.. currentmodule:: pandas
.. 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 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.
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')
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:
.. ipython:: python pd.to_timedelta(np.arange(5), unit='s') pd.to_timedelta(np.arange(5), unit='d')
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
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)
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()
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])
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
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
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()
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')
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']
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()
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]
Similar to :ref:`timeseries resampling <timeseries.resampling>`, we can resample with a TimedeltaIndex.
.. ipython:: python
s.resample('D').mean()