.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   from datetime import datetime, timedelta, time
   import numpy as np
   import pandas as pd
   from pandas import offsets
   np.random.seed(123456)
   randn = np.random.randn
   randint = np.random.randint
   np.set_printoptions(precision=4, suppress=True)
   pd.options.display.max_rows=15
   import dateutil
   import pytz
   from dateutil.relativedelta import relativedelta

Time Series / Date functionality

pandas has proven very successful as a tool for working with time series data, especially in the financial data analysis space. Using the NumPy datetime64 and timedelta64 dtypes, we have 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.

In working with time series data, we will frequently seek to:

generate sequences of fixed-frequency dates and time spans

conform or convert time series to a particular frequency

compute "relative" dates based on various non-standard time increments (e.g. 5 business days before the last business day of the year), or "roll" dates forward or backward

pandas provides a relatively compact and self-contained set of tools for performing the above tasks.

Create a range of dates:

.. ipython:: python

   # 72 hours starting with midnight Jan 1st, 2011
   rng = pd.date_range('1/1/2011', periods=72, freq='H')
   rng[:5]

Index pandas objects with dates:

.. ipython:: python

   ts = pd.Series(np.random.randn(len(rng)), index=rng)
   ts.head()

Change frequency and fill gaps:

.. ipython:: python

   # to 45 minute frequency and forward fill
   converted = ts.asfreq('45Min', method='pad')
   converted.head()

Resample:

.. ipython:: python

   # Daily means
   ts.resample('D').mean()

Overview

Following table shows the type of time-related classes pandas can handle and how to create them.

Class	Remarks	How to create
`Timestamp`	Represents a single time stamp	`to_datetime`, `Timestamp`
`DatetimeIndex`	Index of `Timestamp`	`to_datetime`, `date_range`, `DatetimeIndex`
`Period`	Represents a single time span	`Period`
`PeriodIndex`	Index of `Period`	`period_range`, `PeriodIndex`

Time Stamps vs. Time Spans

Time-stamped data is the most basic type of timeseries data that associates values with points in time. For pandas objects it means using the points in time.

.. ipython:: python

   pd.Timestamp(datetime(2012, 5, 1))
   pd.Timestamp('2012-05-01')
   pd.Timestamp(2012, 5, 1)

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:

.. ipython:: python

   pd.Period('2011-01')

   pd.Period('2012-05', freq='D')

Timestamp and Period can be the index. Lists of Timestamp and Period are automatically coerced to DatetimeIndex and PeriodIndex respectively.

.. ipython:: python

   dates = [pd.Timestamp('2012-05-01'), pd.Timestamp('2012-05-02'), pd.Timestamp('2012-05-03')]
   ts = pd.Series(np.random.randn(3), dates)

   type(ts.index)
   ts.index

   ts

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

   ts = pd.Series(np.random.randn(3), periods)

   type(ts.index)
   ts.index

   ts

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:

.. ipython:: python

    pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None]))

    pd.to_datetime(['2005/11/23', '2010.12.31'])

If you use dates which start with the day first (i.e. European style), you can pass the dayfirst flag:

.. ipython:: python

    pd.to_datetime(['04-01-2012 10:00'], dayfirst=True)

    pd.to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)

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.

Note

Specifying a format argument will potentially speed up the conversion considerably and on versions later then 0.13.0 explicitly specifying a format string of '%Y%m%d' takes a faster path still.

If you pass a single string to to_datetime, it returns single Timestamp. Also, Timestamp can accept the string input. Note that Timestamp doesn't accept string parsing option like dayfirst or format, use to_datetime if these are required.

.. ipython:: python

    pd.to_datetime('2010/11/12')

    pd.Timestamp('2010/11/12')

.. versionadded:: 0.18.1

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

.. ipython:: python

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

You can pass only the columns that you need to assemble.

.. ipython:: python

   pd.to_datetime(df[['year', 'month', 'day']])

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

Note

In version 0.17.0, the default for to_datetime is now errors='raise', rather than errors='ignore'. This means that invalid parsing will raise rather that return the original input as in previous versions.

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

Raise when unparseable, this is the default

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

Return the original input when unparseable

In [4]: pd.to_datetime(['2009/07/31', 'asd'], errors='ignore')
Out[4]: array(['2009/07/31', 'asd'], dtype=object)

Return NaT for input when unparseable

In [6]: pd.to_datetime(['2009/07/31', 'asd'], errors='coerce')
Out[6]: DatetimeIndex(['2009-07-31', 'NaT'], dtype='datetime64[ns]', freq=None)

Epoch Timestamps

It's also possible to convert integer or float epoch times. The default unit for these is nanoseconds (since these are how Timestamp s are stored). However, often epochs are stored in another unit which can be specified. These are computed from the starting point specified by the :ref:`Origin Parameter <timeseries.origin>`.

Typical epoch stored units

.. ipython:: python

   pd.to_datetime([1349720105, 1349806505, 1349892905,
                   1349979305, 1350065705], unit='s')

   pd.to_datetime([1349720105100, 1349720105200, 1349720105300,
                   1349720105400, 1349720105500 ], unit='ms')

Note

Epoch times will be rounded to the nearest nanosecond.

Warning

Conversion of float epoch times can lead to inaccurate and unexpected results. :ref:`Python floats <python:tut-fp-issues>` 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).

.. ipython:: python

   pd.to_datetime([1490195805.433, 1490195805.433502912], unit='s')
   pd.to_datetime(1490195805433502912, unit='ns')

From Timestamps to Epoch

To invert the operation from above, namely, to convert from a Timestamp to a 'unix' epoch:

.. ipython:: python

   stamps = pd.date_range('2012-10-08 18:15:05', periods=4, freq='D')
   stamps

We convert the DatetimeIndex to an int64 array, then divide by the conversion unit.

.. ipython:: python

   stamps.view('int64') // pd.Timedelta(1, unit='s')

Using the Origin Parameter

.. versionadded:: 0.20.0

Using the origin parameter, one can specify an alternative starting point for creation of a DatetimeIndex.

Start with 1960-01-01 as the starting date

.. ipython:: python

   pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))

The default is set at origin='unix', which defaults to 1970-01-01 00:00:00. Commonly called 'unix epoch' or POSIX time.

.. ipython:: python

   pd.to_datetime([1, 2, 3], unit='D')

Generating Ranges of Timestamps

To generate an index with time stamps, you can use either the DatetimeIndex or Index constructor and pass in a list of datetime objects:

.. ipython:: python

   dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]

   # Note the frequency information
   index = pd.DatetimeIndex(dates)
   index

   # Automatically converted to DatetimeIndex
   index = pd.Index(dates)
   index

Practically, 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 pandas functions date_range and bdate_range to create timestamp indexes.

.. ipython:: python

   index = pd.date_range('2000-1-1', periods=1000, freq='M')
   index

   index = pd.bdate_range('2012-1-1', periods=250)
   index

Convenience functions like date_range and bdate_range utilize a variety of frequency aliases. The default frequency for date_range is a calendar day while the default for bdate_range is a business day

.. ipython:: python

   start = datetime(2011, 1, 1)
   end = datetime(2012, 1, 1)

   rng = pd.date_range(start, end)
   rng

   rng = pd.bdate_range(start, end)
   rng

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:

.. ipython:: python

   pd.date_range(start, end, freq='BM')

   pd.date_range(start, end, freq='W')

   pd.bdate_range(end=end, periods=20)

   pd.bdate_range(start=start, periods=20)

The start and end dates are strictly inclusive. So it will not generate any dates outside of those dates if specified.

Timestamp limitations

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

.. ipython:: python

   pd.Timestamp.min
   pd.Timestamp.max

See :ref:`here <timeseries.oob>` for ways to represent data outside these bound.

Indexing

One of the main uses for DatetimeIndex is as an index for pandas objects. The DatetimeIndex class contains many timeseries 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 has all the basic functionality of regular Index objects and a smorgasbord of advanced timeseries-specific methods for easy frequency processing.

.. seealso::
    :ref:`Reindexing methods <basics.reindexing>`

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. So please be careful.

DatetimeIndex can be used like a regular index and offers all of its intelligent functionality like selection, slicing, etc.

.. ipython:: python

   rng = pd.date_range(start, end, freq='BM')
   ts = pd.Series(np.random.randn(len(rng)), index=rng)
   ts.index
   ts[:5].index
   ts[::2].index

Partial String Indexing

You can pass in dates and strings that parse to dates as indexing parameters:

.. ipython:: python

   ts['1/31/2011']

   ts[datetime(2011, 12, 25):]

   ts['10/31/2011':'12/31/2011']

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

.. ipython:: python

   ts['2011']

   ts['2011-6']

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. Here's an example:

.. ipython:: python

   dft = pd.DataFrame(randn(100000,1),
                      columns=['A'],
                      index=pd.date_range('20130101',periods=100000,freq='T'))
   dft
   dft['2013']

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

.. ipython:: python

   dft['2013-1':'2013-2']

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

.. ipython:: python

   dft['2013-1':'2013-2-28']

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

.. ipython:: python

   dft['2013-1':'2013-2-28 00:00:00']

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

.. ipython:: python

   dft['2013-1-15':'2013-1-15 12:30:00']

.. versionadded:: 0.18.0

DatetimeIndex Partial String Indexing also works on DataFrames with a MultiIndex. For example:

.. ipython:: python

   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']]))
   dft2
   dft2.loc['2013-01-05']
   idx = pd.IndexSlice
   dft2 = dft2.swaplevel(0, 1).sort_index()
   dft2.loc[idx[:, '2013-01-05'], :]

Slice vs. exact match

.. versionchanged:: 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 an index. If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match.

For example, let us consider Series object which index has minute resolution.

.. ipython:: python

    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']))
    series_minute.index.resolution

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

.. ipython:: python

    series_minute['2011-12-31 23']

A timestamp string with minute resolution (or more accurate), gives a scalar instead, i.e. it is not casted to a slice.

.. ipython:: python

    series_minute['2011-12-31 23:59']
    series_minute['2011-12-31 23:59:00']

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

.. ipython:: python

    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']))
    series_second.index.resolution
    series_second['2011-12-31 23:59']

If the timestamp string is treated as a slice, it can be used to index DataFrame with [] as well.

.. ipython:: python

    dft_minute = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
                               index=series_minute.index)
    dft_minute['2011-12-31 23']

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 :ref:`Indexing Basics <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 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.

.. ipython:: python

  dft_minute.loc['2011-12-31 23:59']

Note also that DatetimeIndex resolution cannot be less precise than day.

.. ipython:: python

    series_monthly = pd.Series([1, 2, 3],
                              pd.DatetimeIndex(['2011-12',
                                                '2012-01',
                                                '2012-02']))
    series_monthly.index.resolution
    series_monthly['2011-12'] # returns Series

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).

.. ipython:: python

   dft[datetime(2013, 1, 1):datetime(2013,2,28)]

With no defaults.

.. ipython:: python

   dft[datetime(2013, 1, 1, 10, 12, 0):datetime(2013, 2, 28, 10, 12, 0)]

Truncating & Fancy Indexing

A truncate convenience function is provided that is equivalent to slicing:

.. ipython:: python

   ts.truncate(before='10/31/2011', after='12/31/2011')

Even complicated fancy indexing that breaks the DatetimeIndex's frequency regularity will result in a DatetimeIndex (but frequency is lost):

.. ipython:: python

   ts[[0, 2, 6]].index

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)
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, see the :ref:`docs <basics.dt_accessors>`

DateOffset objects

In the preceding examples, we created DatetimeIndex objects at various frequencies by passing in :ref:`frequency strings <timeseries.offset_aliases>` like 'M', 'W', and 'BM to the freq keyword. Under the hood, these frequency strings are being translated into an instance of pandas DateOffset, which represents a regular frequency increment. Specific offset logic like "month", "business day", or "one hour" is represented in its various subclasses.

Class name	Description
DateOffset	Generic offset class, defaults to 1 calendar day
BDay	business day (weekday)
CDay	custom business day (experimental)
Week	one week, optionally anchored on a day of the week
WeekOfMonth	the x-th day of the y-th week of each month
LastWeekOfMonth	the x-th day of the last week of each month
MonthEnd	calendar month end
MonthBegin	calendar month begin
BMonthEnd	business month end
BMonthBegin	business month begin
CBMonthEnd	custom business month end
CBMonthBegin	custom business month begin
SemiMonthEnd	15th (or other day_of_month) and calendar month end
SemiMonthBegin	15th (or other day_of_month) and calendar month begin
QuarterEnd	calendar quarter end
QuarterBegin	calendar quarter begin
BQuarterEnd	business quarter end
BQuarterBegin	business quarter begin
FY5253Quarter	retail (aka 52-53 week) quarter
YearEnd	calendar year end
YearBegin	calendar year begin
BYearEnd	business year end
BYearBegin	business year begin
FY5253	retail (aka 52-53 week) year
BusinessHour	business hour
CustomBusinessHour	custom business hour
Hour	one hour
Minute	one minute
Second	one second
Milli	one millisecond
Micro	one microsecond
Nano	one nanosecond

The basic DateOffset takes the same arguments as dateutil.relativedelta, which works like:

.. ipython:: python

   d = datetime(2008, 8, 18, 9, 0)
   d + relativedelta(months=4, days=5)

We could have done the same thing with DateOffset:

.. ipython:: python

   from pandas.tseries.offsets import *
   d + DateOffset(months=4, days=5)

The key features of a DateOffset object are:

it can be added / subtracted to/from a datetime object to obtain a shifted date
it can be multiplied by an integer (positive or negative) so that the increment will be applied multiple times
it has rollforward and rollback methods for moving a date forward or backward to the next or previous "offset date"

Subclasses of DateOffset define the apply function which dictates custom date increment logic, such as adding business days:

class BDay(DateOffset):
    """DateOffset increments between business days"""
    def apply(self, other):
        ...

.. ipython:: python

   d - 5 * BDay()
   d + BMonthEnd()

The rollforward and rollback methods do exactly what you would expect:

.. ipython:: python

   d
   offset = BMonthEnd()
   offset.rollforward(d)
   offset.rollback(d)

It's definitely worth exploring the pandas.tseries.offsets module and the various docstrings for the classes.

These operations (apply, rollforward and rollback) preserves time (hour, minute, etc) information by default. To reset time, use normalize=True keyword when creating the offset instance. If normalize=True, result is normalized after the function is applied.

.. ipython:: python

   day = Day()
   day.apply(pd.Timestamp('2014-01-01 09:00'))

   day = Day(normalize=True)
   day.apply(pd.Timestamp('2014-01-01 09:00'))

   hour = Hour()
   hour.apply(pd.Timestamp('2014-01-01 22:00'))

   hour = Hour(normalize=True)
   hour.apply(pd.Timestamp('2014-01-01 22:00'))
   hour.apply(pd.Timestamp('2014-01-01 23:00'))

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:

.. ipython:: python

   d
   d + Week()
   d + Week(weekday=4)
   (d + Week(weekday=4)).weekday()

   d - Week()

normalize option will be effective for addition and subtraction.

.. ipython:: python

   d + Week(normalize=True)
   d - Week(normalize=True)

Another example is parameterizing YearEnd with the specific ending month:

.. ipython:: python

   d + YearEnd()
   d + YearEnd(month=6)

Using offsets with `Series` / `DatetimeIndex`

Offsets can be used with either a Series or DatetimeIndex to apply the offset to each element.

.. ipython:: python

   rng = pd.date_range('2012-01-01', '2012-01-03')
   s = pd.Series(rng)
   rng
   rng + DateOffset(months=2)
   s + DateOffset(months=2)
   s - DateOffset(months=2)

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 :ref:`Timedelta section<timedeltas.operations>` for more examples.

.. ipython:: python

   s - Day(2)
   td = s - pd.Series(pd.date_range('2011-12-29', '2011-12-31'))
   td
   td + Minute(15)

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

.. ipython:: python
   :okwarning:

   rng + BQuarterEnd()

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.

.. ipython:: python

    from pandas.tseries.offsets import CustomBusinessDay
    weekmask_egypt = 'Sun Mon Tue Wed Thu'

    # They also observe International Workers' Day so let's
    # add that for a couple of years

    holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]
    bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
    dt = datetime(2013, 4, 30)
    dt + 2 * bday_egypt

Let's map to the weekday names

.. ipython:: python

    dts = pd.date_range(dt, periods=5, freq=bday_egypt)

    pd.Series(dts.weekday, dts).map(pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split()))

Holiday calendars can be used to provide the list of holidays. See the :ref:`holiday calendar<timeseries.holiday>` section for more information.

.. ipython:: python

    from pandas.tseries.holiday import USFederalHolidayCalendar

    bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())

    # Friday before MLK Day
    dt = datetime(2014, 1, 17)

    # Tuesday after MLK Day (Monday is skipped because it's a holiday)
    dt + bday_us

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

.. ipython:: python

    from pandas.tseries.offsets import CustomBusinessMonthBegin
    bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())

    # Skip new years
    dt = datetime(2013, 12, 17)
    dt + bmth_us

    # Define date index with custom offset
    pd.DatetimeIndex(start='20100101',end='20120101',freq=bmth_us)

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. 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, remaining is added to the next business day.

.. ipython:: python

    bh = BusinessHour()
    bh

    # 2014-08-01 is Friday
    pd.Timestamp('2014-08-01 10:00').weekday()
    pd.Timestamp('2014-08-01 10:00') + bh

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

    # If the results is on the end time, move to the next business day
    pd.Timestamp('2014-08-01 16:00') + bh

    # Remainings are added to the next day
    pd.Timestamp('2014-08-01 16:30') + bh

    # Adding 2 business hours
    pd.Timestamp('2014-08-01 10:00') + BusinessHour(2)

    # Subtracting 3 business hours
    pd.Timestamp('2014-08-01 10:00') + BusinessHour(-3)

Also, you can specify start and end time by keywords. Argument must be str which has hour:minute representation or datetime.time instance. Specifying seconds, microseconds and nanoseconds as business hour results in ValueError.

.. ipython:: python

    bh = BusinessHour(start='11:00', end=time(20, 0))
    bh

    pd.Timestamp('2014-08-01 13:00') + bh
    pd.Timestamp('2014-08-01 09:00') + bh
    pd.Timestamp('2014-08-01 18:00') + bh

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.

.. ipython:: python

    bh = BusinessHour(start='17:00', end='09:00')
    bh

    pd.Timestamp('2014-08-01 17:00') + bh
    pd.Timestamp('2014-08-01 23:00') + bh

    # Although 2014-08-02 is Satuaday,
    # it is valid because it starts from 08-01 (Friday).
    pd.Timestamp('2014-08-02 04:00') + bh

    # Although 2014-08-04 is Monday,
    # it is out of business hours because it starts from 08-03 (Sunday).
    pd.Timestamp('2014-08-04 04:00') + bh

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.

.. ipython:: python

    # This adjusts a Timestamp to business hour edge
    BusinessHour().rollback(pd.Timestamp('2014-08-02 15:00'))
    BusinessHour().rollforward(pd.Timestamp('2014-08-02 15:00'))

    # 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'))
    BusinessHour().apply(pd.Timestamp('2014-08-02 15:00'))

    # BusinessDay results (for reference)
    BusinessHour().rollforward(pd.Timestamp('2014-08-02'))

    # It is the same as BusinessDay().apply(pd.Timestamp('2014-08-01'))
    # The result is the same as rollworward because BusinessDay never overlap.
    BusinessHour().apply(pd.Timestamp('2014-08-02'))

BusinessHour regards Saturday and Sunday as holidays. To use arbitrary holidays, you can use CustomBusinessHour offset, see :ref:`Custom Business Hour <timeseries.custombusinesshour>`:

Custom Business Hour

.. versionadded:: 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.

.. ipython:: python

    from pandas.tseries.holiday import USFederalHolidayCalendar
    bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())
    # Friday before MLK Day
    dt = datetime(2014, 1, 17, 15)

    dt + bhour_us

    # Tuesday after MLK Day (Monday is skipped because it's a holiday)
    dt + bhour_us * 2

You can use keyword arguments suported by either BusinessHour and CustomBusinessDay.

.. ipython:: python

    bhour_mon = CustomBusinessHour(start='10:00', weekmask='Tue Wed Thu Fri')

    # Monday is skipped because it's a holiday, business hour starts from 10:00
    dt + bhour_mon * 2

Offset Aliases

A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases (referred to as time rules prior to v0.8.0).

Alias	Description
B	business day frequency
C	custom business day frequency (experimental)
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 endfrequency
QS	quarter start frequency
BQS	business quarter start frequency
A, Y	year end frequency
BA	business year end frequency
AS, YS	year start frequency
BAS	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:

.. ipython:: python

   pd.date_range(start, periods=5, freq='B')

   pd.date_range(start, periods=5, freq=BDay())

You can combine together day and intraday offsets:

.. ipython:: python

   pd.date_range(start, periods=10, freq='2h20min')

   pd.date_range(start, periods=10, freq='1D10U')

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.

.. ipython:: python

   pd.Timestamp('2014-01-02') + MonthBegin(n=1)
   pd.Timestamp('2014-01-02') + MonthEnd(n=1)

   pd.Timestamp('2014-01-02') - MonthBegin(n=1)
   pd.Timestamp('2014-01-02') - MonthEnd(n=1)

   pd.Timestamp('2014-01-02') + MonthBegin(n=4)
   pd.Timestamp('2014-01-02') - MonthBegin(n=4)

If the given date is on an anchor point, it is moved |n| points forwards or backwards.

.. ipython:: python

   pd.Timestamp('2014-01-01') + MonthBegin(n=1)
   pd.Timestamp('2014-01-31') + MonthEnd(n=1)

   pd.Timestamp('2014-01-01') - MonthBegin(n=1)
   pd.Timestamp('2014-01-31') - MonthEnd(n=1)

   pd.Timestamp('2014-01-01') + MonthBegin(n=4)
   pd.Timestamp('2014-01-31') - MonthBegin(n=4)

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.

.. ipython:: python

   pd.Timestamp('2014-01-02') + MonthBegin(n=0)
   pd.Timestamp('2014-01-02') + MonthEnd(n=0)

   pd.Timestamp('2014-01-01') + MonthBegin(n=0)
   pd.Timestamp('2014-01-31') + MonthEnd(n=0)

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. Further, 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:

.. ipython:: python

    from pandas.tseries.holiday import Holiday, USMemorialDay,\
        AbstractHolidayCalendar, nearest_workday, MO
    class ExampleCalendar(AbstractHolidayCalendar):
        rules = [
            USMemorialDay,
            Holiday('July 4th', month=7, day=4, observance=nearest_workday),
            Holiday('Columbus Day', month=10, day=1,
                offset=DateOffset(weekday=MO(2))), #same as 2*Week(weekday=2)
            ]
    cal = ExampleCalendar()
    cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))

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.

.. ipython:: python

    from pandas.tseries.offsets import CDay
    pd.DatetimeIndex(start='7/1/2012', end='7/10/2012',
        freq=CDay(calendar=cal)).to_pydatetime()
    offset = CustomBusinessDay(calendar=cal)
    datetime(2012, 5, 25) + offset
    datetime(2012, 7, 3) + offset
    datetime(2012, 7, 3) + 2 * offset
    datetime(2012, 7, 6) + offset

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

.. ipython:: python

    AbstractHolidayCalendar.start_date
    AbstractHolidayCalendar.end_date

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

.. ipython:: python

    AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)
    AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)
    cal.holidays()

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.

.. ipython:: python

    from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
        USLaborDay
    cal = get_calendar('ExampleCalendar')
    cal.rules
    new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)
    new_cal.rules

Time series-related instance methods

Shifting / lagging

One may want to shift or lag the values in a time series back and forward in time. The method for this is shift, which is available on all of the pandas objects.

.. ipython:: python

   ts = ts[:5]
   ts.shift(1)

The shift method accepts an freq argument which can accept a DateOffset class or other timedelta-like object or also a :ref:`offset alias <timeseries.offset_aliases>`:

.. ipython:: python

   ts.shift(5, freq=offsets.BDay())
   ts.shift(5, freq='BM')

Rather than changing the alignment of the data and the index, DataFrame and Series objects also have a tshift convenience method that changes all the dates in the index by a specified number of offsets:

.. ipython:: python

   ts.tshift(5, freq='D')

Note that with tshift, the leading entry is no longer NaN because the data is not being realigned.

Frequency conversion

The primary function for changing frequencies is the asfreq function. For a DatetimeIndex, this is basically just a thin, but convenient wrapper around reindex which generates a date_range and calls reindex.

.. ipython:: python

   dr = pd.date_range('1/1/2010', periods=3, freq=3 * offsets.BDay())
   ts = pd.Series(randn(3), index=dr)
   ts
   ts.asfreq(BDay())

asfreq provides a further convenience so you can specify an interpolation method for any gaps that may appear after the frequency conversion

.. ipython:: python

   ts.asfreq(BDay(), method='pad')

Filling forward / backward

Related to asfreq and reindex is the fillna function documented in the :ref:`missing data section <missing_data.fillna>`.

Converting to Python datetimes

DatetimeIndex can be converted to an array of Python native datetime.datetime objects using the to_pydatetime method.

Resampling

Warning

The interface to .resample has changed in 0.18.0 to be more groupby-like and hence more flexible. See the :ref:`whatsnew docs <whatsnew_0180.breaking.resample>` 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 :ref:`cookbook examples <cookbook.resample>` for some advanced strategies

Starting in version 0.18.1, the resample() function can be used directly from DataFrameGroupBy objects, see the :ref:`groupby docs <groupby.transform.window_resample>`.

Note

.resample() is similar to using a .rolling() operation with a time-based offset, see a discussion :ref:`here <stats.moments.ts-versus-resampling>`

Basics

.. ipython:: python

   rng = pd.date_range('1/1/2012', periods=100, freq='S')

   ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)

   ts.resample('5Min').sum()

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

The how parameter can be a function name or numpy array function that takes an array and produces aggregated values:

.. ipython:: python

   ts.resample('5Min').mean()

   ts.resample('5Min').ohlc()

   ts.resample('5Min').max()

Any function available via :ref:`dispatching <groupby.dispatch>` can be given to the how parameter by name, including sum, mean, std, sem, max, min, median, first, last, ohlc.

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

.. ipython:: python

   ts.resample('5Min', closed='right').mean()

   ts.resample('5Min', closed='left').mean()

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.

.. ipython:: python

   ts.resample('5Min').mean() # by default label='right'

   ts.resample('5Min', label='left').mean()

   ts.resample('5Min', label='left', loffset='1s').mean()

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 time-stamp 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.

Up Sampling

For upsampling, you can specify a way to upsample and the limit parameter to interpolate over the gaps that are created:

.. ipython:: python

   # from secondly to every 250 milliseconds

   ts[:2].resample('250L').asfreq()

   ts[:2].resample('250L').ffill()

   ts[:2].resample('250L').ffill(limit=2)

Sparse Resampling

Sparse timeseries are 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

.. ipython:: python

    rng = pd.date_range('2014-1-1', periods=100, freq='D') + pd.Timedelta('1s')
    ts = pd.Series(range(100), index=rng)

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

.. ipython:: python

    ts.resample('3T').sum()

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

.. ipython:: python

    from functools import partial
    from pandas.tseries.frequencies import to_offset

    def round(t, freq):
        # round a Timestamp to a specified freq
        freq = to_offset(freq)
        return pd.Timestamp((t.value // freq.delta.value) * freq.delta.value)

    ts.groupby(partial(round, freq='3T')).sum()

Aggregation

Similar to the :ref:`aggregating API <basics.aggregate>`, :ref:`groupby API <groupby.aggregate>`, and the :ref:`window functions API <stats.aggregate>`, a Resampler can be selectively resampled.

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

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 3),
                     index=pd.date_range('1/1/2012', freq='S', periods=1000),
                     columns=['A', 'B', 'C'])
   r = df.resample('3T')
   r.mean()

We can select a specific column or columns using standard getitem.

.. ipython:: python

   r['A'].mean()

   r[['A','B']].mean()

You can pass a list or dict of functions to do aggregation with, outputting a DataFrame:

.. ipython:: python

   r['A'].agg([np.sum, np.mean, np.std])

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:

.. ipython:: python

   r.agg([np.sum, np.mean])

By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:

.. ipython:: python
   :okexcept:

   r.agg({'A' : np.sum,
          'B' : lambda x: np.std(x, ddof=1)})

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

.. ipython:: python

   r.agg({'A' : 'sum', 'B' : 'std'})

Furthermore, you can also specify multiple aggregation functions for each column separately.

.. ipython:: python

   r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })

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.

.. ipython:: python

   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']))
   df
   df.resample('M', on='date').sum()

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

.. ipython:: python

   df.resample('M', level='d').sum()

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".

.. ipython:: python

   pd.Period('2012', freq='A-DEC')

   pd.Period('2012-1-1', freq='D')

   pd.Period('2012-1-1 19:00', freq='H')

   pd.Period('2012-1-1 19:00', freq='5H')

Adding and subtracting integers from periods shifts the period by its own frequency. Arithmetic is not allowed between Period with different freq (span).

.. ipython:: python

   p = pd.Period('2012', freq='A-DEC')
   p + 1
   p - 3
   p = pd.Period('2012-01', freq='2M')
   p + 2
   p - 1
   @okexcept
   p == pd.Period('2012-01', freq='3M')

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.

.. ipython:: python

   p = pd.Period('2014-07-01 09:00', freq='H')
   p + Hour(2)
   p + timedelta(minutes=120)
   p + np.timedelta64(7200, 's')

In [1]: p + Minute(5)
Traceback
   ...
ValueError: Input has different freq from Period(freq=H)

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

.. ipython:: python

   p = pd.Period('2014-07', freq='M')
   p + MonthEnd(3)

In [1]: p + 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:

.. ipython:: python

   pd.Period('2012', freq='A-DEC') - pd.Period('2002', freq='A-DEC')

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:

.. ipython:: python

   prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')
   prng

The PeriodIndex constructor can also be used directly:

.. ipython:: python

   pd.PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')

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

.. ipython:: python

   pd.PeriodIndex(start='2014-01', freq='3M', periods=4)

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

.. ipython:: python

   ps = pd.Series(np.random.randn(len(prng)), prng)
   ps

PeriodIndex supports addition and subtraction with the same rule as Period.

.. ipython:: python

   idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
   idx
   idx + Hour(2)

   idx = pd.period_range('2014-07', periods=5, freq='M')
   idx
   idx + MonthEnd(3)

PeriodIndex has its own dtype named period, refer to :ref:`Period Dtypes <timeseries.period_dtype>`.

Period Dtypes

.. versionadded:: 0.19.0

PeriodIndex has a custom period dtype. This is a pandas extension dtype similar to the :ref:`timezone aware dtype <timeseries.timezone_series>` (datetime64[ns, tz]).

The period dtype holds the freq attribute and is represented with period[freq] like period[D] or period[M], using :ref:`frequency strings <timeseries.offset_aliases>`.

.. ipython:: python

   pi = pd.period_range('2016-01-01', periods=3, freq='M')
   pi
   pi.dtype

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():

.. ipython:: python

   # change monthly freq to daily freq
   pi.astype('period[D]')

   # convert to DatetimeIndex
   pi.astype('datetime64[ns]')

   # convert to PeriodIndex
   dti = pd.date_range('2011-01-01', freq='M', periods=3)
   dti
   dti.astype('period[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 :ref:`DatetimeIndex Partial String Indexing <timeseries.partialindexing>`.

.. ipython:: python

   ps['2011-01']

   ps[datetime(2011, 12, 25):]

   ps['10/31/2011':'12/31/2011']

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

.. ipython:: python

   ps['2011']

   dfp = pd.DataFrame(np.random.randn(600,1),
                      columns=['A'],
                      index=pd.period_range('2013-01-01 9:00', periods=600, freq='T'))
   dfp
   dfp['2013-01-01 10H']

As with DatetimeIndex, the endpoints will be included in the result. The example below slices data starting from 10:00 to 11:59.

.. ipython:: python

   dfp['2013-01-01 10H':'2013-01-01 11H']

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:

.. ipython:: python

   p = pd.Period('2011', freq='A-DEC')
   p

We can convert it to a monthly frequency. Using the how parameter, we can specify whether to return the starting or ending month:

.. ipython:: python

   p.asfreq('M', how='start')

   p.asfreq('M', how='end')

The shorthands 's' and 'e' are provided for convenience:

.. ipython:: python

   p.asfreq('M', 's')
   p.asfreq('M', 'e')

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:

.. ipython:: python

   p = pd.Period('2011-12', freq='M')

   p.asfreq('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:

.. ipython:: python

   p = pd.Period('2012Q1', freq='Q-DEC')

   p.asfreq('D', 's')

   p.asfreq('D', 'e')

Q-MAR defines fiscal year end in March:

.. ipython:: python

   p = pd.Period('2011Q4', freq='Q-MAR')

   p.asfreq('D', 's')

   p.asfreq('D', 'e')

Converting between Representations

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

.. ipython:: python

   rng = pd.date_range('1/1/2012', periods=5, freq='M')

   ts = pd.Series(np.random.randn(len(rng)), index=rng)

   ts

   ps = ts.to_period()

   ps

   ps.to_timestamp()

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

.. ipython:: python

   ps.to_timestamp('D', how='s')

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:

.. ipython:: python

   prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')

   ts = pd.Series(np.random.randn(len(prng)), prng)

   ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9

   ts.head()

Representing out-of-bounds spans

If you have data that is outside of the Timestamp bounds, see :ref:`Timestamp limitations <timeseries.timestamp-limits>`, then you can use a PeriodIndex and/or Series of Periods to do computations.

.. ipython:: python

   span = pd.period_range('1215-01-01', '1381-01-01', freq='D')
   span

To convert from an int64 based YYYYMMDD representation.

.. ipython:: python

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

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

   s.apply(conv)
   s.apply(conv)[2]

These can easily be converted to a PeriodIndex

.. ipython:: python

   span = pd.PeriodIndex(s.apply(conv))
   span

Time Zone Handling

Pandas provides rich support for working with timestamps in different time zones using pytz and dateutil libraries. dateutil support is new in 0.14.1 and currently only supported for fixed offset and tzfile zones. The default library is pytz. Support for dateutil is provided for compatibility with other applications e.g. if you use dateutil in other python packages.

Working with Time Zones

By default, pandas objects are time zone unaware:

.. ipython:: python

   rng = pd.date_range('3/6/2012 00:00', periods=15, freq='D')
   rng.tz is None

To supply the time zone, you can use the tz keyword to date_range and other functions. Dateutil time zone strings are distinguished from pytz time zones by starting with dateutil/.

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 timezones so there isn't a fixed list available. For common zones, the names are the same as pytz.

.. ipython:: python

   # pytz
   rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
                            tz='Europe/London')
   rng_pytz.tz

   # dateutil
   rng_dateutil = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
                                tz='dateutil/Europe/London')
   rng_dateutil.tz

   # dateutil - utc special case
   rng_utc = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
                           tz=dateutil.tz.tzutc())
   rng_utc.tz

Note that the UTC timezone is a special case in dateutil and should be constructed explicitly as an instance of dateutil.tz.tzutc. You can also construct other timezones explicitly first, which gives you more control over which time zone is used:

.. ipython:: python

   # pytz
   tz_pytz = pytz.timezone('Europe/London')
   rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
                            tz=tz_pytz)
   rng_pytz.tz == tz_pytz

   # dateutil
   tz_dateutil = dateutil.tz.gettz('Europe/London')
   rng_dateutil = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
                                tz=tz_dateutil)
   rng_dateutil.tz == tz_dateutil

Timestamps, like Python's datetime.datetime object can be either time zone naive or time zone aware. Naive time series and DatetimeIndex objects can be localized using tz_localize:

.. ipython:: python

   ts = pd.Series(np.random.randn(len(rng)), rng)

   ts_utc = ts.tz_localize('UTC')
   ts_utc

Again, you can explicitly construct the timezone object first. You can use the tz_convert method to convert pandas objects to convert tz-aware data to another time zone:

.. ipython:: python

   ts_utc.tz_convert('US/Eastern')

Warning

Be wary of conversions between libraries. For some zones pytz and dateutil have different definitions of the zone. This is more of a problem for unusual timezones than for 'standard' zones like US/Eastern.

Warning

Be aware that a timezone definition across versions of timezone 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 :ref:`here<io.hdf5-notes>` for how to handle such a situation.

Warning

It is incorrect to pass a timezone directly into the datetime.datetime constructor (e.g., datetime.datetime(2011, 1, 1, tz=timezone('US/Eastern')). Instead, the datetime needs to be localized using the the localize method on the timezone.

Under the hood, all timestamps are stored in UTC. Scalar values from a DatetimeIndex with a time zone will have their fields (day, hour, minute) 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:

.. ipython:: python

   rng_eastern = rng_utc.tz_convert('US/Eastern')
   rng_berlin = rng_utc.tz_convert('Europe/Berlin')

   rng_eastern[5]
   rng_berlin[5]
   rng_eastern[5] == rng_berlin[5]

Like Series, DataFrame, and DatetimeIndex, Timestamp``s can be converted to other time zones using ``tz_convert:

.. ipython:: python

   rng_eastern[5]
   rng_berlin[5]
   rng_eastern[5].tz_convert('Europe/Berlin')

Localization of Timestamp functions just like DatetimeIndex and Series:

.. ipython:: python

   rng[5]
   rng[5].tz_localize('Asia/Shanghai')

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

.. ipython:: python

   eastern = ts_utc.tz_convert('US/Eastern')
   berlin = ts_utc.tz_convert('Europe/Berlin')
   result = eastern + berlin
   result
   result.index

To remove timezone from tz-aware DatetimeIndex, use tz_localize(None) or tz_convert(None). tz_localize(None) will remove timezone holding local time representations. tz_convert(None) will remove timezone after converting to UTC time.

.. ipython:: python

   didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
   didx
   didx.tz_localize(None)
   didx.tz_convert(None)

   # tz_convert(None) is identical with tz_convert('UTC').tz_localize(None)
   didx.tz_convert('UCT').tz_localize(None)

Ambiguous Times when Localizing

In some cases, localize cannot determine the DST and non-DST hours when there are duplicates. This often happens when reading files or database records that simply duplicate the hours. Passing ambiguous='infer' (infer_dst argument in prior releases) into tz_localize will attempt to determine the right offset. Below the top example will fail as it contains ambiguous times and the bottom will infer the right offset.

.. ipython:: python

   rng_hourly = pd.DatetimeIndex(['11/06/2011 00:00', '11/06/2011 01:00',
                                  '11/06/2011 01:00', '11/06/2011 02:00',
                                  '11/06/2011 03:00'])

This will fail as there are ambiguous times

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

Infer the ambiguous times

.. ipython:: python

   rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', ambiguous='infer')
   rng_hourly_eastern.tolist()

In addition to 'infer', there are several other arguments supported. Passing an array-like of bools or 0s/1s where True represents a DST hour and False a non-DST hour, allows for distinguishing more than one DST transition (e.g., if you have multiple records in a database each with their own DST transition). Or passing 'NaT' will fill in transition times with not-a-time values. These methods are available in the DatetimeIndex constructor as well as tz_localize.

.. ipython:: python

   rng_hourly_dst = np.array([1, 1, 0, 0, 0])
   rng_hourly.tz_localize('US/Eastern', ambiguous=rng_hourly_dst).tolist()
   rng_hourly.tz_localize('US/Eastern', ambiguous='NaT').tolist()

   didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
   didx
   didx.tz_localize(None)
   didx.tz_convert(None)

   # tz_convert(None) is identical with tz_convert('UTC').tz_localize(None)
   didx.tz_convert('UCT').tz_localize(None)

TZ aware Dtypes

.. versionadded:: 0.17.0

Series/DatetimeIndex with a timezone naive value are represented with a dtype of datetime64[ns].

.. ipython:: python

   s_naive = pd.Series(pd.date_range('20130101',periods=3))
   s_naive

Series/DatetimeIndex with a timezone aware value are represented with a dtype of datetime64[ns, tz].

.. ipython:: python

   s_aware = pd.Series(pd.date_range('20130101',periods=3,tz='US/Eastern'))
   s_aware

Both of these Series can be manipulated via the .dt accessor, see :ref:`here <basics.dt_accessors>`.

For example, to localize and convert a naive stamp to timezone aware.

.. ipython:: python

   s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

Further more you can .astype(...) timezone aware (and naive). This operation is effectively a localize AND convert on a naive stamp, and a convert on an aware stamp.

.. ipython:: python

   # localize and convert a naive timezone
   s_naive.astype('datetime64[ns, US/Eastern]')

   # make an aware tz naive
   s_aware.astype('datetime64[ns]')

   # convert to a new timezone
   s_aware.astype('datetime64[ns, CET]')

Note

Using the .values accessor on a Series, returns an numpy array of the data. These values are converted to UTC, as numpy does not currently support timezones (even though it is printing in the local timezone!).

.. ipython:: python

   s_naive.values
   s_aware.values

Further note that once converted to a numpy array these would lose the tz tenor.

.. ipython:: python

   pd.Series(s_aware.values)

However, these can be easily converted

.. ipython:: python

   pd.Series(s_aware.values).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

Files

timeseries.rst

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timeseries.rst

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Time Series / Date functionality

Overview

Time Stamps vs. Time Spans

Converting to Timestamps

Invalid Data

Epoch Timestamps

From Timestamps to Epoch

Using the Origin Parameter

Generating Ranges of Timestamps

Timestamp limitations

Indexing

Partial String Indexing

Slice vs. exact match

Exact Indexing

Truncating & Fancy Indexing

Time/Date Components

DateOffset objects

Parametric offsets

Using offsets with Series / DatetimeIndex

Custom Business Days

Business Hour

Custom Business Hour

Offset Aliases

Combining Aliases

Anchored Offsets

Anchored Offset Semantics

Holidays / Holiday Calendars

Time series-related instance methods

Shifting / lagging

Frequency conversion

Filling forward / backward

Converting to Python datetimes

Resampling

Basics

Up Sampling

Sparse Resampling

Aggregation

Time Span Representation

Period

PeriodIndex and period_range

Period Dtypes

PeriodIndex Partial String Indexing

Frequency Conversion and Resampling with PeriodIndex

Converting between Representations

Representing out-of-bounds spans

Time Zone Handling

Working with Time Zones

Ambiguous Times when Localizing

TZ aware Dtypes

Using offsets with `Series` / `DatetimeIndex`