Time Series / Date functionality¶
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:
# 72 hours starting with midnight Jan 1st, 2011
In [1]: rng = date_range('1/1/2011', periods=72, freq='H')
In [2]: rng[:5]
Out[2]:
DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 01:00:00',
'2011-01-01 02:00:00', '2011-01-01 03:00:00',
'2011-01-01 04:00:00'],
dtype='datetime64[ns]', freq='H')
Index pandas objects with dates:
In [3]: ts = Series(randn(len(rng)), index=rng)
In [4]: ts.head()
Out[4]:
2011-01-01 00:00:00 0.469112
2011-01-01 01:00:00 -0.282863
2011-01-01 02:00:00 -1.509059
2011-01-01 03:00:00 -1.135632
2011-01-01 04:00:00 1.212112
Freq: H, dtype: float64
Change frequency and fill gaps:
# to 45 minute frequency and forward fill
In [5]: converted = ts.asfreq('45Min', method='pad')
In [6]: converted.head()
Out[6]:
2011-01-01 00:00:00 0.469112
2011-01-01 00:45:00 0.469112
2011-01-01 01:30:00 -0.282863
2011-01-01 02:15:00 -1.509059
2011-01-01 03:00:00 -1.135632
Freq: 45T, dtype: float64
Resample:
# Daily means
In [7]: ts.resample('D', how='mean')
Out[7]:
2011-01-01 -0.319569
2011-01-02 -0.337703
2011-01-03 0.117258
Freq: D, dtype: float64
Overview¶
Following table shows the type of time-related classes pandas can handle and how to create them.
Class | Remarks | How to create |
---|---|---|
Timestamp |
Represents a single time stamp | to_datetime , Timestamp |
DatetimeIndex |
Index of Timestamps |
to_datetime , date_range , DatetimeIndex |
Period |
Represents a single time span | Period |
PeriodIndex |
Index of Period |
period_range , PeriodIndex |
Time Stamps vs. Time Spans¶
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.
In [8]: Timestamp(datetime(2012, 5, 1))
Out[8]: Timestamp('2012-05-01 00:00:00')
In [9]: Timestamp('2012-05-01')
Out[9]: Timestamp('2012-05-01 00:00:00')
However, in many cases it is more natural to associate things like change
variables with a time span instead. The span represented by Period
can be
specified explicitly, or inferred from datetime string format.
For example:
In [10]: Period('2011-01')
Out[10]: Period('2011-01', 'M')
In [11]: Period('2012-05', freq='D')
Out[11]: Period('2012-05-01', 'D')
Timestamp
and Period
can be the index. Lists of Timestamp
and
Period
are automatically coerce to DatetimeIndex
and PeriodIndex
respectively.
In [12]: dates = [Timestamp('2012-05-01'), Timestamp('2012-05-02'), Timestamp('2012-05-03')]
In [13]: ts = Series(np.random.randn(3), dates)
In [14]: type(ts.index)
Out[14]: pandas.tseries.index.DatetimeIndex
In [15]: ts.index
Out[15]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
In [16]: ts
Out[16]:
2012-05-01 -0.410001
2012-05-02 -0.078638
2012-05-03 0.545952
dtype: float64
In [17]: periods = [Period('2012-01'), Period('2012-02'), Period('2012-03')]
In [18]: ts = Series(np.random.randn(3), periods)
In [19]: type(ts.index)
Out[19]: pandas.tseries.period.PeriodIndex
In [20]: ts.index
Out[20]: PeriodIndex(['2012-01', '2012-02', '2012-03'], dtype='int64', freq='M')
In [21]: ts
Out[21]:
2012-01 -1.219217
2012-02 -1.226825
2012-03 0.769804
Freq: M, dtype: float64
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.
Converting to Timestamps¶
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:
In [22]: to_datetime(Series(['Jul 31, 2009', '2010-01-10', None]))
Out[22]:
0 2009-07-31
1 2010-01-10
2 NaT
dtype: datetime64[ns]
In [23]: to_datetime(['2005/11/23', '2010.12.31'])
Out[23]: DatetimeIndex(['2005-11-23', '2010-12-31'], dtype='datetime64[ns]', freq=None)
If you use dates which start with the day first (i.e. European style),
you can pass the dayfirst
flag:
In [24]: to_datetime(['04-01-2012 10:00'], dayfirst=True)
Out[24]: DatetimeIndex(['2012-01-04 10:00:00'], dtype='datetime64[ns]', freq=None)
In [25]: to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
Out[25]: DatetimeIndex(['2012-01-14', '2012-01-14'], dtype='datetime64[ns]', freq=None)
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.
If you pass a single string to to_datetime
, it returns single Timestamp
.
Also, Timestamp
can accept the string input.
Note that Timestamp
doesn’t accept string parsing option like dayfirst
or format
, use to_datetime
if these are required.
In [26]: to_datetime('2010/11/12')
Out[26]: Timestamp('2010-11-12 00:00:00')
In [27]: Timestamp('2010/11/12')
Out[27]: Timestamp('2010-11-12 00:00:00')
Invalid Data¶
Note
In version 0.17.0, the default for to_datetime
is now errors='raise'
, rather than errors='ignore'
. This means
that invalid parsing will raise rather that return the original input as in previous versions.
Pass errors='coerce'
to convert invalid data to NaT
(not a time):
# this is the default, raise when unparseable
In [28]: to_datetime(['2009/07/31', 'asd'], errors='raise')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-28-5ef6aaff276b> in <module>()
----> 1 to_datetime(['2009/07/31', 'asd'], errors='raise')
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, coerce, unit, infer_datetime_format)
274 return _to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst,
275 utc=utc, box=box, format=format, exact=exact,
--> 276 unit=unit, infer_datetime_format=infer_datetime_format)
277
278
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in _to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, freq, infer_datetime_format)
393 return _convert_listlike(arg, box, format, name=arg.name)
394 elif com.is_list_like(arg):
--> 395 return _convert_listlike(arg, box, format)
396
397 return _convert_listlike(np.array([ arg ]), box, format)[0]
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in _convert_listlike(arg, box, format, name)
381 return DatetimeIndex._simple_new(values, name=name, tz=tz)
382 except (ValueError, TypeError):
--> 383 raise e
384
385 if arg is None:
ValueError: Unknown string format
# return the original input when unparseable
In [29]: to_datetime(['2009/07/31', 'asd'], errors='ignore')
Out[29]: array(['2009/07/31', 'asd'], dtype=object)
# return NaT for input when unparseable
In [30]: to_datetime(['2009/07/31', 'asd'], errors='coerce')
Out[30]: DatetimeIndex(['2009-07-31', 'NaT'], dtype='datetime64[ns]', freq=None)
Epoch Timestamps¶
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
In [31]: to_datetime([1349720105, 1349806505, 1349892905,
....: 1349979305, 1350065705], unit='s')
....:
Out[31]:
DatetimeIndex(['2012-10-08 18:15:05', '2012-10-09 18:15:05',
'2012-10-10 18:15:05', '2012-10-11 18:15:05',
'2012-10-12 18:15:05'],
dtype='datetime64[ns]', freq=None)
In [32]: to_datetime([1349720105100, 1349720105200, 1349720105300,
....: 1349720105400, 1349720105500 ], unit='ms')
....:
Out[32]:
DatetimeIndex(['2012-10-08 18:15:05.100000', '2012-10-08 18:15:05.200000',
'2012-10-08 18:15:05.300000', '2012-10-08 18:15:05.400000',
'2012-10-08 18:15:05.500000'],
dtype='datetime64[ns]', freq=None)
These work, but the results may be unexpected.
In [33]: to_datetime([1])
Out[33]: DatetimeIndex(['1970-01-01 00:00:00.000000001'], dtype='datetime64[ns]', freq=None)
In [34]: to_datetime([1, 3.14], unit='s')
Out[34]: DatetimeIndex(['1970-01-01 00:00:01', '1970-01-01 00:00:03.140000'], dtype='datetime64[ns]', freq=None)
Note
Epoch times will be rounded to the nearest nanosecond.
Generating Ranges of Timestamps¶
To generate an index with time stamps, you can use either the DatetimeIndex or Index constructor and pass in a list of datetime objects:
In [35]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]
In [36]: index = DatetimeIndex(dates)
In [37]: index # Note the frequency information
Out[37]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
In [38]: index = Index(dates)
In [39]: index # Automatically converted to DatetimeIndex
Out[39]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
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.
In [40]: index = date_range('2000-1-1', periods=1000, freq='M')
In [41]: index
Out[41]:
DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-30',
'2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',
'2000-09-30', '2000-10-31',
...
'2082-07-31', '2082-08-31', '2082-09-30', '2082-10-31',
'2082-11-30', '2082-12-31', '2083-01-31', '2083-02-28',
'2083-03-31', '2083-04-30'],
dtype='datetime64[ns]', length=1000, freq='M')
In [42]: index = bdate_range('2012-1-1', periods=250)
In [43]: index
Out[43]:
DatetimeIndex(['2012-01-02', '2012-01-03', '2012-01-04', '2012-01-05',
'2012-01-06', '2012-01-09', '2012-01-10', '2012-01-11',
'2012-01-12', '2012-01-13',
...
'2012-12-03', '2012-12-04', '2012-12-05', '2012-12-06',
'2012-12-07', '2012-12-10', '2012-12-11', '2012-12-12',
'2012-12-13', '2012-12-14'],
dtype='datetime64[ns]', length=250, freq='B')
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
In [44]: start = datetime(2011, 1, 1)
In [45]: end = datetime(2012, 1, 1)
In [46]: rng = date_range(start, end)
In [47]: rng
Out[47]:
DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
'2011-01-05', '2011-01-06', '2011-01-07', '2011-01-08',
'2011-01-09', '2011-01-10',
...
'2011-12-23', '2011-12-24', '2011-12-25', '2011-12-26',
'2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30',
'2011-12-31', '2012-01-01'],
dtype='datetime64[ns]', length=366, freq='D')
In [48]: rng = bdate_range(start, end)
In [49]: rng
Out[49]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12',
'2011-01-13', '2011-01-14',
...
'2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22',
'2011-12-23', '2011-12-26', '2011-12-27', '2011-12-28',
'2011-12-29', '2011-12-30'],
dtype='datetime64[ns]', length=260, freq='B')
date_range
and bdate_range
make it easy to generate a range of dates
using various combinations of parameters like start
, end
,
periods
, and freq
:
In [50]: date_range(start, end, freq='BM')
Out[50]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31',
'2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30'],
dtype='datetime64[ns]', freq='BM')
In [51]: date_range(start, end, freq='W')
Out[51]:
DatetimeIndex(['2011-01-02', '2011-01-09', '2011-01-16', '2011-01-23',
'2011-01-30', '2011-02-06', '2011-02-13', '2011-02-20',
'2011-02-27', '2011-03-06', '2011-03-13', '2011-03-20',
'2011-03-27', '2011-04-03', '2011-04-10', '2011-04-17',
'2011-04-24', '2011-05-01', '2011-05-08', '2011-05-15',
'2011-05-22', '2011-05-29', '2011-06-05', '2011-06-12',
'2011-06-19', '2011-06-26', '2011-07-03', '2011-07-10',
'2011-07-17', '2011-07-24', '2011-07-31', '2011-08-07',
'2011-08-14', '2011-08-21', '2011-08-28', '2011-09-04',
'2011-09-11', '2011-09-18', '2011-09-25', '2011-10-02',
'2011-10-09', '2011-10-16', '2011-10-23', '2011-10-30',
'2011-11-06', '2011-11-13', '2011-11-20', '2011-11-27',
'2011-12-04', '2011-12-11', '2011-12-18', '2011-12-25',
'2012-01-01'],
dtype='datetime64[ns]', freq='W-SUN')
In [52]: bdate_range(end=end, periods=20)
Out[52]:
DatetimeIndex(['2011-12-05', '2011-12-06', '2011-12-07', '2011-12-08',
'2011-12-09', '2011-12-12', '2011-12-13', '2011-12-14',
'2011-12-15', '2011-12-16', '2011-12-19', '2011-12-20',
'2011-12-21', '2011-12-22', '2011-12-23', '2011-12-26',
'2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30'],
dtype='datetime64[ns]', freq='B')
In [53]: bdate_range(start=start, periods=20)
Out[53]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12',
'2011-01-13', '2011-01-14', '2011-01-17', '2011-01-18',
'2011-01-19', '2011-01-20', '2011-01-21', '2011-01-24',
'2011-01-25', '2011-01-26', '2011-01-27', '2011-01-28'],
dtype='datetime64[ns]', freq='B')
The start and end dates are strictly inclusive. So it will not generate any dates outside of those dates if specified.
DatetimeIndex¶
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
shift
andtshift
method 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
snap
and very fastasof
logic
DatetimeIndex objects has all the basic functionality of regular Index objects and a smorgasbord of advanced timeseries-specific methods for easy frequency processing.
See also
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.
In [54]: rng = date_range(start, end, freq='BM')
In [55]: ts = Series(randn(len(rng)), index=rng)
In [56]: ts.index
Out[56]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31',
'2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30'],
dtype='datetime64[ns]', freq='BM')
In [57]: ts[:5].index
Out[57]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31'],
dtype='datetime64[ns]', freq='BM')
In [58]: ts[::2].index
Out[58]:
DatetimeIndex(['2011-01-31', '2011-03-31', '2011-05-31', '2011-07-29',
'2011-09-30', '2011-11-30'],
dtype='datetime64[ns]', freq='2BM')
DatetimeIndex Partial String Indexing¶
You can pass in dates and strings that parse to dates as indexing parameters:
In [59]: ts['1/31/2011']
Out[59]: -1.2812473076599531
In [60]: ts[datetime(2011, 12, 25):]
Out[60]:
2011-12-30 0.687738
Freq: BM, dtype: float64
In [61]: ts['10/31/2011':'12/31/2011']
Out[61]:
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
To provide convenience for accessing longer time series, you can also pass in the year or year and month as strings:
In [62]: ts['2011']
Out[62]:
2011-01-31 -1.281247
2011-02-28 -0.727707
2011-03-31 -0.121306
2011-04-29 -0.097883
2011-05-31 0.695775
2011-06-30 0.341734
2011-07-29 0.959726
2011-08-31 -1.110336
2011-09-30 -0.619976
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
In [63]: ts['2011-6']
Out[63]:
2011-06-30 0.341734
Freq: BM, dtype: float64
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:
In [64]: dft = DataFrame(randn(100000,1),columns=['A'],index=date_range('20130101',periods=100000,freq='T'))
In [65]: dft
Out[65]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-03-11 10:33:00 -0.293083
2013-03-11 10:34:00 -0.059881
2013-03-11 10:35:00 1.252450
2013-03-11 10:36:00 0.046611
2013-03-11 10:37:00 0.059478
2013-03-11 10:38:00 -0.286539
2013-03-11 10:39:00 0.841669
[100000 rows x 1 columns]
In [66]: dft['2013']
Out[66]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-03-11 10:33:00 -0.293083
2013-03-11 10:34:00 -0.059881
2013-03-11 10:35:00 1.252450
2013-03-11 10:36:00 0.046611
2013-03-11 10:37:00 0.059478
2013-03-11 10:38:00 -0.286539
2013-03-11 10:39:00 0.841669
[100000 rows x 1 columns]
This starts on the very first time in the month, and includes the last date & time for the month
In [67]: dft['2013-1':'2013-2']
Out[67]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-28 23:53:00 0.103114
2013-02-28 23:54:00 -1.303422
2013-02-28 23:55:00 0.451943
2013-02-28 23:56:00 0.220534
2013-02-28 23:57:00 -1.624220
2013-02-28 23:58:00 0.093915
2013-02-28 23:59:00 -1.087454
[84960 rows x 1 columns]
This specifies a stop time that includes all of the times on the last day
In [68]: dft['2013-1':'2013-2-28']
Out[68]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-28 23:53:00 0.103114
2013-02-28 23:54:00 -1.303422
2013-02-28 23:55:00 0.451943
2013-02-28 23:56:00 0.220534
2013-02-28 23:57:00 -1.624220
2013-02-28 23:58:00 0.093915
2013-02-28 23:59:00 -1.087454
[84960 rows x 1 columns]
This specifies an exact stop time (and is not the same as the above)
In [69]: dft['2013-1':'2013-2-28 00:00:00']
Out[69]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-27 23:54:00 0.897051
2013-02-27 23:55:00 -0.309230
2013-02-27 23:56:00 1.944713
2013-02-27 23:57:00 0.369265
2013-02-27 23:58:00 0.053071
2013-02-27 23:59:00 -0.019734
2013-02-28 00:00:00 1.388189
[83521 rows x 1 columns]
We are stopping on the included end-point as it is part of the index
In [70]: dft['2013-1-15':'2013-1-15 12:30:00']
Out[70]:
A
2013-01-15 00:00:00 0.501288
2013-01-15 00:01:00 -0.605198
2013-01-15 00:02:00 0.215146
2013-01-15 00:03:00 0.924732
2013-01-15 00:04:00 -2.228519
2013-01-15 00:05:00 1.517331
2013-01-15 00:06:00 -1.188774
... ...
2013-01-15 12:24:00 1.358314
2013-01-15 12:25:00 -0.737727
2013-01-15 12:26:00 1.838323
2013-01-15 12:27:00 -0.774090
2013-01-15 12:28:00 0.622261
2013-01-15 12:29:00 -0.631649
2013-01-15 12:30:00 0.193284
[751 rows x 1 columns]
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
In [71]: dft.loc['2013-1-15 12:30:00']
Out[71]:
A 0.193284
Name: 2013-01-15 12:30:00, dtype: float64
Datetime Indexing¶
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
).
In [72]: dft[datetime(2013, 1, 1):datetime(2013,2,28)]
Out[72]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-27 23:54:00 0.897051
2013-02-27 23:55:00 -0.309230
2013-02-27 23:56:00 1.944713
2013-02-27 23:57:00 0.369265
2013-02-27 23:58:00 0.053071
2013-02-27 23:59:00 -0.019734
2013-02-28 00:00:00 1.388189
[83521 rows x 1 columns]
With no defaults.
In [73]: dft[datetime(2013, 1, 1, 10, 12, 0):datetime(2013, 2, 28, 10, 12, 0)]
Out[73]:
A
2013-01-01 10:12:00 -0.246733
2013-01-01 10:13:00 -1.429225
2013-01-01 10:14:00 -1.265339
2013-01-01 10:15:00 0.710986
2013-01-01 10:16:00 -0.818200
2013-01-01 10:17:00 0.543542
2013-01-01 10:18:00 1.577713
... ...
2013-02-28 10:06:00 0.311249
2013-02-28 10:07:00 2.366080
2013-02-28 10:08:00 -0.490372
2013-02-28 10:09:00 0.373340
2013-02-28 10:10:00 0.638442
2013-02-28 10:11:00 1.330135
2013-02-28 10:12:00 -0.945450
[83521 rows x 1 columns]
Truncating & Fancy Indexing¶
A truncate
convenience function is provided that is equivalent to slicing:
In [74]: ts.truncate(before='10/31/2011', after='12/31/2011')
Out[74]:
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
Even complicated fancy indexing that breaks the DatetimeIndex’s frequency
regularity will result in a DatetimeIndex
(but frequency is lost):
In [75]: ts[[0, 2, 6]].index
Out[75]: DatetimeIndex(['2011-01-31', '2011-03-31', '2011-07-29'], dtype='datetime64[ns]', freq=None)
Time/Date Components¶
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. |
days_in_month | The number of days in the month of the datetime |
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 docs
DateOffset objects¶
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 |
BusinessHour | business hour |
Hour | one hour |
Minute | one minute |
Second | one second |
Milli | one millisecond |
Micro | one microsecond |
Nano | one nanosecond |
The basic DateOffset
takes the same arguments as
dateutil.relativedelta
, which works like:
In [76]: d = datetime(2008, 8, 18, 9, 0)
In [77]: d + relativedelta(months=4, days=5)
Out[77]: datetime.datetime(2008, 12, 23, 9, 0)
We could have done the same thing with DateOffset
:
In [78]: from pandas.tseries.offsets import *
In [79]: d + DateOffset(months=4, days=5)
Out[79]: Timestamp('2008-12-23 09:00:00')
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
rollforward
androllback
methods 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):
...
In [80]: d - 5 * BDay()
Out[80]: Timestamp('2008-08-11 09:00:00')
In [81]: d + BMonthEnd()
Out[81]: Timestamp('2008-08-29 09:00:00')
The rollforward
and rollback
methods do exactly what you would expect:
In [82]: d
Out[82]: datetime.datetime(2008, 8, 18, 9, 0)
In [83]: offset = BMonthEnd()
In [84]: offset.rollforward(d)
Out[84]: Timestamp('2008-08-29 09:00:00')
In [85]: offset.rollback(d)
Out[85]: Timestamp('2008-07-31 09:00:00')
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 creating the offset instance. If normalize=True
, result is normalized after the function is applied.
In [86]: day = Day()
In [87]: day.apply(Timestamp('2014-01-01 09:00'))
Out[87]: Timestamp('2014-01-02 09:00:00')
In [88]: day = Day(normalize=True)
In [89]: day.apply(Timestamp('2014-01-01 09:00'))
Out[89]: Timestamp('2014-01-02 00:00:00')
In [90]: hour = Hour()
In [91]: hour.apply(Timestamp('2014-01-01 22:00'))
Out[91]: Timestamp('2014-01-01 23:00:00')
In [92]: hour = Hour(normalize=True)
In [93]: hour.apply(Timestamp('2014-01-01 22:00'))
Out[93]: Timestamp('2014-01-01 00:00:00')
In [94]: hour.apply(Timestamp('2014-01-01 23:00'))
Out[94]: Timestamp('2014-01-02 00:00:00')
Parametric offsets¶
Some of the offsets can be “parameterized” when created to result in different
behaviors. 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:
In [95]: d
Out[95]: datetime.datetime(2008, 8, 18, 9, 0)
In [96]: d + Week()
Out[96]: Timestamp('2008-08-25 09:00:00')
In [97]: d + Week(weekday=4)
Out[97]: Timestamp('2008-08-22 09:00:00')
In [98]: (d + Week(weekday=4)).weekday()
Out[98]: 4
In [99]: d - Week()
Out[99]: Timestamp('2008-08-11 09:00:00')
normalize
option will be effective for addition and subtraction.
In [100]: d + Week(normalize=True)
Out[100]: Timestamp('2008-08-25 00:00:00')
In [101]: d - Week(normalize=True)
Out[101]: Timestamp('2008-08-11 00:00:00')
Another example is parameterizing YearEnd
with the specific ending month:
In [102]: d + YearEnd()
Out[102]: Timestamp('2008-12-31 09:00:00')
In [103]: d + YearEnd(month=6)
Out[103]: Timestamp('2009-06-30 09:00:00')
Using offsets with Series
/ DatetimeIndex
¶
Offsets can be used with either a Series
or DatetimeIndex
to
apply the offset to each element.
In [104]: rng = date_range('2012-01-01', '2012-01-03')
In [105]: s = Series(rng)
In [106]: rng
Out[106]: DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03'], dtype='datetime64[ns]', freq='D')
In [107]: rng + DateOffset(months=2)
Out[107]: DatetimeIndex(['2012-03-01', '2012-03-02', '2012-03-03'], dtype='datetime64[ns]', freq='D')
In [108]: s + DateOffset(months=2)
Out[108]:
0 2012-03-01
1 2012-03-02
2 2012-03-03
dtype: datetime64[ns]
In [109]: s - DateOffset(months=2)
Out[109]:
0 2011-11-01
1 2011-11-02
2 2011-11-03
dtype: datetime64[ns]
If the offset class maps directly to a Timedelta
(Day
, Hour
,
Minute
, Second
, Micro
, Milli
, Nano
) it can be
used exactly like a Timedelta
- see the
Timedelta section for more examples.
In [110]: s - Day(2)
Out[110]:
0 2011-12-30
1 2011-12-31
2 2012-01-01
dtype: datetime64[ns]
In [111]: td = s - Series(date_range('2011-12-29', '2011-12-31'))
In [112]: td
Out[112]:
0 3 days
1 3 days
2 3 days
dtype: timedelta64[ns]
In [113]: td + Minute(15)
Out[113]:
0 3 days 00:15:00
1 3 days 00:15:00
2 3 days 00:15:00
dtype: timedelta64[ns]
Note that some offsets (such as BQuarterEnd
) do not have a
vectorized implementation. They can still be used but may
calculate signficantly slower and will raise a PerformanceWarning
In [114]: rng + BQuarterEnd()
Out[114]: DatetimeIndex(['2012-03-30', '2012-03-30', '2012-03-30'], dtype='datetime64[ns]', freq=None)
Custom Business Days (Experimental)¶
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.
In [115]: from pandas.tseries.offsets import CustomBusinessDay
# As an interesting example, let's look at Egypt where
# a Friday-Saturday weekend is observed.
In [116]: weekmask_egypt = 'Sun Mon Tue Wed Thu'
# They also observe International Workers' Day so let's
# add that for a couple of years
In [117]: holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]
In [118]: bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
In [119]: dt = datetime(2013, 4, 30)
In [120]: dt + 2 * bday_egypt
Out[120]: Timestamp('2013-05-05 00:00:00')
In [121]: dts = date_range(dt, periods=5, freq=bday_egypt)
In [122]: Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split()))
Out[122]:
2013-04-30 Tue
2013-05-02 Thu
2013-05-05 Sun
2013-05-06 Mon
2013-05-07 Tue
Freq: C, dtype: object
As of v0.14 holiday calendars can be used to provide the list of holidays. See the holiday calendar section for more information.
In [123]: from pandas.tseries.holiday import USFederalHolidayCalendar
In [124]: bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
In [125]: dt = datetime(2014, 1, 17)
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
In [126]: dt + bday_us
Out[126]: Timestamp('2014-01-21 00:00:00')
Monthly offsets that respect a certain holiday calendar can be defined in the usual way.
In [127]: from pandas.tseries.offsets import CustomBusinessMonthBegin
In [128]: bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())
# Skip new years
In [129]: dt = datetime(2013, 12, 17)
In [130]: dt + bmth_us
Out[130]: Timestamp('2014-01-02 00:00:00')
# Define date index with custom offset
In [131]: from pandas import DatetimeIndex
In [132]: DatetimeIndex(start='20100101',end='20120101',freq=bmth_us)
Out[132]:
DatetimeIndex(['2010-01-04', '2010-02-01', '2010-03-01', '2010-04-01',
'2010-05-03', '2010-06-01', '2010-07-01', '2010-08-02',
'2010-09-01', '2010-10-01', '2010-11-01', '2010-12-01',
'2011-01-03', '2011-02-01', '2011-03-01', '2011-04-01',
'2011-05-02', '2011-06-01', '2011-07-01', '2011-08-01',
'2011-09-01', '2011-10-03', '2011-11-01', '2011-12-01'],
dtype='datetime64[ns]', freq='CBMS')
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.
Business Hour¶
The BusinessHour
class provides a business hour representation on BusinessDay
,
allowing to use specific start and end times.
By default, BusinessHour
uses 9:00 - 17:00 as business hours.
Adding BusinessHour
will increment Timestamp
by hourly.
If target Timestamp
is out of business hours, move to the next business hour then increment it.
If the result exceeds the business hours end, remaining is added to the next business day.
In [133]: bh = BusinessHour()
In [134]: bh
Out[134]: <BusinessHour: BH=09:00-17:00>
# 2014-08-01 is Friday
In [135]: Timestamp('2014-08-01 10:00').weekday()
Out[135]: 4
In [136]: Timestamp('2014-08-01 10:00') + bh
Out[136]: Timestamp('2014-08-01 11:00:00')
# Below example is the same as Timestamp('2014-08-01 09:00') + bh
In [137]: Timestamp('2014-08-01 08:00') + bh
Out[137]: Timestamp('2014-08-01 10:00:00')
# If the results is on the end time, move to the next business day
In [138]: Timestamp('2014-08-01 16:00') + bh
Out[138]: Timestamp('2014-08-04 09:00:00')
# Remainings are added to the next day
In [139]: Timestamp('2014-08-01 16:30') + bh
Out[139]: Timestamp('2014-08-04 09:30:00')
# Adding 2 business hours
In [140]: Timestamp('2014-08-01 10:00') + BusinessHour(2)
Out[140]: Timestamp('2014-08-01 12:00:00')
# Subtracting 3 business hours
In [141]: Timestamp('2014-08-01 10:00') + BusinessHour(-3)
Out[141]: Timestamp('2014-07-31 15:00:00')
Also, you can specify start
and end
time by keywords.
Argument must be str
which has hour:minute
representation or datetime.time
instance.
Specifying seconds, microseconds and nanoseconds as business hour results in ValueError
.
In [142]: bh = BusinessHour(start='11:00', end=time(20, 0))
In [143]: bh
Out[143]: <BusinessHour: BH=11:00-20:00>
In [144]: Timestamp('2014-08-01 13:00') + bh
Out[144]: Timestamp('2014-08-01 14:00:00')
In [145]: Timestamp('2014-08-01 09:00') + bh
Out[145]: Timestamp('2014-08-01 12:00:00')
In [146]: Timestamp('2014-08-01 18:00') + bh
Out[146]: Timestamp('2014-08-01 19:00:00')
Passing start
time later than end
represents midnight business hour.
In this case, business hour exceeds midnight and overlap to the next day.
Valid business hours are distinguished by whether it started from valid BusinessDay
.
In [147]: bh = BusinessHour(start='17:00', end='09:00')
In [148]: bh
Out[148]: <BusinessHour: BH=17:00-09:00>
In [149]: Timestamp('2014-08-01 17:00') + bh
Out[149]: Timestamp('2014-08-01 18:00:00')
In [150]: Timestamp('2014-08-01 23:00') + bh
Out[150]: Timestamp('2014-08-02 00:00:00')
# Although 2014-08-02 is Satuaday,
# it is valid because it starts from 08-01 (Friday).
In [151]: Timestamp('2014-08-02 04:00') + bh
Out[151]: Timestamp('2014-08-02 05:00:00')
# Although 2014-08-04 is Monday,
# it is out of business hours because it starts from 08-03 (Sunday).
In [152]: Timestamp('2014-08-04 04:00') + bh
Out[152]: Timestamp('2014-08-04 18:00:00')
Applying BusinessHour.rollforward
and rollback
to out of business hours results in
the next business hour start or previous day’s end. Different from other offsets, BusinessHour.rollforward
may output different results from apply
by definition.
This is because one day’s business hour end is equal to next day’s business hour start. For example,
under the default business hours (9:00 - 17:00), there is no gap (0 minutes) between 2014-08-01 17:00
and
2014-08-04 09:00
.
# This adjusts a Timestamp to business hour edge
In [153]: BusinessHour().rollback(Timestamp('2014-08-02 15:00'))
Out[153]: Timestamp('2014-08-01 17:00:00')
In [154]: BusinessHour().rollforward(Timestamp('2014-08-02 15:00'))
Out[154]: Timestamp('2014-08-04 09:00:00')
# It is the same as BusinessHour().apply(Timestamp('2014-08-01 17:00')).
# And it is the same as BusinessHour().apply(Timestamp('2014-08-04 09:00'))
In [155]: BusinessHour().apply(Timestamp('2014-08-02 15:00'))
Out[155]: Timestamp('2014-08-04 10:00:00')
# BusinessDay results (for reference)
In [156]: BusinessHour().rollforward(Timestamp('2014-08-02'))
Out[156]: Timestamp('2014-08-04 09:00:00')
# It is the same as BusinessDay().apply(Timestamp('2014-08-01'))
# The result is the same as rollworward because BusinessDay never overlap.
In [157]: BusinessHour().apply(Timestamp('2014-08-02'))
Out[157]: Timestamp('2014-08-04 10:00:00')
Offset Aliases¶
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 |
BH | business hour frequency |
H | hourly frequency |
T, min | minutely frequency |
S | secondly frequency |
L, ms | milliseonds |
U, us | microseconds |
N | nanoseconds |
Combining Aliases¶
As we have seen previously, the alias and the offset instance are fungible in most functions:
In [158]: date_range(start, periods=5, freq='B')
Out[158]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07'],
dtype='datetime64[ns]', freq='B')
In [159]: date_range(start, periods=5, freq=BDay())
Out[159]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07'],
dtype='datetime64[ns]', freq='B')
You can combine together day and intraday offsets:
In [160]: date_range(start, periods=10, freq='2h20min')
Out[160]:
DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 02:20:00',
'2011-01-01 04:40:00', '2011-01-01 07:00:00',
'2011-01-01 09:20:00', '2011-01-01 11:40:00',
'2011-01-01 14:00:00', '2011-01-01 16:20:00',
'2011-01-01 18:40:00', '2011-01-01 21:00:00'],
dtype='datetime64[ns]', freq='140T')
In [161]: date_range(start, periods=10, freq='1D10U')
Out[161]:
DatetimeIndex([ '2011-01-01 00:00:00', '2011-01-02 00:00:00.000010',
'2011-01-03 00:00:00.000020', '2011-01-04 00:00:00.000030',
'2011-01-05 00:00:00.000040', '2011-01-06 00:00:00.000050',
'2011-01-07 00:00:00.000060', '2011-01-08 00:00:00.000070',
'2011-01-09 00:00:00.000080', '2011-01-10 00:00:00.000090'],
dtype='datetime64[ns]', freq='86400000010U')
Anchored Offsets¶
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.
Legacy Aliases¶
Note that prior to v0.8.0, time rules had a slightly different look. These are deprecated in v0.17.0, and removed in future version.
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 |
As you can see, legacy quarterly and annual frequencies are business quarters
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 / Holiday Calendars¶
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:
In [162]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
.....: AbstractHolidayCalendar, nearest_workday, MO
.....:
In [163]: 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)
.....: ]
.....:
In [164]: cal = ExampleCalendar()
In [165]: cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))
Out[165]: DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None)
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,
it can be used to create a DatetimeIndex
or added to datetime
or Timestamp
objects.
In [166]: from pandas.tseries.offsets import CDay
In [167]: DatetimeIndex(start='7/1/2012', end='7/10/2012',
.....: freq=CDay(calendar=cal)).to_pydatetime()
.....:
Out[167]:
array([datetime.datetime(2012, 7, 2, 0, 0),
datetime.datetime(2012, 7, 3, 0, 0),
datetime.datetime(2012, 7, 5, 0, 0),
datetime.datetime(2012, 7, 6, 0, 0),
datetime.datetime(2012, 7, 9, 0, 0),
datetime.datetime(2012, 7, 10, 0, 0)], dtype=object)
In [168]: offset = CustomBusinessDay(calendar=cal)
In [169]: datetime(2012, 5, 25) + offset
Out[169]: Timestamp('2012-05-29 00:00:00')
In [170]: datetime(2012, 7, 3) + offset
Out[170]: Timestamp('2012-07-05 00:00:00')
In [171]: datetime(2012, 7, 3) + 2 * offset
Out[171]: Timestamp('2012-07-06 00:00:00')
In [172]: datetime(2012, 7, 6) + offset
Out[172]: Timestamp('2012-07-09 00:00:00')
Ranges are defined by the start_date
and end_date
class attributes
of AbstractHolidayCalendar
. The defaults are below.
In [173]: AbstractHolidayCalendar.start_date
Out[173]: Timestamp('1970-01-01 00:00:00')
In [174]: AbstractHolidayCalendar.end_date
Out[174]: Timestamp('2030-12-31 00:00:00')
These dates can be overwritten by setting the attributes as datetime/Timestamp/string.
In [175]: AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)
In [176]: AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)
In [177]: cal.holidays()
Out[177]: DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None)
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.
In [178]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
.....: USLaborDay
.....:
In [179]: cal = get_calendar('ExampleCalendar')
In [180]: cal.rules
Out[180]:
[Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7f81b82d4938>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>)]
In [181]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)
In [182]: new_cal.rules
Out[182]:
[Holiday: Labor Day (month=9, day=1, offset=<DateOffset: kwds={'weekday': MO(+1)}>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7f81b82d4938>),
Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>)]
Resampling¶
Pandas has a 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.
resample
is a time-based groupby, followed by a reduction method on each of its groups.
See some cookbook examples for some advanced strategies
In [193]: rng = date_range('1/1/2012', periods=100, freq='S')
In [194]: ts = Series(randint(0, 500, len(rng)), index=rng)
In [195]: ts.resample('5Min', how='sum')
Out[195]:
2012-01-01 25103
Freq: 5T, dtype: int64
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:
In [196]: ts.resample('5Min') # default is mean
Out[196]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [197]: ts.resample('5Min', how='ohlc')
Out[197]:
open high low close
2012-01-01 308 460 9 205
In [198]: ts.resample('5Min', how=np.max)
Out[198]:
2012-01-01 460
Freq: 5T, dtype: int64
Any function available via dispatching 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:
In [199]: ts.resample('5Min', closed='right')
Out[199]:
2011-12-31 23:55:00 308.000000
2012-01-01 00:00:00 250.454545
Freq: 5T, dtype: float64
In [200]: ts.resample('5Min', closed='left')
Out[200]:
2012-01-01 251.03
Freq: 5T, dtype: float64
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.
In [201]: ts.resample('5Min') # by default label='right'
Out[201]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [202]: ts.resample('5Min', label='left')
Out[202]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [203]: ts.resample('5Min', label='left', loffset='1s')
Out[203]:
2012-01-01 00:00:01 251.03
dtype: float64
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.
Up Sampling¶
For upsampling, the fill_method
and limit
parameters can be specified
to interpolate over the gaps that are created:
# from secondly to every 250 milliseconds
In [204]: ts[:2].resample('250L')
Out[204]:
2012-01-01 00:00:00.000 308
2012-01-01 00:00:00.250 NaN
2012-01-01 00:00:00.500 NaN
2012-01-01 00:00:00.750 NaN
2012-01-01 00:00:01.000 204
Freq: 250L, dtype: float64
In [205]: ts[:2].resample('250L', fill_method='pad')
Out[205]:
2012-01-01 00:00:00.000 308
2012-01-01 00:00:00.250 308
2012-01-01 00:00:00.500 308
2012-01-01 00:00:00.750 308
2012-01-01 00:00:01.000 204
Freq: 250L, dtype: int64
In [206]: ts[:2].resample('250L', fill_method='pad', limit=2)
Out[206]:
2012-01-01 00:00:00.000 308
2012-01-01 00:00:00.250 308
2012-01-01 00:00:00.500 308
2012-01-01 00:00:00.750 NaN
2012-01-01 00:00:01.000 204
Freq: 250L, dtype: float64
Sparse Resampling¶
Sparse timeseries are ones where you have a lot fewer points relative
to the amount of time you are looking to resample. Naively upsampling a sparse series can potentially
generate lots of intermediate values. When you don’t want to use a method to fill these values, e.g. fill_method
is None
,
then intermediate values will be filled with NaN
.
Since resample
is a time-based groupby, the following is a method to efficiently
resample only the groups that are not all NaN
In [207]: rng = date_range('2014-1-1', periods=100, freq='D') + Timedelta('1s')
In [208]: ts = Series(range(100), index=rng)
If we want to resample to the full range of the series
In [209]: ts.resample('3T',how='sum')
Out[209]:
2014-01-01 00:00:00 0
2014-01-01 00:03:00 NaN
2014-01-01 00:06:00 NaN
2014-01-01 00:09:00 NaN
2014-01-01 00:12:00 NaN
2014-01-01 00:15:00 NaN
2014-01-01 00:18:00 NaN
..
2014-04-09 23:42:00 NaN
2014-04-09 23:45:00 NaN
2014-04-09 23:48:00 NaN
2014-04-09 23:51:00 NaN
2014-04-09 23:54:00 NaN
2014-04-09 23:57:00 NaN
2014-04-10 00:00:00 99
Freq: 3T, dtype: float64
We can instead only resample those groups where we have points as follows:
In [210]: from functools import partial
In [211]: from pandas.tseries.frequencies import to_offset
In [212]: def round(t, freq):
.....: freq = to_offset(freq)
.....: return Timestamp((t.value // freq.delta.value) * freq.delta.value)
.....:
In [213]: ts.groupby(partial(round, freq='3T')).sum()
Out[213]:
2014-01-01 0
2014-01-02 1
2014-01-03 2
2014-01-04 3
2014-01-05 4
2014-01-06 5
2014-01-07 6
..
2014-04-04 93
2014-04-05 94
2014-04-06 95
2014-04-07 96
2014-04-08 97
2014-04-09 98
2014-04-10 99
dtype: int64
Time Span Representation¶
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
.
Period¶
A Period
represents a span of time (e.g., a day, a month, a quarter, etc).
You can specify the span via freq
keyword using a frequency alias like below.
Because freq
represents a span of Period
, it cannot be negative like “-3D”.
In [214]: Period('2012', freq='A-DEC')
Out[214]: Period('2012', 'A-DEC')
In [215]: Period('2012-1-1', freq='D')
Out[215]: Period('2012-01-01', 'D')
In [216]: Period('2012-1-1 19:00', freq='H')
Out[216]: Period('2012-01-01 19:00', 'H')
In [217]: Period('2012-1-1 19:00', freq='5H')
Out[217]: Period('2012-01-01 19:00', '5H')
Adding and subtracting integers from periods shifts the period by its own
frequency. Arithmetic is not allowed between Period
with different freq
(span).
In [218]: p = Period('2012', freq='A-DEC')
In [219]: p + 1
Out[219]: Period('2013', 'A-DEC')
In [220]: p - 3
Out[220]: Period('2009', 'A-DEC')
In [221]: p = Period('2012-01', freq='2M')
In [222]: p + 2
Out[222]: Period('2012-05', '2M')
In [223]: p - 1
Out[223]: Period('2011-11', '2M')
In [224]: p == Period('2012-01', freq='3M')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-224-196036327bc8> in <module>()
----> 1 p == Period('2012-01', freq='3M')
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/_period.so in pandas._period.Period.__richcmp__ (pandas/src/period.c:12558)()
ValueError: Input has different freq=3M from Period(freq=2M)
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 the same freq. Otherise, ValueError
will be raised.
In [225]: p = Period('2014-07-01 09:00', freq='H')
In [226]: p + Hour(2)
Out[226]: Period('2014-07-01 11:00', 'H')
In [227]: p + timedelta(minutes=120)
Out[227]: Period('2014-07-01 11:00', 'H')
In [228]: p + np.timedelta64(7200, 's')
Out[228]: Period('2014-07-01 11:00', 'H')
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.
In [229]: p = Period('2014-07', freq='M')
In [230]: p + MonthEnd(3)
Out[230]: Period('2014-10', 'M')
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:
In [231]: Period('2012', freq='A-DEC') - Period('2002', freq='A-DEC')
Out[231]: 10
PeriodIndex and period_range¶
Regular sequences of Period
objects can be collected in a PeriodIndex
,
which can be constructed using the period_range
convenience function:
In [232]: prng = period_range('1/1/2011', '1/1/2012', freq='M')
In [233]: prng
Out[233]:
PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06',
'2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12',
'2012-01'],
dtype='int64', freq='M')
The PeriodIndex
constructor can also be used directly:
In [234]: PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Out[234]: PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='int64', freq='M')
Passing multiplied frequency outputs a sequence of Period
which
has multiplied span.
In [235]: PeriodIndex(start='2014-01', freq='3M', periods=4)
Out[235]: PeriodIndex(['2014-01', '2014-04', '2014-07', '2014-10'], dtype='int64', freq='3M')
Just like DatetimeIndex
, a PeriodIndex
can also be used to index pandas
objects:
In [236]: ps = Series(randn(len(prng)), prng)
In [237]: ps
Out[237]:
2011-01 -0.253355
2011-02 -1.426908
2011-03 1.548971
2011-04 -0.088718
2011-05 -1.771348
2011-06 -0.989328
2011-07 -1.584789
2011-08 -0.288786
2011-09 -2.029806
2011-10 -0.761200
2011-11 -1.603608
2011-12 1.756171
2012-01 0.256502
Freq: M, dtype: float64
PeriodIndex
supports addition and subtraction with the same rule as Period
.
In [238]: idx = period_range('2014-07-01 09:00', periods=5, freq='H')
In [239]: idx
Out[239]:
PeriodIndex(['2014-07-01 09:00', '2014-07-01 10:00', '2014-07-01 11:00',
'2014-07-01 12:00', '2014-07-01 13:00'],
dtype='int64', freq='H')
In [240]: idx + Hour(2)
Out[240]:
PeriodIndex(['2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00',
'2014-07-01 14:00', '2014-07-01 15:00'],
dtype='int64', freq='H')
In [241]: idx = period_range('2014-07', periods=5, freq='M')
In [242]: idx
Out[242]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='int64', freq='M')
In [243]: idx + MonthEnd(3)
Out[243]: PeriodIndex(['2014-10', '2014-11', '2014-12', '2015-01', '2015-02'], dtype='int64', freq='M')
PeriodIndex Partial String Indexing¶
You can pass in dates and strings to Series
and DataFrame
with PeriodIndex
, in the same manner as DatetimeIndex
. For details, refer to DatetimeIndex Partial String Indexing.
In [244]: ps['2011-01']
Out[244]: -0.25335528290092818
In [245]: ps[datetime(2011, 12, 25):]
Out[245]:
2011-12 1.756171
2012-01 0.256502
Freq: M, dtype: float64
In [246]: ps['10/31/2011':'12/31/2011']
Out[246]:
2011-10 -0.761200
2011-11 -1.603608
2011-12 1.756171
Freq: M, dtype: float64
Passing a string representing a lower frequency than PeriodIndex
returns partial sliced data.
In [247]: ps['2011']
Out[247]:
2011-01 -0.253355
2011-02 -1.426908
2011-03 1.548971
2011-04 -0.088718
2011-05 -1.771348
2011-06 -0.989328
2011-07 -1.584789
2011-08 -0.288786
2011-09 -2.029806
2011-10 -0.761200
2011-11 -1.603608
2011-12 1.756171
Freq: M, dtype: float64
In [248]: dfp = DataFrame(randn(600,1), columns=['A'],
.....: index=period_range('2013-01-01 9:00', periods=600, freq='T'))
.....:
In [249]: dfp
Out[249]:
A
2013-01-01 09:00 0.020601
2013-01-01 09:01 -0.411719
2013-01-01 09:02 2.079413
2013-01-01 09:03 -1.077911
2013-01-01 09:04 0.099258
2013-01-01 09:05 -0.089851
2013-01-01 09:06 0.711329
... ...
2013-01-01 18:53 -1.340038
2013-01-01 18:54 1.315461
2013-01-01 18:55 2.396188
2013-01-01 18:56 -0.501527
2013-01-01 18:57 -3.171938
2013-01-01 18:58 0.142019
2013-01-01 18:59 0.606998
[600 rows x 1 columns]
In [250]: dfp['2013-01-01 10H']
Out[250]:
A
2013-01-01 10:00 -0.745396
2013-01-01 10:01 0.141880
2013-01-01 10:02 -1.077754
2013-01-01 10:03 -1.301174
2013-01-01 10:04 -0.269628
2013-01-01 10:05 -0.456347
2013-01-01 10:06 0.157766
... ...
2013-01-01 10:53 0.168057
2013-01-01 10:54 -0.214306
2013-01-01 10:55 -0.069739
2013-01-01 10:56 -1.511809
2013-01-01 10:57 0.307021
2013-01-01 10:58 1.449776
2013-01-01 10:59 0.782537
[60 rows x 1 columns]
As with DatetimeIndex
, the endpoints will be included in the result. The example below slices data starting from 10:00 to 11:59.
In [251]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[251]:
A
2013-01-01 10:00 -0.745396
2013-01-01 10:01 0.141880
2013-01-01 10:02 -1.077754
2013-01-01 10:03 -1.301174
2013-01-01 10:04 -0.269628
2013-01-01 10:05 -0.456347
2013-01-01 10:06 0.157766
... ...
2013-01-01 11:53 -0.064395
2013-01-01 11:54 0.350193
2013-01-01 11:55 1.336433
2013-01-01 11:56 -0.438701
2013-01-01 11:57 -0.915841
2013-01-01 11:58 0.294215
2013-01-01 11:59 0.040959
[120 rows x 1 columns]
Frequency Conversion and Resampling with PeriodIndex¶
The frequency of Period
and PeriodIndex
can be converted via the asfreq
method. Let’s start with the fiscal year 2011, ending in December:
In [252]: p = Period('2011', freq='A-DEC')
In [253]: p
Out[253]: Period('2011', 'A-DEC')
We can convert it to a monthly frequency. Using the how
parameter, we can
specify whether to return the starting or ending month:
In [254]: p.asfreq('M', how='start')
Out[254]: Period('2011-01', 'M')
In [255]: p.asfreq('M', how='end')
Out[255]: Period('2011-12', 'M')
The shorthands ‘s’ and ‘e’ are provided for convenience:
In [256]: p.asfreq('M', 's')
Out[256]: Period('2011-01', 'M')
In [257]: p.asfreq('M', 'e')
Out[257]: Period('2011-12', 'M')
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:
In [258]: p = Period('2011-12', freq='M')
In [259]: p.asfreq('A-NOV')
Out[259]: Period('2012', '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 starts and ends. Thus, first quarter of 2011 could start in 2010 or
a few months into 2011. Via anchored frequencies, pandas works for all quarterly
frequencies Q-JAN
through Q-DEC
.
Q-DEC
define regular calendar quarters:
In [260]: p = Period('2012Q1', freq='Q-DEC')
In [261]: p.asfreq('D', 's')
Out[261]: Period('2012-01-01', 'D')
In [262]: p.asfreq('D', 'e')
Out[262]: Period('2012-03-31', 'D')
Q-MAR
defines fiscal year end in March:
In [263]: p = Period('2011Q4', freq='Q-MAR')
In [264]: p.asfreq('D', 's')
Out[264]: Period('2011-01-01', 'D')
In [265]: p.asfreq('D', 'e')
Out[265]: Period('2011-03-31', 'D')
Converting between Representations¶
Timestamped data can be converted to PeriodIndex-ed data using to_period
and vice-versa using to_timestamp
:
In [266]: rng = date_range('1/1/2012', periods=5, freq='M')
In [267]: ts = Series(randn(len(rng)), index=rng)
In [268]: ts
Out[268]:
2012-01-31 -0.016142
2012-02-29 0.865782
2012-03-31 0.246439
2012-04-30 -1.199736
2012-05-31 0.407620
Freq: M, dtype: float64
In [269]: ps = ts.to_period()
In [270]: ps
Out[270]:
2012-01 -0.016142
2012-02 0.865782
2012-03 0.246439
2012-04 -1.199736
2012-05 0.407620
Freq: M, dtype: float64
In [271]: ps.to_timestamp()
Out[271]:
2012-01-01 -0.016142
2012-02-01 0.865782
2012-03-01 0.246439
2012-04-01 -1.199736
2012-05-01 0.407620
Freq: MS, dtype: float64
Remember that ‘s’ and ‘e’ can be used to return the timestamps at the start or end of the period:
In [272]: ps.to_timestamp('D', how='s')
Out[272]:
2012-01-01 -0.016142
2012-02-01 0.865782
2012-03-01 0.246439
2012-04-01 -1.199736
2012-05-01 0.407620
Freq: MS, dtype: float64
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:
In [273]: prng = period_range('1990Q1', '2000Q4', freq='Q-NOV')
In [274]: ts = Series(randn(len(prng)), prng)
In [275]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In [276]: ts.head()
Out[276]:
1990-03-01 09:00 -2.470970
1990-06-01 09:00 -0.929915
1990-09-01 09:00 1.385889
1990-12-01 09:00 -1.830966
1991-03-01 09:00 -0.328505
Freq: H, dtype: float64
Representing out-of-bounds spans¶
If you have data that is outside of the Timestamp
bounds, see Timestamp limitations,
then you can use a PeriodIndex
and/or Series
of Periods
to do computations.
In [277]: span = period_range('1215-01-01', '1381-01-01', freq='D')
In [278]: span
Out[278]:
PeriodIndex(['1215-01-01', '1215-01-02', '1215-01-03', '1215-01-04',
'1215-01-05', '1215-01-06', '1215-01-07', '1215-01-08',
'1215-01-09', '1215-01-10',
...
'1380-12-23', '1380-12-24', '1380-12-25', '1380-12-26',
'1380-12-27', '1380-12-28', '1380-12-29', '1380-12-30',
'1380-12-31', '1381-01-01'],
dtype='int64', length=60632, freq='D')
To convert from a int64
based YYYYMMDD representation.
In [279]: s = Series([20121231, 20141130, 99991231])
In [280]: s
Out[280]:
0 20121231
1 20141130
2 99991231
dtype: int64
In [281]: def conv(x):
.....: return Period(year = x // 10000, month = x//100 % 100, day = x%100, freq='D')
.....:
In [282]: s.apply(conv)
Out[282]:
0 2012-12-31
1 2014-11-30
2 9999-12-31
dtype: object
In [283]: s.apply(conv)[2]
Out[283]: Period('9999-12-31', 'D')
These can easily be converted to a PeriodIndex
In [284]: span = PeriodIndex(s.apply(conv))
In [285]: span
Out[285]: PeriodIndex(['2012-12-31', '2014-11-30', '9999-12-31'], dtype='int64', freq='D')
Time Zone Handling¶
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.
Working with Time Zones¶
By default, pandas objects are time zone unaware:
In [286]: rng = date_range('3/6/2012 00:00', periods=15, freq='D')
In [287]: rng.tz is None
Out[287]: True
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
pytz
you can find a list of common (and less common) time zones usingfrom pytz import common_timezones, all_timezones
. dateutil
uses the OS timezones so there isn’t a fixed list available. For common zones, the names are the same aspytz
.
# pytz
In [288]: rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz='Europe/London')
.....:
In [289]: rng_pytz.tz
Out[289]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>
# dateutil
In [290]: rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz='dateutil/Europe/London')
.....:
In [291]: rng_dateutil.tz
Out[291]: tzfile('/usr/share/zoneinfo/Europe/London')
# dateutil - utc special case
In [292]: rng_utc = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=dateutil.tz.tzutc())
.....:
In [293]: rng_utc.tz
Out[293]: tzutc()
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:
# pytz
In [294]: tz_pytz = pytz.timezone('Europe/London')
In [295]: rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=tz_pytz)
.....:
In [296]: rng_pytz.tz == tz_pytz
Out[296]: True
# dateutil
In [297]: tz_dateutil = dateutil.tz.gettz('Europe/London')
In [298]: rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=tz_dateutil)
.....:
In [299]: rng_dateutil.tz == tz_dateutil
Out[299]: True
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
:
In [300]: ts = Series(randn(len(rng)), rng)
In [301]: ts_utc = ts.tz_localize('UTC')
In [302]: ts_utc
Out[302]:
2012-03-06 00:00:00+00:00 0.758606
2012-03-07 00:00:00+00:00 2.190827
2012-03-08 00:00:00+00:00 0.706087
2012-03-09 00:00:00+00:00 1.798831
2012-03-10 00:00:00+00:00 1.228481
2012-03-11 00:00:00+00:00 -0.179494
2012-03-12 00:00:00+00:00 0.634073
2012-03-13 00:00:00+00:00 0.262123
2012-03-14 00:00:00+00:00 1.928233
2012-03-15 00:00:00+00:00 0.322573
2012-03-16 00:00:00+00:00 -0.711113
2012-03-17 00:00:00+00:00 1.444272
2012-03-18 00:00:00+00:00 -0.352268
2012-03-19 00:00:00+00:00 0.213008
2012-03-20 00:00:00+00:00 -0.619340
Freq: D, dtype: float64
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:
In [303]: ts_utc.tz_convert('US/Eastern')
Out[303]:
2012-03-05 19:00:00-05:00 0.758606
2012-03-06 19:00:00-05:00 2.190827
2012-03-07 19:00:00-05:00 0.706087
2012-03-08 19:00:00-05:00 1.798831
2012-03-09 19:00:00-05:00 1.228481
2012-03-10 19:00:00-05:00 -0.179494
2012-03-11 20:00:00-04:00 0.634073
2012-03-12 20:00:00-04:00 0.262123
2012-03-13 20:00:00-04:00 1.928233
2012-03-14 20:00:00-04:00 0.322573
2012-03-15 20:00:00-04:00 -0.711113
2012-03-16 20:00:00-04:00 1.444272
2012-03-17 20:00:00-04:00 -0.352268
2012-03-18 20:00:00-04:00 0.213008
2012-03-19 20:00:00-04:00 -0.619340
Freq: D, dtype: float64
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 here 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:
In [304]: rng_eastern = rng_utc.tz_convert('US/Eastern')
In [305]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')
In [306]: rng_eastern[5]
Out[306]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', offset='D')
In [307]: rng_berlin[5]
Out[307]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', offset='D')
In [308]: rng_eastern[5] == rng_berlin[5]
Out[308]: True
Like Series, DataFrame, and DatetimeIndex, Timestamps can be converted to other
time zones using tz_convert
:
In [309]: rng_eastern[5]
Out[309]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', offset='D')
In [310]: rng_berlin[5]
Out[310]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', offset='D')
In [311]: rng_eastern[5].tz_convert('Europe/Berlin')
Out[311]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin')
Localization of Timestamps functions just like DatetimeIndex and Series:
In [312]: rng[5]
Out[312]: Timestamp('2012-03-11 00:00:00', offset='D')
In [313]: rng[5].tz_localize('Asia/Shanghai')
Out[313]: Timestamp('2012-03-11 00:00:00+0800', tz='Asia/Shanghai')
Operations between Series in different time zones will yield UTC Series, aligning the data on the UTC timestamps:
In [314]: eastern = ts_utc.tz_convert('US/Eastern')
In [315]: berlin = ts_utc.tz_convert('Europe/Berlin')
In [316]: result = eastern + berlin
In [317]: result
Out[317]:
2012-03-06 00:00:00+00:00 1.517212
2012-03-07 00:00:00+00:00 4.381654
2012-03-08 00:00:00+00:00 1.412174
2012-03-09 00:00:00+00:00 3.597662
2012-03-10 00:00:00+00:00 2.456962
2012-03-11 00:00:00+00:00 -0.358988
2012-03-12 00:00:00+00:00 1.268146
2012-03-13 00:00:00+00:00 0.524245
2012-03-14 00:00:00+00:00 3.856466
2012-03-15 00:00:00+00:00 0.645146
2012-03-16 00:00:00+00:00 -1.422226
2012-03-17 00:00:00+00:00 2.888544
2012-03-18 00:00:00+00:00 -0.704537
2012-03-19 00:00:00+00:00 0.426017
2012-03-20 00:00:00+00:00 -1.238679
Freq: D, dtype: float64
In [318]: result.index
Out[318]:
DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',
'2012-03-10', '2012-03-11', '2012-03-12', '2012-03-13',
'2012-03-14', '2012-03-15', '2012-03-16', '2012-03-17',
'2012-03-18', '2012-03-19', '2012-03-20'],
dtype='datetime64[ns, UTC]', freq='D')
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.
In [319]: didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
In [320]: didx
Out[320]:
DatetimeIndex(['2014-08-01 09:00:00-04:00', '2014-08-01 10:00:00-04:00',
'2014-08-01 11:00:00-04:00', '2014-08-01 12:00:00-04:00',
'2014-08-01 13:00:00-04:00', '2014-08-01 14:00:00-04:00',
'2014-08-01 15:00:00-04:00', '2014-08-01 16:00:00-04:00',
'2014-08-01 17:00:00-04:00', '2014-08-01 18:00:00-04:00'],
dtype='datetime64[ns, US/Eastern]', freq='H')
In [321]: didx.tz_localize(None)
Out[321]:
DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00',
'2014-08-01 11:00:00', '2014-08-01 12:00:00',
'2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00', '2014-08-01 16:00:00',
'2014-08-01 17:00:00', '2014-08-01 18:00:00'],
dtype='datetime64[ns]', freq='H')
In [322]: didx.tz_convert(None)
Out[322]:
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00', '2014-08-01 16:00:00',
'2014-08-01 17:00:00', '2014-08-01 18:00:00',
'2014-08-01 19:00:00', '2014-08-01 20:00:00',
'2014-08-01 21:00:00', '2014-08-01 22:00:00'],
dtype='datetime64[ns]', freq='H')
# tz_convert(None) is identical with tz_convert('UTC').tz_localize(None)
In [323]: didx.tz_convert('UCT').tz_localize(None)
Out[323]:
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00', '2014-08-01 16:00:00',
'2014-08-01 17:00:00', '2014-08-01 18:00:00',
'2014-08-01 19:00:00', '2014-08-01 20:00:00',
'2014-08-01 21:00:00', '2014-08-01 22:00:00'],
dtype='datetime64[ns]', freq='H')
Ambiguous Times when Localizing¶
In some cases, localize cannot determine the DST and non-DST hours when there are
duplicates. This often happens when reading files or database records that simply
duplicate the hours. Passing ambiguous='infer'
(infer_dst
argument in prior
releases) into tz_localize
will attempt to determine the right offset. Below
the top example will fail as it contains ambiguous times and the bottom will
infer the right offset.
In [324]: 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'])
.....:
# This will fail as there are ambiguous times
In [325]: rng_hourly.tz_localize('US/Eastern')
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-325-8c5fa6a37f5b> in <module>()
----> 1 rng_hourly.tz_localize('US/Eastern')
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tseries/index.pyc in tz_localize(self, tz, ambiguous)
1722
1723 new_dates = tslib.tz_localize_to_utc(self.asi8, tz,
-> 1724 ambiguous=ambiguous)
1725 new_dates = new_dates.view(_NS_DTYPE)
1726 return self._shallow_copy(new_dates, tz=tz)
/build/pandas-8apiR_/pandas-0.17.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tslib.so in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64823)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2011-11-06 01:00:00'), try using the 'ambiguous' argument
In [326]: rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', ambiguous='infer')
In [327]: rng_hourly_eastern.tolist()
Out[327]:
[Timestamp('2011-11-06 00:00:00-0400', tz='US/Eastern'),
Timestamp('2011-11-06 01:00:00-0400', tz='US/Eastern'),
Timestamp('2011-11-06 01:00:00-0500', tz='US/Eastern'),
Timestamp('2011-11-06 02:00:00-0500', tz='US/Eastern'),
Timestamp('2011-11-06 03:00:00-0500', tz='US/Eastern')]
In addition to ‘infer’, there are several other arguments supported. Passing
an array-like of bools or 0s/1s where True represents a DST hour and False a
non-DST hour, allows for distinguishing more than one DST
transition (e.g., if you have multiple records in a database each with their
own DST transition). Or passing ‘NaT’ will fill in transition times
with not-a-time values. These methods are available in the DatetimeIndex
constructor as well as tz_localize
.
In [328]: rng_hourly_dst = np.array([1, 1, 0, 0, 0])
In [329]: rng_hourly.tz_localize('US/Eastern', ambiguous=rng_hourly_dst).tolist()
Out[329]:
[Timestamp('2011-11-06 00:00:00-0400', tz='US/Eastern'),
Timestamp('2011-11-06 01:00:00-0400', tz='US/Eastern'),
Timestamp('2011-11-06 01:00:00-0500', tz='US/Eastern'),
Timestamp('2011-11-06 02:00:00-0500', tz='US/Eastern'),
Timestamp('2011-11-06 03:00:00-0500', tz='US/Eastern')]
In [330]: rng_hourly.tz_localize('US/Eastern', ambiguous='NaT').tolist()
Out[330]:
[Timestamp('2011-11-06 00:00:00-0400', tz='US/Eastern'),
NaT,
NaT,
Timestamp('2011-11-06 02:00:00-0500', tz='US/Eastern'),
Timestamp('2011-11-06 03:00:00-0500', tz='US/Eastern')]
In [331]: didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
In [332]: didx
Out[332]:
DatetimeIndex(['2014-08-01 09:00:00-04:00', '2014-08-01 10:00:00-04:00',
'2014-08-01 11:00:00-04:00', '2014-08-01 12:00:00-04:00',
'2014-08-01 13:00:00-04:00', '2014-08-01 14:00:00-04:00',
'2014-08-01 15:00:00-04:00', '2014-08-01 16:00:00-04:00',
'2014-08-01 17:00:00-04:00', '2014-08-01 18:00:00-04:00'],
dtype='datetime64[ns, US/Eastern]', freq='H')
In [333]: didx.tz_localize(None)
Out[333]:
DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00',
'2014-08-01 11:00:00', '2014-08-01 12:00:00',
'2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00', '2014-08-01 16:00:00',
'2014-08-01 17:00:00', '2014-08-01 18:00:00'],
dtype='datetime64[ns]', freq='H')
In [334]: didx.tz_convert(None)
Out[334]:
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00', '2014-08-01 16:00:00',
'2014-08-01 17:00:00', '2014-08-01 18:00:00',
'2014-08-01 19:00:00', '2014-08-01 20:00:00',
'2014-08-01 21:00:00', '2014-08-01 22:00:00'],
dtype='datetime64[ns]', freq='H')
# tz_convert(None) is identical with tz_convert('UTC').tz_localize(None)
In [335]: didx.tz_convert('UCT').tz_localize(None)
Out[335]:
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00', '2014-08-01 16:00:00',
'2014-08-01 17:00:00', '2014-08-01 18:00:00',
'2014-08-01 19:00:00', '2014-08-01 20:00:00',
'2014-08-01 21:00:00', '2014-08-01 22:00:00'],
dtype='datetime64[ns]', freq='H')
TZ aware Dtypes¶
New in version 0.17.0.
Series/DatetimeIndex
with a timezone naive value are represented with a dtype of datetime64[ns]
.
In [336]: s_naive = pd.Series(pd.date_range('20130101',periods=3))
In [337]: s_naive
Out[337]:
0 2013-01-01
1 2013-01-02
2 2013-01-03
dtype: datetime64[ns]
Series/DatetimeIndex
with a timezone aware value are represented with a dtype of datetime64[ns, tz]
.
In [338]: s_aware = pd.Series(pd.date_range('20130101',periods=3,tz='US/Eastern'))
In [339]: s_aware
Out[339]:
0 2013-01-01 00:00:00-05:00
1 2013-01-02 00:00:00-05:00
2 2013-01-03 00:00:00-05:00
dtype: datetime64[ns, US/Eastern]
Both of these Series
can be manipulated via the .dt
accessor, see here.
For example, to localize and convert a naive stamp to timezone aware.
In [340]: s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[340]:
0 2012-12-31 19:00:00-05:00
1 2013-01-01 19:00:00-05:00
2 2013-01-02 19:00:00-05:00
dtype: datetime64[ns, US/Eastern]
Further more you can .astype(...)
timezone aware (and naive). This operation is effectively a localize AND convert on a naive stamp, and
a convert on an aware stamp.
# localize and convert a naive timezone
In [341]: s_naive.astype('datetime64[ns, US/Eastern]')
Out[341]:
0 2012-12-31 19:00:00-05:00
1 2013-01-01 19:00:00-05:00
2 2013-01-02 19:00:00-05:00
dtype: datetime64[ns, US/Eastern]
# make an aware tz naive
In [342]: s_aware.astype('datetime64[ns]')
Out[342]:
0 2013-01-01 05:00:00
1 2013-01-02 05:00:00
2 2013-01-03 05:00:00
dtype: datetime64[ns]
# convert to a new timezone
In [343]: s_aware.astype('datetime64[ns, CET]')
Out[343]:
0 2013-01-01 06:00:00+01:00
1 2013-01-02 06:00:00+01:00
2 2013-01-03 06:00:00+01:00
dtype: datetime64[ns, CET]
Note
Using the .values
accessor on a Series
, returns an numpy array of the data.
These values are converted to UTC, as numpy does not currently support timezones (even though it is printing in the local timezone!).
In [344]: s_naive.values
Out[344]:
array(['2013-01-01T00:00:00.000000000+0000',
'2013-01-02T00:00:00.000000000+0000',
'2013-01-03T00:00:00.000000000+0000'], dtype='datetime64[ns]')
In [345]: s_aware.values
Out[345]:
array(['2013-01-01T05:00:00.000000000+0000',
'2013-01-02T05:00:00.000000000+0000',
'2013-01-03T05:00:00.000000000+0000'], dtype='datetime64[ns]')
Further note that once converted to a numpy array these would lose the tz tenor.
In [346]: Series(s_aware.values)
Out[346]:
0 2013-01-01 05:00:00
1 2013-01-02 05:00:00
2 2013-01-03 05:00:00
dtype: datetime64[ns]
However, these can be easily converted
In [347]: pd.Series(s_aware.values).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[347]:
0 2013-01-01 00:00:00-05:00
1 2013-01-02 00:00:00-05:00
2 2013-01-03 00:00:00-05:00
dtype: datetime64[ns, US/Eastern]