Skip to content

DOC: Fix PEP-8 issues in timeseries.rst #23898

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Dec 2, 2018
Merged

DOC: Fix PEP-8 issues in timeseries.rst #23898

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
5d1e3fd
Fix PEP-8 issues in visualization.rst
FHaase Nov 25, 2018
ed22297
Merge branch 'master' into fix-pep8-timeseries
FHaase Dec 2, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
126 changes: 69 additions & 57 deletions doc/source/timeseries.rst
View file
Open in desktop
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
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
pd.options.display.max_rows = 15

********************************
Time Series / Date functionality
Expand All @@ -32,7 +26,10 @@ Parsing time series information from various sources and formats

.. ipython:: python

dti = pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01'), datetime.datetime(2018, 1, 1)])
import datetime

dti = pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01'),
datetime.datetime(2018, 1, 1)])
dti

Generate sequences of fixed-frequency dates and time spans
Expand Down Expand Up @@ -165,7 +162,9 @@ and :class:`PeriodIndex` respectively.

.. ipython:: python

dates = [pd.Timestamp('2012-05-01'), pd.Timestamp('2012-05-02'), pd.Timestamp('2012-05-03')]
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)
Expand Down Expand Up @@ -329,7 +328,7 @@ which can be specified. These are computed from the starting point specified by
1349979305, 1350065705], unit='s')

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

.. note::

Expand Down Expand Up @@ -402,7 +401,9 @@ To generate an index with timestamps, you can use either the ``DatetimeIndex`` o

.. ipython:: python

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

# Note the frequency information
index = pd.DatetimeIndex(dates)
Expand Down Expand Up @@ -585,9 +586,8 @@ would include matching times on an included date:

.. ipython:: python

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

Expand Down Expand Up @@ -624,10 +624,9 @@ We are stopping on the included end-point as it is part of the index:

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']]))
index=pd.MultiIndex.from_product(
[pd.date_range('20130101', periods=10, freq='12H'),
['a', 'b']]))
dft2
dft2.loc['2013-01-05']
idx = pd.IndexSlice
Expand Down Expand Up @@ -683,7 +682,7 @@ If the timestamp string is treated as a slice, it can be used to index ``DataFra
.. ipython:: python

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


Expand All @@ -695,18 +694,16 @@ If the timestamp string is treated as a slice, it can be used to index ``DataFra

.. ipython:: python

dft_minute.loc['2011-12-31 23:59']
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']))
pd.DatetimeIndex(['2011-12', '2012-01', '2012-02']))
series_monthly.index.resolution
series_monthly['2011-12'] # returns Series
series_monthly['2011-12'] # returns Series


Exact Indexing
Expand All @@ -718,13 +715,14 @@ These ``Timestamp`` and ``datetime`` objects have exact ``hours, minutes,`` and

.. ipython:: python

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

With no defaults.

.. ipython:: python

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


Truncating & Fancy Indexing
Expand Down Expand Up @@ -1045,14 +1043,16 @@ As an interesting example, let's look at Egypt where a Friday-Saturday weekend i

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

Expand All @@ -1062,7 +1062,8 @@ Let's map to the weekday names:

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()))
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.
Expand All @@ -1071,7 +1072,7 @@ Holiday calendars can be used to provide the list of holidays. See the

from pandas.tseries.holiday import USFederalHolidayCalendar

bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())
bday_us = pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar())

# Friday before MLK Day
dt = datetime.datetime(2014, 1, 17)
Expand All @@ -1084,15 +1085,15 @@ in the usual way.

.. ipython:: python

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

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

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

.. note::

Expand Down Expand Up @@ -1231,7 +1232,8 @@ You can use keyword arguments supported by either ``BusinessHour`` and ``CustomB

.. ipython:: python

bhour_mon = pd.offsets.CustomBusinessHour(start='10:00', weekmask='Tue Wed Thu Fri')
bhour_mon = pd.offsets.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
Expand Down Expand Up @@ -1429,11 +1431,14 @@ An example of how holidays and holiday calendars are defined:
USMemorialDay,
Holiday('July 4th', month=7, day=4, observance=nearest_workday),
Holiday('Columbus Day', month=10, day=1,
offset=pd.DateOffset(weekday=MO(2))), #same as 2*Week(weekday=2)
]
offset=pd.DateOffset(weekday=MO(2)))]

cal = ExampleCalendar()
cal.holidays(datetime.datetime(2012, 1, 1), datetime.datetime(2012, 12, 31))

:hint:
**weekday=MO(2)** is same as **2 * Week(weekday=2)**

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,
Expand All @@ -1442,10 +1447,9 @@ 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)
freq=pd.offsets.CDay(calendar=cal)).to_pydatetime()
offset = pd.offsets.CustomBusinessDay(calendar=cal)
datetime.datetime(2012, 5, 25) + offset
datetime.datetime(2012, 7, 3) + offset
datetime.datetime(2012, 7, 3) + 2 * offset
Expand Down Expand Up @@ -1532,7 +1536,7 @@ calls ``reindex``.
.. ipython:: python

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

Expand Down Expand Up @@ -1626,7 +1630,7 @@ labels.

.. ipython:: python

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

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

Expand Down Expand Up @@ -1739,7 +1743,7 @@ We can select a specific column or columns using standard getitem.

r['A'].mean()

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

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

Expand All @@ -1760,21 +1764,21 @@ columns of a ``DataFrame``:
.. ipython:: python
:okexcept:

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


If a ``DataFrame`` does not have a datetimelike index, but instead you want
Expand All @@ -1786,9 +1790,9 @@ to resample based on datetimelike column in the frame, it can passed to the
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']))
[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()

Expand Down Expand Up @@ -1989,9 +1993,11 @@ Passing a string representing a lower frequency than ``PeriodIndex`` returns par

ps['2011']

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

Expand Down Expand Up @@ -2180,6 +2186,8 @@ time zones by starting with ``dateutil/``.

.. ipython:: python

import dateutil

# pytz
rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
tz='Europe/London')
Expand All @@ -2201,6 +2209,8 @@ which gives you more control over which time zone is used:

.. ipython:: python

import pytz

# pytz
tz_pytz = pytz.timezone('Europe/London')
rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
Expand Down Expand Up @@ -2299,7 +2309,8 @@ To remove timezone from tz-aware ``DatetimeIndex``, use ``tz_localize(None)`` or

.. ipython:: python

didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
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)
Expand Down Expand Up @@ -2352,7 +2363,8 @@ constructor as well as ``tz_localize``.
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 = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H',
periods=10, tz='US/Eastern')
didx
didx.tz_localize(None)
didx.tz_convert(None)
Expand Down Expand Up @@ -2403,14 +2415,14 @@ TZ Aware Dtypes

.. ipython:: python

s_naive = pd.Series(pd.date_range('20130101',periods=3))
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 = 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>`.
Expand Down