.. currentmodule:: pandas
.. _timedeltas:

.. ipython:: python
   :suppress:

   import datetime
   import numpy as np
   import pandas as pd
   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
   from pandas.tseries.offsets import *

.. _timedeltas.timedeltas:

***********
Time Deltas
***********

.. note::

   Starting in v0.15.0, we introduce a new scalar type ``Timedelta``, which is a subclass of ``datetime.timedelta``, and behaves in a similar manner,
   but allows compatibility with ``np.timedelta64`` types as well as a host of custom representation, parsing, and attributes.

Timedeltas are differences in times, expressed in difference units, e.g. days, hours, minutes, seconds.
They can be both positive and negative.

Parsing
-------

You can construct a ``Timedelta`` scalar through various arguments:

.. ipython:: python

   # strings
   pd.Timedelta('1 days')
   pd.Timedelta('1 days 00:00:00')
   pd.Timedelta('1 days 2 hours')
   pd.Timedelta('-1 days 2 min 3us')

   # like datetime.timedelta
   # note: these MUST be specified as keyword arguments
   pd.Timedelta(days=1, seconds=1)

   # integers with a unit
   pd.Timedelta(1, unit='d')

   # from a datetime.timedelta/np.timedelta64
   pd.Timedelta(datetime.timedelta(days=1, seconds=1))
   pd.Timedelta(np.timedelta64(1, 'ms'))

   # negative Timedeltas have this string repr
   # to be more consistent with datetime.timedelta conventions
   pd.Timedelta('-1us')

   # a NaT
   pd.Timedelta('nan')
   pd.Timedelta('nat')

:ref:`DateOffsets<timeseries.offsets>` (``Day, Hour, Minute, Second, Milli, Micro, Nano``) can also be used in construction.

.. ipython:: python

   pd.Timedelta(Second(2))

Further, operations among the scalars yield another scalar ``Timedelta``.

.. ipython:: python

   pd.Timedelta(Day(2)) + pd.Timedelta(Second(2)) + pd.Timedelta('00:00:00.000123')

to_timedelta
~~~~~~~~~~~~

.. warning::

       Prior to 0.15.0 ``pd.to_timedelta`` would return a ``Series`` for list-like/Series input, and a ``np.timedelta64`` for scalar input.
       It will now return a ``TimedeltaIndex`` for list-like input, ``Series`` for Series input, and ``Timedelta`` for scalar input.

       The arguments to ``pd.to_timedelta`` are now ``(arg, unit='ns', box=True)``, previously were ``(arg, box=True, unit='ns')`` as these are more logical.

Using the top-level ``pd.to_timedelta``, you can convert a scalar, array, list, or Series from a recognized timedelta format / value into a ``Timedelta`` type.
It will construct Series if the input is a Series, a scalar if the input is scalar-like, otherwise will output a ``TimedeltaIndex``.

You can parse a single string to a Timedelta:

.. ipython:: python

   pd.to_timedelta('1 days 06:05:01.00003')
   pd.to_timedelta('15.5us')

or a list/array of strings:

.. ipython:: python

   pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])

The ``unit`` keyword argument specifies the unit of the Timedelta:

.. ipython:: python

   pd.to_timedelta(np.arange(5), unit='s')
   pd.to_timedelta(np.arange(5), unit='d')

.. _timedeltas.limitations:

Timedelta limitations
~~~~~~~~~~~~~~~~~~~~~

Pandas represents ``Timedeltas`` in nanosecond resolution using
64 bit integers. As such, the 64 bit integer limits determine
the ``Timedelta`` limits.

.. ipython:: python

   pd.Timedelta.min
   pd.Timedelta.max

.. _timedeltas.operations:

Operations
----------

You can operate on Series/DataFrames and construct ``timedelta64[ns]`` Series through
subtraction operations on ``datetime64[ns]`` Series, or ``Timestamps``.

.. ipython:: python

   s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
   td = pd.Series([ pd.Timedelta(days=i) for i in range(3) ])
   df = pd.DataFrame(dict(A = s, B = td))
   df
   df['C'] = df['A'] + df['B']
   df
   df.dtypes

   s - s.max()
   s - datetime.datetime(2011, 1, 1, 3, 5)
   s + datetime.timedelta(minutes=5)
   s + Minute(5)
   s + Minute(5) + Milli(5)

Operations with scalars from a ``timedelta64[ns]`` series:

.. ipython:: python

   y = s - s[0]
   y

Series of timedeltas with ``NaT`` values are supported:

.. ipython:: python

   y = s - s.shift()
   y

Elements can be set to ``NaT`` using ``np.nan`` analogously to datetimes:

.. ipython:: python

   y[1] = np.nan
   y

Operands can also appear in a reversed order (a singular object operated with a Series):

.. ipython:: python

   s.max() - s
   datetime.datetime(2011, 1, 1, 3, 5) - s
   datetime.timedelta(minutes=5) + s

``min, max`` and the corresponding ``idxmin, idxmax`` operations are supported on frames:

.. ipython:: python

   A = s - pd.Timestamp('20120101') - pd.Timedelta('00:05:05')
   B = s - pd.Series(pd.date_range('2012-1-2', periods=3, freq='D'))

   df = pd.DataFrame(dict(A=A, B=B))
   df

   df.min()
   df.min(axis=1)

   df.idxmin()
   df.idxmax()

``min, max, idxmin, idxmax`` operations are supported on Series as well. A scalar result will be a ``Timedelta``.

.. ipython:: python

   df.min().max()
   df.min(axis=1).min()

   df.min().idxmax()
   df.min(axis=1).idxmin()

You can fillna on timedeltas. Integers will be interpreted as seconds. You can
pass a timedelta to get a particular value.

.. ipython:: python

   y.fillna(0)
   y.fillna(10)
   y.fillna(pd.Timedelta('-1 days, 00:00:05'))

You can also negate, multiply and use ``abs`` on ``Timedeltas``:

.. ipython:: python

   td1 = pd.Timedelta('-1 days 2 hours 3 seconds')
   td1
   -1 * td1
   - td1
   abs(td1)

.. _timedeltas.timedeltas_reductions:

Reductions
----------

Numeric reduction operation for ``timedelta64[ns]`` will return ``Timedelta`` objects. As usual
``NaT`` are skipped during evaluation.

.. ipython:: python

   y2 = pd.Series(pd.to_timedelta(['-1 days +00:00:05', 'nat', '-1 days +00:00:05', '1 days']))
   y2
   y2.mean()
   y2.median()
   y2.quantile(.1)
   y2.sum()

.. _timedeltas.timedeltas_convert:

Frequency Conversion
--------------------

.. versionadded:: 0.13

Timedelta Series, ``TimedeltaIndex``, and ``Timedelta`` scalars can be converted to other 'frequencies' by dividing by another timedelta,
or by astyping to a specific timedelta type. These operations yield Series and propagate ``NaT`` -> ``nan``.
Note that division by the numpy scalar is true division, while astyping is equivalent of floor division.

.. ipython:: python

   td = pd.Series(pd.date_range('20130101', periods=4)) - \
        pd.Series(pd.date_range('20121201', periods=4))
   td[2] += datetime.timedelta(minutes=5, seconds=3)
   td[3] = np.nan
   td

   # to days
   td / np.timedelta64(1, 'D')
   td.astype('timedelta64[D]')

   # to seconds
   td / np.timedelta64(1, 's')
   td.astype('timedelta64[s]')

   # to months (these are constant months)
   td / np.timedelta64(1, 'M')

Dividing or multiplying a ``timedelta64[ns]`` Series by an integer or integer Series
yields another ``timedelta64[ns]`` dtypes Series.

.. ipython:: python

   td * -1
   td * pd.Series([1, 2, 3, 4])

Attributes
----------

You can access various components of the ``Timedelta`` or ``TimedeltaIndex`` directly using the attributes ``days,seconds,microseconds,nanoseconds``. These are identical to the values returned by ``datetime.timedelta``, in that, for example, the ``.seconds`` attribute represents the number of seconds >= 0 and < 1 day. These are signed according to whether the ``Timedelta`` is signed.

These operations can also be directly accessed via the ``.dt`` property of the ``Series`` as well.

.. note::

   Note that the attributes are NOT the displayed values of the ``Timedelta``. Use ``.components`` to retrieve the displayed values.

For a ``Series``:

.. ipython:: python

   td.dt.days
   td.dt.seconds

You can access the value of the fields for a scalar ``Timedelta`` directly.

.. ipython:: python

   tds = pd.Timedelta('31 days 5 min 3 sec')
   tds.days
   tds.seconds
   (-tds).seconds

You can use the ``.components`` property to access a reduced form of the timedelta. This returns a ``DataFrame`` indexed
similarly to the ``Series``. These are the *displayed* values of the ``Timedelta``.

.. ipython:: python

   td.dt.components
   td.dt.components.seconds

.. _timedeltas.isoformat:

You can convert a ``Timedelta`` to an `ISO 8601 Duration`_ string with the
``.isoformat`` method

.. versionadded:: 0.20.0

.. ipython:: python

    pd.Timedelta(days=6, minutes=50, seconds=3,
                 milliseconds=10, microseconds=10,
                 nanoseconds=12).isoformat()

.. _ISO 8601 Duration: https://en.wikipedia.org/wiki/ISO_8601#Durations

.. _timedeltas.index:

TimedeltaIndex
--------------

.. versionadded:: 0.15.0

To generate an index with time delta, you can use either the ``TimedeltaIndex`` or
the ``timedelta_range`` constructor.

Using ``TimedeltaIndex`` you can pass string-like, ``Timedelta``, ``timedelta``,
or ``np.timedelta64`` objects. Passing ``np.nan/pd.NaT/nat`` will represent missing values.

.. ipython:: python

   pd.TimedeltaIndex(['1 days', '1 days, 00:00:05',
                     np.timedelta64(2,'D'), datetime.timedelta(days=2,seconds=2)])

Similarly to ``date_range``, you can construct regular ranges of a ``TimedeltaIndex``:

.. ipython:: python

   pd.timedelta_range(start='1 days', periods=5, freq='D')
   pd.timedelta_range(start='1 days', end='2 days', freq='30T')

Using the TimedeltaIndex
~~~~~~~~~~~~~~~~~~~~~~~~

Similarly to other of the datetime-like indices, ``DatetimeIndex`` and ``PeriodIndex``, you can use
``TimedeltaIndex`` as the index of pandas objects.

.. ipython:: python

   s = pd.Series(np.arange(100),
                 index=pd.timedelta_range('1 days', periods=100, freq='h'))
   s

Selections work similarly, with coercion on string-likes and slices:

.. ipython:: python

   s['1 day':'2 day']
   s['1 day 01:00:00']
   s[pd.Timedelta('1 day 1h')]

Furthermore you can use partial string selection and the range will be inferred:

.. ipython:: python

   s['1 day':'1 day 5 hours']

Operations
~~~~~~~~~~

Finally, the combination of ``TimedeltaIndex`` with ``DatetimeIndex`` allow certain combination operations that are NaT preserving:

.. ipython:: python

   tdi = pd.TimedeltaIndex(['1 days', pd.NaT, '2 days'])
   tdi.tolist()
   dti = pd.date_range('20130101', periods=3)
   dti.tolist()
   (dti + tdi).tolist()
   (dti - tdi).tolist()

Conversions
~~~~~~~~~~~

Similarly to frequency conversion on a ``Series`` above, you can convert these indices to yield another Index.

.. ipython:: python

   tdi / np.timedelta64(1,'s')
   tdi.astype('timedelta64[s]')

Scalars type ops work as well. These can potentially return a *different* type of index.

.. ipython:: python

   # adding or timedelta and date -> datelike
   tdi + pd.Timestamp('20130101')

   # subtraction of a date and a timedelta -> datelike
   # note that trying to subtract a date from a Timedelta will raise an exception
   (pd.Timestamp('20130101') - tdi).tolist()

   # timedelta + timedelta -> timedelta
   tdi + pd.Timedelta('10 days')

   # division can result in a Timedelta if the divisor is an integer
   tdi / 2

   # or a Float64Index if the divisor is a Timedelta
   tdi / tdi[0]

.. _timedeltas.resampling:

Resampling
----------

Similar to :ref:`timeseries resampling <timeseries.resampling>`, we can resample with a ``TimedeltaIndex``.

.. ipython:: python

   s.resample('D').mean()
