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.. _timedeltas:
{{ header }}
.. _timedeltas.timedeltas:
***********
Time deltas
***********
Timedeltas are differences in times, expressed in difference units, e.g. days, hours, minutes,
seconds. They can be both positive and negative.
``Timedelta`` 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.
Parsing
-------
You can construct a ``Timedelta`` scalar through various arguments, including `ISO 8601 Duration`_ strings.
.. ipython:: python
import datetime
# 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")
# ISO 8601 Duration strings
pd.Timedelta("P0DT0H1M0S")
pd.Timedelta("P0DT0H0M0.000000123S")
:ref:`DateOffsets<timeseries.offsets>` (``Day, Hour, Minute, Second, Milli, Micro, Nano``) can also be used in construction.
.. ipython:: python
pd.Timedelta(pd.offsets.Second(2))
Further, operations among the scalars yield another scalar ``Timedelta``.
.. ipython:: python
pd.Timedelta(pd.offsets.Day(2)) + pd.Timedelta(pd.offsets.Second(2)) + pd.Timedelta(
"00:00:00.000123"
)
to_timedelta
~~~~~~~~~~~~
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 it 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 if the input
is numeric:
.. ipython:: python
pd.to_timedelta(np.arange(5), unit="s")
pd.to_timedelta(np.arange(5), unit="d")
.. warning::
If a string or array of strings is passed as an input then the ``unit`` keyword
argument will be ignored. If a string without units is passed then the default
unit of nanoseconds is assumed.
.. _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({"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 + pd.offsets.Minute(5)
s + pd.offsets.Minute(5) + pd.offsets.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({"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, passing a timedelta to get a particular value.
.. ipython:: python
y.fillna(pd.Timedelta(0))
y.fillna(pd.Timedelta(10, unit="s"))
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(0.1)
y2.sum()
.. _timedeltas.timedeltas_convert:
Frequency conversion
--------------------
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
december = pd.Series(pd.date_range("20121201", periods=4))
january = pd.Series(pd.date_range("20130101", periods=4))
td = january - december
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])
Rounded division (floor-division) of a ``timedelta64[ns]`` Series by a scalar
``Timedelta`` gives a series of integers.
.. ipython:: python
td // pd.Timedelta(days=3, hours=4)
pd.Timedelta(days=3, hours=4) // td
.. _timedeltas.mod_divmod:
The mod (%) and divmod operations are defined for ``Timedelta`` when operating with another timedelta-like or with a numeric argument.
.. ipython:: python
pd.Timedelta(hours=37) % datetime.timedelta(hours=2)
# divmod against a timedelta-like returns a pair (int, Timedelta)
divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11))
# divmod against a numeric returns a pair (Timedelta, Timedelta)
divmod(pd.Timedelta(hours=25), 86400000000000)
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
.. 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
--------------
To generate an index with time delta, you can use either the :class:`TimedeltaIndex` or
the :func:`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),
]
)
The string 'infer' can be passed in order to set the frequency of the index as the
inferred frequency upon creation:
.. ipython:: python
pd.TimedeltaIndex(["0 days", "10 days", "20 days"], freq="infer")
Generating ranges of time deltas
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Similar to :func:`date_range`, you can construct regular ranges of a ``TimedeltaIndex``
using :func:`timedelta_range`. The default frequency for ``timedelta_range`` is
calendar day:
.. ipython:: python
pd.timedelta_range(start="1 days", periods=5)
Various combinations of ``start``, ``end``, and ``periods`` can be used with
``timedelta_range``:
.. ipython:: python
pd.timedelta_range(start="1 days", end="5 days")
pd.timedelta_range(end="10 days", periods=4)
The ``freq`` parameter can passed a variety of :ref:`frequency aliases <timeseries.offset_aliases>`:
.. ipython:: python
pd.timedelta_range(start="1 days", end="2 days", freq="30T")
pd.timedelta_range(start="1 days", periods=5, freq="2D5H")
Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
timedeltas from ``start`` to ``end`` inclusively, with ``periods`` number of elements
in the resulting ``TimedeltaIndex``:
.. ipython:: python
pd.timedelta_range("0 days", "4 days", periods=5)
pd.timedelta_range("0 days", "4 days", periods=10)
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.to_list()
dti = pd.date_range("20130101", periods=3)
dti.to_list()
(dti + tdi).to_list()
(dti - tdi).to_list()
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).to_list()
# 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()
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