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').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')
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/tseries/tools.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, coerce, unit, infer_datetime_format)
285 yearfirst=yearfirst,
286 utc=utc, box=box, format=format, exact=exact,
--> 287 unit=unit, infer_datetime_format=infer_datetime_format)
288
289
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/tseries/tools.py in _to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, freq, infer_datetime_format)
412 return _convert_listlike(arg, box, format, name=arg.name)
413 elif com.is_list_like(arg):
--> 414 return _convert_listlike(arg, box, format)
415
416 return _convert_listlike(np.array([arg]), box, format)[0]
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/tseries/tools.py in _convert_listlike(arg, box, format, name)
400 return DatetimeIndex._simple_new(values, name=name, tz=tz)
401 except (ValueError, TypeError):
--> 402 raise e
403
404 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 and tshift 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 fast asof 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 and rollback 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 significantly 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 | milliseconds |
| 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.
Anchored Offset Semantics¶
For those offsets that are anchored to the start or end of specific frequency (MonthEnd, MonthBegin, WeekEnd, etc) the following rules apply to rolling forward and backwards.
When n is not 0, if the given date is not on an anchor point, it snapped to the next(previous) anchor point, and moved |n|-1 additional steps forwards or backwards.
In [162]: pd.Timestamp('2014-01-02') + MonthBegin(n=1)
Out[162]: Timestamp('2014-02-01 00:00:00')
In [163]: pd.Timestamp('2014-01-02') + MonthEnd(n=1)
Out[163]: Timestamp('2014-01-31 00:00:00')
In [164]: pd.Timestamp('2014-01-02') - MonthBegin(n=1)
Out[164]: Timestamp('2014-01-01 00:00:00')
In [165]: pd.Timestamp('2014-01-02') - MonthEnd(n=1)
Out[165]: Timestamp('2013-12-31 00:00:00')
In [166]: pd.Timestamp('2014-01-02') + MonthBegin(n=4)
Out[166]: Timestamp('2014-05-01 00:00:00')
In [167]: pd.Timestamp('2014-01-02') - MonthBegin(n=4)
Out[167]: Timestamp('2013-10-01 00:00:00')
If the given date is on an anchor point, it is moved |n| points forwards or backwards.
In [168]: pd.Timestamp('2014-01-01') + MonthBegin(n=1)
Out[168]: Timestamp('2014-02-01 00:00:00')
In [169]: pd.Timestamp('2014-01-31') + MonthEnd(n=1)
Out[169]: Timestamp('2014-02-28 00:00:00')
In [170]: pd.Timestamp('2014-01-01') - MonthBegin(n=1)
Out[170]: Timestamp('2013-12-01 00:00:00')
In [171]: pd.Timestamp('2014-01-31') - MonthEnd(n=1)
Out[171]: Timestamp('2013-12-31 00:00:00')
In [172]: pd.Timestamp('2014-01-01') + MonthBegin(n=4)
Out[172]: Timestamp('2014-05-01 00:00:00')
In [173]: pd.Timestamp('2014-01-31') - MonthBegin(n=4)
Out[173]: Timestamp('2013-10-01 00:00:00')
For the case when n=0, the date is not moved if on an anchor point, otherwise it is rolled forward to the next anchor point.
In [174]: pd.Timestamp('2014-01-02') + MonthBegin(n=0)
Out[174]: Timestamp('2014-02-01 00:00:00')
In [175]: pd.Timestamp('2014-01-02') + MonthEnd(n=0)
Out[175]: Timestamp('2014-01-31 00:00:00')
In [176]: pd.Timestamp('2014-01-01') + MonthBegin(n=0)
Out[176]: Timestamp('2014-01-01 00:00:00')
In [177]: pd.Timestamp('2014-01-31') + MonthEnd(n=0)
Out[177]: Timestamp('2014-01-31 00:00:00')
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 [178]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
.....: AbstractHolidayCalendar, nearest_workday, MO
.....:
In [179]: 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 [180]: cal = ExampleCalendar()
In [181]: cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))
Out[181]: 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 [182]: from pandas.tseries.offsets import CDay
In [183]: DatetimeIndex(start='7/1/2012', end='7/10/2012',
.....: freq=CDay(calendar=cal)).to_pydatetime()
.....:
Out[183]:
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 [184]: offset = CustomBusinessDay(calendar=cal)
In [185]: datetime(2012, 5, 25) + offset
Out[185]: Timestamp('2012-05-29 00:00:00')
In [186]: datetime(2012, 7, 3) + offset
Out[186]: Timestamp('2012-07-05 00:00:00')
In [187]: datetime(2012, 7, 3) + 2 * offset
Out[187]: Timestamp('2012-07-06 00:00:00')
In [188]: datetime(2012, 7, 6) + offset
Out[188]: 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 [189]: AbstractHolidayCalendar.start_date
Out[189]: Timestamp('1970-01-01 00:00:00')
In [190]: AbstractHolidayCalendar.end_date
Out[190]: Timestamp('2030-12-31 00:00:00')
These dates can be overwritten by setting the attributes as datetime/Timestamp/string.
In [191]: AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)
In [192]: AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)
In [193]: cal.holidays()
Out[193]: 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 [194]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
.....: USLaborDay
.....:
In [195]: cal = get_calendar('ExampleCalendar')
In [196]: cal.rules
Out[196]:
[Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x133b711b8>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>)]
In [197]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)
In [198]: new_cal.rules
Out[198]:
[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 0x133b711b8>),
Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>)]
Resampling¶
Warning
The interface to .resample has changed in 0.18.0 to be more groupby-like and hence more flexible. See the whatsnew docs for a comparison with prior versions.
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 [209]: rng = date_range('1/1/2012', periods=100, freq='S')
In [210]: ts = Series(randint(0, 500, len(rng)), index=rng)
In [211]: ts.resample('5Min').sum()
Out[211]:
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 [212]: ts.resample('5Min').mean()
Out[212]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [213]: ts.resample('5Min').ohlc()
Out[213]:
open high low close
2012-01-01 308 460 9 205
In [214]: ts.resample('5Min').max()
Out[214]:
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 [215]: ts.resample('5Min', closed='right').mean()
Out[215]:
2011-12-31 23:55:00 308.000000
2012-01-01 00:00:00 250.454545
Freq: 5T, dtype: float64
In [216]: ts.resample('5Min', closed='left').mean()
Out[216]:
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 [217]: ts.resample('5Min').mean() # by default label='right'
Out[217]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [218]: ts.resample('5Min', label='left').mean()
Out[218]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [219]: ts.resample('5Min', label='left', loffset='1s').mean()
Out[219]:
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, you can specify a way to upsample and the limit parameter to interpolate over the gaps that are created:
# from secondly to every 250 milliseconds
In [220]: ts[:2].resample('250L').asfreq()
Out[220]:
2012-01-01 00:00:00.000 308.0
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 00:00:01.000 204.0
Freq: 250L, dtype: float64
In [221]: ts[:2].resample('250L').ffill()
Out[221]:
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 [222]: ts[:2].resample('250L').ffill(limit=2)
Out[222]:
2012-01-01 00:00:00.000 308.0
2012-01-01 00:00:00.250 308.0
2012-01-01 00:00:00.500 308.0
2012-01-01 00:00:00.750 NaN
2012-01-01 00:00:01.000 204.0
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 [223]: rng = date_range('2014-1-1', periods=100, freq='D') + Timedelta('1s')
In [224]: ts = Series(range(100), index=rng)
If we want to resample to the full range of the series
In [225]: ts.resample('3T').sum()
Out[225]:
2014-01-01 00:00:00 0.0
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 99.0
Freq: 3T, dtype: float64
We can instead only resample those groups where we have points as follows:
In [226]: from functools import partial
In [227]: from pandas.tseries.frequencies import to_offset
In [228]: def round(t, freq):
.....: freq = to_offset(freq)
.....: return Timestamp((t.value // freq.delta.value) * freq.delta.value)
.....:
In [229]: ts.groupby(partial(round, freq='3T')).sum()
Out[229]:
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
Aggregation¶
Similar to groupby aggregates and the window functions, a Resampler can be selectively resampled.
Resampling a DataFrame, the default will be to act on all columns with the same function.
In [230]: df = pd.DataFrame(np.random.randn(1000, 3),
.....: index=pd.date_range('1/1/2012', freq='S', periods=1000),
.....: columns=['A', 'B', 'C'])
.....:
In [231]: r = df.resample('3T')
In [232]: r.mean()
Out[232]:
A B C
2012-01-01 00:00:00 -0.064588 -0.033823 -0.121514
2012-01-01 00:03:00 -0.041387 0.056909 0.146731
2012-01-01 00:06:00 -0.060262 -0.058837 0.047046
2012-01-01 00:09:00 -0.052470 0.063123 -0.026158
2012-01-01 00:12:00 0.057242 0.186340 -0.003144
2012-01-01 00:15:00 -0.033235 -0.085954 -0.016287
We can select a specific column or columns using standard getitem.
In [233]: r['A'].mean()
Out[233]:
2012-01-01 00:00:00 -0.064588
2012-01-01 00:03:00 -0.041387
2012-01-01 00:06:00 -0.060262
2012-01-01 00:09:00 -0.052470
2012-01-01 00:12:00 0.057242
2012-01-01 00:15:00 -0.033235
Freq: 3T, Name: A, dtype: float64
In [234]: r[['A','B']].mean()
Out[234]:
A B
2012-01-01 00:00:00 -0.064588 -0.033823
2012-01-01 00:03:00 -0.041387 0.056909
2012-01-01 00:06:00 -0.060262 -0.058837
2012-01-01 00:09:00 -0.052470 0.063123
2012-01-01 00:12:00 0.057242 0.186340
2012-01-01 00:15:00 -0.033235 -0.085954
You can pass a list or dict of functions to do aggregation with, outputting a DataFrame:
In [235]: r['A'].agg([np.sum, np.mean, np.std])
Out[235]:
sum mean std
2012-01-01 00:00:00 -11.625881 -0.064588 1.041692
2012-01-01 00:03:00 -7.449739 -0.041387 1.068487
2012-01-01 00:06:00 -10.847234 -0.060262 0.961939
2012-01-01 00:09:00 -9.444580 -0.052470 0.976687
2012-01-01 00:12:00 10.303564 0.057242 1.049493
2012-01-01 00:15:00 -3.323491 -0.033235 1.029529
If a dict is passed, the keys will be used to name the columns. Otherwise the function’s name (stored in the function object) will be used.
In [236]: r['A'].agg({'result1' : np.sum,
.....: 'result2' : np.mean})
.....:
Out[236]:
result2 result1
2012-01-01 00:00:00 -0.064588 -11.625881
2012-01-01 00:03:00 -0.041387 -7.449739
2012-01-01 00:06:00 -0.060262 -10.847234
2012-01-01 00:09:00 -0.052470 -9.444580
2012-01-01 00:12:00 0.057242 10.303564
2012-01-01 00:15:00 -0.033235 -3.323491
On a resampled DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index:
In [237]: r.agg([np.sum, np.mean])
Out[237]:
A B C \
sum mean sum mean sum
2012-01-01 00:00:00 -11.625881 -0.064588 -6.088060 -0.033823 -21.872530
2012-01-01 00:03:00 -7.449739 -0.041387 10.243678 0.056909 26.411633
2012-01-01 00:06:00 -10.847234 -0.060262 -10.590584 -0.058837 8.468289
2012-01-01 00:09:00 -9.444580 -0.052470 11.362228 0.063123 -4.708526
2012-01-01 00:12:00 10.303564 0.057242 33.541257 0.186340 -0.565895
2012-01-01 00:15:00 -3.323491 -0.033235 -8.595393 -0.085954 -1.628689
mean
2012-01-01 00:00:00 -0.121514
2012-01-01 00:03:00 0.146731
2012-01-01 00:06:00 0.047046
2012-01-01 00:09:00 -0.026158
2012-01-01 00:12:00 -0.003144
2012-01-01 00:15:00 -0.016287
By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:
In [238]: r.agg({'A' : np.sum,
.....: 'B' : lambda x: np.std(x, ddof=1)})
.....:
Out[238]:
A B
2012-01-01 00:00:00 -11.625881 1.043263
2012-01-01 00:03:00 -7.449739 1.058534
2012-01-01 00:06:00 -10.847234 0.949264
2012-01-01 00:09:00 -9.444580 1.028096
2012-01-01 00:12:00 10.303564 0.884586
2012-01-01 00:15:00 -3.323491 1.035476
The function names can also be strings. In order for a string to be valid it must be implemented on the Resampled object
In [239]: r.agg({'A' : 'sum', 'B' : 'std'})
Out[239]:
A B
2012-01-01 00:00:00 -11.625881 1.043263
2012-01-01 00:03:00 -7.449739 1.058534
2012-01-01 00:06:00 -10.847234 0.949264
2012-01-01 00:09:00 -9.444580 1.028096
2012-01-01 00:12:00 10.303564 0.884586
2012-01-01 00:15:00 -3.323491 1.035476
Furthermore, you can also specify multiple aggregation functions for each column separately.
In [240]: r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })
Out[240]:
A B
sum std mean std
2012-01-01 00:00:00 -11.625881 1.041692 -0.033823 1.043263
2012-01-01 00:03:00 -7.449739 1.068487 0.056909 1.058534
2012-01-01 00:06:00 -10.847234 0.961939 -0.058837 0.949264
2012-01-01 00:09:00 -9.444580 0.976687 0.063123 1.028096
2012-01-01 00:12:00 10.303564 1.049493 0.186340 0.884586
2012-01-01 00:15:00 -3.323491 1.029529 -0.085954 1.035476
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 [241]: Period('2012', freq='A-DEC')
Out[241]: Period('2012', 'A-DEC')
In [242]: Period('2012-1-1', freq='D')
Out[242]: Period('2012-01-01', 'D')
In [243]: Period('2012-1-1 19:00', freq='H')
Out[243]: Period('2012-01-01 19:00', 'H')
In [244]: Period('2012-1-1 19:00', freq='5H')
Out[244]: 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 [245]: p = Period('2012', freq='A-DEC')
In [246]: p + 1
Out[246]: Period('2013', 'A-DEC')
In [247]: p - 3
Out[247]: Period('2009', 'A-DEC')
In [248]: p = Period('2012-01', freq='2M')
In [249]: p + 2
Out[249]: Period('2012-05', '2M')
In [250]: p - 1
Out[250]: Period('2011-11', '2M')
In [251]: p == Period('2012-01', freq='3M')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-251-196036327bc8> in <module>()
----> 1 p == Period('2012-01', freq='3M')
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/src/period.pyx in pandas._period.Period.__richcmp__ (pandas/src/period.c:12558)()
768 if other.freq != self.freq:
769 msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr)
--> 770 raise ValueError(msg)
771 if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT:
772 return _nat_scalar_rules[op]
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. Otherwise, ValueError will be raised.
In [252]: p = Period('2014-07-01 09:00', freq='H')
In [253]: p + Hour(2)
Out[253]: Period('2014-07-01 11:00', 'H')
In [254]: p + timedelta(minutes=120)
Out[254]: Period('2014-07-01 11:00', 'H')
In [255]: p + np.timedelta64(7200, 's')
Out[255]: 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 [256]: p = Period('2014-07', freq='M')
In [257]: p + MonthEnd(3)
Out[257]: 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 [258]: Period('2012', freq='A-DEC') - Period('2002', freq='A-DEC')
Out[258]: 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 [259]: prng = period_range('1/1/2011', '1/1/2012', freq='M')
In [260]: prng
Out[260]:
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 [261]: PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Out[261]: PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='int64', freq='M')
Passing multiplied frequency outputs a sequence of Period which has multiplied span.
In [262]: PeriodIndex(start='2014-01', freq='3M', periods=4)
Out[262]: 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 [263]: ps = Series(randn(len(prng)), prng)
In [264]: ps
Out[264]:
2011-01 0.206847
2011-02 -2.916901
2011-03 0.514474
2011-04 1.346470
2011-05 0.816397
2011-06 2.258648
2011-07 0.494789
2011-08 0.301239
2011-09 0.464776
2011-10 -1.393581
2011-11 0.056780
2011-12 0.197035
2012-01 2.261385
Freq: M, dtype: float64
PeriodIndex supports addition and subtraction with the same rule as Period.
In [265]: idx = period_range('2014-07-01 09:00', periods=5, freq='H')
In [266]: idx
Out[266]:
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 [267]: idx + Hour(2)
Out[267]:
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 [268]: idx = period_range('2014-07', periods=5, freq='M')
In [269]: idx
Out[269]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='int64', freq='M')
In [270]: idx + MonthEnd(3)
Out[270]: 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 [271]: ps['2011-01']
Out[271]: 0.20684702140409306
In [272]: ps[datetime(2011, 12, 25):]
Out[272]:
2011-12 0.197035
2012-01 2.261385
Freq: M, dtype: float64
In [273]: ps['10/31/2011':'12/31/2011']
Out[273]:
2011-10 -1.393581
2011-11 0.056780
2011-12 0.197035
Freq: M, dtype: float64
Passing a string representing a lower frequency than PeriodIndex returns partial sliced data.
In [274]: ps['2011']
Out[274]:
2011-01 0.206847
2011-02 -2.916901
2011-03 0.514474
2011-04 1.346470
2011-05 0.816397
2011-06 2.258648
2011-07 0.494789
2011-08 0.301239
2011-09 0.464776
2011-10 -1.393581
2011-11 0.056780
2011-12 0.197035
Freq: M, dtype: float64
In [275]: dfp = DataFrame(randn(600,1), columns=['A'],
.....: index=period_range('2013-01-01 9:00', periods=600, freq='T'))
.....:
In [276]: dfp
Out[276]:
A
2013-01-01 09:00 -0.329583
2013-01-01 09:01 -0.538468
2013-01-01 09:02 -1.365819
2013-01-01 09:03 -0.969051
2013-01-01 09:04 -0.331152
2013-01-01 09:05 -0.245334
2013-01-01 09:06 0.182165
... ...
2013-01-01 18:53 -1.732799
2013-01-01 18:54 0.313342
2013-01-01 18:55 1.741982
2013-01-01 18:56 0.522460
2013-01-01 18:57 0.118710
2013-01-01 18:58 0.167517
2013-01-01 18:59 0.922883
[600 rows x 1 columns]
In [277]: dfp['2013-01-01 10H']
Out[277]:
A
2013-01-01 10:00 -1.132955
2013-01-01 10:01 -0.308975
2013-01-01 10:02 0.542520
2013-01-01 10:03 1.061068
2013-01-01 10:04 0.754005
2013-01-01 10:05 0.352933
2013-01-01 10:06 0.671551
... ...
2013-01-01 10:53 -0.210061
2013-01-01 10:54 -0.266928
2013-01-01 10:55 0.327742
2013-01-01 10:56 -0.865621
2013-01-01 10:57 -1.167818
2013-01-01 10:58 -2.081748
2013-01-01 10:59 -0.527146
[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 [278]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[278]:
A
2013-01-01 10:00 -1.132955
2013-01-01 10:01 -0.308975
2013-01-01 10:02 0.542520
2013-01-01 10:03 1.061068
2013-01-01 10:04 0.754005
2013-01-01 10:05 0.352933
2013-01-01 10:06 0.671551
... ...
2013-01-01 11:53 2.241511
2013-01-01 11:54 -1.219875
2013-01-01 11:55 2.568241
2013-01-01 11:56 -0.590204
2013-01-01 11:57 1.539990
2013-01-01 11:58 -1.224826
2013-01-01 11:59 0.578798
[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 [279]: p = Period('2011', freq='A-DEC')
In [280]: p
Out[280]: 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 [281]: p.asfreq('M', how='start')
Out[281]: Period('2011-01', 'M')
In [282]: p.asfreq('M', how='end')
Out[282]: Period('2011-12', 'M')
The shorthands ‘s’ and ‘e’ are provided for convenience:
In [283]: p.asfreq('M', 's')
Out[283]: Period('2011-01', 'M')
In [284]: p.asfreq('M', 'e')
Out[284]: 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 [285]: p = Period('2011-12', freq='M')
In [286]: p.asfreq('A-NOV')
Out[286]: 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 [287]: p = Period('2012Q1', freq='Q-DEC')
In [288]: p.asfreq('D', 's')
Out[288]: Period('2012-01-01', 'D')
In [289]: p.asfreq('D', 'e')
Out[289]: Period('2012-03-31', 'D')
Q-MAR defines fiscal year end in March:
In [290]: p = Period('2011Q4', freq='Q-MAR')
In [291]: p.asfreq('D', 's')
Out[291]: Period('2011-01-01', 'D')
In [292]: p.asfreq('D', 'e')
Out[292]: 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 [293]: rng = date_range('1/1/2012', periods=5, freq='M')
In [294]: ts = Series(randn(len(rng)), index=rng)
In [295]: ts
Out[295]:
2012-01-31 1.721104
2012-02-29 1.931253
2012-03-31 -0.184594
2012-04-30 0.249656
2012-05-31 -0.978151
Freq: M, dtype: float64
In [296]: ps = ts.to_period()
In [297]: ps
Out[297]:
2012-01 1.721104
2012-02 1.931253
2012-03 -0.184594
2012-04 0.249656
2012-05 -0.978151
Freq: M, dtype: float64
In [298]: ps.to_timestamp()
Out[298]:
2012-01-01 1.721104
2012-02-01 1.931253
2012-03-01 -0.184594
2012-04-01 0.249656
2012-05-01 -0.978151
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 [299]: ps.to_timestamp('D', how='s')
Out[299]:
2012-01-01 1.721104
2012-02-01 1.931253
2012-03-01 -0.184594
2012-04-01 0.249656
2012-05-01 -0.978151
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 [300]: prng = period_range('1990Q1', '2000Q4', freq='Q-NOV')
In [301]: ts = Series(randn(len(prng)), prng)
In [302]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In [303]: ts.head()
Out[303]:
1990-03-01 09:00 -0.873389
1990-06-01 09:00 -0.109291
1990-09-01 09:00 -0.637235
1990-12-01 09:00 -1.735925
1991-03-01 09:00 2.096946
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 [304]: span = period_range('1215-01-01', '1381-01-01', freq='D')
In [305]: span
Out[305]:
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 [306]: s = Series([20121231, 20141130, 99991231])
In [307]: s
Out[307]:
0 20121231
1 20141130
2 99991231
dtype: int64
In [308]: def conv(x):
.....: return Period(year = x // 10000, month = x//100 % 100, day = x%100, freq='D')
.....:
In [309]: s.apply(conv)
Out[309]:
0 2012-12-31
1 2014-11-30
2 9999-12-31
dtype: object
In [310]: s.apply(conv)[2]
Out[310]: Period('9999-12-31', 'D')
These can easily be converted to a PeriodIndex
In [311]: span = PeriodIndex(s.apply(conv))
In [312]: span
Out[312]: 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 [313]: rng = date_range('3/6/2012 00:00', periods=15, freq='D')
In [314]: rng.tz is None
Out[314]: 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 using from 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 as pytz.
# pytz
In [315]: rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz='Europe/London')
.....:
In [316]: rng_pytz.tz
Out[316]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>
# dateutil
In [317]: rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz='dateutil/Europe/London')
.....:
In [318]: rng_dateutil.tz
Out[318]: tzfile('/usr/share/zoneinfo/Europe/London')
# dateutil - utc special case
In [319]: rng_utc = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=dateutil.tz.tzutc())
.....:
In [320]: rng_utc.tz
Out[320]: 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 [321]: tz_pytz = pytz.timezone('Europe/London')
In [322]: rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=tz_pytz)
.....:
In [323]: rng_pytz.tz == tz_pytz
Out[323]: True
# dateutil
In [324]: tz_dateutil = dateutil.tz.gettz('Europe/London')
In [325]: rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=tz_dateutil)
.....:
In [326]: rng_dateutil.tz == tz_dateutil
Out[326]: 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 [327]: ts = Series(randn(len(rng)), rng)
In [328]: ts_utc = ts.tz_localize('UTC')
In [329]: ts_utc
Out[329]:
2012-03-06 00:00:00+00:00 0.406331
2012-03-07 00:00:00+00:00 0.326152
2012-03-08 00:00:00+00:00 0.455487
2012-03-09 00:00:00+00:00 -0.173426
2012-03-10 00:00:00+00:00 0.832223
2012-03-11 00:00:00+00:00 -0.166404
2012-03-12 00:00:00+00:00 -0.918468
2012-03-13 00:00:00+00:00 0.076835
2012-03-14 00:00:00+00:00 0.039694
2012-03-15 00:00:00+00:00 -1.246487
2012-03-16 00:00:00+00:00 -0.146705
2012-03-17 00:00:00+00:00 -1.392724
2012-03-18 00:00:00+00:00 0.523910
2012-03-19 00:00:00+00:00 1.578829
2012-03-20 00:00:00+00:00 0.654179
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 [330]: ts_utc.tz_convert('US/Eastern')
Out[330]:
2012-03-05 19:00:00-05:00 0.406331
2012-03-06 19:00:00-05:00 0.326152
2012-03-07 19:00:00-05:00 0.455487
2012-03-08 19:00:00-05:00 -0.173426
2012-03-09 19:00:00-05:00 0.832223
2012-03-10 19:00:00-05:00 -0.166404
2012-03-11 20:00:00-04:00 -0.918468
2012-03-12 20:00:00-04:00 0.076835
2012-03-13 20:00:00-04:00 0.039694
2012-03-14 20:00:00-04:00 -1.246487
2012-03-15 20:00:00-04:00 -0.146705
2012-03-16 20:00:00-04:00 -1.392724
2012-03-17 20:00:00-04:00 0.523910
2012-03-18 20:00:00-04:00 1.578829
2012-03-19 20:00:00-04:00 0.654179
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 [331]: rng_eastern = rng_utc.tz_convert('US/Eastern')
In [332]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')
In [333]: rng_eastern[5]
Out[333]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', offset='D')
In [334]: rng_berlin[5]
Out[334]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', offset='D')
In [335]: rng_eastern[5] == rng_berlin[5]
Out[335]: True
Like Series, DataFrame, and DatetimeIndex, Timestamps can be converted to other time zones using tz_convert:
In [336]: rng_eastern[5]
Out[336]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', offset='D')
In [337]: rng_berlin[5]
Out[337]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', offset='D')
In [338]: rng_eastern[5].tz_convert('Europe/Berlin')
Out[338]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin')
Localization of Timestamps functions just like DatetimeIndex and Series:
In [339]: rng[5]
Out[339]: Timestamp('2012-03-11 00:00:00', offset='D')
In [340]: rng[5].tz_localize('Asia/Shanghai')
Out[340]: 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 [341]: eastern = ts_utc.tz_convert('US/Eastern')
In [342]: berlin = ts_utc.tz_convert('Europe/Berlin')
In [343]: result = eastern + berlin
In [344]: result
Out[344]:
2012-03-06 00:00:00+00:00 0.812662
2012-03-07 00:00:00+00:00 0.652304
2012-03-08 00:00:00+00:00 0.910974
2012-03-09 00:00:00+00:00 -0.346851
2012-03-10 00:00:00+00:00 1.664446
2012-03-11 00:00:00+00:00 -0.332807
2012-03-12 00:00:00+00:00 -1.836936
2012-03-13 00:00:00+00:00 0.153669
2012-03-14 00:00:00+00:00 0.079388
2012-03-15 00:00:00+00:00 -2.492974
2012-03-16 00:00:00+00:00 -0.293409
2012-03-17 00:00:00+00:00 -2.785448
2012-03-18 00:00:00+00:00 1.047819
2012-03-19 00:00:00+00:00 3.157658
2012-03-20 00:00:00+00:00 1.308359
Freq: D, dtype: float64
In [345]: result.index
Out[345]:
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 [346]: didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
In [347]: didx
Out[347]:
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 [348]: didx.tz_localize(None)
Out[348]:
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 [349]: didx.tz_convert(None)
Out[349]:
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 [350]: didx.tz_convert('UCT').tz_localize(None)
Out[350]:
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 [351]: 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 [352]: rng_hourly.tz_localize('US/Eastern')
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-352-8c5fa6a37f5b> in <module>()
----> 1 rng_hourly.tz_localize('US/Eastern')
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/tseries/index.pyc in tz_localize(self, tz, ambiguous)
1841
1842 new_dates = tslib.tz_localize_to_utc(self.asi8, tz,
-> 1843 ambiguous=ambiguous)
1844 new_dates = new_dates.view(_NS_DTYPE)
1845 return self._shallow_copy(new_dates, tz=tz)
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:67354)()
3903 else:
3904 stamp = Timestamp(vals[i])
-> 3905 raise pytz.AmbiguousTimeError("Cannot infer dst time from %r, "\
3906 "try using the 'ambiguous' argument"
3907 % stamp)
AmbiguousTimeError: Cannot infer dst time from Timestamp('2011-11-06 01:00:00'), try using the 'ambiguous' argument
In [353]: rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', ambiguous='infer')
In [354]: rng_hourly_eastern.tolist()
Out[354]:
[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 [355]: rng_hourly_dst = np.array([1, 1, 0, 0, 0])
In [356]: rng_hourly.tz_localize('US/Eastern', ambiguous=rng_hourly_dst).tolist()
Out[356]:
[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 [357]: rng_hourly.tz_localize('US/Eastern', ambiguous='NaT').tolist()
Out[357]:
[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 [358]: didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
In [359]: didx
Out[359]:
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 [360]: didx.tz_localize(None)
Out[360]:
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 [361]: didx.tz_convert(None)
Out[361]:
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 [362]: didx.tz_convert('UCT').tz_localize(None)
Out[362]:
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 [363]: s_naive = pd.Series(pd.date_range('20130101',periods=3))
In [364]: s_naive
Out[364]:
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 [365]: s_aware = pd.Series(pd.date_range('20130101',periods=3,tz='US/Eastern'))
In [366]: s_aware
Out[366]:
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 [367]: s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[367]:
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 [368]: s_naive.astype('datetime64[ns, US/Eastern]')
Out[368]:
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 [369]: s_aware.astype('datetime64[ns]')
Out[369]:
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 [370]: s_aware.astype('datetime64[ns, CET]')
Out[370]:
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 [371]: s_naive.values
Out[371]:
array(['2012-12-31T18:00:00.000000000-0600',
'2013-01-01T18:00:00.000000000-0600',
'2013-01-02T18:00:00.000000000-0600'], dtype='datetime64[ns]')
In [372]: s_aware.values
Out[372]:
array(['2012-12-31T23:00:00.000000000-0600',
'2013-01-01T23:00:00.000000000-0600',
'2013-01-02T23:00:00.000000000-0600'], dtype='datetime64[ns]')
Further note that once converted to a numpy array these would lose the tz tenor.
In [373]: Series(s_aware.values)
Out[373]:
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 [374]: pd.Series(s_aware.values).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[374]:
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]