.. currentmodule:: pandas
.. ipython:: python :suppress: from datetime import datetime, timedelta import numpy as np np.random.seed(123456) from pandas import * randn = np.random.randn randint = np.random.randint np.set_printoptions(precision=4, suppress=True) options.display.max_rows=15 import dateutil import pytz from dateutil.relativedelta import relativedelta from pandas.tseries.api import * from pandas.tseries.offsets import *
pandas has proven very successful as a tool for working with time series data,
especially in the financial data analysis space. With the 0.8 release, we have
further improved the time series API in pandas by leaps and bounds. Using the
new NumPy datetime64 dtype, we have consolidated a large number of features
from other Python libraries like scikits.timeseries as well as created
a tremendous amount of new functionality for manipulating time series data.
In working with time series data, we will frequently seek to:
- generate sequences of fixed-frequency dates and time spans
- conform or convert time series to a particular frequency
- compute "relative" dates based on various non-standard time increments (e.g. 5 business days before the last business day of the year), or "roll" dates forward or backward
pandas provides a relatively compact and self-contained set of tools for performing the above tasks.
Create a range of dates:
.. ipython:: python
# 72 hours starting with midnight Jan 1st, 2011
rng = date_range('1/1/2011', periods=72, freq='H')
rng[:5]
Index pandas objects with dates:
.. ipython:: python ts = Series(randn(len(rng)), index=rng) ts.head()
Change frequency and fill gaps:
.. ipython:: python
# to 45 minute frequency and forward fill
converted = ts.asfreq('45Min', method='pad')
converted.head()
Resample:
.. ipython:: python
# Daily means
ts.resample('D', how='mean')
Time-stamped data is the most basic type of timeseries data that associates values with points in time. For pandas objects it means using the points in time to create the index
.. ipython:: python dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)] ts = Series(np.random.randn(3), dates) type(ts.index) ts
However, in many cases it is more natural to associate things like change variables with a time span instead.
For example:
.. ipython:: python
periods = PeriodIndex([Period('2012-01'), Period('2012-02'),
Period('2012-03')])
ts = Series(np.random.randn(3), periods)
type(ts.index)
ts
Starting with 0.8, pandas allows you to capture both representations and
convert between them. Under the hood, pandas represents timestamps using
instances of Timestamp and sequences of timestamps using instances of
DatetimeIndex. For regular time spans, pandas uses Period objects for
scalar values and PeriodIndex for sequences of spans. Better support for
irregular intervals with arbitrary start and end points are forth-coming in
future releases.
To convert a Series or list-like object of date-like objects e.g. strings,
epochs, or a mixture, you can use the to_datetime function. When passed
a Series, this returns a Series (with the same index), while a list-like
is converted to a DatetimeIndex:
.. ipython:: python
to_datetime(Series(['Jul 31, 2009', '2010-01-10', None]))
to_datetime(['2005/11/23', '2010.12.31'])
If you use dates which start with the day first (i.e. European style),
you can pass the dayfirst flag:
.. ipython:: python
to_datetime(['04-01-2012 10:00'], dayfirst=True)
to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
Warning
You see in the above example that dayfirst isn't strict, so if a date
can't be parsed with the day being first it will be parsed as if
dayfirst were False.
Note
Specifying a format argument will potentially speed up the conversion
considerably and on versions later then 0.13.0 explicitly specifying
a format string of '%Y%m%d' takes a faster path still.
Pass coerce=True to convert invalid data to NaT (not a time):
.. ipython:: python to_datetime(['2009-07-31', 'asd']) to_datetime(['2009-07-31', 'asd'], coerce=True)
Take care, to_datetime may not act as you expect on mixed data:
.. ipython:: python to_datetime([1, '1'])
It's also possible to convert integer or float epoch times. The default unit
for these is nanoseconds (since these are how Timestamps are stored). However,
often epochs are stored in another unit which can be specified:
Typical epoch stored units
.. ipython:: python
to_datetime([1349720105, 1349806505, 1349892905,
1349979305, 1350065705], unit='s')
to_datetime([1349720105100, 1349720105200, 1349720105300,
1349720105400, 1349720105500 ], unit='ms')
These work, but the results may be unexpected.
.. ipython:: python to_datetime([1]) to_datetime([1, 3.14], unit='s')
Note
Epoch times will be rounded to the nearest nanosecond.
To generate an index with time stamps, you can use either the DatetimeIndex or Index constructor and pass in a list of datetime objects:
.. ipython:: python dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)] index = DatetimeIndex(dates) index # Note the frequency information index = Index(dates) index # Automatically converted to DatetimeIndex
Practically, this becomes very cumbersome because we often need a very long
index with a large number of timestamps. If we need timestamps on a regular
frequency, we can use the pandas functions date_range and bdate_range
to create timestamp indexes.
.. ipython:: python
index = date_range('2000-1-1', periods=1000, freq='M')
index
index = bdate_range('2012-1-1', periods=250)
index
Convenience functions like date_range and bdate_range utilize a
variety of frequency aliases. The default frequency for date_range is a
calendar day while the default for bdate_range is a business day
.. ipython:: python start = datetime(2011, 1, 1) end = datetime(2012, 1, 1) rng = date_range(start, end) rng rng = bdate_range(start, end) rng
date_range and bdate_range makes it easy to generate a range of dates
using various combinations of parameters like start, end,
periods, and freq:
.. ipython:: python date_range(start, end, freq='BM') date_range(start, end, freq='W') bdate_range(end=end, periods=20) bdate_range(start=start, periods=20)
The start and end dates are strictly inclusive. So it will not generate any dates outside of those dates if specified.
One of the main uses for DatetimeIndex is as an index for pandas objects.
The DatetimeIndex class contains many timeseries related optimizations:
- A large range of dates for various offsets are pre-computed and cached under the hood in order to make generating subsequent date ranges very fast (just have to grab a slice)
- Fast shifting using the
shiftandtshiftmethod on pandas objects- Unioning of overlapping DatetimeIndex objects with the same frequency is very fast (important for fast data alignment)
- Quick access to date fields via properties such as
year,month, etc.- Regularization functions like
snapand very fastasoflogic
DatetimeIndex objects has all the basic functionality of regular Index objects and a smorgasbord of advanced timeseries-specific methods for easy frequency processing.
.. seealso::
:ref:`Reindexing methods <basics.reindexing>`
Note
While pandas does not force you to have a sorted date index, some of these methods may have unexpected or incorrect behavior if the dates are unsorted. So please be careful.
DatetimeIndex can be used like a regular index and offers all of its
intelligent functionality like selection, slicing, etc.
.. ipython:: python rng = date_range(start, end, freq='BM') ts = Series(randn(len(rng)), index=rng) ts.index ts[:5].index ts[::2].index
You can pass in dates and strings that parse to dates as indexing parameters:
.. ipython:: python ts['1/31/2011'] ts[datetime(2011, 12, 25):] ts['10/31/2011':'12/31/2011']
To provide convenience for accessing longer time series, you can also pass in the year or year and month as strings:
.. ipython:: python ts['2011'] ts['2011-6']
This type of slicing will work on a DataFrame with a DateTimeIndex as well. Since the
partial string selection is a form of label slicing, the endpoints will be included. This
would include matching times on an included date. Here's an example:
.. ipython:: python
dft = DataFrame(randn(100000,1),columns=['A'],index=date_range('20130101',periods=100000,freq='T'))
dft
dft['2013']
This starts on the very first time in the month, and includes the last date & time for the month
.. ipython:: python dft['2013-1':'2013-2']
This specifies a stop time that includes all of the times on the last day
.. ipython:: python dft['2013-1':'2013-2-28']
This specifies an exact stop time (and is not the same as the above)
.. ipython:: python dft['2013-1':'2013-2-28 00:00:00']
We are stopping on the included end-point as its part of the index
.. ipython:: python dft['2013-1-15':'2013-1-15 12:30:00']
Warning
The following selection will raise a KeyError; otherwise this selection methodology
would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it
resolve to one)
dft['2013-1-15 12:30:00']To select a single row, use .loc
.. ipython:: python dft.loc['2013-1-15 12:30:00']
Indexing a DateTimeIndex with a partial string depends on the "accuracy" of the period, in other words how specific the interval is in relation to the frequency of the index. In contrast, indexing with datetime objects is exact, because the objects have exact meaning. These also follow the semantics of including both endpoints.
These datetime objects are specific hours, minutes, and seconds even though they were not explicitly specified (they are 0).
.. ipython:: python dft[datetime(2013, 1, 1):datetime(2013,2,28)]
With no defaults.
.. ipython:: python dft[datetime(2013, 1, 1, 10, 12, 0):datetime(2013, 2, 28, 10, 12, 0)]
A truncate convenience function is provided that is equivalent to slicing:
.. ipython:: python ts.truncate(before='10/31/2011', after='12/31/2011')
Even complicated fancy indexing that breaks the DatetimeIndex's frequency
regularity will result in a DatetimeIndex (but frequency is lost):
.. ipython:: python ts[[0, 2, 6]].index
There are several time/date properties that one can access from Timestamp or a collection of timestamps like a DateTimeIndex.
| Property | Description |
|---|---|
| year | The year of the datetime |
| month | The month of the datetime |
| day | The days of the datetime |
| hour | The hour of the datetime |
| minute | The minutes of the datetime |
| second | The seconds of the datetime |
| microsecond | The microseconds of the datetime |
| nanosecond | The nanoseconds of the datetime |
| date | Returns datetime.date |
| time | Returns datetime.time |
| dayofyear | The ordinal day of year |
| weekofyear | The week ordinal of the year |
| week | The week ordinal of the year |
| dayofweek | The day of the week with Monday=0, Sunday=6 |
| weekday | The day of the week with Monday=0, Sunday=6 |
| quarter | Quarter of the date: Jan=Mar = 1, Apr-Jun = 2, etc. |
| is_month_start | Logical indicating if first day of month (defined by frequency) |
| is_month_end | Logical indicating if last day of month (defined by frequency) |
| is_quarter_start | Logical indicating if first day of quarter (defined by frequency) |
| is_quarter_end | Logical indicating if last day of quarter (defined by frequency) |
| is_year_start | Logical indicating if first day of year (defined by frequency) |
| is_year_end | Logical indicating if last day of year (defined by frequency) |
Furthermore, if you have a Series with datetimelike values, then you can access these properties via the .dt accessor, see the :ref:`docs <basics.dt_accessors>`
In the preceding examples, we created DatetimeIndex objects at various
frequencies by passing in frequency strings like 'M', 'W', and 'BM to the
freq keyword. Under the hood, these frequency strings are being translated
into an instance of pandas DateOffset, which represents a regular
frequency increment. Specific offset logic like "month", "business day", or
"one hour" is represented in its various subclasses.
| Class name | Description |
|---|---|
| DateOffset | Generic offset class, defaults to 1 calendar day |
| BDay | business day (weekday) |
| CDay | custom business day (experimental) |
| Week | one week, optionally anchored on a day of the week |
| WeekOfMonth | the x-th day of the y-th week of each month |
| LastWeekOfMonth | the x-th day of the last week of each month |
| MonthEnd | calendar month end |
| MonthBegin | calendar month begin |
| BMonthEnd | business month end |
| BMonthBegin | business month begin |
| CBMonthEnd | custom business month end |
| CBMonthBegin | custom business month begin |
| QuarterEnd | calendar quarter end |
| QuarterBegin | calendar quarter begin |
| BQuarterEnd | business quarter end |
| BQuarterBegin | business quarter begin |
| FY5253Quarter | retail (aka 52-53 week) quarter |
| YearEnd | calendar year end |
| YearBegin | calendar year begin |
| BYearEnd | business year end |
| BYearBegin | business year begin |
| FY5253 | retail (aka 52-53 week) year |
| Hour | one hour |
| Minute | one minute |
| Second | one second |
| Milli | one millisecond |
| Micro | one microsecond |
The basic DateOffset takes the same arguments as
dateutil.relativedelta, which works like:
.. ipython:: python d = datetime(2008, 8, 18, 9, 0) d + relativedelta(months=4, days=5)
We could have done the same thing with DateOffset:
.. ipython:: python from pandas.tseries.offsets import * d + DateOffset(months=4, days=5)
The key features of a DateOffset object are:
- it can be added / subtracted to/from a datetime object to obtain a shifted date
- it can be multiplied by an integer (positive or negative) so that the increment will be applied multiple times
- it has
rollforwardandrollbackmethods for moving a date forward or backward to the next or previous "offset date"
Subclasses of DateOffset define the apply function which dictates
custom date increment logic, such as adding business days:
class BDay(DateOffset):
"""DateOffset increments between business days"""
def apply(self, other):
..... ipython:: python d - 5 * BDay() d + BMonthEnd()
The rollforward and rollback methods do exactly what you would expect:
.. ipython:: python d offset = BMonthEnd() offset.rollforward(d) offset.rollback(d)
It's definitely worth exploring the pandas.tseries.offsets module and the
various docstrings for the classes.
These operations (apply, rollforward and rollback) preserves time (hour, minute, etc) information by default. To reset time, use normalize=True keyword when create offset instance. If normalize=True, result is normalized after the function is applied.
.. ipython:: python
day = Day()
day.apply(Timestamp('2014-01-01 09:00'))
day = Day(normalize=True)
day.apply(Timestamp('2014-01-01 09:00'))
hour = Hour()
hour.apply(Timestamp('2014-01-01 22:00'))
hour = Hour(normalize=True)
hour.apply(Timestamp('2014-01-01 22:00'))
hour.apply(Timestamp('2014-01-01 23:00'))
Some of the offsets can be "parameterized" when created to result in different
behavior. For example, the Week offset for generating weekly data accepts a
weekday parameter which results in the generated dates always lying on a
particular day of the week:
.. ipython:: python d d + Week() d + Week(weekday=4) (d + Week(weekday=4)).weekday() d - Week()
normalize option will be effective for addition and subtraction.
.. ipython:: python d + Week(normalize=True) d - Week(normalize=True)
Another example is parameterizing YearEnd with the specific ending month:
.. ipython:: python d + YearEnd() d + YearEnd(month=6)
The CDay or CustomBusinessDay class provides a parametric
BusinessDay class which can be used to create customized business day
calendars which account for local holidays and local weekend conventions.
.. ipython:: python
from pandas.tseries.offsets import CustomBusinessDay
# As an interesting example, let's look at Egypt where
# a Friday-Saturday weekend is observed.
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(2013, 5, 1), np.datetime64('2014-05-01')]
bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
dt = datetime(2013, 4, 30)
dt + 2 * bday_egypt
dts = date_range(dt, periods=5, freq=bday_egypt)
Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split()))
As of v0.14 holiday calendars can be used to provide the list of holidays. See the :ref:`holiday calendar<timeseries.holiday>` section for more information.
.. ipython:: python
from pandas.tseries.holiday import USFederalHolidayCalendar
bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
dt = datetime(2014, 1, 17)
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
dt + bday_us
Monthly offsets that respect a certain holiday calendar can be defined in the usual way.
.. ipython:: python
from pandas.tseries.offsets import CustomBusinessMonthBegin
bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())
# Skip new years
dt = datetime(2013, 12, 17)
dt + bmth_us
# Define date index with custom offset
from pandas import DatetimeIndex
DatetimeIndex(start='20100101',end='20120101',freq=bmth_us)
Note
The frequency string 'C' is used to indicate that a CustomBusinessDay DateOffset is used, it is important to note that since CustomBusinessDay is a parameterised type, instances of CustomBusinessDay may differ and this is not detectable from the 'C' frequency string. The user therefore needs to ensure that the 'C' frequency string is used consistently within the user's application.
Note
This uses the numpy.busdaycalendar API introduced in Numpy 1.7 and
therefore requires Numpy 1.7.0 or newer.
Warning
There are known problems with the timezone handling in Numpy 1.7 and users should therefore use this experimental(!) feature with caution and at their own risk.
To the extent that the datetime64 and busdaycalendar APIs in Numpy
have to change to fix the timezone issues, the behaviour of the
CustomBusinessDay class may have to change in future versions.
A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases (referred to as time rules prior to v0.8.0).
| Alias | Description |
|---|---|
| B | business day frequency |
| C | custom business day frequency (experimental) |
| D | calendar day frequency |
| W | weekly frequency |
| M | month end frequency |
| BM | business month end frequency |
| CBM | custom business month end frequency |
| MS | month start frequency |
| BMS | business month start frequency |
| CBMS | custom business month start frequency |
| Q | quarter end frequency |
| BQ | business quarter endfrequency |
| QS | quarter start frequency |
| BQS | business quarter start frequency |
| A | year end frequency |
| BA | business year end frequency |
| AS | year start frequency |
| BAS | business year start frequency |
| H | hourly frequency |
| T | minutely frequency |
| S | secondly frequency |
| L | milliseonds |
| U | microseconds |
| N | nanoseconds |
As we have seen previously, the alias and the offset instance are fungible in most functions:
.. ipython:: python date_range(start, periods=5, freq='B') date_range(start, periods=5, freq=BDay())
You can combine together day and intraday offsets:
.. ipython:: python date_range(start, periods=10, freq='2h20min') date_range(start, periods=10, freq='1D10U')
For some frequencies you can specify an anchoring suffix:
| Alias | Description |
|---|---|
| W-SUN | weekly frequency (sundays). Same as 'W' |
| W-MON | weekly frequency (mondays) |
| W-TUE | weekly frequency (tuesdays) |
| W-WED | weekly frequency (wednesdays) |
| W-THU | weekly frequency (thursdays) |
| W-FRI | weekly frequency (fridays) |
| W-SAT | weekly frequency (saturdays) |
| (B)Q(S)-DEC | quarterly frequency, year ends in December. Same as 'Q' |
| (B)Q(S)-JAN | quarterly frequency, year ends in January |
| (B)Q(S)-FEB | quarterly frequency, year ends in February |
| (B)Q(S)-MAR | quarterly frequency, year ends in March |
| (B)Q(S)-APR | quarterly frequency, year ends in April |
| (B)Q(S)-MAY | quarterly frequency, year ends in May |
| (B)Q(S)-JUN | quarterly frequency, year ends in June |
| (B)Q(S)-JUL | quarterly frequency, year ends in July |
| (B)Q(S)-AUG | quarterly frequency, year ends in August |
| (B)Q(S)-SEP | quarterly frequency, year ends in September |
| (B)Q(S)-OCT | quarterly frequency, year ends in October |
| (B)Q(S)-NOV | quarterly frequency, year ends in November |
| (B)A(S)-DEC | annual frequency, anchored end of December. Same as 'A' |
| (B)A(S)-JAN | annual frequency, anchored end of January |
| (B)A(S)-FEB | annual frequency, anchored end of February |
| (B)A(S)-MAR | annual frequency, anchored end of March |
| (B)A(S)-APR | annual frequency, anchored end of April |
| (B)A(S)-MAY | annual frequency, anchored end of May |
| (B)A(S)-JUN | annual frequency, anchored end of June |
| (B)A(S)-JUL | annual frequency, anchored end of July |
| (B)A(S)-AUG | annual frequency, anchored end of August |
| (B)A(S)-SEP | annual frequency, anchored end of September |
| (B)A(S)-OCT | annual frequency, anchored end of October |
| (B)A(S)-NOV | annual frequency, anchored end of November |
These can be used as arguments to date_range, bdate_range, constructors
for DatetimeIndex, as well as various other timeseries-related functions
in pandas.
Note that prior to v0.8.0, time rules had a slightly different look. pandas will continue to support the legacy time rules for the time being but it is strongly recommended that you switch to using the new offset aliases.
| Legacy Time Rule | Offset Alias |
|---|---|
| WEEKDAY | B |
| EOM | BM |
| W@MON | W-MON |
| W@TUE | W-TUE |
| W@WED | W-WED |
| W@THU | W-THU |
| W@FRI | W-FRI |
| W@SAT | W-SAT |
| W@SUN | W-SUN |
| Q@JAN | BQ-JAN |
| Q@FEB | BQ-FEB |
| Q@MAR | BQ-MAR |
| A@JAN | BA-JAN |
| A@FEB | BA-FEB |
| A@MAR | BA-MAR |
| A@APR | BA-APR |
| A@MAY | BA-MAY |
| A@JUN | BA-JUN |
| A@JUL | BA-JUL |
| A@AUG | BA-AUG |
| A@SEP | BA-SEP |
| A@OCT | BA-OCT |
| A@NOV | BA-NOV |
| A@DEC | BA-DEC |
| min | T |
| ms | L |
| us | U |
As you can see, legacy quarterly and annual frequencies are business quarter
and business year ends. Please also note the legacy time rule for milliseconds
ms versus the new offset alias for month start MS. This means that
offset alias parsing is case sensitive.
Holidays and calendars provide a simple way to define holiday rules to be used
with CustomBusinessDay or in other analysis that requires a predefined
set of holidays. The AbstractHolidayCalendar class provides all the necessary
methods to return a list of holidays and only rules need to be defined
in a specific holiday calendar class. Further, start_date and end_date
class attributes determine over what date range holidays are generated. These
should be overwritten on the AbstractHolidayCalendar class to have the range
apply to all calendar subclasses. USFederalHolidayCalendar is the
only calendar that exists and primarily serves as an example for developing
other calendars.
For holidays that occur on fixed dates (e.g., US Memorial Day or July 4th) an observance rule determines when that holiday is observed if it falls on a weekend or some other non-observed day. Defined observance rules are:
| Rule | Description |
|---|---|
| nearest_workday | move Saturday to Friday and Sunday to Monday |
| sunday_to_monday | move Sunday to following Monday |
| next_monday_or_tuesday | move Saturday to Monday and Sunday/Monday to Tuesday |
| previous_friday | move Saturday and Sunday to previous Friday" |
| next_monday | move Saturday and Sunday to following Monday |
An example of how holidays and holiday calendars are defined:
.. ipython:: python
from pandas.tseries.holiday import Holiday, USMemorialDay,\
AbstractHolidayCalendar, nearest_workday, MO
class ExampleCalendar(AbstractHolidayCalendar):
rules = [
USMemorialDay,
Holiday('July 4th', month=7, day=4, observance=nearest_workday),
Holiday('Columbus Day', month=10, day=1,
offset=DateOffset(weekday=MO(2))), #same as 2*Week(weekday=2)
]
cal = ExampleCalendar()
cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))
Using this calendar, creating an index or doing offset arithmetic skips weekends and holidays (i.e., Memorial Day/July 4th).
.. ipython:: python
DatetimeIndex(start='7/1/2012', end='7/10/2012',
freq=CDay(calendar=cal)).to_pydatetime()
offset = CustomBusinessDay(calendar=cal)
datetime(2012, 5, 25) + offset
datetime(2012, 7, 3) + offset
datetime(2012, 7, 3) + 2 * offset
datetime(2012, 7, 6) + offset
Ranges are defined by the start_date and end_date class attributes
of AbstractHolidayCalendar. The defaults are below.
.. ipython:: python
AbstractHolidayCalendar.start_date
AbstractHolidayCalendar.end_date
These dates can be overwritten by setting the attributes as datetime/Timestamp/string.
.. ipython:: python
AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)
AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)
cal.holidays()
Every calendar class is accessible by name using the get_calendar function
which returns a holiday class instance. Any imported calendar class will
automatically be available by this function. Also, HolidayCalendarFactory
provides an easy interface to create calendars that are combinations of calendars
or calendars with additional rules.
.. ipython:: python
from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
USLaborDay
cal = get_calendar('ExampleCalendar')
cal.rules
new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)
new_cal.rules
One may want to shift or lag the values in a TimeSeries back and forward in
time. The method for this is shift, which is available on all of the pandas
objects. In DataFrame, shift will currently only shift along the index
and in Panel along the major_axis.
.. ipython:: python ts = ts[:5] ts.shift(1)
The shift method accepts an freq argument which can accept a
DateOffset class or other timedelta-like object or also a :ref:`offset alias <timeseries.alias>`:
.. ipython:: python ts.shift(5, freq=datetools.bday) ts.shift(5, freq='BM')
Rather than changing the alignment of the data and the index, DataFrame and
TimeSeries objects also have a tshift convenience method that changes
all the dates in the index by a specified number of offsets:
.. ipython:: python ts.tshift(5, freq='D')
Note that with tshift, the leading entry is no longer NaN because the data
is not being realigned.
The primary function for changing frequencies is the asfreq function.
For a DatetimeIndex, this is basically just a thin, but convenient wrapper
around reindex which generates a date_range and calls reindex.
.. ipython:: python
dr = date_range('1/1/2010', periods=3, freq=3 * datetools.bday)
ts = Series(randn(3), index=dr)
ts
ts.asfreq(BDay())
asfreq provides a further convenience so you can specify an interpolation
method for any gaps that may appear after the frequency conversion
.. ipython:: python ts.asfreq(BDay(), method='pad')
Related to asfreq and reindex is the fillna function documented in
the :ref:`missing data section <missing_data.fillna>`.
DatetimeIndex can be converted to an array of Python native datetime.datetime objects using the
to_pydatetime method.
With 0.8, pandas introduces simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications.
See some :ref:`cookbook examples <cookbook.resample>` for some advanced strategies
.. ipython:: python
rng = date_range('1/1/2012', periods=100, freq='S')
ts = Series(randint(0, 500, len(rng)), index=rng)
ts.resample('5Min', how='sum')
The resample function is very flexible and allows you to specify many
different parameters to control the frequency conversion and resampling
operation.
The how parameter can be a function name or numpy array function that takes
an array and produces aggregated values:
.. ipython:: python
ts.resample('5Min') # default is mean
ts.resample('5Min', how='ohlc')
ts.resample('5Min', how=np.max)
Any function available via :ref:`dispatching <groupby.dispatch>` can be given to
the how parameter by name, including sum, mean, std, sem,
max, min, median, first, last, ohlc.
For downsampling, closed can be set to 'left' or 'right' to specify which
end of the interval is closed:
.. ipython:: python
ts.resample('5Min', closed='right')
ts.resample('5Min', closed='left')
For upsampling, the fill_method and limit parameters can be specified
to interpolate over the gaps that are created:
.. ipython:: python
# from secondly to every 250 milliseconds
ts[:2].resample('250L')
ts[:2].resample('250L', fill_method='pad')
ts[:2].resample('250L', fill_method='pad', limit=2)
Parameters like label and loffset are used to manipulate the resulting
labels. label specifies whether the result is labeled with the beginning or
the end of the interval. loffset performs a time adjustment on the output
labels.
.. ipython:: python
ts.resample('5Min') # by default label='right'
ts.resample('5Min', label='left')
ts.resample('5Min', label='left', loffset='1s')
The axis parameter can be set to 0 or 1 and allows you to resample the
specified axis for a DataFrame.
kind can be set to 'timestamp' or 'period' to convert the resulting index
to/from time-stamp and time-span representations. By default resample
retains the input representation.
convention can be set to 'start' or 'end' when resampling period data
(detail below). It specifies how low frequency periods are converted to higher
frequency periods.
Note that 0.8 marks a watershed in the timeseries functionality in pandas. In
previous versions, resampling had to be done using a combination of
date_range, groupby with asof, and then calling an aggregation
function on the grouped object. This was not nearly convenient or performant as
the new pandas timeseries API.
Regular intervals of time are represented by Period objects in pandas while
sequences of Period objects are collected in a PeriodIndex, which can
be created with the convenience function period_range.
A Period represents a span of time (e.g., a day, a month, a quarter, etc).
It can be created using a frequency alias:
.. ipython:: python
Period('2012', freq='A-DEC')
Period('2012-1-1', freq='D')
Period('2012-1-1 19:00', freq='H')
Unlike time stamped data, pandas does not support frequencies at multiples of DateOffsets (e.g., '3Min') for periods.
Adding and subtracting integers from periods shifts the period by its own frequency.
.. ipython:: python
p = Period('2012', freq='A-DEC')
p + 1
p - 3
If Period freq is daily or higher (D, H, T, S, L, U, N), offsets and timedelta-like can be added if the result can have same freq. Otherise, ValueError will be raised.
.. ipython:: python
p = Period('2014-07-01 09:00', freq='H')
p + Hour(2)
p + timedelta(minutes=120)
p + np.timedelta64(7200, 's')
In [1]: p + Minute(5)
Traceback
...
ValueError: Input has different freq from Period(freq=H)If Period has other freqs, only the same offsets can be added. Otherwise, ValueError will be raised.
.. ipython:: python
p = Period('2014-07', freq='M')
p + MonthEnd(3)
In [1]: p + MonthBegin(3)
Traceback
...
ValueError: Input has different freq from Period(freq=M)Taking the difference of Period instances with the same frequency will
return the number of frequency units between them:
.. ipython:: python
Period('2012', freq='A-DEC') - Period('2002', freq='A-DEC')
Regular sequences of Period objects can be collected in a PeriodIndex,
which can be constructed using the period_range convenience function:
.. ipython:: python
prng = period_range('1/1/2011', '1/1/2012', freq='M')
prng
The PeriodIndex constructor can also be used directly:
.. ipython:: python PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Just like DatetimeIndex, a PeriodIndex can also be used to index pandas
objects:
.. ipython:: python ps = Series(randn(len(prng)), prng) ps
PeriodIndex supports addition and subtraction as the same rule as Period.
.. ipython:: python
idx = period_range('2014-07-01 09:00', periods=5, freq='H')
idx
idx + Hour(2)
idx = period_range('2014-07', periods=5, freq='M')
idx
idx + MonthEnd(3)
You can pass in dates and strings to Series and DataFrame with PeriodIndex, as the same manner as DatetimeIndex. For details, refer to :ref:`DatetimeIndex Partial String Indexing <timeseries.partialindexing>`.
.. ipython:: python ps['2011-01'] ps[datetime(2011, 12, 25):] ps['10/31/2011':'12/31/2011']
Passing string represents lower frequency than PeriodIndex returns partial sliced data.
.. ipython:: python
ps['2011']
dfp = DataFrame(randn(600,1), columns=['A'],
index=period_range('2013-01-01 9:00', periods=600, freq='T'))
dfp
dfp['2013-01-01 10H']
As the same as DatetimeIndex, the endpoints will be included in the result. Below example slices data starting from 10:00 to 11:59.
.. ipython:: python dfp['2013-01-01 10H':'2013-01-01 11H']
The frequency of Periods and PeriodIndex can be converted via the asfreq
method. Let's start with the fiscal year 2011, ending in December:
.. ipython:: python
p = Period('2011', freq='A-DEC')
p
We can convert it to a monthly frequency. Using the how parameter, we can
specify whether to return the starting or ending month:
.. ipython:: python
p.asfreq('M', how='start')
p.asfreq('M', how='end')
The shorthands 's' and 'e' are provided for convenience:
.. ipython:: python
p.asfreq('M', 's')
p.asfreq('M', 'e')
Converting to a "super-period" (e.g., annual frequency is a super-period of quarterly frequency) automatically returns the super-period that includes the input period:
.. ipython:: python
p = Period('2011-12', freq='M')
p.asfreq('A-NOV')
Note that since we converted to an annual frequency that ends the year in November, the monthly period of December 2011 is actually in the 2012 A-NOV period.
Period conversions with anchored frequencies are particularly useful for
working with various quarterly data common to economics, business, and other
fields. Many organizations define quarters relative to the month in which their
fiscal year start and ends. Thus, first quarter of 2011 could start in 2010 or
a few months into 2011. Via anchored frequencies, pandas works all quarterly
frequencies Q-JAN through Q-DEC.
Q-DEC define regular calendar quarters:
.. ipython:: python
p = Period('2012Q1', freq='Q-DEC')
p.asfreq('D', 's')
p.asfreq('D', 'e')
Q-MAR defines fiscal year end in March:
.. ipython:: python
p = Period('2011Q4', freq='Q-MAR')
p.asfreq('D', 's')
p.asfreq('D', 'e')
Timestamped data can be converted to PeriodIndex-ed data using to_period
and vice-versa using to_timestamp:
.. ipython:: python
rng = date_range('1/1/2012', periods=5, freq='M')
ts = Series(randn(len(rng)), index=rng)
ts
ps = ts.to_period()
ps
ps.to_timestamp()
Remember that 's' and 'e' can be used to return the timestamps at the start or end of the period:
.. ipython:: python
ps.to_timestamp('D', how='s')
Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:
.. ipython:: python
prng = period_range('1990Q1', '2000Q4', freq='Q-NOV')
ts = Series(randn(len(prng)), prng)
ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
ts.head()
If you have data that is outside of the Timestamp bounds, see :ref:`Timestamp limitations <gotchas.timestamp-limits>`,
then you can use a PeriodIndex and/or Series of Periods to do computations.
.. ipython:: python
span = period_range('1215-01-01', '1381-01-01', freq='D')
span
To convert from a int64 based YYYYMMDD representation.
.. ipython:: python
s = Series([20121231, 20141130, 99991231])
s
def conv(x):
return Period(year = x // 10000, month = x//100 % 100, day = x%100, freq='D')
s.apply(conv)
s.apply(conv)[2]
These can easily be converted to a PeriodIndex
.. ipython:: python span = PeriodIndex(s.apply(conv)) span
Pandas provides rich support for working with timestamps in different time zones using pytz and dateutil libraries.
dateutil support is new [in 0.14.1] and currently only supported for fixed offset and tzfile zones. The default library is pytz.
Support for dateutil is provided for compatibility with other applications e.g. if you use dateutil in other python packages.
By default, pandas objects are time zone unaware:
.. ipython:: python
rng = date_range('3/6/2012 00:00', periods=15, freq='D')
rng.tz is None
To supply the time zone, you can use the tz keyword to date_range and
other functions. Dateutil time zone strings are distinguished from pytz
time zones by starting with dateutil/.
- In
pytzyou can find a list of common (and less common) time zones usingfrom pytz import common_timezones, all_timezones. dateutiluses the OS timezones so there isn't a fixed list available. For common zones, the names are the same aspytz.
.. ipython:: python
# pytz
rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
tz='Europe/London')
rng_pytz.tz
# dateutil
rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
tz='dateutil/Europe/London')
rng_dateutil.tz
# dateutil - utc special case
rng_utc = date_range('3/6/2012 00:00', periods=10, freq='D',
tz=dateutil.tz.tzutc())
rng_utc.tz
Note that the UTC timezone is a special case in dateutil and should be constructed explicitly
as an instance of dateutil.tz.tzutc. You can also construct other timezones explicitly first,
which gives you more control over which time zone is used:
.. ipython:: python
# pytz
tz_pytz = pytz.timezone('Europe/London')
rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
tz=tz_pytz)
rng_pytz.tz == tz_pytz
# dateutil
tz_dateutil = dateutil.tz.gettz('Europe/London')
rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
tz=tz_dateutil)
rng_dateutil.tz == tz_dateutil
Timestamps, like Python's datetime.datetime object can be either time zone
naive or time zone aware. Naive time series and DatetimeIndex objects can be
localized using tz_localize:
.. ipython:: python
ts = Series(randn(len(rng)), rng)
ts_utc = ts.tz_localize('UTC')
ts_utc
Again, you can explicitly construct the timezone object first.
You can use the tz_convert method to convert pandas objects to convert
tz-aware data to another time zone:
.. ipython:: python
ts_utc.tz_convert('US/Eastern')
Warning
Be wary of conversions between libraries. For some zones pytz and dateutil have different
definitions of the zone. This is more of a problem for unusual timezones than for
'standard' zones like US/Eastern.
Warning
Be aware that a timezone definition across versions of timezone libraries may not be considered equal. This may cause problems when working with stored data that is localized using one version and operated on with a different version. See :ref:`here<io.hdf5-notes>` for how to handle such a situation.
Warning
It is incorrect to pass a timezone directly into the datetime.datetime constructor (e.g.,
datetime.datetime(2011, 1, 1, tz=timezone('US/Eastern')). Instead, the datetime
needs to be localized using the the localize method on the timezone.
Under the hood, all timestamps are stored in UTC. Scalar values from a
DatetimeIndex with a time zone will have their fields (day, hour, minute)
localized to the time zone. However, timestamps with the same UTC value are
still considered to be equal even if they are in different time zones:
.. ipython:: python
rng_eastern = rng_utc.tz_convert('US/Eastern')
rng_berlin = rng_utc.tz_convert('Europe/Berlin')
rng_eastern[5]
rng_berlin[5]
rng_eastern[5] == rng_berlin[5]
Like Series, DataFrame, and DatetimeIndex, Timestamps can be converted to other
time zones using tz_convert:
.. ipython:: python
rng_eastern[5]
rng_berlin[5]
rng_eastern[5].tz_convert('Europe/Berlin')
Localization of Timestamps functions just like DatetimeIndex and TimeSeries:
.. ipython:: python
rng[5]
rng[5].tz_localize('Asia/Shanghai')
Operations between TimeSeries in different time zones will yield UTC TimeSeries, aligning the data on the UTC timestamps:
.. ipython:: python
eastern = ts_utc.tz_convert('US/Eastern')
berlin = ts_utc.tz_convert('Europe/Berlin')
result = eastern + berlin
result
result.index
In some cases, localize cannot determine the DST and non-DST hours when there are duplicates. This often happens when reading files that simply duplicate the hours. The infer_dst argument in tz_localize will attempt to determine the right offset.
.. ipython:: python
:okexcept:
rng_hourly = DatetimeIndex(['11/06/2011 00:00', '11/06/2011 01:00',
'11/06/2011 01:00', '11/06/2011 02:00',
'11/06/2011 03:00'])
rng_hourly.tz_localize('US/Eastern')
rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', infer_dst=True)
rng_hourly_eastern.values
To remove timezone from tz-aware DatetimeIndex, use tz_localize(None) or tz_convert(None). tz_localize(None) will remove timezone holding local time representations. tz_convert(None) will remove timezone after converting to UTC time.
.. ipython:: python
didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
didx
didx.tz_localize(None)
didx.tz_convert(None)
# tz_convert(None) is identical with tz_convert('UTC').tz_localize(None)
didx.tz_convert('UCT').tz_localize(None)
Timedeltas are differences in times, expressed in difference units, e.g. days,hours,minutes,seconds.
They can be both positive and negative. :ref:`DateOffsets<timeseries.offsets>` that are absolute in nature
(Day, Hour, Minute, Second, Milli, Micro, Nano) can be used as timedeltas.
.. ipython:: python
from datetime import datetime, timedelta
s = Series(date_range('2012-1-1', periods=3, freq='D'))
td = Series([ timedelta(days=i) for i in range(3) ])
df = DataFrame(dict(A = s, B = td))
df
df['C'] = df['A'] + df['B']
df
df.dtypes
s - s.max()
s - datetime(2011,1,1,3,5)
s + timedelta(minutes=5)
s + Minute(5)
s + Minute(5) + Milli(5)
Getting scalar results 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(2011,1,1,3,5) - s timedelta(minutes=5) + s
Some timedelta numeric like operations are supported.
.. ipython:: python td - timedelta(minutes=5, seconds=5, microseconds=5)
min, max and the corresponding idxmin, idxmax operations are supported on frames
.. ipython:: python
A = s - Timestamp('20120101') - timedelta(minutes=5, seconds=5)
B = s - Series(date_range('2012-1-2', periods=3, freq='D'))
df = DataFrame(dict(A=A, B=B))
df
df.min()
df.min(axis=1)
df.idxmin()
df.idxmax()
min, max operations are supported on series; these return a single element
timedelta64[ns] Series (this avoids having to deal with numpy timedelta64
issues). idxmin, idxmax are supported as well.
.. 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(timedelta(days=-1,seconds=5))
Warning
A numeric reduction operation for timedelta64[ns] can return a single-element Series of
dtype timedelta64[ns].
You can do numeric reduction operations on timedeltas.
.. ipython:: python y2 = y.fillna(timedelta(days=-1,seconds=5)) y2 y2.mean() y2.quantile(.1)
.. versionadded:: 0.13
string/integer conversion
Using the top-level to_timedelta, you can convert a scalar or array from the standard
timedelta format (produced by to_csv) into a timedelta type (np.timedelta64 in nanoseconds).
It can also construct Series.
Warning
This requires numpy >= 1.7
.. ipython:: python
to_timedelta('1 days 06:05:01.00003')
to_timedelta('15.5us')
to_timedelta(['1 days 06:05:01.00003','15.5us','nan'])
to_timedelta(np.arange(5),unit='s')
to_timedelta(np.arange(5),unit='d')
frequency conversion
Timedeltas can be converted to other 'frequencies' by dividing by another timedelta,
or by astyping to a specific timedelta type. These operations yield float64 dtyped Series.
.. ipython:: python
td = Series(date_range('20130101',periods=4))-Series(date_range('20121201',periods=4))
td[2] += np.timedelta64(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]')
Dividing or multiplying a timedelta64[ns] Series by an integer or integer Series
yields another timedelta64[ns] dtypes Series.
.. ipython:: python td * -1 td * Series([1,2,3,4])