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. Using the NumPy datetime64 and timedelta64 dtypes,
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 = pd.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 = pd.Series(np.random.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 Timestamp |
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]: pd.Timestamp(datetime(2012, 5, 1))
Out[8]: Timestamp('2012-05-01 00:00:00')
In [9]: pd.Timestamp('2012-05-01')
Out[9]: Timestamp('2012-05-01 00:00:00')
In [10]: pd.Timestamp(2012, 5, 1)
Out[10]: 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 [11]: pd.Period('2011-01')
Out[11]: Period('2011-01', 'M')
In [12]: pd.Period('2012-05', freq='D')
Out[12]: Period('2012-05-01', 'D')
Timestamp and Period can be the index. Lists of Timestamp and
Period are automatically coerced to DatetimeIndex and PeriodIndex
respectively.
In [13]: dates = [pd.Timestamp('2012-05-01'), pd.Timestamp('2012-05-02'), pd.Timestamp('2012-05-03')]
In [14]: ts = pd.Series(np.random.randn(3), dates)
In [15]: type(ts.index)
Out[15]: pandas.core.indexes.datetimes.DatetimeIndex
In [16]: ts.index
Out[16]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
In [17]: ts
Out[17]:
2012-05-01 -0.410001
2012-05-02 -0.078638
2012-05-03 0.545952
dtype: float64
In [18]: periods = [pd.Period('2012-01'), pd.Period('2012-02'), pd.Period('2012-03')]
In [19]: ts = pd.Series(np.random.randn(3), periods)
In [20]: type(ts.index)
Out[20]: pandas.core.indexes.period.PeriodIndex
In [21]: ts.index
Out[21]: PeriodIndex(['2012-01', '2012-02', '2012-03'], dtype='period[M]', freq='M')
In [22]: ts
Out[22]:
2012-01 -1.219217
2012-02 -1.226825
2012-03 0.769804
Freq: M, dtype: float64
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 [23]: pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None]))
Out[23]:
0 2009-07-31
1 2010-01-10
2 NaT
dtype: datetime64[ns]
In [24]: pd.to_datetime(['2005/11/23', '2010.12.31'])
Out[24]: 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 [25]: pd.to_datetime(['04-01-2012 10:00'], dayfirst=True)
Out[25]: DatetimeIndex(['2012-01-04 10:00:00'], dtype='datetime64[ns]', freq=None)
In [26]: pd.to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
Out[26]: 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 [27]: pd.to_datetime('2010/11/12')
Out[27]: Timestamp('2010-11-12 00:00:00')
In [28]: pd.Timestamp('2010/11/12')
Out[28]: Timestamp('2010-11-12 00:00:00')
New in version 0.18.1.
You can also pass a DataFrame of integer or string columns to assemble into a Series of Timestamps.
In [29]: df = pd.DataFrame({'year': [2015, 2016],
....: 'month': [2, 3],
....: 'day': [4, 5],
....: 'hour': [2, 3]})
....:
In [30]: pd.to_datetime(df)
Out[30]:
0 2015-02-04 02:00:00
1 2016-03-05 03:00:00
dtype: datetime64[ns]
You can pass only the columns that you need to assemble.
In [31]: pd.to_datetime(df[['year', 'month', 'day']])
Out[31]:
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
pd.to_datetime looks for standard designations of the datetime component in the column names, including:
- required:
year,month,day - optional:
hour,minute,second,millisecond,microsecond,nanosecond
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):
Raise when unparseable, this is the default
In [2]: pd.to_datetime(['2009/07/31', 'asd'], errors='raise')
ValueError: Unknown string format
Return the original input when unparseable
In [4]: pd.to_datetime(['2009/07/31', 'asd'], errors='ignore')
Out[4]: array(['2009/07/31', 'asd'], dtype=object)
Return NaT for input when unparseable
In [6]: pd.to_datetime(['2009/07/31', 'asd'], errors='coerce')
Out[6]: 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 Timestamp s are stored). However,
often epochs are stored in another unit which can be specified. These are computed
from the starting point specified by the Origin Parameter.
Typical epoch stored units
In [32]: pd.to_datetime([1349720105, 1349806505, 1349892905,
....: 1349979305, 1350065705], unit='s')
....:
Out[32]:
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 [33]: pd.to_datetime([1349720105100, 1349720105200, 1349720105300,
....: 1349720105400, 1349720105500 ], unit='ms')
....:
Out[33]:
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)
Note
Epoch times will be rounded to the nearest nanosecond.
Warning
Conversion of float epoch times can lead to inaccurate and unexpected results.
Python floats have about 15 digits precision in
decimal. Rounding during conversion from float to high precision Timestamp is
unavoidable. The only way to achieve exact precision is to use a fixed-width
types (e.g. an int64).
In [34]: pd.to_datetime([1490195805.433, 1490195805.433502912], unit='s')
Out[34]: DatetimeIndex(['2017-03-22 15:16:45.433000', '2017-03-22 15:16:45.433503'], dtype='datetime64[ns]', freq=None)
In [35]: pd.to_datetime(1490195805433502912, unit='ns')
Out[35]: Timestamp('2017-03-22 15:16:45.433502912')
From Timestamps to Epoch¶
To invert the operation from above, namely, to convert from a Timestamp to a ‘unix’ epoch:
In [36]: stamps = pd.date_range('2012-10-08 18:15:05', periods=4, freq='D')
In [37]: stamps
Out[37]:
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'],
dtype='datetime64[ns]', freq='D')
We convert the DatetimeIndex to an int64 array, then divide by the conversion unit.
In [38]: stamps.view('int64') // pd.Timedelta(1, unit='s')
Out[38]: array([1349720105, 1349806505, 1349892905, 1349979305])
Using the Origin Parameter¶
New in version 0.20.0.
Using the origin parameter, one can specify an alternative starting point for creation
of a DatetimeIndex.
Start with 1960-01-01 as the starting date
In [39]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
Out[39]: DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)
The default is set at origin='unix', which defaults to 1970-01-01 00:00:00.
Commonly called ‘unix epoch’ or POSIX time.
In [40]: pd.to_datetime([1, 2, 3], unit='D')
Out[40]: DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)
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 [41]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]
# Note the frequency information
In [42]: index = pd.DatetimeIndex(dates)
In [43]: index
Out[43]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
# Automatically converted to DatetimeIndex
In [44]: index = pd.Index(dates)
In [45]: index
Out[45]: 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 [46]: index = pd.date_range('2000-1-1', periods=1000, freq='M')
In [47]: index
Out[47]:
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 [48]: index = pd.bdate_range('2012-1-1', periods=250)
In [49]: index
Out[49]:
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 [50]: start = datetime(2011, 1, 1)
In [51]: end = datetime(2012, 1, 1)
In [52]: rng = pd.date_range(start, end)
In [53]: rng
Out[53]:
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 [54]: rng = pd.bdate_range(start, end)
In [55]: rng
Out[55]:
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 [56]: pd.date_range(start, end, freq='BM')
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]: pd.date_range(start, end, freq='W')
Out[57]:
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 [58]: pd.bdate_range(end=end, periods=20)
Out[58]:
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 [59]: pd.bdate_range(start=start, periods=20)
Out[59]:
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.
Timestamp limitations¶
Since pandas represents timestamps in nanosecond resolution, the timespan that can be represented using a 64-bit integer is limited to approximately 584 years:
In [60]: pd.Timestamp.min
Out[60]: Timestamp('1677-09-21 00:12:43.145225')
In [61]: pd.Timestamp.max
Out[61]: Timestamp('2262-04-11 23:47:16.854775807')
See here for ways to represent data outside these bound.
Indexing¶
One of the main uses for DatetimeIndex is as an index for pandas objects.
The DatetimeIndex class contains many timeseries related optimizations:
- A large range of dates for various offsets are pre-computed and cached under the hood in order to make generating subsequent date ranges very fast (just have to grab a slice)
- Fast shifting using the
shiftandtshiftmethod on pandas objects- Unioning of overlapping DatetimeIndex objects with the same frequency is very fast (important for fast data alignment)
- Quick access to date fields via properties such as
year,month, etc.- Regularization functions like
snapand very fastasoflogic
DatetimeIndex objects has all the basic functionality of regular Index objects and a smorgasbord of advanced timeseries-specific methods for easy frequency processing.
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 [62]: rng = pd.date_range(start, end, freq='BM')
In [63]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [64]: ts.index
Out[64]:
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 [65]: ts[:5].index
Out[65]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31'],
dtype='datetime64[ns]', freq='BM')
In [66]: ts[::2].index
Out[66]:
DatetimeIndex(['2011-01-31', '2011-03-31', '2011-05-31', '2011-07-29',
'2011-09-30', '2011-11-30'],
dtype='datetime64[ns]', freq='2BM')
Partial String Indexing¶
You can pass in dates and strings that parse to dates as indexing parameters:
In [67]: ts['1/31/2011']
Out[67]: -1.2812473076599531
In [68]: ts[datetime(2011, 12, 25):]
Out[68]:
2011-12-30 0.687738
Freq: BM, dtype: float64
In [69]: ts['10/31/2011':'12/31/2011']
Out[69]:
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 [70]: ts['2011']
Out[70]:
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 [71]: ts['2011-6']
Out[71]:
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 [72]: dft = pd.DataFrame(randn(100000,1),
....: columns=['A'],
....: index=pd.date_range('20130101',periods=100000,freq='T'))
....:
In [73]: dft
Out[73]:
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 [74]: dft['2013']
Out[74]:
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 [75]: dft['2013-1':'2013-2']
Out[75]:
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 [76]: dft['2013-1':'2013-2-28']
Out[76]:
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 [77]: dft['2013-1':'2013-2-28 00:00:00']
Out[77]:
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 [78]: dft['2013-1-15':'2013-1-15 12:30:00']
Out[78]:
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]
New in version 0.18.0.
DatetimeIndex Partial String Indexing also works on DataFrames with a MultiIndex. For example:
In [79]: dft2 = pd.DataFrame(np.random.randn(20, 1),
....: columns=['A'],
....: index=pd.MultiIndex.from_product([pd.date_range('20130101',
....: periods=10,
....: freq='12H'),
....: ['a', 'b']]))
....:
In [80]: dft2
Out[80]:
A
2013-01-01 00:00:00 a -0.659574
b 1.494522
2013-01-01 12:00:00 a -0.778425
b -0.253355
2013-01-02 00:00:00 a -2.816159
b -1.210929
2013-01-02 12:00:00 a 0.144669
... ...
2013-01-04 00:00:00 b -1.624463
2013-01-04 12:00:00 a 0.056912
b 0.149867
2013-01-05 00:00:00 a -1.256173
b 2.324544
2013-01-05 12:00:00 a -1.067396
b -0.660996
[20 rows x 1 columns]
In [81]: dft2.loc['2013-01-05']
Out[81]:
A
2013-01-05 00:00:00 a -1.256173
b 2.324544
2013-01-05 12:00:00 a -1.067396
b -0.660996
In [82]: idx = pd.IndexSlice
In [83]: dft2 = dft2.swaplevel(0, 1).sort_index()
In [84]: dft2.loc[idx[:, '2013-01-05'], :]
Out[84]:
A
a 2013-01-05 00:00:00 -1.256173
2013-01-05 12:00:00 -1.067396
b 2013-01-05 00:00:00 2.324544
2013-01-05 12:00:00 -0.660996
Slice vs. exact match¶
Changed in version 0.20.0.
The same string used as an indexing parameter can be treated either as a slice or as an exact match depending on the resolution of an index. If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match.
For example, let us consider Series object which index has minute resolution.
In [85]: series_minute = pd.Series([1, 2, 3],
....: pd.DatetimeIndex(['2011-12-31 23:59:00',
....: '2012-01-01 00:00:00',
....: '2012-01-01 00:02:00']))
....:
In [86]: series_minute.index.resolution
Out[86]: 'minute'
A timestamp string less accurate than a minute gives a Series object.
In [87]: series_minute['2011-12-31 23']
Out[87]:
2011-12-31 23:59:00 1
dtype: int64
A timestamp string with minute resolution (or more accurate), gives a scalar instead, i.e. it is not casted to a slice.
In [88]: series_minute['2011-12-31 23:59']
Out[88]: 1
In [89]: series_minute['2011-12-31 23:59:00']
Out[89]: 1
If index resolution is second, then, the minute-accurate timestamp gives a Series.
In [90]: series_second = pd.Series([1, 2, 3],
....: pd.DatetimeIndex(['2011-12-31 23:59:59',
....: '2012-01-01 00:00:00',
....: '2012-01-01 00:00:01']))
....:
In [91]: series_second.index.resolution
Out[91]: 'second'
In [92]: series_second['2011-12-31 23:59']
Out[92]:
2011-12-31 23:59:59 1
dtype: int64
If the timestamp string is treated as a slice, it can be used to index DataFrame with [] as well.
In [93]: dft_minute = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
....: index=series_minute.index)
....:
In [94]: dft_minute['2011-12-31 23']
Out[94]:
a b
2011-12-31 23:59:00 1 4
Warning
However if the string is treated as an exact match, the selection in DataFrame’s [] will be column-wise and not row-wise, see Indexing Basics. For example dft_minute['2011-12-31 23:59'] will raise KeyError as '2012-12-31 23:59' has the same resolution as index and there is no column with such name:
To always have unambiguous selection, whether the row is treated as a slice or a single selection, use .loc.
In [95]: dft_minute.loc['2011-12-31 23:59']
Out[95]:
a 1
b 4
Name: 2011-12-31 23:59:00, dtype: int64
Note also that DatetimeIndex resolution cannot be less precise than day.
In [96]: series_monthly = pd.Series([1, 2, 3],
....: pd.DatetimeIndex(['2011-12',
....: '2012-01',
....: '2012-02']))
....:
In [97]: series_monthly.index.resolution
Out[97]: 'day'
In [98]: series_monthly['2011-12'] # returns Series
Out[98]:
2011-12-01 1
dtype: int64
Exact Indexing¶
As discussed in previous section, 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 resolution of the index. In contrast, indexing with Timestamp or datetime objects is exact, because the objects have exact meaning. These also follow the semantics of including both endpoints.
These Timestamp and datetime objects have exact hours, minutes, and seconds, even though they were not explicitly specified (they are 0).
In [99]: dft[datetime(2013, 1, 1):datetime(2013,2,28)]
Out[99]:
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 [100]: dft[datetime(2013, 1, 1, 10, 12, 0):datetime(2013, 2, 28, 10, 12, 0)]
Out[100]:
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 [101]: ts.truncate(before='10/31/2011', after='12/31/2011')
Out[101]:
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 [102]: ts[[0, 2, 6]].index
Out[102]: 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 (does not contain timezone information) |
| time | Returns datetime.time (does not contain timezone information) |
| dayofyear | The ordinal day of year |
| weekofyear | The week ordinal of the year |
| week | The week ordinal of the year |
| dayofweek | The number of the day of the week with Monday=0, Sunday=6 |
| weekday | The number of the day of the week with Monday=0, Sunday=6 |
| weekday_name | The name of the day in a week (ex: Friday) |
| 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) |
| is_leap_year | Logical indicating if the date belongs to a leap year |
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 |
| SemiMonthEnd | 15th (or other day_of_month) and calendar month end |
| SemiMonthBegin | 15th (or other day_of_month) and calendar 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 |
| CustomBusinessHour | custom 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 [103]: d = datetime(2008, 8, 18, 9, 0)
In [104]: d + relativedelta(months=4, days=5)
Out[104]: datetime.datetime(2008, 12, 23, 9, 0)
We could have done the same thing with DateOffset:
In [105]: from pandas.tseries.offsets import *
In [106]: d + DateOffset(months=4, days=5)
Out[106]: 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
rollforwardandrollbackmethods for moving a date forward or backward to the next or previous “offset date”
Subclasses of DateOffset define the apply function which dictates
custom date increment logic, such as adding business days:
class BDay(DateOffset):
"""DateOffset increments between business days"""
def apply(self, other):
...
In [107]: d - 5 * BDay()
Out[107]: Timestamp('2008-08-11 09:00:00')
In [108]: d + BMonthEnd()
Out[108]: Timestamp('2008-08-29 09:00:00')
The rollforward and rollback methods do exactly what you would expect:
In [109]: d
Out[109]: datetime.datetime(2008, 8, 18, 9, 0)
In [110]: offset = BMonthEnd()
In [111]: offset.rollforward(d)
Out[111]: Timestamp('2008-08-29 09:00:00')
In [112]: offset.rollback(d)
Out[112]: 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 [113]: day = Day()
In [114]: day.apply(pd.Timestamp('2014-01-01 09:00'))
Out[114]: Timestamp('2014-01-02 09:00:00')
In [115]: day = Day(normalize=True)
In [116]: day.apply(pd.Timestamp('2014-01-01 09:00'))
Out[116]: Timestamp('2014-01-02 00:00:00')
In [117]: hour = Hour()
In [118]: hour.apply(pd.Timestamp('2014-01-01 22:00'))
Out[118]: Timestamp('2014-01-01 23:00:00')
In [119]: hour = Hour(normalize=True)
In [120]: hour.apply(pd.Timestamp('2014-01-01 22:00'))
Out[120]: Timestamp('2014-01-01 00:00:00')
In [121]: hour.apply(pd.Timestamp('2014-01-01 23:00'))
Out[121]: 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 [122]: d
Out[122]: datetime.datetime(2008, 8, 18, 9, 0)
In [123]: d + Week()
Out[123]: Timestamp('2008-08-25 09:00:00')
In [124]: d + Week(weekday=4)
Out[124]: Timestamp('2008-08-22 09:00:00')
In [125]: (d + Week(weekday=4)).weekday()
Out[125]: 4
In [126]: d - Week()
Out[126]: Timestamp('2008-08-11 09:00:00')
normalize option will be effective for addition and subtraction.
In [127]: d + Week(normalize=True)
Out[127]: Timestamp('2008-08-25 00:00:00')
In [128]: d - Week(normalize=True)
Out[128]: Timestamp('2008-08-11 00:00:00')
Another example is parameterizing YearEnd with the specific ending month:
In [129]: d + YearEnd()
Out[129]: Timestamp('2008-12-31 09:00:00')
In [130]: d + YearEnd(month=6)
Out[130]: 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 [131]: rng = pd.date_range('2012-01-01', '2012-01-03')
In [132]: s = pd.Series(rng)
In [133]: rng
Out[133]: DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03'], dtype='datetime64[ns]', freq='D')
In [134]: rng + DateOffset(months=2)
Out[134]: DatetimeIndex(['2012-03-01', '2012-03-02', '2012-03-03'], dtype='datetime64[ns]', freq='D')
In [135]: s + DateOffset(months=2)
Out[135]:
0 2012-03-01
1 2012-03-02
2 2012-03-03
dtype: datetime64[ns]
In [136]: s - DateOffset(months=2)
Out[136]:
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 [137]: s - Day(2)
Out[137]:
0 2011-12-30
1 2011-12-31
2 2012-01-01
dtype: datetime64[ns]
In [138]: td = s - pd.Series(pd.date_range('2011-12-29', '2011-12-31'))
In [139]: td
Out[139]:
0 3 days
1 3 days
2 3 days
dtype: timedelta64[ns]
In [140]: td + Minute(15)
Out[140]:
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 show a PerformanceWarning
In [141]: rng + BQuarterEnd()
Out[141]: DatetimeIndex(['2012-03-30', '2012-03-30', '2012-03-30'], dtype='datetime64[ns]', freq=None)
Custom Business Days¶
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.
As an interesting example, let’s look at Egypt where a Friday-Saturday weekend is observed.
In [142]: from pandas.tseries.offsets import CustomBusinessDay
In [143]: weekmask_egypt = 'Sun Mon Tue Wed Thu'
# They also observe International Workers' Day so let's
# add that for a couple of years
In [144]: holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]
In [145]: bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
In [146]: dt = datetime(2013, 4, 30)
In [147]: dt + 2 * bday_egypt
Out[147]: Timestamp('2013-05-05 00:00:00')
Let’s map to the weekday names
In [148]: dts = pd.date_range(dt, periods=5, freq=bday_egypt)
In [149]: pd.Series(dts.weekday, dts).map(pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split()))
Out[149]:
2013-04-30 Tue
2013-05-02 Thu
2013-05-05 Sun
2013-05-06 Mon
2013-05-07 Tue
Freq: C, dtype: object
Holiday calendars can be used to provide the list of holidays. See the holiday calendar section for more information.
In [150]: from pandas.tseries.holiday import USFederalHolidayCalendar
In [151]: bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
In [152]: dt = datetime(2014, 1, 17)
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
In [153]: dt + bday_us
Out[153]: Timestamp('2014-01-21 00:00:00')
Monthly offsets that respect a certain holiday calendar can be defined in the usual way.
In [154]: from pandas.tseries.offsets import CustomBusinessMonthBegin
In [155]: bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())
# Skip new years
In [156]: dt = datetime(2013, 12, 17)
In [157]: dt + bmth_us
Out[157]: Timestamp('2014-01-02 00:00:00')
# Define date index with custom offset
In [158]: pd.DatetimeIndex(start='20100101',end='20120101',freq=bmth_us)
Out[158]:
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.
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 [159]: bh = BusinessHour()
In [160]: bh
Out[160]: <BusinessHour: BH=09:00-17:00>
# 2014-08-01 is Friday
In [161]: pd.Timestamp('2014-08-01 10:00').weekday()
Out[161]: 4
In [162]: pd.Timestamp('2014-08-01 10:00') + bh
Out[162]: Timestamp('2014-08-01 11:00:00')
# Below example is the same as: pd.Timestamp('2014-08-01 09:00') + bh
In [163]: pd.Timestamp('2014-08-01 08:00') + bh
Out[163]: Timestamp('2014-08-01 10:00:00')
# If the results is on the end time, move to the next business day
In [164]: pd.Timestamp('2014-08-01 16:00') + bh
Out[164]: Timestamp('2014-08-04 09:00:00')
# Remainings are added to the next day
In [165]: pd.Timestamp('2014-08-01 16:30') + bh
Out[165]: Timestamp('2014-08-04 09:30:00')
# Adding 2 business hours
In [166]: pd.Timestamp('2014-08-01 10:00') + BusinessHour(2)
Out[166]: Timestamp('2014-08-01 12:00:00')
# Subtracting 3 business hours
In [167]: pd.Timestamp('2014-08-01 10:00') + BusinessHour(-3)
Out[167]: 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 [168]: bh = BusinessHour(start='11:00', end=time(20, 0))
In [169]: bh
Out[169]: <BusinessHour: BH=11:00-20:00>
In [170]: pd.Timestamp('2014-08-01 13:00') + bh
Out[170]: Timestamp('2014-08-01 14:00:00')
In [171]: pd.Timestamp('2014-08-01 09:00') + bh
Out[171]: Timestamp('2014-08-01 12:00:00')
In [172]: pd.Timestamp('2014-08-01 18:00') + bh
Out[172]: 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 [173]: bh = BusinessHour(start='17:00', end='09:00')
In [174]: bh
Out[174]: <BusinessHour: BH=17:00-09:00>
In [175]: pd.Timestamp('2014-08-01 17:00') + bh
Out[175]: Timestamp('2014-08-01 18:00:00')
In [176]: pd.Timestamp('2014-08-01 23:00') + bh
Out[176]: Timestamp('2014-08-02 00:00:00')
# Although 2014-08-02 is Satuaday,
# it is valid because it starts from 08-01 (Friday).
In [177]: pd.Timestamp('2014-08-02 04:00') + bh
Out[177]: 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 [178]: pd.Timestamp('2014-08-04 04:00') + bh
Out[178]: 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 [179]: BusinessHour().rollback(pd.Timestamp('2014-08-02 15:00'))
Out[179]: Timestamp('2014-08-01 17:00:00')
In [180]: BusinessHour().rollforward(pd.Timestamp('2014-08-02 15:00'))
Out[180]: Timestamp('2014-08-04 09:00:00')
# It is the same as BusinessHour().apply(pd.Timestamp('2014-08-01 17:00')).
# And it is the same as BusinessHour().apply(pd.Timestamp('2014-08-04 09:00'))
In [181]: BusinessHour().apply(pd.Timestamp('2014-08-02 15:00'))
Out[181]: Timestamp('2014-08-04 10:00:00')
# BusinessDay results (for reference)
In [182]: BusinessHour().rollforward(pd.Timestamp('2014-08-02'))
Out[182]: Timestamp('2014-08-04 09:00:00')
# It is the same as BusinessDay().apply(pd.Timestamp('2014-08-01'))
# The result is the same as rollworward because BusinessDay never overlap.
In [183]: BusinessHour().apply(pd.Timestamp('2014-08-02'))
Out[183]: Timestamp('2014-08-04 10:00:00')
BusinessHour regards Saturday and Sunday as holidays. To use arbitrary holidays,
you can use CustomBusinessHour offset, see Custom Business Hour:
Custom Business Hour¶
New in version 0.18.1.
The CustomBusinessHour is a mixture of BusinessHour and CustomBusinessDay which
allows you to specify arbitrary holidays. CustomBusinessHour works as the same
as BusinessHour except that it skips specified custom holidays.
In [184]: from pandas.tseries.holiday import USFederalHolidayCalendar
In [185]: bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
In [186]: dt = datetime(2014, 1, 17, 15)
In [187]: dt + bhour_us
Out[187]: Timestamp('2014-01-17 16:00:00')
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
In [188]: dt + bhour_us * 2
Out[188]: Timestamp('2014-01-21 09:00:00')
You can use keyword arguments suported by either BusinessHour and CustomBusinessDay.
In [189]: bhour_mon = CustomBusinessHour(start='10:00', weekmask='Tue Wed Thu Fri')
# Monday is skipped because it's a holiday, business hour starts from 10:00
In [190]: dt + bhour_mon * 2
Out[190]: Timestamp('2014-01-21 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 |
| SM | semi-month end frequency (15th and end of month) |
| BM | business month end frequency |
| CBM | custom business month end frequency |
| MS | month start frequency |
| SMS | semi-month start frequency (1st and 15th) |
| 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 [191]: pd.date_range(start, periods=5, freq='B')
Out[191]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07'],
dtype='datetime64[ns]', freq='B')
In [192]: pd.date_range(start, periods=5, freq=BDay())
Out[192]:
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 [193]: pd.date_range(start, periods=10, freq='2h20min')
Out[193]:
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 [194]: pd.date_range(start, periods=10, freq='1D10U')
Out[194]:
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 [195]: pd.Timestamp('2014-01-02') + MonthBegin(n=1)
Out[195]: Timestamp('2014-02-01 00:00:00')
In [196]: pd.Timestamp('2014-01-02') + MonthEnd(n=1)
Out[196]: Timestamp('2014-01-31 00:00:00')
In [197]: pd.Timestamp('2014-01-02') - MonthBegin(n=1)
Out[197]: Timestamp('2014-01-01 00:00:00')
In [198]: pd.Timestamp('2014-01-02') - MonthEnd(n=1)
Out[198]: Timestamp('2013-12-31 00:00:00')
In [199]: pd.Timestamp('2014-01-02') + MonthBegin(n=4)
Out[199]: Timestamp('2014-05-01 00:00:00')
In [200]: pd.Timestamp('2014-01-02') - MonthBegin(n=4)
Out[200]: 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 [201]: pd.Timestamp('2014-01-01') + MonthBegin(n=1)
Out[201]: Timestamp('2014-02-01 00:00:00')
In [202]: pd.Timestamp('2014-01-31') + MonthEnd(n=1)
Out[202]: Timestamp('2014-02-28 00:00:00')
In [203]: pd.Timestamp('2014-01-01') - MonthBegin(n=1)
Out[203]: Timestamp('2013-12-01 00:00:00')
In [204]: pd.Timestamp('2014-01-31') - MonthEnd(n=1)
Out[204]: Timestamp('2013-12-31 00:00:00')
In [205]: pd.Timestamp('2014-01-01') + MonthBegin(n=4)
Out[205]: Timestamp('2014-05-01 00:00:00')
In [206]: pd.Timestamp('2014-01-31') - MonthBegin(n=4)
Out[206]: 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 [207]: pd.Timestamp('2014-01-02') + MonthBegin(n=0)
Out[207]: Timestamp('2014-02-01 00:00:00')
In [208]: pd.Timestamp('2014-01-02') + MonthEnd(n=0)
Out[208]: Timestamp('2014-01-31 00:00:00')
In [209]: pd.Timestamp('2014-01-01') + MonthBegin(n=0)
Out[209]: Timestamp('2014-01-01 00:00:00')
In [210]: pd.Timestamp('2014-01-31') + MonthEnd(n=0)
Out[210]: Timestamp('2014-01-31 00:00:00')
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 [211]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
.....: AbstractHolidayCalendar, nearest_workday, MO
.....:
In [212]: 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 [213]: cal = ExampleCalendar()
In [214]: cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))
Out[214]: 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 [215]: from pandas.tseries.offsets import CDay
In [216]: pd.DatetimeIndex(start='7/1/2012', end='7/10/2012',
.....: freq=CDay(calendar=cal)).to_pydatetime()
.....:
Out[216]:
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 [217]: offset = CustomBusinessDay(calendar=cal)
In [218]: datetime(2012, 5, 25) + offset
Out[218]: Timestamp('2012-05-29 00:00:00')
In [219]: datetime(2012, 7, 3) + offset
Out[219]: Timestamp('2012-07-05 00:00:00')
In [220]: datetime(2012, 7, 3) + 2 * offset
Out[220]: Timestamp('2012-07-06 00:00:00')
In [221]: datetime(2012, 7, 6) + offset
Out[221]: 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 [222]: AbstractHolidayCalendar.start_date
Out[222]: Timestamp('1970-01-01 00:00:00')
In [223]: AbstractHolidayCalendar.end_date
Out[223]: Timestamp('2030-12-31 00:00:00')
These dates can be overwritten by setting the attributes as datetime/Timestamp/string.
In [224]: AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)
In [225]: AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)
In [226]: cal.holidays()
Out[226]: 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 [227]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
.....: USLaborDay
.....:
In [228]: cal = get_calendar('ExampleCalendar')
In [229]: cal.rules
Out[229]:
[Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x13bdbcb70>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>)]
In [230]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)
In [231]: new_cal.rules
Out[231]:
[Holiday: Labor Day (month=9, day=1, offset=<DateOffset: kwds={'weekday': MO(+1)}>),
Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x13bdbcb70>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>)]
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
Starting in version 0.18.1, the resample() function can be used directly from
DataFrameGroupBy objects, see the groupby docs.
Note
.resample() is similar to using a .rolling() operation with a time-based offset, see a discussion here
Basics¶
In [242]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
In [243]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [244]: ts.resample('5Min').sum()
Out[244]:
2012-01-01 24390
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 [245]: ts.resample('5Min').mean()
Out[245]:
2012-01-01 243.9
Freq: 5T, dtype: float64
In [246]: ts.resample('5Min').ohlc()
Out[246]:
open high low close
2012-01-01 161 495 1 245
In [247]: ts.resample('5Min').max()
Out[247]:
2012-01-01 495
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 [248]: ts.resample('5Min', closed='right').mean()
Out[248]:
2011-12-31 23:55:00 161.000000
2012-01-01 00:00:00 244.737374
Freq: 5T, dtype: float64
In [249]: ts.resample('5Min', closed='left').mean()
Out[249]:
2012-01-01 243.9
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 [250]: ts.resample('5Min').mean() # by default label='right'
Out[250]:
2012-01-01 243.9
Freq: 5T, dtype: float64
In [251]: ts.resample('5Min', label='left').mean()
Out[251]:
2012-01-01 243.9
Freq: 5T, dtype: float64
In [252]: ts.resample('5Min', label='left', loffset='1s').mean()
Out[252]:
2012-01-01 00:00:01 243.9
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 [253]: ts[:2].resample('250L').asfreq()
Out[253]:
2012-01-01 00:00:00.000 161.0
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 00:00:01.000 199.0
Freq: 250L, dtype: float64
In [254]: ts[:2].resample('250L').ffill()
Out[254]:
2012-01-01 00:00:00.000 161
2012-01-01 00:00:00.250 161
2012-01-01 00:00:00.500 161
2012-01-01 00:00:00.750 161
2012-01-01 00:00:01.000 199
Freq: 250L, dtype: int64
In [255]: ts[:2].resample('250L').ffill(limit=2)
Out[255]:
2012-01-01 00:00:00.000 161.0
2012-01-01 00:00:00.250 161.0
2012-01-01 00:00:00.500 161.0
2012-01-01 00:00:00.750 NaN
2012-01-01 00:00:01.000 199.0
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 [256]: rng = pd.date_range('2014-1-1', periods=100, freq='D') + pd.Timedelta('1s')
In [257]: ts = pd.Series(range(100), index=rng)
If we want to resample to the full range of the series
In [258]: ts.resample('3T').sum()
Out[258]:
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, Length: 47521, dtype: float64
We can instead only resample those groups where we have points as follows:
In [259]: from functools import partial
In [260]: from pandas.tseries.frequencies import to_offset
In [261]: def round(t, freq):
.....: freq = to_offset(freq)
.....: return pd.Timestamp((t.value // freq.delta.value) * freq.delta.value)
.....:
In [262]: ts.groupby(partial(round, freq='3T')).sum()
Out[262]:
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
Length: 100, dtype: int64
Aggregation¶
Similar to the aggregating API, groupby API, and the window functions API,
a Resampler can be selectively resampled.
Resampling a DataFrame, the default will be to act on all columns with the same function.
In [263]: df = pd.DataFrame(np.random.randn(1000, 3),
.....: index=pd.date_range('1/1/2012', freq='S', periods=1000),
.....: columns=['A', 'B', 'C'])
.....:
In [264]: r = df.resample('3T')
In [265]: r.mean()
Out[265]:
A B C
2012-01-01 00:00:00 -0.220339 0.034854 -0.073757
2012-01-01 00:03:00 0.037070 0.040013 0.053754
2012-01-01 00:06:00 -0.041597 -0.144562 -0.007614
2012-01-01 00:09:00 0.043127 -0.076432 -0.032570
2012-01-01 00:12:00 -0.027609 0.054618 0.056878
2012-01-01 00:15:00 -0.014181 0.043958 0.077734
We can select a specific column or columns using standard getitem.
In [266]: r['A'].mean()
Out[266]:
2012-01-01 00:00:00 -0.220339
2012-01-01 00:03:00 0.037070
2012-01-01 00:06:00 -0.041597
2012-01-01 00:09:00 0.043127
2012-01-01 00:12:00 -0.027609
2012-01-01 00:15:00 -0.014181
Freq: 3T, Name: A, dtype: float64
In [267]: r[['A','B']].mean()
Out[267]:
A B
2012-01-01 00:00:00 -0.220339 0.034854
2012-01-01 00:03:00 0.037070 0.040013
2012-01-01 00:06:00 -0.041597 -0.144562
2012-01-01 00:09:00 0.043127 -0.076432
2012-01-01 00:12:00 -0.027609 0.054618
2012-01-01 00:15:00 -0.014181 0.043958
You can pass a list or dict of functions to do aggregation with, outputting a DataFrame:
In [268]: r['A'].agg([np.sum, np.mean, np.std])
Out[268]:
sum mean std
2012-01-01 00:00:00 -39.660974 -0.220339 1.033912
2012-01-01 00:03:00 6.672559 0.037070 0.971503
2012-01-01 00:06:00 -7.487453 -0.041597 1.018418
2012-01-01 00:09:00 7.762901 0.043127 1.025842
2012-01-01 00:12:00 -4.969624 -0.027609 0.961649
2012-01-01 00:15:00 -1.418119 -0.014181 0.978847
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 [269]: r.agg([np.sum, np.mean])
Out[269]:
A B C \
sum mean sum mean sum
2012-01-01 00:00:00 -39.660974 -0.220339 6.273786 0.034854 -13.276324
2012-01-01 00:03:00 6.672559 0.037070 7.202361 0.040013 9.675632
2012-01-01 00:06:00 -7.487453 -0.041597 -26.021155 -0.144562 -1.370600
2012-01-01 00:09:00 7.762901 0.043127 -13.757837 -0.076432 -5.862640
2012-01-01 00:12:00 -4.969624 -0.027609 9.831208 0.054618 10.237970
2012-01-01 00:15:00 -1.418119 -0.014181 4.395766 0.043958 7.773442
mean
2012-01-01 00:00:00 -0.073757
2012-01-01 00:03:00 0.053754
2012-01-01 00:06:00 -0.007614
2012-01-01 00:09:00 -0.032570
2012-01-01 00:12:00 0.056878
2012-01-01 00:15:00 0.077734
By passing a dict to aggregate you can apply a different aggregation to the
columns of a DataFrame:
In [270]: r.agg({'A' : np.sum,
.....: 'B' : lambda x: np.std(x, ddof=1)})
.....:
Out[270]:
A B
2012-01-01 00:00:00 -39.660974 1.004756
2012-01-01 00:03:00 6.672559 0.963559
2012-01-01 00:06:00 -7.487453 0.950766
2012-01-01 00:09:00 7.762901 0.949182
2012-01-01 00:12:00 -4.969624 1.093736
2012-01-01 00:15:00 -1.418119 1.028869
The function names can also be strings. In order for a string to be valid it must be implemented on the Resampled object
In [271]: r.agg({'A' : 'sum', 'B' : 'std'})
Out[271]:
A B
2012-01-01 00:00:00 -39.660974 1.004756
2012-01-01 00:03:00 6.672559 0.963559
2012-01-01 00:06:00 -7.487453 0.950766
2012-01-01 00:09:00 7.762901 0.949182
2012-01-01 00:12:00 -4.969624 1.093736
2012-01-01 00:15:00 -1.418119 1.028869
Furthermore, you can also specify multiple aggregation functions for each column separately.
In [272]: r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })
Out[272]:
A B
sum std mean std
2012-01-01 00:00:00 -39.660974 1.033912 0.034854 1.004756
2012-01-01 00:03:00 6.672559 0.971503 0.040013 0.963559
2012-01-01 00:06:00 -7.487453 1.018418 -0.144562 0.950766
2012-01-01 00:09:00 7.762901 1.025842 -0.076432 0.949182
2012-01-01 00:12:00 -4.969624 0.961649 0.054618 1.093736
2012-01-01 00:15:00 -1.418119 0.978847 0.043958 1.028869
If a DataFrame does not have a datetimelike index, but instead you want
to resample based on datetimelike column in the frame, it can passed to the
on keyword.
In [273]: df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
.....: 'a': np.arange(5)},
.....: index=pd.MultiIndex.from_arrays([
.....: [1,2,3,4,5],
.....: pd.date_range('2015-01-01', freq='W', periods=5)],
.....: names=['v','d']))
.....:
In [274]: df
Out[274]:
a date
v d
1 2015-01-04 0 2015-01-04
2 2015-01-11 1 2015-01-11
3 2015-01-18 2 2015-01-18
4 2015-01-25 3 2015-01-25
5 2015-02-01 4 2015-02-01
In [275]: df.resample('M', on='date').sum()
Out[275]:
a
date
2015-01-31 6
2015-02-28 4
Similarly, if you instead want to resample by a datetimelike
level of MultiIndex, its name or location can be passed to the
level keyword.
In [276]: df.resample('M', level='d').sum()
Out[276]:
a
d
2015-01-31 6
2015-02-28 4
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 [277]: pd.Period('2012', freq='A-DEC')
Out[277]: Period('2012', 'A-DEC')
In [278]: pd.Period('2012-1-1', freq='D')
Out[278]: Period('2012-01-01', 'D')
In [279]: pd.Period('2012-1-1 19:00', freq='H')
Out[279]: Period('2012-01-01 19:00', 'H')
In [280]: pd.Period('2012-1-1 19:00', freq='5H')
Out[280]: 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 [281]: p = pd.Period('2012', freq='A-DEC')
In [282]: p + 1
Out[282]: Period('2013', 'A-DEC')
In [283]: p - 3
Out[283]: Period('2009', 'A-DEC')
In [284]: p = pd.Period('2012-01', freq='2M')
In [285]: p + 2
Out[285]: Period('2012-05', '2M')
In [286]: p - 1
Out[286]: Period('2011-11', '2M')
In [287]: p == pd.Period('2012-01', freq='3M')
---------------------------------------------------------------------------
IncompatibleFrequency Traceback (most recent call last)
<ipython-input-287-ff54ce3238f5> in <module>()
----> 1 p == pd.Period('2012-01', freq='3M')
~/sandbox/pandas/pandas/_libs/period.pyx in pandas._libs.period._Period.__richcmp__ (pandas/_libs/period.c:11842)()
IncompatibleFrequency: 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 [288]: p = pd.Period('2014-07-01 09:00', freq='H')
In [289]: p + Hour(2)
Out[289]: Period('2014-07-01 11:00', 'H')
In [290]: p + timedelta(minutes=120)
Out[290]: Period('2014-07-01 11:00', 'H')
In [291]: p + np.timedelta64(7200, 's')
Out[291]: 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 [292]: p = pd.Period('2014-07', freq='M')
In [293]: p + MonthEnd(3)
Out[293]: 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 [294]: pd.Period('2012', freq='A-DEC') - pd.Period('2002', freq='A-DEC')
Out[294]: 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 [295]: prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')
In [296]: prng
Out[296]:
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='period[M]', freq='M')
The PeriodIndex constructor can also be used directly:
In [297]: pd.PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Out[297]: PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]', freq='M')
Passing multiplied frequency outputs a sequence of Period which
has multiplied span.
In [298]: pd.PeriodIndex(start='2014-01', freq='3M', periods=4)
Out[298]: PeriodIndex(['2014-01', '2014-04', '2014-07', '2014-10'], dtype='period[3M]', freq='3M')
Just like DatetimeIndex, a PeriodIndex can also be used to index pandas
objects:
In [299]: ps = pd.Series(np.random.randn(len(prng)), prng)
In [300]: ps
Out[300]:
2011-01 -1.022670
2011-02 1.371155
2011-03 1.035277
2011-04 1.694400
2011-05 -1.659733
2011-06 0.511432
2011-07 0.433176
2011-08 -0.317955
2011-09 -0.517114
2011-10 -0.310466
2011-11 0.543957
2011-12 0.492003
2012-01 0.193420
Freq: M, dtype: float64
PeriodIndex supports addition and subtraction with the same rule as Period.
In [301]: idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
In [302]: idx
Out[302]:
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='period[H]', freq='H')
In [303]: idx + Hour(2)
Out[303]:
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='period[H]', freq='H')
In [304]: idx = pd.period_range('2014-07', periods=5, freq='M')
In [305]: idx
Out[305]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]', freq='M')
In [306]: idx + MonthEnd(3)
Out[306]: PeriodIndex(['2014-10', '2014-11', '2014-12', '2015-01', '2015-02'], dtype='period[M]', freq='M')
PeriodIndex has its own dtype named period, refer to Period Dtypes.
Period Dtypes¶
New in version 0.19.0.
PeriodIndex has a custom period dtype. This is a pandas extension
dtype similar to the timezone aware dtype (datetime64[ns, tz]).
The period dtype holds the freq attribute and is represented with
period[freq] like period[D] or period[M], using frequency strings.
In [307]: pi = pd.period_range('2016-01-01', periods=3, freq='M')
In [308]: pi
Out[308]: PeriodIndex(['2016-01', '2016-02', '2016-03'], dtype='period[M]', freq='M')
In [309]: pi.dtype
Out[309]: period[M]
The period dtype can be used in .astype(...). It allows one to change the
freq of a PeriodIndex like .asfreq() and convert a
DatetimeIndex to PeriodIndex like to_period():
# change monthly freq to daily freq
In [310]: pi.astype('period[D]')
Out[310]: PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]', freq='D')
# convert to DatetimeIndex
In [311]: pi.astype('datetime64[ns]')
Out[311]: DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='datetime64[ns]', freq='MS')
# convert to PeriodIndex
In [312]: dti = pd.date_range('2011-01-01', freq='M', periods=3)
In [313]: dti
Out[313]: DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], dtype='datetime64[ns]', freq='M')
In [314]: dti.astype('period[M]')
Out[314]: PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]', 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 [315]: ps['2011-01']
Out[315]: -1.022669594890105
In [316]: ps[datetime(2011, 12, 25):]
Out[316]:
2011-12 0.492003
2012-01 0.193420
Freq: M, dtype: float64
In [317]: ps['10/31/2011':'12/31/2011']
Out[317]:
2011-10 -0.310466
2011-11 0.543957
2011-12 0.492003
Freq: M, dtype: float64
Passing a string representing a lower frequency than PeriodIndex returns partial sliced data.
In [318]: ps['2011']
Out[318]:
2011-01 -1.022670
2011-02 1.371155
2011-03 1.035277
2011-04 1.694400
2011-05 -1.659733
2011-06 0.511432
2011-07 0.433176
2011-08 -0.317955
2011-09 -0.517114
2011-10 -0.310466
2011-11 0.543957
2011-12 0.492003
Freq: M, dtype: float64
In [319]: dfp = pd.DataFrame(np.random.randn(600,1),
.....: columns=['A'],
.....: index=pd.period_range('2013-01-01 9:00', periods=600, freq='T'))
.....:
In [320]: dfp
Out[320]:
A
2013-01-01 09:00 0.197720
2013-01-01 09:01 -0.284769
2013-01-01 09:02 0.061491
2013-01-01 09:03 1.630257
2013-01-01 09:04 2.042442
2013-01-01 09:05 -0.804392
2013-01-01 09:06 0.212760
... ...
2013-01-01 18:53 0.150586
2013-01-01 18:54 -0.679569
2013-01-01 18:55 -0.910216
2013-01-01 18:56 -0.413168
2013-01-01 18:57 -0.247752
2013-01-01 18:58 1.590875
2013-01-01 18:59 -2.005294
[600 rows x 1 columns]
In [321]: dfp['2013-01-01 10H']
Out[321]:
A
2013-01-01 10:00 -0.569936
2013-01-01 10:01 -1.179183
2013-01-01 10:02 -0.838602
2013-01-01 10:03 -1.727539
2013-01-01 10:04 1.334027
2013-01-01 10:05 0.417423
2013-01-01 10:06 -0.221189
... ...
2013-01-01 10:53 -0.375925
2013-01-01 10:54 0.212750
2013-01-01 10:55 -0.592417
2013-01-01 10:56 -0.466064
2013-01-01 10:57 -1.715347
2013-01-01 10:58 -0.634913
2013-01-01 10:59 -0.809471
[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 [322]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[322]:
A
2013-01-01 10:00 -0.569936
2013-01-01 10:01 -1.179183
2013-01-01 10:02 -0.838602
2013-01-01 10:03 -1.727539
2013-01-01 10:04 1.334027
2013-01-01 10:05 0.417423
2013-01-01 10:06 -0.221189
... ...
2013-01-01 11:53 0.616198
2013-01-01 11:54 2.843156
2013-01-01 11:55 0.572537
2013-01-01 11:56 1.709706
2013-01-01 11:57 -0.205490
2013-01-01 11:58 1.759719
2013-01-01 11:59 -1.181485
[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 [323]: p = pd.Period('2011', freq='A-DEC')
In [324]: p
Out[324]: 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 [325]: p.asfreq('M', how='start')
Out[325]: Period('2011-01', 'M')
In [326]: p.asfreq('M', how='end')
Out[326]: Period('2011-12', 'M')
The shorthands ‘s’ and ‘e’ are provided for convenience:
In [327]: p.asfreq('M', 's')
Out[327]: Period('2011-01', 'M')
In [328]: p.asfreq('M', 'e')
Out[328]: 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 [329]: p = pd.Period('2011-12', freq='M')
In [330]: p.asfreq('A-NOV')
Out[330]: 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 [331]: p = pd.Period('2012Q1', freq='Q-DEC')
In [332]: p.asfreq('D', 's')
Out[332]: Period('2012-01-01', 'D')
In [333]: p.asfreq('D', 'e')
Out[333]: Period('2012-03-31', 'D')
Q-MAR defines fiscal year end in March:
In [334]: p = pd.Period('2011Q4', freq='Q-MAR')
In [335]: p.asfreq('D', 's')
Out[335]: Period('2011-01-01', 'D')
In [336]: p.asfreq('D', 'e')
Out[336]: 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 [337]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
In [338]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [339]: ts
Out[339]:
2012-01-31 2.167674
2012-02-29 -1.505130
2012-03-31 1.005802
2012-04-30 0.481525
2012-05-31 -0.352151
Freq: M, dtype: float64
In [340]: ps = ts.to_period()
In [341]: ps
Out[341]:
2012-01 2.167674
2012-02 -1.505130
2012-03 1.005802
2012-04 0.481525
2012-05 -0.352151
Freq: M, dtype: float64
In [342]: ps.to_timestamp()
Out[342]:
2012-01-01 2.167674
2012-02-01 -1.505130
2012-03-01 1.005802
2012-04-01 0.481525
2012-05-01 -0.352151
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 [343]: ps.to_timestamp('D', how='s')
Out[343]:
2012-01-01 2.167674
2012-02-01 -1.505130
2012-03-01 1.005802
2012-04-01 0.481525
2012-05-01 -0.352151
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 [344]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
In [345]: ts = pd.Series(np.random.randn(len(prng)), prng)
In [346]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In [347]: ts.head()
Out[347]:
1990-03-01 09:00 -0.608988
1990-06-01 09:00 0.412294
1990-09-01 09:00 -0.715938
1990-12-01 09:00 1.297773
1991-03-01 09:00 -2.260765
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 [348]: span = pd.period_range('1215-01-01', '1381-01-01', freq='D')
In [349]: span
Out[349]:
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='period[D]', length=60632, freq='D')
To convert from a int64 based YYYYMMDD representation.
In [350]: s = pd.Series([20121231, 20141130, 99991231])
In [351]: s
Out[351]:
0 20121231
1 20141130
2 99991231
dtype: int64
In [352]: def conv(x):
.....: return pd.Period(year = x // 10000, month = x//100 % 100, day = x%100, freq='D')
.....:
In [353]: s.apply(conv)
Out[353]:
0 2012-12-31
1 2014-11-30
2 9999-12-31
dtype: object
In [354]: s.apply(conv)[2]
Out[354]: Period('9999-12-31', 'D')
These can easily be converted to a PeriodIndex
In [355]: span = pd.PeriodIndex(s.apply(conv))
In [356]: span
Out[356]: PeriodIndex(['2012-12-31', '2014-11-30', '9999-12-31'], dtype='period[D]', 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 [357]: rng = pd.date_range('3/6/2012 00:00', periods=15, freq='D')
In [358]: rng.tz is None
Out[358]: 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
pytzyou can find a list of common (and less common) time zones usingfrom pytz import common_timezones, all_timezones. dateutiluses the OS timezones so there isn’t a fixed list available. For common zones, the names are the same aspytz.
# pytz
In [359]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz='Europe/London')
.....:
In [360]: rng_pytz.tz
Out[360]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>
# dateutil
In [361]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz='dateutil/Europe/London')
.....:
In [362]: rng_dateutil.tz
Out[362]: tzfile('/usr/share/zoneinfo/Europe/London')
# dateutil - utc special case
In [363]: rng_utc = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=dateutil.tz.tzutc())
.....:
In [364]: rng_utc.tz
Out[364]: 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 [365]: tz_pytz = pytz.timezone('Europe/London')
In [366]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=tz_pytz)
.....:
In [367]: rng_pytz.tz == tz_pytz
Out[367]: True
# dateutil
In [368]: tz_dateutil = dateutil.tz.gettz('Europe/London')
In [369]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
.....: tz=tz_dateutil)
.....:
In [370]: rng_dateutil.tz == tz_dateutil
Out[370]: 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 [371]: ts = pd.Series(np.random.randn(len(rng)), rng)
In [372]: ts_utc = ts.tz_localize('UTC')
In [373]: ts_utc
Out[373]:
2012-03-06 00:00:00+00:00 0.679135
2012-03-07 00:00:00+00:00 0.345668
2012-03-08 00:00:00+00:00 -1.143903
2012-03-09 00:00:00+00:00 0.487087
2012-03-10 00:00:00+00:00 -1.421073
2012-03-11 00:00:00+00:00 -0.327463
2012-03-12 00:00:00+00:00 0.169899
2012-03-13 00:00:00+00:00 0.867568
2012-03-14 00:00:00+00:00 -0.834122
2012-03-15 00:00:00+00:00 -1.698494
2012-03-16 00:00:00+00:00 0.974717
2012-03-17 00:00:00+00:00 0.966771
2012-03-18 00:00:00+00:00 -0.754168
2012-03-19 00:00:00+00:00 -1.434246
2012-03-20 00:00:00+00:00 0.848935
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 [374]: ts_utc.tz_convert('US/Eastern')
Out[374]:
2012-03-05 19:00:00-05:00 0.679135
2012-03-06 19:00:00-05:00 0.345668
2012-03-07 19:00:00-05:00 -1.143903
2012-03-08 19:00:00-05:00 0.487087
2012-03-09 19:00:00-05:00 -1.421073
2012-03-10 19:00:00-05:00 -0.327463
2012-03-11 20:00:00-04:00 0.169899
2012-03-12 20:00:00-04:00 0.867568
2012-03-13 20:00:00-04:00 -0.834122
2012-03-14 20:00:00-04:00 -1.698494
2012-03-15 20:00:00-04:00 0.974717
2012-03-16 20:00:00-04:00 0.966771
2012-03-17 20:00:00-04:00 -0.754168
2012-03-18 20:00:00-04:00 -1.434246
2012-03-19 20:00:00-04:00 0.848935
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 [375]: rng_eastern = rng_utc.tz_convert('US/Eastern')
In [376]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')
In [377]: rng_eastern[5]
Out[377]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', freq='D')
In [378]: rng_berlin[5]
Out[378]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', freq='D')
In [379]: rng_eastern[5] == rng_berlin[5]
Out[379]: True
Like Series, DataFrame, and DatetimeIndex, Timestamp``s can be converted to other
time zones using ``tz_convert:
In [380]: rng_eastern[5]
Out[380]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', freq='D')
In [381]: rng_berlin[5]
Out[381]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', freq='D')
In [382]: rng_eastern[5].tz_convert('Europe/Berlin')
Out[382]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin')
Localization of Timestamp functions just like DatetimeIndex and Series:
In [383]: rng[5]
Out[383]: Timestamp('2012-03-11 00:00:00', freq='D')
In [384]: rng[5].tz_localize('Asia/Shanghai')
Out[384]: 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 [385]: eastern = ts_utc.tz_convert('US/Eastern')
In [386]: berlin = ts_utc.tz_convert('Europe/Berlin')
In [387]: result = eastern + berlin
In [388]: result
Out[388]:
2012-03-06 00:00:00+00:00 1.358269
2012-03-07 00:00:00+00:00 0.691336
2012-03-08 00:00:00+00:00 -2.287805
2012-03-09 00:00:00+00:00 0.974174
2012-03-10 00:00:00+00:00 -2.842146
2012-03-11 00:00:00+00:00 -0.654926
2012-03-12 00:00:00+00:00 0.339798
2012-03-13 00:00:00+00:00 1.735136
2012-03-14 00:00:00+00:00 -1.668245
2012-03-15 00:00:00+00:00 -3.396988
2012-03-16 00:00:00+00:00 1.949435
2012-03-17 00:00:00+00:00 1.933541
2012-03-18 00:00:00+00:00 -1.508335
2012-03-19 00:00:00+00:00 -2.868493
2012-03-20 00:00:00+00:00 1.697870
Freq: D, dtype: float64
In [389]: result.index
Out[389]:
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 [390]: didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
In [391]: didx
Out[391]:
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 [392]: didx.tz_localize(None)
Out[392]:
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 [393]: didx.tz_convert(None)
Out[393]:
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 [394]: didx.tz_convert('UCT').tz_localize(None)
Out[394]:
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 [395]: rng_hourly = pd.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 [2]: rng_hourly.tz_localize('US/Eastern')
AmbiguousTimeError: Cannot infer dst time from Timestamp('2011-11-06 01:00:00'), try using the 'ambiguous' argument
Infer the ambiguous times
In [396]: rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', ambiguous='infer')
In [397]: rng_hourly_eastern.tolist()
Out[397]:
[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 [398]: rng_hourly_dst = np.array([1, 1, 0, 0, 0])
In [399]: rng_hourly.tz_localize('US/Eastern', ambiguous=rng_hourly_dst).tolist()
Out[399]:
[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 [400]: rng_hourly.tz_localize('US/Eastern', ambiguous='NaT').tolist()
Out[400]:
[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 [401]: didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
In [402]: didx
Out[402]:
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 [403]: didx.tz_localize(None)
Out[403]:
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 [404]: didx.tz_convert(None)
Out[404]:
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 [405]: didx.tz_convert('UCT').tz_localize(None)
Out[405]:
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 [406]: s_naive = pd.Series(pd.date_range('20130101',periods=3))
In [407]: s_naive
Out[407]:
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 [408]: s_aware = pd.Series(pd.date_range('20130101',periods=3,tz='US/Eastern'))
In [409]: s_aware
Out[409]:
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 [410]: s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[410]:
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 [411]: s_naive.astype('datetime64[ns, US/Eastern]')
Out[411]:
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 [412]: s_aware.astype('datetime64[ns]')
Out[412]:
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 [413]: s_aware.astype('datetime64[ns, CET]')
Out[413]:
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 [414]: s_naive.values
Out[414]:
array(['2013-01-01T00:00:00.000000000', '2013-01-02T00:00:00.000000000',
'2013-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
In [415]: s_aware.values
Out[415]:
array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000',
'2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
Further note that once converted to a numpy array these would lose the tz tenor.
In [416]: pd.Series(s_aware.values)
Out[416]:
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 [417]: pd.Series(s_aware.values).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[417]:
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]