Time Series / Date functionality

pandas has proven very successful as a tool for working with time series data, especially in the financial data analysis space. With the 0.8 release, we have further improved the time series API in pandas by leaps and bounds. Using the new NumPy datetime64 dtype, we have consolidated a large number of features from other Python libraries like scikits.timeseries as well as created a tremendous amount of new functionality for manipulating time series data.

In working with time series data, we will frequently seek to:

• generate sequences of fixed-frequency dates and time spans
• conform or convert time series to a particular frequency
• compute “relative” dates based on various non-standard time increments (e.g. 5 business days before the last business day of the year), or “roll” dates forward or backward

pandas provides a relatively compact and self-contained set of tools for performing the above tasks.

Create a range of dates:

# 72 hours starting with midnight Jan 1st, 2011
In [1]: rng = date_range('1/1/2011', periods=72, freq='H')

In [2]: rng[:5]
Out[2]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00, ..., 2011-01-01 04:00:00]
Length: 5, Freq: H, Timezone: None

Index pandas objects with dates:

In [3]: ts = Series(randn(len(rng)), index=rng)

In [4]: ts.head()
Out[4]: 
2011-01-01 00:00:00    0.469112
2011-01-01 01:00:00   -0.282863
2011-01-01 02:00:00   -1.509059
2011-01-01 03:00:00   -1.135632
2011-01-01 04:00:00    1.212112
Freq: H, dtype: float64

Change frequency and fill gaps:

# to 45 minute frequency and forward fill
In [5]: converted = ts.asfreq('45Min', method='pad')

In [6]: converted.head()
Out[6]: 
2011-01-01 00:00:00    0.469112
2011-01-01 00:45:00    0.469112
2011-01-01 01:30:00   -0.282863
2011-01-01 02:15:00   -1.509059
2011-01-01 03:00:00   -1.135632
Freq: 45T, dtype: float64

Resample:

# Daily means
In [7]: ts.resample('D', how='mean')
Out[7]: 
2011-01-01   -0.319569
2011-01-02   -0.337703
2011-01-03    0.117258
Freq: D, dtype: float64

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 to create the index

In [8]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]

In [9]: ts = Series(np.random.randn(3), dates)

In [10]: type(ts.index)
Out[10]: pandas.tseries.index.DatetimeIndex

In [11]: ts
Out[11]: 
2012-05-01   -0.410001
2012-05-02   -0.078638
2012-05-03    0.545952
dtype: float64

However, in many cases it is more natural to associate things like change variables with a time span instead.

For example:

In [12]: periods = PeriodIndex([Period('2012-01'), Period('2012-02'),
   ....:                        Period('2012-03')])
   ....: 

In [13]: ts = Series(np.random.randn(3), periods)

In [14]: type(ts.index)
Out[14]: pandas.tseries.period.PeriodIndex

In [15]: ts
Out[15]: 
2012-01   -1.219217
2012-02   -1.226825
2012-03    0.769804
Freq: M, dtype: float64

Starting with 0.8, pandas allows you to capture both representations and convert between them. Under the hood, pandas represents timestamps using instances of Timestamp and sequences of timestamps using instances of DatetimeIndex. For regular time spans, pandas uses Period objects for scalar values and PeriodIndex for sequences of spans. Better support for irregular intervals with arbitrary start and end points are forth-coming in future releases.

Converting to Timestamps

To convert a Series or list-like object of date-like objects e.g. strings, epochs, or a mixture, you can use the to_datetime function. When passed a Series, this returns a Series (with the same index), while a list-like is converted to a DatetimeIndex:

In [16]: to_datetime(Series(['Jul 31, 2009', '2010-01-10', None]))
Out[16]: 
0   2009-07-31
1   2010-01-10
2          NaT
dtype: datetime64[ns]

In [17]: to_datetime(['2005/11/23', '2010.12.31'])
Out[17]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-11-23, 2010-12-31]
Length: 2, Freq: None, Timezone: None

If you use dates which start with the day first (i.e. European style), you can pass the dayfirst flag:

In [18]: to_datetime(['04-01-2012 10:00'], dayfirst=True)
Out[18]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-04 10:00:00]
Length: 1, Freq: None, Timezone: None

In [19]: to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
Out[19]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-14, 2012-01-14]
Length: 2, Freq: None, Timezone: 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.

Invalid Data

Pass coerce=True to convert invalid data to NaT (not a time):

In [20]: to_datetime(['2009-07-31', 'asd'])
Out[20]: array(['2009-07-31', 'asd'], dtype=object)

In [21]: to_datetime(['2009-07-31', 'asd'], coerce=True)
Out[21]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2009-07-31, NaT]
Length: 2, Freq: None, Timezone: None

Take care, to_datetime may not act as you expect on mixed data:

In [22]: to_datetime([1, '1'])
Out[22]: array([1, '1'], dtype=object)

Epoch Timestamps

It’s also possible to convert integer or float epoch times. The default unit for these is nanoseconds (since these are how Timestamps are stored). However, often epochs are stored in another unit which can be specified:

Typical epoch stored units

In [23]: to_datetime([1349720105, 1349806505, 1349892905,
   ....:              1349979305, 1350065705], unit='s')
   ....: 
Out[23]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-10-08 18:15:05, ..., 2012-10-12 18:15:05]
Length: 5, Freq: None, Timezone: None

In [24]: to_datetime([1349720105100, 1349720105200, 1349720105300,
   ....:              1349720105400, 1349720105500 ], unit='ms')
   ....: 
Out[24]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-10-08 18:15:05.100000, ..., 2012-10-08 18:15:05.500000]
Length: 5, Freq: None, Timezone: None

These work, but the results may be unexpected.

In [25]: to_datetime([1])
Out[25]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[1970-01-01 00:00:00.000000001]
Length: 1, Freq: None, Timezone: None

In [26]: to_datetime([1, 3.14], unit='s')
Out[26]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[1970-01-01 00:00:01, 1970-01-01 00:00:03.140000]
Length: 2, Freq: None, Timezone: None

Note

Epoch times will be rounded to the nearest nanosecond.

Generating Ranges of Timestamps

To generate an index with time stamps, you can use either the DatetimeIndex or Index constructor and pass in a list of datetime objects:

In [27]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]

In [28]: index = DatetimeIndex(dates)

In [29]: index # Note the frequency information
Out[29]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-05-01, ..., 2012-05-03]
Length: 3, Freq: None, Timezone: None

In [30]: index = Index(dates)

In [31]: index # Automatically converted to DatetimeIndex
Out[31]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-05-01, ..., 2012-05-03]
Length: 3, Freq: None, Timezone: 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 [32]: index = date_range('2000-1-1', periods=1000, freq='M')

In [33]: index
Out[33]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-31, ..., 2083-04-30]
Length: 1000, Freq: M, Timezone: None

In [34]: index = bdate_range('2012-1-1', periods=250)

In [35]: index
Out[35]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-02, ..., 2012-12-14]
Length: 250, Freq: B, Timezone: None

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 [36]: start = datetime(2011, 1, 1)

In [37]: end = datetime(2012, 1, 1)

In [38]: rng = date_range(start, end)

In [39]: rng
Out[39]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01, ..., 2012-01-01]
Length: 366, Freq: D, Timezone: None

In [40]: rng = bdate_range(start, end)

In [41]: rng
Out[41]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-03, ..., 2011-12-30]
Length: 260, Freq: B, Timezone: None

date_range and bdate_range makes it easy to generate a range of dates using various combinations of parameters like start, end, periods, and freq:

In [42]: date_range(start, end, freq='BM')
Out[42]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-31, ..., 2011-12-30]
Length: 12, Freq: BM, Timezone: None

In [43]: date_range(start, end, freq='W')
Out[43]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-02, ..., 2012-01-01]
Length: 53, Freq: W-SUN, Timezone: None

In [44]: bdate_range(end=end, periods=20)
Out[44]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-12-05, ..., 2011-12-30]
Length: 20, Freq: B, Timezone: None

In [45]: bdate_range(start=start, periods=20)
Out[45]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-03, ..., 2011-01-28]
Length: 20, Freq: B, Timezone: None

The start and end dates are strictly inclusive. So it will not generate any dates outside of those dates if specified.

DatetimeIndex

One of the main uses for DatetimeIndex is as an index for pandas objects. The DatetimeIndex class contains many timeseries related optimizations:

• A large range of dates for various offsets are pre-computed and cached under the hood in order to make generating subsequent date ranges very fast (just have to grab a slice)
• Fast shifting using the shift and tshift method on pandas objects
• Unioning of overlapping DatetimeIndex objects with the same frequency is very fast (important for fast data alignment)
• Quick access to date fields via properties such as year, month, etc.
• Regularization functions like snap and very fast asof logic

DatetimeIndex objects has all the basic functionality of regular Index objects and a smorgasbord of advanced timeseries-specific methods for easy frequency processing.

See also

Reindexing methods

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 [46]: rng = date_range(start, end, freq='BM')

In [47]: ts = Series(randn(len(rng)), index=rng)

In [48]: ts.index
Out[48]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-31, ..., 2011-12-30]
Length: 12, Freq: BM, Timezone: None

In [49]: ts[:5].index
Out[49]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-31, ..., 2011-05-31]
Length: 5, Freq: BM, Timezone: None

In [50]: ts[::2].index
Out[50]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-31, ..., 2011-11-30]
Length: 6, Freq: 2BM, Timezone: None

DatetimeIndex Partial String Indexing

You can pass in dates and strings that parse to dates as indexing parameters:

In [51]: ts['1/31/2011']
Out[51]: -1.2812473076599529

In [52]: ts[datetime(2011, 12, 25):]
Out[52]: 
2011-12-30    0.687738
Freq: BM, dtype: float64

In [53]: ts['10/31/2011':'12/31/2011']
Out[53]: 
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 [54]: ts['2011']
Out[54]: 
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 [55]: ts['2011-6']
Out[55]: 
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 [56]: dft = DataFrame(randn(100000,1),columns=['A'],index=date_range('20130101',periods=100000,freq='T'))

In [57]: dft
Out[57]: 
                            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 [58]: dft['2013']
Out[58]: 
                            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 [59]: dft['2013-1':'2013-2']
Out[59]: 
                            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 [60]: dft['2013-1':'2013-2-28']
Out[60]: 
                            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 [61]: dft['2013-1':'2013-2-28 00:00:00']
Out[61]: 
                            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 its part of the index

In [62]: dft['2013-1-15':'2013-1-15 12:30:00']
Out[62]: 
                            A
2013-01-15 00:00:00  0.501288
2013-01-15 00:01:00 -0.605198
2013-01-15 00:02:00  0.215146
2013-01-15 00:03:00  0.924732
2013-01-15 00:04:00 -2.228519
2013-01-15 00:05:00  1.517331
2013-01-15 00:06:00 -1.188774
...                       ...
2013-01-15 12:24:00  1.358314
2013-01-15 12:25:00 -0.737727
2013-01-15 12:26:00  1.838323
2013-01-15 12:27:00 -0.774090
2013-01-15 12:28:00  0.622261
2013-01-15 12:29:00 -0.631649
2013-01-15 12:30:00  0.193284

[751 rows x 1 columns]

Warning

The following selection will raise a KeyError; otherwise this selection methodology would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it resolve to one)

dft['2013-1-15 12:30:00']

To select a single row, use .loc

In [63]: dft.loc['2013-1-15 12:30:00']
Out[63]: 
A    0.193284
Name: 2013-01-15 12:30:00, dtype: float64

Datetime Indexing

Indexing a DateTimeIndex with a partial string depends on the “accuracy” of the period, in other words how specific the interval is in relation to the frequency of the index. In contrast, indexing with datetime objects is exact, because the objects have exact meaning. These also follow the sematics of including both endpoints.

These datetime objects are specific hours, minutes, and seconds even though they were not explicity specified (they are 0).

In [64]: dft[datetime(2013, 1, 1):datetime(2013,2,28)]
Out[64]: 
                            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 [65]: dft[datetime(2013, 1, 1, 10, 12, 0):datetime(2013, 2, 28, 10, 12, 0)]
Out[65]: 
                            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 [66]: ts.truncate(before='10/31/2011', after='12/31/2011')
Out[66]: 
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 [67]: ts[[0, 2, 6]].index
Out[67]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-31, ..., 2011-07-29]
Length: 3, Freq: None, Timezone: None

Time/Date Components

There are several time/date properties that one can access from Timestamp or a collection of timestamps like a DateTimeIndex.

Property	Description
year	The year of the datetime
month	The month of the datetime
day	The days of the datetime
hour	The hour of the datetime
minute	The minutes of the datetime
second	The seconds of the datetime
microsecond	The microseconds of the datetime
nanosecond	The nanoseconds of the datetime
date	Returns datetime.date
time	Returns datetime.time
dayofyear	The ordinal day of year
weekofyear	The week ordinal of the year
week	The week ordinal of the year
dayofweek	The day of the week with Monday=0, Sunday=6
weekday	The day of the week with Monday=0, Sunday=6
quarter	Quarter of the date: Jan=Mar = 1, Apr-Jun = 2, etc.
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)

DateOffset objects

In the preceding examples, we created DatetimeIndex objects at various frequencies by passing in frequency strings like ‘M’, ‘W’, and ‘BM to the freq keyword. Under the hood, these frequency strings are being translated into an instance of pandas DateOffset, which represents a regular frequency increment. Specific offset logic like “month”, “business day”, or “one hour” is represented in its various subclasses.

Class name	Description
DateOffset	Generic offset class, defaults to 1 calendar day
BDay	business day (weekday)
CDay	custom business day (experimental)
Week	one week, optionally anchored on a day of the week
WeekOfMonth	the x-th day of the y-th week of each month
LastWeekOfMonth	the x-th day of the last week of each month
MonthEnd	calendar month end
MonthBegin	calendar month begin
BMonthEnd	business month end
BMonthBegin	business month begin
CBMonthEnd	custom business month end
CBMonthBegin	custom business month begin
QuarterEnd	calendar quarter end
QuarterBegin	calendar quarter begin
BQuarterEnd	business quarter end
BQuarterBegin	business quarter begin
FY5253Quarter	retail (aka 52-53 week) quarter
YearEnd	calendar year end
YearBegin	calendar year begin
BYearEnd	business year end
BYearBegin	business year begin
FY5253	retail (aka 52-53 week) year
Hour	one hour
Minute	one minute
Second	one second
Milli	one millisecond
Micro	one microsecond

The basic DateOffset takes the same arguments as dateutil.relativedelta, which works like:

In [68]: d = datetime(2008, 8, 18, 9, 0)

In [69]: d + relativedelta(months=4, days=5)
Out[69]: datetime.datetime(2008, 12, 23, 9, 0)

We could have done the same thing with DateOffset:

In [70]: from pandas.tseries.offsets import *

In [71]: d + DateOffset(months=4, days=5)
Out[71]: Timestamp('2008-12-23 09:00:00')

The key features of a DateOffset object are:

• it can be added / subtracted to/from a datetime object to obtain a shifted date
• it can be multiplied by an integer (positive or negative) so that the increment will be applied multiple times
• it has rollforward and rollback methods for moving a date forward or backward to the next or previous “offset date”

Subclasses of DateOffset define the apply function which dictates custom date increment logic, such as adding business days:

class BDay(DateOffset):
    """DateOffset increments between business days"""
    def apply(self, other):
        ...

In [72]: d - 5 * BDay()
Out[72]: Timestamp('2008-08-11 09:00:00')

In [73]: d + BMonthEnd()
Out[73]: Timestamp('2008-08-29 09:00:00')

The rollforward and rollback methods do exactly what you would expect:

In [74]: d
Out[74]: datetime.datetime(2008, 8, 18, 9, 0)

In [75]: offset = BMonthEnd()

In [76]: offset.rollforward(d)
Out[76]: Timestamp('2008-08-29 09:00:00')

In [77]: offset.rollback(d)
Out[77]: 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 create offset instance. If normalize=True, result is normalized after the function is applied.

In [78]: day = Day()

In [79]: day.apply(Timestamp('2014-01-01 09:00'))
Out[79]: Timestamp('2014-01-02 09:00:00')

In [80]: day = Day(normalize=True)

In [81]: day.apply(Timestamp('2014-01-01 09:00'))
Out[81]: Timestamp('2014-01-02 00:00:00')

In [82]: hour = Hour()

In [83]: hour.apply(Timestamp('2014-01-01 22:00'))
Out[83]: Timestamp('2014-01-01 23:00:00')

In [84]: hour = Hour(normalize=True)

In [85]: hour.apply(Timestamp('2014-01-01 22:00'))
Out[85]: Timestamp('2014-01-01 00:00:00')

In [86]: hour.apply(Timestamp('2014-01-01 23:00'))
Out[86]: Timestamp('2014-01-02 00:00:00')

Parametric offsets

Some of the offsets can be “parameterized” when created to result in different behavior. For example, the Week offset for generating weekly data accepts a weekday parameter which results in the generated dates always lying on a particular day of the week:

In [87]: d
Out[87]: datetime.datetime(2008, 8, 18, 9, 0)

In [88]: d + Week()
Out[88]: Timestamp('2008-08-25 09:00:00')

In [89]: d + Week(weekday=4)
Out[89]: Timestamp('2008-08-22 09:00:00')

In [90]: (d + Week(weekday=4)).weekday()
Out[90]: 4

In [91]: d - Week()
Out[91]: Timestamp('2008-08-11 09:00:00')

normalize option will be effective for addition and subtraction.

In [92]: d + Week(normalize=True)
Out[92]: Timestamp('2008-08-25 00:00:00')

In [93]: d - Week(normalize=True)
Out[93]: Timestamp('2008-08-11 00:00:00')

Another example is parameterizing YearEnd with the specific ending month:

In [94]: d + YearEnd()
Out[94]: Timestamp('2008-12-31 09:00:00')

In [95]: d + YearEnd(month=6)
Out[95]: Timestamp('2009-06-30 09:00:00')

Custom Business Days (Experimental)

The CDay or CustomBusinessDay class provides a parametric BusinessDay class which can be used to create customized business day calendars which account for local holidays and local weekend conventions.

In [96]: from pandas.tseries.offsets import CustomBusinessDay

# As an interesting example, let's look at Egypt where
# a Friday-Saturday weekend is observed.
In [97]: weekmask_egypt = 'Sun Mon Tue Wed Thu'

# They also observe International Workers' Day so let's
# add that for a couple of years
In [98]: holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]

In [99]: bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)

In [100]: dt = datetime(2013, 4, 30)

In [101]: dt + 2 * bday_egypt
Out[101]: Timestamp('2013-05-05 00:00:00')

In [102]: dts = date_range(dt, periods=5, freq=bday_egypt)

In [103]: Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split()))
Out[103]: 
2013-04-30    Tue
2013-05-02    Thu
2013-05-05    Sun
2013-05-06    Mon
2013-05-07    Tue
Freq: C, dtype: object

As of v0.14 holiday calendars can be used to provide the list of holidays. See the holiday calendar section for more information.

In [104]: from pandas.tseries.holiday import USFederalHolidayCalendar

In [105]: bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())

# Friday before MLK Day
In [106]: dt = datetime(2014, 1, 17)

# Tuesday after MLK Day (Monday is skipped because it's a holiday)
In [107]: dt + bday_us
Out[107]: Timestamp('2014-01-21 00:00:00')

Monthly offsets that respect a certain holiday calendar can be defined in the usual way.

In [108]: from pandas.tseries.offsets import CustomBusinessMonthBegin

In [109]: bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())

# Skip new years
In [110]: dt = datetime(2013, 12, 17)

In [111]: dt + bmth_us
Out[111]: Timestamp('2014-01-02 00:00:00')

# Define date index with custom offset
In [112]: from pandas import DatetimeIndex

In [113]: DatetimeIndex(start='20100101',end='20120101',freq=bmth_us)
Out[113]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-01-04, ..., 2011-12-01]
Length: 24, Freq: CBMS, Timezone: None

Note

The frequency string ‘C’ is used to indicate that a CustomBusinessDay DateOffset is used, it is important to note that since CustomBusinessDay is a parameterised type, instances of CustomBusinessDay may differ and this is not detectable from the ‘C’ frequency string. The user therefore needs to ensure that the ‘C’ frequency string is used consistently within the user’s application.

Note

This uses the numpy.busdaycalendar API introduced in Numpy 1.7 and therefore requires Numpy 1.7.0 or newer.

Warning

There are known problems with the timezone handling in Numpy 1.7 and users should therefore use this experimental(!) feature with caution and at their own risk.

To the extent that the datetime64 and busdaycalendar APIs in Numpy have to change to fix the timezone issues, the behaviour of the CustomBusinessDay class may have to change in future versions.

Offset Aliases

A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases (referred to as time rules prior to v0.8.0).

Alias	Description
B	business day frequency
C	custom business day frequency (experimental)
D	calendar day frequency
W	weekly frequency
M	month end frequency
BM	business month end frequency
CBM	custom business month end frequency
MS	month start frequency
BMS	business month start frequency
CBMS	custom business month start frequency
Q	quarter end frequency
BQ	business quarter endfrequency
QS	quarter start frequency
BQS	business quarter start frequency
A	year end frequency
BA	business year end frequency
AS	year start frequency
BAS	business year start frequency
H	hourly frequency
T	minutely frequency
S	secondly frequency
L	milliseonds
U	microseconds

Combining Aliases

As we have seen previously, the alias and the offset instance are fungible in most functions:

In [114]: date_range(start, periods=5, freq='B')
Out[114]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-03, ..., 2011-01-07]
Length: 5, Freq: B, Timezone: None

In [115]: date_range(start, periods=5, freq=BDay())
Out[115]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-03, ..., 2011-01-07]
Length: 5, Freq: B, Timezone: None

You can combine together day and intraday offsets:

In [116]: date_range(start, periods=10, freq='2h20min')
Out[116]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00, ..., 2011-01-01 21:00:00]
Length: 10, Freq: 140T, Timezone: None

In [117]: date_range(start, periods=10, freq='1D10U')
Out[117]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00, ..., 2011-01-10 00:00:00.000090]
Length: 10, Freq: 86400000010U, Timezone: None

Anchored Offsets

For some frequencies you can specify an anchoring suffix:

Alias	Description
W-SUN	weekly frequency (sundays). Same as ‘W’
W-MON	weekly frequency (mondays)
W-TUE	weekly frequency (tuesdays)
W-WED	weekly frequency (wednesdays)
W-THU	weekly frequency (thursdays)
W-FRI	weekly frequency (fridays)
W-SAT	weekly frequency (saturdays)
(B)Q(S)-DEC	quarterly frequency, year ends in December. Same as ‘Q’
(B)Q(S)-JAN	quarterly frequency, year ends in January
(B)Q(S)-FEB	quarterly frequency, year ends in February
(B)Q(S)-MAR	quarterly frequency, year ends in March
(B)Q(S)-APR	quarterly frequency, year ends in April
(B)Q(S)-MAY	quarterly frequency, year ends in May
(B)Q(S)-JUN	quarterly frequency, year ends in June
(B)Q(S)-JUL	quarterly frequency, year ends in July
(B)Q(S)-AUG	quarterly frequency, year ends in August
(B)Q(S)-SEP	quarterly frequency, year ends in September
(B)Q(S)-OCT	quarterly frequency, year ends in October
(B)Q(S)-NOV	quarterly frequency, year ends in November
(B)A(S)-DEC	annual frequency, anchored end of December. Same as ‘A’
(B)A(S)-JAN	annual frequency, anchored end of January
(B)A(S)-FEB	annual frequency, anchored end of February
(B)A(S)-MAR	annual frequency, anchored end of March
(B)A(S)-APR	annual frequency, anchored end of April
(B)A(S)-MAY	annual frequency, anchored end of May
(B)A(S)-JUN	annual frequency, anchored end of June
(B)A(S)-JUL	annual frequency, anchored end of July
(B)A(S)-AUG	annual frequency, anchored end of August
(B)A(S)-SEP	annual frequency, anchored end of September
(B)A(S)-OCT	annual frequency, anchored end of October
(B)A(S)-NOV	annual frequency, anchored end of November

These can be used as arguments to date_range, bdate_range, constructors for DatetimeIndex, as well as various other timeseries-related functions in pandas.

Legacy Aliases

Note that prior to v0.8.0, time rules had a slightly different look. pandas will continue to support the legacy time rules for the time being but it is strongly recommended that you switch to using the new offset aliases.

Legacy Time Rule	Offset Alias
WEEKDAY	B
EOM	BM
W@MON	W-MON
W@TUE	W-TUE
W@WED	W-WED
W@THU	W-THU
W@FRI	W-FRI
W@SAT	W-SAT
W@SUN	W-SUN
Q@JAN	BQ-JAN
Q@FEB	BQ-FEB
Q@MAR	BQ-MAR
A@JAN	BA-JAN
A@FEB	BA-FEB
A@MAR	BA-MAR
A@APR	BA-APR
A@MAY	BA-MAY
A@JUN	BA-JUN
A@JUL	BA-JUL
A@AUG	BA-AUG
A@SEP	BA-SEP
A@OCT	BA-OCT
A@NOV	BA-NOV
A@DEC	BA-DEC
min	T
ms	L
us	U

As you can see, legacy quarterly and annual frequencies are business quarter and business year ends. Please also note the legacy time rule for milliseconds ms versus the new offset alias for month start MS. This means that offset alias parsing is case sensitive.

Holidays / 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 [118]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
   .....:     AbstractHolidayCalendar, nearest_workday, MO
   .....: 

In [119]: 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 [120]: cal = ExampleCalendar()

In [121]: cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))
Out[121]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-05-28, ..., 2012-10-08]
Length: 3, Freq: None, Timezone: None

Using this calendar, creating an index or doing offset arithmetic skips weekends and holidays (i.e., Memorial Day/July 4th).

In [122]: DatetimeIndex(start='7/1/2012', end='7/10/2012',
   .....:     freq=CDay(calendar=cal)).to_pydatetime()
   .....: 
Out[122]: 
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 [123]: offset = CustomBusinessDay(calendar=cal)

In [124]: datetime(2012, 5, 25) + offset
Out[124]: Timestamp('2012-05-29 00:00:00')

In [125]: datetime(2012, 7, 3) + offset
Out[125]: Timestamp('2012-07-05 00:00:00')

In [126]: datetime(2012, 7, 3) + 2 * offset
Out[126]: Timestamp('2012-07-06 00:00:00')

In [127]: datetime(2012, 7, 6) + offset
Out[127]: 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 [128]: AbstractHolidayCalendar.start_date
Out[128]: Timestamp('1970-01-01 00:00:00')

In [129]: AbstractHolidayCalendar.end_date
Out[129]: Timestamp('2030-12-31 00:00:00')

These dates can be overwritten by setting the attributes as datetime/Timestamp/string.

In [130]: AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)

In [131]: AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)

In [132]: cal.holidays()
Out[132]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-05-28, ..., 2012-10-08]
Length: 3, Freq: None, Timezone: 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 [133]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
   .....:     USLaborDay
   .....: 

In [134]: cal = get_calendar('ExampleCalendar')

In [135]: cal.rules
Out[135]: 
[Holiday: MemorialDay (month=5, day=24, offset=<DateOffset: kwds={'weekday': MO(+1)}>),
 Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0xa83966f4>),
 Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>)]

In [136]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)

In [137]: new_cal.rules
Out[137]: 
[Holiday: Labor Day (month=9, day=1, offset=<DateOffset: kwds={'weekday': MO(+1)}>),
 Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>),
 Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0xa83966f4>),
 Holiday: MemorialDay (month=5, day=24, offset=<DateOffset: kwds={'weekday': MO(+1)}>)]

Time series-related instance methods

Shifting / lagging

One may want to shift or lag the values in a TimeSeries back and forward in time. The method for this is shift, which is available on all of the pandas objects. In DataFrame, shift will currently only shift along the index and in Panel along the major_axis.

In [138]: ts = ts[:5]

In [139]: ts.shift(1)
Out[139]: 
2011-01-31         NaN
2011-02-28   -1.281247
2011-03-31   -0.727707
2011-04-29   -0.121306
2011-05-31   -0.097883
Freq: BM, dtype: float64

The shift method accepts an freq argument which can accept a DateOffset class or other timedelta-like object or also a offset alias:

In [140]: ts.shift(5, freq=datetools.bday)
Out[140]: 
2011-02-07   -1.281247
2011-03-07   -0.727707
2011-04-07   -0.121306
2011-05-06   -0.097883
2011-06-07    0.695775
dtype: float64

In [141]: ts.shift(5, freq='BM')
Out[141]: 
2011-06-30   -1.281247
2011-07-29   -0.727707
2011-08-31   -0.121306
2011-09-30   -0.097883
2011-10-31    0.695775
Freq: BM, dtype: float64

Rather than changing the alignment of the data and the index, DataFrame and TimeSeries objects also have a tshift convenience method that changes all the dates in the index by a specified number of offsets:

In [142]: ts.tshift(5, freq='D')
Out[142]: 
2011-02-05   -1.281247
2011-03-05   -0.727707
2011-04-05   -0.121306
2011-05-04   -0.097883
2011-06-05    0.695775
dtype: float64

Note that with tshift, the leading entry is no longer NaN because the data is not being realigned.

Frequency conversion

The primary function for changing frequencies is the asfreq function. For a DatetimeIndex, this is basically just a thin, but convenient wrapper around reindex which generates a date_range and calls reindex.

In [143]: dr = date_range('1/1/2010', periods=3, freq=3 * datetools.bday)

In [144]: ts = Series(randn(3), index=dr)

In [145]: ts
Out[145]: 
2010-01-01   -0.659574
2010-01-06    1.494522
2010-01-11   -0.778425
Freq: 3B, dtype: float64

In [146]: ts.asfreq(BDay())
Out[146]: 
2010-01-01   -0.659574
2010-01-04         NaN
2010-01-05         NaN
2010-01-06    1.494522
2010-01-07         NaN
2010-01-08         NaN
2010-01-11   -0.778425
Freq: B, dtype: float64

asfreq provides a further convenience so you can specify an interpolation method for any gaps that may appear after the frequency conversion

In [147]: ts.asfreq(BDay(), method='pad')
Out[147]: 
2010-01-01   -0.659574
2010-01-04   -0.659574
2010-01-05   -0.659574
2010-01-06    1.494522
2010-01-07    1.494522
2010-01-08    1.494522
2010-01-11   -0.778425
Freq: B, dtype: float64

Filling forward / backward

Related to asfreq and reindex is the fillna function documented in the missing data section.

Converting to Python datetimes

DatetimeIndex can be converted to an array of Python native datetime.datetime objects using the to_pydatetime method.

Up- and downsampling

With 0.8, pandas introduces simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications.

See some cookbook examples for some advanced strategies

In [148]: rng = date_range('1/1/2012', periods=100, freq='S')

In [149]: ts = Series(randint(0, 500, len(rng)), index=rng)

In [150]: ts.resample('5Min', how='sum')
Out[150]: 
2012-01-01    25103
Freq: 5T, dtype: int32

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 [151]: ts.resample('5Min') # default is mean
Out[151]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

In [152]: ts.resample('5Min', how='ohlc')
Out[152]: 
            open  high  low  close
2012-01-01   308   460    9    205

In [153]: ts.resample('5Min', how=np.max)
Out[153]: 
2012-01-01    460
Freq: 5T, dtype: int32

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 [154]: ts.resample('5Min', closed='right')
Out[154]: 
2011-12-31 23:55:00    308.000000
2012-01-01 00:00:00    250.454545
Freq: 5T, dtype: float64

In [155]: ts.resample('5Min', closed='left')
Out[155]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

For upsampling, the fill_method and limit parameters can be specified to interpolate over the gaps that are created:

# from secondly to every 250 milliseconds
In [156]: ts[:2].resample('250L')
Out[156]: 
2012-01-01 00:00:00           308
2012-01-01 00:00:00.250000    NaN
2012-01-01 00:00:00.500000    NaN
2012-01-01 00:00:00.750000    NaN
2012-01-01 00:00:01           204
Freq: 250L, dtype: float64

In [157]: ts[:2].resample('250L', fill_method='pad')
Out[157]: 
2012-01-01 00:00:00           308
2012-01-01 00:00:00.250000    308
2012-01-01 00:00:00.500000    308
2012-01-01 00:00:00.750000    308
2012-01-01 00:00:01           204
Freq: 250L, dtype: int32

In [158]: ts[:2].resample('250L', fill_method='pad', limit=2)
Out[158]: 
2012-01-01 00:00:00           308
2012-01-01 00:00:00.250000    308
2012-01-01 00:00:00.500000    308
2012-01-01 00:00:00.750000    NaN
2012-01-01 00:00:01           204
Freq: 250L, 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 [159]: ts.resample('5Min') # by default label='right'
Out[159]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

In [160]: ts.resample('5Min', label='left')
Out[160]: 
2012-01-01    251.03
Freq: 5T, dtype: float64

In [161]: ts.resample('5Min', label='left', loffset='1s')
Out[161]: 
2012-01-01 00:00:01    251.03
dtype: float64

The axis parameter can be set to 0 or 1 and allows you to resample the specified axis for a DataFrame.

kind can be set to ‘timestamp’ or ‘period’ to convert the resulting index to/from time-stamp and time-span representations. By default resample retains the input representation.

convention can be set to ‘start’ or ‘end’ when resampling period data (detail below). It specifies how low frequency periods are converted to higher frequency periods.

Note that 0.8 marks a watershed in the timeseries functionality in pandas. In previous versions, resampling had to be done using a combination of date_range, groupby with asof, and then calling an aggregation function on the grouped object. This was not nearly convenient or performant as the new pandas timeseries API.

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). It can be created using a frequency alias:

In [162]: Period('2012', freq='A-DEC')
Out[162]: Period('2012', 'A-DEC')

In [163]: Period('2012-1-1', freq='D')
Out[163]: Period('2012-01-01', 'D')

In [164]: Period('2012-1-1 19:00', freq='H')
Out[164]: Period('2012-01-01 19:00', 'H')

Unlike time stamped data, pandas does not support frequencies at multiples of DateOffsets (e.g., ‘3Min’) for periods.

Adding and subtracting integers from periods shifts the period by its own frequency.

In [165]: p = Period('2012', freq='A-DEC')

In [166]: p + 1
Out[166]: Period('2013', 'A-DEC')

In [167]: p - 3
Out[167]: Period('2009', 'A-DEC')

Taking the difference of Period instances with the same frequency will return the number of frequency units between them:

In [168]: Period('2012', freq='A-DEC') - Period('2002', freq='A-DEC')
Out[168]: 10L

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 [169]: prng = period_range('1/1/2011', '1/1/2012', freq='M')

In [170]: prng
Out[170]: 
<class 'pandas.tseries.period.PeriodIndex'>
[2011-01, ..., 2012-01]
Length: 13, Freq: M

The PeriodIndex constructor can also be used directly:

In [171]: PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Out[171]: 
<class 'pandas.tseries.period.PeriodIndex'>
[2011-01, ..., 2011-03]
Length: 3, Freq: M

Just like DatetimeIndex, a PeriodIndex can also be used to index pandas objects:

In [172]: ps = Series(randn(len(prng)), prng)

In [173]: ps
Out[173]: 
2011-01   -0.253355
2011-02   -1.426908
2011-03    1.548971
2011-04   -0.088718
2011-05   -1.771348
2011-06   -0.989328
2011-07   -1.584789
2011-08   -0.288786
2011-09   -2.029806
2011-10   -0.761200
2011-11   -1.603608
2011-12    1.756171
2012-01    0.256502
Freq: M, dtype: float64

PeriodIndex Partial String Indexing

You can pass in dates and strings to Series and DataFrame with PeriodIndex, as the same manner as DatetimeIndex. For details, refer to DatetimeIndex Partial String Indexing.

In [174]: ps['2011-01']
Out[174]: -0.25335528290092818

In [175]: ps[datetime(2011, 12, 25):]
Out[175]: 
2011-12    1.756171
2012-01    0.256502
Freq: M, dtype: float64

In [176]: ps['10/31/2011':'12/31/2011']
Out[176]: 
2011-10   -0.761200
2011-11   -1.603608
2011-12    1.756171
Freq: M, dtype: float64

Passing string represents lower frequency than PeriodIndex returns partial sliced data.

In [177]: ps['2011']
Out[177]: 
2011-01   -0.253355
2011-02   -1.426908
2011-03    1.548971
2011-04   -0.088718
2011-05   -1.771348
2011-06   -0.989328
2011-07   -1.584789
2011-08   -0.288786
2011-09   -2.029806
2011-10   -0.761200
2011-11   -1.603608
2011-12    1.756171
Freq: M, dtype: float64

In [178]: dfp = DataFrame(randn(600,1), columns=['A'],
   .....:                 index=period_range('2013-01-01 9:00', periods=600, freq='T'))
   .....: 

In [179]: dfp
Out[179]: 
                         A
2013-01-01 09:00  0.020601
2013-01-01 09:01 -0.411719
2013-01-01 09:02  2.079413
2013-01-01 09:03 -1.077911
2013-01-01 09:04  0.099258
2013-01-01 09:05 -0.089851
2013-01-01 09:06  0.711329
...                    ...
2013-01-01 18:53 -1.340038
2013-01-01 18:54  1.315461
2013-01-01 18:55  2.396188
2013-01-01 18:56 -0.501527
2013-01-01 18:57 -3.171938
2013-01-01 18:58  0.142019
2013-01-01 18:59  0.606998

[600 rows x 1 columns]

In [180]: dfp['2013-01-01 10H']
Out[180]: 
                         A
2013-01-01 10:00 -0.745396
2013-01-01 10:01  0.141880
2013-01-01 10:02 -1.077754
2013-01-01 10:03 -1.301174
2013-01-01 10:04 -0.269628
2013-01-01 10:05 -0.456347
2013-01-01 10:06  0.157766
...                    ...
2013-01-01 10:53  0.168057
2013-01-01 10:54 -0.214306
2013-01-01 10:55 -0.069739
2013-01-01 10:56 -1.511809
2013-01-01 10:57  0.307021
2013-01-01 10:58  1.449776
2013-01-01 10:59  0.782537

[60 rows x 1 columns]

As the same as DatetimeIndex, the endpoints will be included in the result. Below example slices data starting from 10:00 to 11:59.

In [181]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[181]: 
                         A
2013-01-01 10:00 -0.745396
2013-01-01 10:01  0.141880
2013-01-01 10:02 -1.077754
2013-01-01 10:03 -1.301174
2013-01-01 10:04 -0.269628
2013-01-01 10:05 -0.456347
2013-01-01 10:06  0.157766
...                    ...
2013-01-01 11:53 -0.064395
2013-01-01 11:54  0.350193
2013-01-01 11:55  1.336433
2013-01-01 11:56 -0.438701
2013-01-01 11:57 -0.915841
2013-01-01 11:58  0.294215
2013-01-01 11:59  0.040959

[120 rows x 1 columns]

Frequency Conversion and Resampling with PeriodIndex

The frequency of Periods and PeriodIndex can be converted via the asfreq method. Let’s start with the fiscal year 2011, ending in December:

In [182]: p = Period('2011', freq='A-DEC')

In [183]: p
Out[183]: 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 [184]: p.asfreq('M', how='start')
Out[184]: Period('2011-01', 'M')

In [185]: p.asfreq('M', how='end')
Out[185]: Period('2011-12', 'M')

The shorthands ‘s’ and ‘e’ are provided for convenience:

In [186]: p.asfreq('M', 's')
Out[186]: Period('2011-01', 'M')

In [187]: p.asfreq('M', 'e')
Out[187]: 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 [188]: p = Period('2011-12', freq='M')

In [189]: p.asfreq('A-NOV')
Out[189]: 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 start and ends. Thus, first quarter of 2011 could start in 2010 or a few months into 2011. Via anchored frequencies, pandas works all quarterly frequencies Q-JAN through Q-DEC.

Q-DEC define regular calendar quarters:

In [190]: p = Period('2012Q1', freq='Q-DEC')

In [191]: p.asfreq('D', 's')
Out[191]: Period('2012-01-01', 'D')

In [192]: p.asfreq('D', 'e')
Out[192]: Period('2012-03-31', 'D')

Q-MAR defines fiscal year end in March:

In [193]: p = Period('2011Q4', freq='Q-MAR')

In [194]: p.asfreq('D', 's')
Out[194]: Period('2011-01-01', 'D')

In [195]: p.asfreq('D', 'e')
Out[195]: 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 [196]: rng = date_range('1/1/2012', periods=5, freq='M')

In [197]: ts = Series(randn(len(rng)), index=rng)

In [198]: ts
Out[198]: 
2012-01-31   -0.016142
2012-02-29    0.865782
2012-03-31    0.246439
2012-04-30   -1.199736
2012-05-31    0.407620
Freq: M, dtype: float64

In [199]: ps = ts.to_period()

In [200]: ps
Out[200]: 
2012-01   -0.016142
2012-02    0.865782
2012-03    0.246439
2012-04   -1.199736
2012-05    0.407620
Freq: M, dtype: float64

In [201]: ps.to_timestamp()
Out[201]: 
2012-01-01   -0.016142
2012-02-01    0.865782
2012-03-01    0.246439
2012-04-01   -1.199736
2012-05-01    0.407620
Freq: MS, dtype: float64

Remember that ‘s’ and ‘e’ can be used to return the timestamps at the start or end of the period:

In [202]: ps.to_timestamp('D', how='s')
Out[202]: 
2012-01-01   -0.016142
2012-02-01    0.865782
2012-03-01    0.246439
2012-04-01   -1.199736
2012-05-01    0.407620
Freq: MS, dtype: float64

Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:

In [203]: prng = period_range('1990Q1', '2000Q4', freq='Q-NOV')

In [204]: ts = Series(randn(len(prng)), prng)

In [205]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9

In [206]: ts.head()
Out[206]: 
1990-03-01 09:00   -2.470970
1990-06-01 09:00   -0.929915
1990-09-01 09:00    1.385889
1990-12-01 09:00   -1.830966
1991-03-01 09:00   -0.328505
Freq: H, dtype: float64

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.

By default, pandas objects are time zone unaware:

In [207]: rng = date_range('3/6/2012 00:00', periods=15, freq='D')

In [208]: rng.tz is None
Out[208]: True

To supply the time zone, you can use the tz keyword to date_range and other functions. Dateutil time zone strings are distinguished from pytz time zones by starting with dateutil/.

• In pytz you can find a list of common (and less common) time zones using from pytz import common_timezones, all_timezones.
• dateutil uses the OS timezones so there isn’t a fixed list available. For common zones, the names are the same as pytz.

# pytz
In [209]: rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
   .....:                       tz='Europe/London')
   .....: 

In [210]: rng_pytz.tz
Out[210]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>

# dateutil
In [211]: rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
   .....:                           tz='dateutil/Europe/London')
   .....: 

In [212]: rng_dateutil.tz
Out[212]: tzfile('/usr/share/zoneinfo/Europe/London')

# dateutil - utc special case
In [213]: rng_utc = date_range('3/6/2012 00:00', periods=10, freq='D',
   .....:                      tz=dateutil.tz.tzutc())
   .....: 

In [214]: rng_utc.tz
Out[214]: 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 [215]: tz_pytz = pytz.timezone('Europe/London')

In [216]: rng_pytz = date_range('3/6/2012 00:00', periods=10, freq='D',
   .....:                       tz=tz_pytz)
   .....: 

In [217]: rng_pytz.tz == tz_pytz
Out[217]: True

# dateutil
In [218]: tz_dateutil = dateutil.tz.gettz('Europe/London')

In [219]: rng_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
   .....:                           tz=tz_dateutil)
   .....: 

In [220]: rng_dateutil.tz == tz_dateutil
Out[220]: 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 [221]: ts = Series(randn(len(rng)), rng)

In [222]: ts_utc = ts.tz_localize('UTC')

In [223]: ts_utc
Out[223]: 
2012-03-06 00:00:00+00:00    0.758606
2012-03-07 00:00:00+00:00    2.190827
2012-03-08 00:00:00+00:00    0.706087
2012-03-09 00:00:00+00:00    1.798831
2012-03-10 00:00:00+00:00    1.228481
2012-03-11 00:00:00+00:00   -0.179494
2012-03-12 00:00:00+00:00    0.634073
2012-03-13 00:00:00+00:00    0.262123
2012-03-14 00:00:00+00:00    1.928233
2012-03-15 00:00:00+00:00    0.322573
2012-03-16 00:00:00+00:00   -0.711113
2012-03-17 00:00:00+00:00    1.444272
2012-03-18 00:00:00+00:00   -0.352268
2012-03-19 00:00:00+00:00    0.213008
2012-03-20 00:00:00+00:00   -0.619340
Freq: D, dtype: float64

Again, you can explicitly construct the timezone object first. You can use the tz_convert method to convert pandas objects to convert tz-aware data to another time zone:

In [224]: ts_utc.tz_convert('US/Eastern')
Out[224]: 
2012-03-05 19:00:00-05:00    0.758606
2012-03-06 19:00:00-05:00    2.190827
2012-03-07 19:00:00-05:00    0.706087
2012-03-08 19:00:00-05:00    1.798831
2012-03-09 19:00:00-05:00    1.228481
2012-03-10 19:00:00-05:00   -0.179494
2012-03-11 20:00:00-04:00    0.634073
2012-03-12 20:00:00-04:00    0.262123
2012-03-13 20:00:00-04:00    1.928233
2012-03-14 20:00:00-04:00    0.322573
2012-03-15 20:00:00-04:00   -0.711113
2012-03-16 20:00:00-04:00    1.444272
2012-03-17 20:00:00-04:00   -0.352268
2012-03-18 20:00:00-04:00    0.213008
2012-03-19 20:00:00-04:00   -0.619340
Freq: D, dtype: float64

Warning

Be wary of conversions between libraries. For some zones pytz and dateutil have different definitions of the zone. This is more of a problem for unusual timezones than for ‘standard’ zones like US/Eastern.

Warning

Be aware that a timezone definition across versions of timezone libraries may not be considered equal. This may cause problems when working with stored data that is localized using one version and operated on with a different version. See here for how to handle such a situation.

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 [225]: rng_eastern = rng_utc.tz_convert('US/Eastern')

In [226]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')

In [227]: rng_eastern[5]
Out[227]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', offset='D')

In [228]: rng_berlin[5]
Out[228]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', offset='D')

In [229]: rng_eastern[5] == rng_berlin[5]
Out[229]: True

Like Series, DataFrame, and DatetimeIndex, Timestamps can be converted to other time zones using tz_convert:

In [230]: rng_eastern[5]
Out[230]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', offset='D')

In [231]: rng_berlin[5]
Out[231]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', offset='D')

In [232]: rng_eastern[5].tz_convert('Europe/Berlin')
Out[232]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin')

Localization of Timestamps functions just like DatetimeIndex and TimeSeries:

In [233]: rng[5]
Out[233]: Timestamp('2012-03-11 00:00:00', offset='D')

In [234]: rng[5].tz_localize('Asia/Shanghai')
Out[234]: Timestamp('2012-03-11 00:00:00+0800', tz='Asia/Shanghai')

Operations between TimeSeries in different time zones will yield UTC TimeSeries, aligning the data on the UTC timestamps:

In [235]: eastern = ts_utc.tz_convert('US/Eastern')

In [236]: berlin = ts_utc.tz_convert('Europe/Berlin')

In [237]: result = eastern + berlin

In [238]: result
Out[238]: 
2012-03-06 00:00:00+00:00    1.517212
2012-03-07 00:00:00+00:00    4.381654
2012-03-08 00:00:00+00:00    1.412174
2012-03-09 00:00:00+00:00    3.597662
2012-03-10 00:00:00+00:00    2.456962
2012-03-11 00:00:00+00:00   -0.358988
2012-03-12 00:00:00+00:00    1.268146
2012-03-13 00:00:00+00:00    0.524245
2012-03-14 00:00:00+00:00    3.856466
2012-03-15 00:00:00+00:00    0.645146
2012-03-16 00:00:00+00:00   -1.422226
2012-03-17 00:00:00+00:00    2.888544
2012-03-18 00:00:00+00:00   -0.704537
2012-03-19 00:00:00+00:00    0.426017
2012-03-20 00:00:00+00:00   -1.238679
Freq: D, dtype: float64

In [239]: result.index
Out[239]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-03-06, ..., 2012-03-20]
Length: 15, Freq: D, Timezone: UTC

In some cases, localize cannot determine the DST and non-DST hours when there are duplicates. This often happens when reading files that simply duplicate the hours. The infer_dst argument in tz_localize will attempt to determine the right offset.

In [240]: rng_hourly = DatetimeIndex(['11/06/2011 00:00', '11/06/2011 01:00',
   .....:                             '11/06/2011 01:00', '11/06/2011 02:00',
   .....:                             '11/06/2011 03:00'])
   .....: 

In [241]: rng_hourly.tz_localize('US/Eastern')
---------------------------------------------------------------------------
AmbiguousTimeError                        Traceback (most recent call last)
<ipython-input-241-8c5fa6a37f5b> in <module>()
----> 1 rng_hourly.tz_localize('US/Eastern')

/home/joris/scipy/pandas/pandas/tseries/index.pyc in tz_localize(self, tz, infer_dst)
   1676 
   1677         # Convert to UTC
-> 1678         new_dates = tslib.tz_localize_to_utc(self.asi8, tz, infer_dst=infer_dst)
   1679         new_dates = new_dates.view(_NS_DTYPE)
   1680 

/home/joris/scipy/pandas/pandas/tslib.so in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:34935)()

AmbiguousTimeError: Cannot infer dst time from Timestamp('2011-11-06 01:00:00'), try using the 'infer_dst' argument

In [242]: rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', infer_dst=True)

In [243]: rng_hourly_eastern.values
Out[243]: 
array(['2011-11-06T05:00:00.000000000+0100',
       '2011-11-06T06:00:00.000000000+0100',
       '2011-11-06T07:00:00.000000000+0100',
       '2011-11-06T08:00:00.000000000+0100',
       '2011-11-06T09:00:00.000000000+0100'], dtype='datetime64[ns]')

Time Deltas

Timedeltas are differences in times, expressed in difference units, e.g. days,hours,minutes,seconds. They can be both positive and negative. DateOffsets that are absolute in nature (Day, Hour, Minute, Second, Milli, Micro, Nano) can be used as timedeltas.

In [244]: from datetime import datetime, timedelta

In [245]: s = Series(date_range('2012-1-1', periods=3, freq='D'))

In [246]: td = Series([ timedelta(days=i) for i in range(3) ])

In [247]: df = DataFrame(dict(A = s, B = td))

In [248]: df
Out[248]: 
           A      B
0 2012-01-01 0 days
1 2012-01-02 1 days
2 2012-01-03 2 days

In [249]: df['C'] = df['A'] + df['B']

In [250]: df
Out[250]: 
           A      B          C
0 2012-01-01 0 days 2012-01-01
1 2012-01-02 1 days 2012-01-03
2 2012-01-03 2 days 2012-01-05

In [251]: df.dtypes
Out[251]: 
A     datetime64[ns]
B    timedelta64[ns]
C     datetime64[ns]
dtype: object

In [252]: s - s.max()
Out[252]: 
0   -2 days
1   -1 days
2    0 days
dtype: timedelta64[ns]

In [253]: s - datetime(2011,1,1,3,5)
Out[253]: 
0   364 days, 20:55:00
1   365 days, 20:55:00
2   366 days, 20:55:00
dtype: timedelta64[ns]

In [254]: s + timedelta(minutes=5)
Out[254]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
dtype: datetime64[ns]

In [255]: s + Minute(5)
Out[255]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
dtype: datetime64[ns]

In [256]: s + Minute(5) + Milli(5)
Out[256]: 
0   2012-01-01 00:05:00.005000
1   2012-01-02 00:05:00.005000
2   2012-01-03 00:05:00.005000
dtype: datetime64[ns]

Getting scalar results from a timedelta64[ns] series

In [257]: y = s - s[0]

In [258]: y
Out[258]: 
0   0 days
1   1 days
2   2 days
dtype: timedelta64[ns]

Series of timedeltas with NaT values are supported

In [259]: y = s - s.shift()

In [260]: y
Out[260]: 
0      NaT
1   1 days
2   1 days
dtype: timedelta64[ns]

Elements can be set to NaT using np.nan analagously to datetimes

In [261]: y[1] = np.nan

In [262]: y
Out[262]: 
0      NaT
1      NaT
2   1 days
dtype: timedelta64[ns]

Operands can also appear in a reversed order (a singular object operated with a Series)

In [263]: s.max() - s
Out[263]: 
0   2 days
1   1 days
2   0 days
dtype: timedelta64[ns]

In [264]: datetime(2011,1,1,3,5) - s
Out[264]: 
0   -364 days, 20:55:00
1   -365 days, 20:55:00
2   -366 days, 20:55:00
dtype: timedelta64[ns]

In [265]: timedelta(minutes=5) + s
Out[265]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
dtype: datetime64[ns]

Some timedelta numeric like operations are supported.

In [266]: td - timedelta(minutes=5, seconds=5, microseconds=5)
Out[266]: 
0   -0 days, 00:05:05.000005
1    0 days, 23:54:54.999995
2    1 days, 23:54:54.999995
dtype: timedelta64[ns]

min, max and the corresponding idxmin, idxmax operations are supported on frames

In [267]: A = s - Timestamp('20120101') - timedelta(minutes=5, seconds=5)

In [268]: B = s - Series(date_range('2012-1-2', periods=3, freq='D'))

In [269]: df = DataFrame(dict(A=A, B=B))

In [270]: df
Out[270]: 
                  A       B
0 -0 days, 00:05:05 -1 days
1  0 days, 23:54:55 -1 days
2  1 days, 23:54:55 -1 days

In [271]: df.min()
Out[271]: 
A   -0 days, 00:05:05
B   -1 days, 00:00:00
dtype: timedelta64[ns]

In [272]: df.min(axis=1)
Out[272]: 
0   -1 days
1   -1 days
2   -1 days
dtype: timedelta64[ns]

In [273]: df.idxmin()
Out[273]: 
A    0
B    0
dtype: int64

In [274]: df.idxmax()
Out[274]: 
A    2
B    0
dtype: int64

min, max operations are supported on series; these return a single element timedelta64[ns] Series (this avoids having to deal with numpy timedelta64 issues). idxmin, idxmax are supported as well.

In [275]: df.min().max()
Out[275]: 
0   -00:05:05
dtype: timedelta64[ns]

In [276]: df.min(axis=1).min()
Out[276]: 
0   -1 days
dtype: timedelta64[ns]

In [277]: df.min().idxmax()
Out[277]: 'A'

In [278]: df.min(axis=1).idxmin()
Out[278]: 0

You can fillna on timedeltas. Integers will be interpreted as seconds. You can pass a timedelta to get a particular value.

In [279]: y.fillna(0)
Out[279]: 
0   0 days
1   0 days
2   1 days
dtype: timedelta64[ns]

In [280]: y.fillna(10)
Out[280]: 
0   0 days, 00:00:10
1   0 days, 00:00:10
2   1 days, 00:00:00
dtype: timedelta64[ns]

In [281]: y.fillna(timedelta(days=-1,seconds=5))
Out[281]: 
0   -0 days, 23:59:55
1   -0 days, 23:59:55
2    1 days, 00:00:00
dtype: timedelta64[ns]

Time Deltas & Reductions

Warning

A numeric reduction operation for timedelta64[ns] can return a single-element Series of dtype timedelta64[ns].

You can do numeric reduction operations on timedeltas.

In [282]: y2 = y.fillna(timedelta(days=-1,seconds=5))

In [283]: y2
Out[283]: 
0   -0 days, 23:59:55
1   -0 days, 23:59:55
2    1 days, 00:00:00
dtype: timedelta64[ns]

In [284]: y2.mean()
Out[284]: 
0   -07:59:56.666667
dtype: timedelta64[ns]

In [285]: y2.quantile(.1)
Out[285]: numpy.timedelta64(-86395000000000,'ns')

Time Deltas & Conversions

New in version 0.13.

string/integer conversion

Using the top-level to_timedelta, you can convert a scalar or array from the standard timedelta format (produced by to_csv) into a timedelta type (np.timedelta64 in nanoseconds). It can also construct Series.

Warning

This requires numpy >= 1.7

In [286]: to_timedelta('1 days 06:05:01.00003')
Out[286]: numpy.timedelta64(108301000030000,'ns')

In [287]: to_timedelta('15.5us')
Out[287]: numpy.timedelta64(15500,'ns')

In [288]: to_timedelta(['1 days 06:05:01.00003','15.5us','nan'])
Out[288]: 
0   1 days, 06:05:01.000030
1   0 days, 00:00:00.000016
2                       NaT
dtype: timedelta64[ns]

In [289]: to_timedelta(np.arange(5),unit='s')
Out[289]: 
0   00:00:00
1   00:00:01
2   00:00:02
3   00:00:03
4   00:00:04
dtype: timedelta64[ns]

In [290]: to_timedelta(np.arange(5),unit='d')
Out[290]: 
0   0 days
1   1 days
2   2 days
3   3 days
4   4 days
dtype: timedelta64[ns]

frequency conversion

Timedeltas can be converted to other ‘frequencies’ by dividing by another timedelta, or by astyping to a specific timedelta type. These operations yield float64 dtyped Series.

In [291]: td = Series(date_range('20130101',periods=4))-Series(date_range('20121201',periods=4))

In [292]: td[2] += np.timedelta64(timedelta(minutes=5,seconds=3))

In [293]: td[3] = np.nan

In [294]: td
Out[294]: 
0   31 days, 00:00:00
1   31 days, 00:00:00
2   31 days, 00:05:03
3                 NaT
dtype: timedelta64[ns]

# to days
In [295]: td / np.timedelta64(1,'D')
Out[295]: 
0    31.000000
1    31.000000
2    31.003507
3          NaN
dtype: float64

In [296]: td.astype('timedelta64[D]')
Out[296]: 
0    31
1    31
2    31
3   NaN
dtype: float64

# to seconds
In [297]: td / np.timedelta64(1,'s')
Out[297]: 
0    2678400
1    2678400
2    2678703
3        NaN
dtype: float64

In [298]: td.astype('timedelta64[s]')
Out[298]: 
0    2678400
1    2678400
2    2678703
3        NaN
dtype: float64

Dividing or multiplying a timedelta64[ns] Series by an integer or integer Series yields another timedelta64[ns] dtypes Series.

In [299]: td * -1
Out[299]: 
0   -31 days, 00:00:00
1   -31 days, 00:00:00
2   -31 days, 00:05:03
3                  NaT
dtype: timedelta64[ns]

In [300]: td * Series([1,2,3,4])
Out[300]: 
0   31 days, 00:00:00
1   62 days, 00:00:00
2   93 days, 00:15:09
3                 NaT
dtype: timedelta64[ns]

Numpy < 1.7 Compatibility

Numpy < 1.7 has a broken timedelta64 type that does not correctly work for arithmetic. pandas bypasses this, but for frequency conversion as above, you need to create the divisor yourself. The np.timetimedelta64 type only has 1 argument, the number of micro seconds.

The following are equivalent statements in the two versions of numpy.

from distutils.version import LooseVersion
if LooseVersion(np.__version__) <= '1.6.2':
    y / np.timedelta(86400*int(1e6))
    y / np.timedelta(int(1e6))
else:
    y / np.timedelta64(1,'D')
    y / np.timedelta64(1,'s')