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DOC: Fix PEP-8 issues in timedeltas.rst #23905

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DOC: Fix PEP-8 issues in timedeltas.rst #23905

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42 changes: 21 additions & 21 deletions doc/source/timedeltas.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -4,18 +4,12 @@
.. 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 *
pd.options.display.max_rows = 15

.. _timedeltas.timedeltas:

Expand All @@ -37,6 +31,8 @@ You can construct a ``Timedelta`` scalar through various arguments:

.. ipython:: python

import datetime

# strings
pd.Timedelta('1 days')
pd.Timedelta('1 days 00:00:00')
Expand Down Expand Up @@ -74,13 +70,14 @@ You can construct a ``Timedelta`` scalar through various arguments:

.. ipython:: python

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

to_timedelta
~~~~~~~~~~~~
Expand Down Expand Up @@ -135,8 +132,8 @@ 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))
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
Expand All @@ -145,8 +142,8 @@ subtraction operations on ``datetime64[ns]`` Series, or ``Timestamps``.
s - s.max()
s - datetime.datetime(2011, 1, 1, 3, 5)
s + datetime.timedelta(minutes=5)
s + Minute(5)
s + Minute(5) + Milli(5)
s + pd.offsets.Minute(5)
s + pd.offsets.Minute(5) + pd.offsets.Milli(5)

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

Expand Down Expand Up @@ -184,7 +181,7 @@ Operands can also appear in a reversed order (a singular object operated with a
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 = pd.DataFrame({'A': A, 'B': B})
df

df.min()
Expand Down Expand Up @@ -232,7 +229,8 @@ Numeric reduction operation for ``timedelta64[ns]`` will return ``Timedelta`` ob

.. ipython:: python

y2 = pd.Series(pd.to_timedelta(['-1 days +00:00:05', 'nat', '-1 days +00:00:05', '1 days']))
y2 = pd.Series(pd.to_timedelta(['-1 days +00:00:05', 'nat',
'-1 days +00:00:05', '1 days']))
y2
y2.mean()
y2.median()
Expand All @@ -250,8 +248,10 @@ Note that division by the NumPy scalar is true division, while astyping is equiv

.. ipython:: python

td = pd.Series(pd.date_range('20130101', periods=4)) - \
pd.Series(pd.date_range('20121201', periods=4))
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
Expand Down Expand Up @@ -360,8 +360,8 @@ or ``np.timedelta64`` objects. Passing ``np.nan/pd.NaT/nat`` will represent miss

.. ipython:: python

pd.TimedeltaIndex(['1 days', '1 days, 00:00:05',
np.timedelta64(2,'D'), datetime.timedelta(days=2,seconds=2)])
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:
Expand Down Expand Up @@ -458,7 +458,7 @@ Similarly to frequency conversion on a ``Series`` above, you can convert these i

.. ipython:: python

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

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