be selectively resampled.

Resampling a DataFrame, the default will be to act on all columns with the same function.

In [312]: df = pd.DataFrame(np.random.randn(1000, 3),
   .....:                   index=pd.date_range('1/1/2012', freq='S', periods=1000),
   .....:                   columns=['A', 'B', 'C'])
   .....: 

In [313]: r = df.resample('3T')

In [314]: r.mean()
Out[314]: 
                            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 [315]: r['A'].mean()
Out[315]: 
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: 3T, Name: A, dtype: float64

In [316]: r[['A', 'B']].mean()
Out[316]: 
                            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 [317]: r['A'].agg([np.sum, np.mean, np.std])
Out[317]: 
                           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 [318]: r.agg([np.sum, np.mean])
Out[318]: 
                             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 [319]: r.agg({'A': np.sum,
   .....:        'B': lambda x: np.std(x, ddof=1)})
   .....: 
Out[319]: 
                             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 [320]: r.agg({'A': 'sum', 'B': 'std'})
Out[320]: 
                             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 [321]: r.agg({'A': ['sum', 'std'], 'B': ['mean', 'std']})
Out[321]: 
                             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 the on keyword.

In [322]: 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 [323]: df
Out[323]: 
                   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 [324]: df.resample('M', on='date').sum()
Out[324]: 
            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 the level keyword.

In [325]: df.resample('M', level='d').sum()
Out[325]: 
            a
d            
2015-01-31  6
2015-02-28  4

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 [326]: pd.Period('2012', freq='A-DEC')
Out[326]: Period('2012', 'A-DEC')

In [327]: pd.Period('2012-1-1', freq='D')
Out[327]: Period('2012-01-01', 'D')

In [328]: pd.Period('2012-1-1 19:00', freq='H')
Out[328]: Period('2012-01-01 19:00', 'H')

In [329]: pd.Period('2012-1-1 19:00', freq='5H')
Out[329]: 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 [330]: p = pd.Period('2012', freq='A-DEC')

In [331]: p + 1
Out[331]: Period('2013', 'A-DEC')

In [332]: p - 3
Out[332]: Period('2009', 'A-DEC')

In [333]: p = pd.Period('2012-01', freq='2M')

In [334]: p + 2
Out[334]: Period('2012-05', '2M')

In [335]: p - 1
Out[335]: Period('2011-11', '2M')

In [336]: p == pd.Period('2012-01', freq='3M')
---------------------------------------------------------------------------
IncompatibleFrequency                     Traceback (most recent call last)
<ipython-input-336-4b67dc0b596c> in <module>
----> 1 p == pd.Period('2012-01', freq='3M')

~/build/pandas-dev/pandas/pandas/_libs/tslibs/period.pyx in pandas._libs.tslibs.period._Period.__richcmp__()

IncompatibleFrequency: Input has different freq=3M from Period(freq=2M)

If Period freq is daily or higher (D, H, T, S, L, U, N), offsets and timedelta-like can be added if the result can have the same freq. Otherwise, ValueError will be raised.

In [337]: p = pd.Period('2014-07-01 09:00', freq='H')

In [338]: p + pd.offsets.Hour(2)
Out[338]: Period('2014-07-01 11:00', 'H')

In [339]: p + datetime.timedelta(minutes=120)
Out[339]: Period('2014-07-01 11:00', 'H')

In [340]: p + np.timedelta64(7200, 's')
Out[340]: Period('2014-07-01 11:00', 'H')
In [1]: p + pd.offsets.Minute(5)
Traceback
   ...
ValueError: Input has different freq from Period(freq=H)

If Period has other frequencies, only the same offsets can be added. Otherwise, ValueError will be raised.

In [341]: p = pd.Period('2014-07', freq='M')

In [342]: p + pd.offsets.MonthEnd(3)
Out[342]: Period('2014-10', 'M')
In [1]: p + pd.offsets.MonthBegin(3)
Traceback
   ...
ValueError: Input has different freq from Period(freq=M)

Taking the difference of Period instances with the same frequency will return the number of frequency units between them:

In [343]: pd.Period('2012', freq='A-DEC') - pd.Period('2002', freq='A-DEC')
Out[343]: <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 [344]: prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')

In [345]: prng
Out[345]: 
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]', freq='M')

The PeriodIndex constructor can also be used directly:

In [346]: pd.PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Out[346]: PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]', freq='M')

Passing multiplied frequency outputs a sequence of Period which has multiplied span.

In [347]: pd.period_range(start='2014-01', freq='3M', periods=4)
Out[347]: PeriodIndex(['2014-01', '2014-04', '2014-07', '2014-10'], dtype='period[3M]', freq='3M')

If start or end are Period objects, they will be used as anchor endpoints for a PeriodIndex with frequency matching that of the PeriodIndex constructor.

In [348]: pd.period_range(start=pd.Period('2017Q1', freq='Q'),
   .....:                 end=pd.Period('2017Q2', freq='Q'), freq='M')
   .....: 
Out[348]: PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'], dtype='period[M]', freq='M')

Just like DatetimeIndex, a PeriodIndex can also be used to index pandas objects:

In [349]: ps = pd.Series(np.random.randn(len(prng)), prng)

In [350]: ps
Out[350]: 
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 [351]: idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')

In [352]: idx
Out[352]: 
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]', freq='H')

In [353]: idx + pd.offsets.Hour(2)
Out[353]: 
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]', freq='H')

In [354]: idx = pd.period_range('2014-07', periods=5, freq='M')

In [355]: idx
Out[355]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]', freq='M')

In [356]: idx + pd.offsets.MonthEnd(3)
Out[356]: PeriodIndex(['2014-10', '2014-11', '2014-12', '2015-01', '2015-02'], dtype='period[M]', freq='M')

PeriodIndex has its own dtype named period, refer to Period Dtypes.

Period Dtypes

New in version 0.19.0.

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 with period[freq] like period[D] or period[M], using frequency strings.

In [357]: pi = pd.period_range('2016-01-01', periods=3, freq='M')

In [358]: pi
Out[358]: PeriodIndex(['2016-01', '2016-02', '2016-03'], dtype='period[M]', freq='M')

In [359]: pi.dtype
Out[359]: period[M]

The period dtype can be used in .astype(...). It allows one to change the freq of a PeriodIndex like .asfreq() and convert a DatetimeIndex to PeriodIndex like to_period():

# change monthly freq to daily freq
In [360]: pi.astype('period[D]')
Out[360]: PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]', freq='D')

# convert to DatetimeIndex
In [361]: pi.astype('datetime64[ns]')
Out[361]: DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='datetime64[ns]', freq='MS')

# convert to PeriodIndex
In [362]: dti = pd.date_range('2011-01-01', freq='M', periods=3)

In [363]: dti
Out[363]: DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], dtype='datetime64[ns]', freq='M')

In [364]: dti.astype('period[M]')
Out[364]: PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]', freq='M')

PeriodIndex Partial String Indexing

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 [365]: ps['2011-01']
Out[365]: -2.9169013294054507

In [366]: ps[datetime.datetime(2011, 12, 25):]
Out[366]: 
2011-12    2.261385
2012-01   -0.329583
Freq: M, dtype: float64

In [367]: ps['10/31/2011':'12/31/2011']
Out[367]: 
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 [368]: ps['2011']
Out[368]: 
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 [369]: dfp = pd.DataFrame(np.random.randn(600, 1),
   .....:                    columns=['A'],
   .....:                    index=pd.period_range('2013-01-01 9:00',
   .....:                                          periods=600,
   .....:                                          freq='T'))
   .....: 

In [370]: dfp
Out[370]: 
                         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 09:05  0.182165
2013-01-01 09:06  0.509697
...                    ...
2013-01-01 18:53  0.313342
2013-01-01 18:54  1.741982
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 [371]: dfp['2013-01-01 10H']
Out[371]: 
                         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 10:05  0.671551
2013-01-01 10:06  0.475667
...                    ...
2013-01-01 10:53 -0.266928
2013-01-01 10:54  0.327742
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 [372]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[372]: 
                         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 10:05  0.671551
2013-01-01 10:06  0.475667
...                    ...
2013-01-01 11:53 -1.219875
2013-01-01 11:54  2.568241
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 asfreq method. Let’s start with the fiscal year 2011, ending in December:

In [373]: p = pd.Period('2011', freq='A-DEC')

In [374]: p
Out[374]: Period('2011', 'A-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 [375]: p.asfreq('M', how='start')
Out[375]: Period('2011-01', 'M')

In [376]: p.asfreq('M', how='end')
Out[376]: Period('2011-12', 'M')

The shorthands ‘s’ and ‘e’ are provided for convenience:

In [377]: p.asfreq('M', 's')
Out[377]: Period('2011-01', 'M')

In [378]: p.asfreq('M', 'e')
Out[378]: 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 [379]: p = pd.Period('2011-12', freq='M')

In [380]: p.asfreq('A-NOV')
Out[380]: Period('2012', 'A-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 A-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 [381]: p = pd.Period('2012Q1', freq='Q-DEC')

In [382]: p.asfreq('D', 's')
Out[382]: Period('2012-01-01', 'D')

In [383]: p.asfreq('D', 'e')
Out[383]: Period('2012-03-31', 'D')

Q-MAR defines fiscal year end in March:

In [384]: p = pd.Period('2011Q4', freq='Q-MAR')

In [385]: p.asfreq('D', 's')
Out[385]: Period('2011-01-01', 'D')

In [386]: p.asfreq('D', 'e')
Out[386]: Period('2011-03-31', 'D')

Converting Between Representations

Timestamped data can be converted to PeriodIndex-ed data using to_period and vice-versa using to_timestamp:

In [387]: rng = pd.date_range('1/1/2012', periods=5, freq='M')

In [388]: ts = pd.Series(np.random.randn(len(rng)), index=rng)

In [389]: ts
Out[389]: 
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: M, dtype: float64

In [390]: ps = ts.to_period()

In [391]: ps
Out[391]: 
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 [392]: ps.to_timestamp()
Out[392]: 
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 [393]: ps.to_timestamp('D', how='s')
Out[393]: 
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 [394]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')

In [395]: ts = pd.Series(np.random.randn(len(prng)), prng)

In [396]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9

In [397]: ts.head()
Out[397]: 
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 [398]: span = pd.period_range('1215-01-01', '1381-01-01', freq='D')

In [399]: span
Out[399]: 
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, freq='D')

To convert from an int64 based YYYYMMDD representation.

In [400]: s = pd.Series([20121231, 20141130, 99991231])

In [401]: s
Out[401]: 
0    20121231
1    20141130
2    99991231
dtype: int64

In [402]: def conv(x):
   .....:     return pd.Period(year=x // 10000, month=x // 100 % 100,
   .....:                      day=x % 100, freq='D')
   .....: 

In [403]: s.apply(conv)
Out[403]: 
0    2012-12-31
1    2014-11-30
2    9999-12-31
dtype: period[D]

In [404]: s.apply(conv)[2]
Out[404]: Period('9999-12-31', 'D')

These can easily be converted to a PeriodIndex:

In [405]: span = pd.PeriodIndex(s.apply(conv))

In [406]: span
Out[406]: PeriodIndex(['2012-12-31', '2014-11-30', '9999-12-31'], dtype='period[D]', freq='D')

Time Zone Handling

pandas provides rich support for working with timestamps in different time zones using the pytz and dateutil libraries or class:datetime.timezone objects from the standard library.

Working with Time Zones

By default, pandas objects are time zone unaware:

In [407]: rng = pd.date_range('3/6/2012 00:00', periods=15, freq='D')

In [408]: rng.tz is None
Out[408]: 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 in date_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 using from 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 [409]: import dateutil

# pytz
In [410]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
   .....:                          tz='Europe/London')
   .....: 

In [411]: rng_pytz.tz
Out[411]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>

# dateutil
In [412]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=3, freq='D')

In [413]: rng_dateutil = rng_dateutil.tz_localize('dateutil/Europe/London')

In [414]: rng_dateutil.tz
Out[414]: tzfile('/usr/share/zoneinfo/Europe/London')

# dateutil - utc special case
In [415]: rng_utc = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
   .....:                         tz=dateutil.tz.tzutc())
   .....: 

In [416]: rng_utc.tz
Out[416]: tzutc()

New in version 0.25.0.

# datetime.timezone
In [417]: rng_utc = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
   .....:                         tz=datetime.timezone.utc)
   .....: 

In [418]: rng_utc.tz
Out[418]: 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 [419]: import pytz

# pytz
In [420]: tz_pytz = pytz.timezone('Europe/London')

In [421]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=3, freq='D')

In [422]: rng_pytz = rng_pytz.tz_localize(tz_pytz)

In [423]: rng_pytz.tz == tz_pytz
Out[423]: True

# dateutil
In [424]: tz_dateutil = dateutil.tz.gettz('Europe/London')

In [425]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
   .....:                              tz=tz_dateutil)
   .....: 

In [426]: rng_dateutil.tz == tz_dateutil
Out[426]: True

To convert a time zone aware pandas object from one time zone to another, you can use the tz_convert method.

In [427]: rng_pytz.tz_convert('US/Eastern')
Out[427]: 
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='D')

Note

When using pytz time zones, DatetimeIndex will construct a different time zone object than a Timestamp for the same time zone input. A DatetimeIndex can 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 Timestamp represents one point in time with a specific UTC offset.

In [428]: dti = pd.date_range('2019-01-01', periods=3, freq='D', tz='US/Pacific')

In [429]: dti.tz
Out[429]: <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>

In [430]: ts = pd.Timestamp('2019-01-01', tz='US/Pacific')

In [431]: ts.tz
Out[431]: <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, tz=pytz.timezone('US/Eastern')). Instead, the datetime needs to be localized using the localize method on the pytz time zone object.

Under the hood, all timestamps are stored in UTC. Values from a time zone aware DatetimeIndex 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 [432]: rng_eastern = rng_utc.tz_convert('US/Eastern')

In [433]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')

In [434]: rng_eastern[2]
Out[434]: Timestamp('2012-03-07 19:00:00-0500', tz='US/Eastern', freq='D')

In [435]: rng_berlin[2]
Out[435]: Timestamp('2012-03-08 01:00:00+0100', tz='Europe/Berlin', freq='D')

In [436]: rng_eastern[2] == rng_berlin[2]
Out[436]: True

Operations between Series in different time zones will yield UTC Series, aligning the data on the UTC timestamps:

In [437]: ts_utc = pd.Series(range(3), pd.date_range('20130101', periods=3, tz='UTC'))

In [438]: eastern = ts_utc.tz_convert('US/Eastern')

In [439]: berlin = ts_utc.tz_convert('Europe/Berlin')

In [440]: result = eastern + berlin

In [441]: result
Out[441]: 
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 [442]: result.index
Out[442]: 
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 [443]: didx = pd.date_range(start='2014-08-01 09:00', freq='H',
   .....:                      periods=3, tz='US/Eastern')
   .....: 

In [444]: didx
Out[444]: 
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 [445]: didx.tz_localize(None)
Out[445]: 
DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00',
               '2014-08-01 11:00:00'],
              dtype='datetime64[ns]', freq='H')

In [446]: didx.tz_convert(None)
Out[446]: 
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 [447]: didx.tz_convert('UTC').tz_localize(None)
Out[447]: 
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
               '2014-08-01 15:00:00'],
              dtype='datetime64[ns]', freq='H')

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 [448]: 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 [2]: rng_hourly.tz_localize('US/Eastern')
AmbiguousTimeError: Cannot infer dst time from Timestamp('2011-11-06 01:00:00'), try using the 'ambiguous' argument

Handle these ambiguous times by specifying the following.

In [449]: rng_hourly.tz_localize('US/Eastern', ambiguous='infer')
Out[449]: 
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 [450]: rng_hourly.tz_localize('US/Eastern', ambiguous='NaT')
Out[450]: 
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 [451]: rng_hourly.tz_localize('US/Eastern', ambiguous=[True, True, False, False])
Out[451]: 
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 [452]: 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 [2]: dti.tz_localize('Europe/Warsaw')
NonExistentTimeError: 2015-03-29 02:30:00

Transform nonexistent times to NaT or shift the times.

In [453]: dti
Out[453]: 
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 [454]: dti.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
Out[454]: 
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='H')

In [455]: dti.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
Out[455]: 
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='H')

In [456]: dti.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta(1, unit='H'))
Out[456]: 
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='H')

In [457]: dti.tz_localize('Europe/Warsaw', nonexistent='NaT')
Out[457]: 
DatetimeIndex(['NaT', '2015-03-29 03:30:00+02:00',
               '2015-03-29 04:30:00+02:00'],
              dtype='datetime64[ns, Europe/Warsaw]', freq='H')

Time Zone Series Operations

A Series with time zone naive values is represented with a dtype of datetime64[ns].

In [458]: s_naive = pd.Series(pd.date_range('20130101', periods=3))

In [459]: s_naive
Out[459]: 
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 [460]: s_aware = pd.Series(pd.date_range('20130101', periods=3, tz='US/Eastern'))

In [461]: s_aware
Out[461]: 
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 [462]: s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[462]: 
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 localize and convert time zone naive timestamps or convert time zone aware timestamps.

# localize and convert a naive time zone
In [463]: s_naive.astype('datetime64[ns, US/Eastern]')
Out[463]: 
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]

# make an aware tz naive
In [464]: s_aware.astype('datetime64[ns]')
Out[464]: 
0   2013-01-01 05:00:00
1   2013-01-02 05:00:00
2   2013-01-03 05:00:00
dtype: datetime64[ns]

# convert to a new time zone
In [465]: s_aware.astype('datetime64[ns, CET]')
Out[465]: 
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 [466]: s_naive.to_numpy()
Out[466]: 
array(['2013-01-01T00:00:00.000000000', '2013-01-02T00:00:00.000000000',
       '2013-01-03T00:00:00.000000000'], dtype='datetime64[ns]')

In [467]: s_aware.to_numpy()
Out[467]: 
array([Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern', freq='D'),
       Timestamp('2013-01-02 00:00:00-0500', tz='US/Eastern', freq='D'),
       Timestamp('2013-01-03 00:00:00-0500', tz='US/Eastern', freq='D')],
      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 [468]: pd.Series(s_aware.to_numpy())
Out[468]: 
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 the dtype argument:

In [469]: s_aware.to_numpy(dtype='datetime64[ns]')
Out[469]: 
array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000',
       '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
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