# 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 timestamp | `to_datetime`, `Timestamp`
`DatetimeIndex` | Index of `Timestamp` | `to_datetime`, `date_range`, `bdate_range`, `DatetimeIndex`
`Period` | Represents a single time span | `Period`
`PeriodIndex` | Index of `Period` | `period_range`, `PeriodIndex`

## Timestamps vs. Time Spans

Timestamped data is the most basic type of time series 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.

If you pass a single string to `to_datetime`, it returns a single `Timestamp`. `Timestamp` can also accept string input, but it doesn’t accept string parsing options like `dayfirst` or `format`, so 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')

### Providing a Format Argument

In addition to the required datetime string, a `format` argument can be passed to ensure specific parsing. This could also potentially speed up the conversion considerably.

    In [29]: pd.to_datetime('2010/11/12', format='%Y/%m/%d')
    Out[29]: Timestamp('2010-11-12 00:00:00')

    In [30]: pd.to_datetime('12-11-2010 00:00', format='%d-%m-%Y %H:%M')
    Out[30]: Timestamp('2010-11-12 00:00:00')

For more information on how to specify the `format` options, see https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.

### Assembling Datetime from Multiple DataFrame Columns

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 [31]: df = pd.DataFrame({'year': [2015, 2016],
       ....:                    'month': [2, 3],
       ....:                    'day': [4, 5],
       ....:                    'hour': [2, 3]})
       ....:

    In [32]: pd.to_datetime(df)
    Out[32]:
    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 [33]: pd.to_datetime(df[['year', 'month', 'day']])
    Out[33]:
    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.

The default behavior, `errors='raise'`, is to raise when unparseable:

    In [2]: pd.to_datetime(['2009/07/31', 'asd'], errors='raise')
    ValueError: Unknown string format

Pass `errors='ignore'` to return the original input when unparseable:

    In [34]: pd.to_datetime(['2009/07/31', 'asd'], errors='ignore')
    Out[34]: array(['2009/07/31', 'asd'], dtype=object)

Pass `errors='coerce'` to convert unparseable data to `NaT` (not a time):

    In [35]: pd.to_datetime(['2009/07/31', 'asd'], errors='coerce')
    Out[35]: DatetimeIndex(['2009-07-31', 'NaT'], dtype='datetime64[ns]', freq=None)

### Epoch Timestamps

pandas supports converting integer or float epoch times to `Timestamp` and `DatetimeIndex`. The default unit is nanoseconds, since that is how `Timestamp` objects are stored internally. However, epochs are often stored in another `unit` which can be specified. These are computed from the starting point specified by the `origin` parameter.

    In [36]: pd.to_datetime([1349720105, 1349806505, 1349892905,
       ....:                 1349979305, 1350065705], unit='s')
       ....:
    Out[36]:
    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 [37]: pd.to_datetime([1349720105100, 1349720105200, 1349720105300,
       ....:                 1349720105400, 1349720105500 ], unit='ms')
       ....:
    Out[37]:
    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 [38]: pd.to_datetime([1490195805.433, 1490195805.433502912], unit='s')
    Out[38]: DatetimeIndex(['2017-03-22 15:16:45.433000', '2017-03-22 15:16:45.433503'], dtype='datetime64[ns]', freq=None)

    In [39]: pd.to_datetime(1490195805433502912, unit='ns')
    Out[39]: Timestamp('2017-03-22 15:16:45.433502912')

See also

Using the origin Parameter

### From Timestamps to Epoch

To invert the operation from above, namely, to convert from a `Timestamp` to a ‘unix’ epoch:

    In [40]: stamps = pd.date_range('2012-10-08 18:15:05', periods=4, freq='D')

    In [41]: stamps
    Out[41]:
    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 [42]: stamps.view('int64') // pd.Timedelta(1, unit='s')
    Out[42]: 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`. For example, to use 1960-01-01 as the starting date:

    In [43]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
    Out[43]: 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 [44]: pd.to_datetime([1, 2, 3], unit='D')
    Out[44]: DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)

## Generating Ranges of Timestamps

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

    In [45]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]

    # Note the frequency information
    In [46]: index = pd.DatetimeIndex(dates)

    In [47]: index
    Out[47]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)

    # Automatically converted to DatetimeIndex
    In [48]: index = pd.Index(dates)

    In [49]: index
    Out[49]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)

In practice 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 `date_range()` and `bdate_range()` functions to create a `DatetimeIndex`. 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]: index = pd.date_range(start, end)

    In [53]: index
    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]: index = pd.bdate_range(start, end)

    In [55]: index
    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')

Convenience functions like `date_range` and `bdate_range` can utilize a variety of frequency aliases:

    In [56]: pd.date_range(start, periods=1000, freq='M')
    Out[56]:
    DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30',
                   '2011-05-31', '2011-06-30', '2011-07-31', '2011-08-31',
                   '2011-09-30', '2011-10-31',
                   ...
                   '2093-07-31', '2093-08-31', '2093-09-30', '2093-10-31',
                   '2093-11-30', '2093-12-31', '2094-01-31', '2094-02-28',
                   '2094-03-31', '2094-04-30'],
                  dtype='datetime64[ns]', length=1000, freq='M')

    In [57]: pd.bdate_range(start, periods=250, freq='BQS')
    Out[57]:
    DatetimeIndex(['2011-01-03', '2011-04-01', '2011-07-01', '2011-10-03',
                   '2012-01-02', '2012-04-02', '2012-07-02', '2012-10-01',
                   '2013-01-01', '2013-04-01',
                   ...
                   '2071-01-01', '2071-04-01', '2071-07-01', '2071-10-01',
                   '2072-01-01', '2072-04-01', '2072-07-01', '2072-10-03',
                   '2073-01-02', '2073-04-03'],
                  dtype='datetime64[ns]', length=250, freq='BQS-JAN')

`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`. The start and end dates are strictly inclusive, so dates outside of those specified will not be generated:

    In [58]: pd.date_range(start, end, freq='BM')
    Out[58]:
    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 [59]: pd.date_range(start, end, freq='W')
    Out[59]:
    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 [60]: pd.bdate_range(end=end, periods=20)
    Out[60]:
    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 [61]: pd.bdate_range(start=start, periods=20)
    Out[61]:
    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')

### Custom Frequency Ranges

Warning

This functionality was originally exclusive to `cdate_range`, which is deprecated as of version 0.21.0 in favor of `bdate_range`. Note that `cdate_range` only utilizes the `weekmask` and `holidays` parameters when custom business day, ‘C’, is passed as the frequency string. Support has been expanded with `bdate_range` to work with any custom frequency string.

New in version 0.21.0.

`bdate_range` can also generate a range of custom frequency dates by using the `weekmask` and `holidays` parameters. These parameters will only be used if a custom frequency string is passed.

    In [62]: weekmask = 'Mon Wed Fri'

    In [63]: holidays = [datetime(2011, 1, 5), datetime(2011, 3, 14)]

    In [64]: pd.bdate_range(start, end, freq='C', weekmask=weekmask, holidays=holidays)
    Out[64]:
    DatetimeIndex(['2011-01-03', '2011-01-07', '2011-01-10', '2011-01-12',
                   '2011-01-14', '2011-01-17', '2011-01-19', '2011-01-21',
                   '2011-01-24', '2011-01-26',
                   ...
                   '2011-12-09', '2011-12-12', '2011-12-14', '2011-12-16',
                   '2011-12-19', '2011-12-21', '2011-12-23', '2011-12-26',
                   '2011-12-28', '2011-12-30'],
                  dtype='datetime64[ns]', length=154, freq='C')

    In [65]: pd.bdate_range(start, end, freq='CBMS', weekmask=weekmask)
    Out[65]:
    DatetimeIndex(['2011-01-03', '2011-02-02', '2011-03-02', '2011-04-01',
                   '2011-05-02', '2011-06-01', '2011-07-01', '2011-08-01',
                   '2011-09-02', '2011-10-03', '2011-11-02', '2011-12-02'],
                  dtype='datetime64[ns]', freq='CBMS')

See also

Custom Business Days

## Timestamp Limitations

Since pandas represents timestamps in nanosecond resolution, the time span that can be represented using a 64-bit integer is limited to approximately 584 years:

    In [66]: pd.Timestamp.min
    Out[66]: Timestamp('1677-09-21 00:12:43.145225')

    In [67]: pd.Timestamp.max
    Out[67]: Timestamp('2262-04-11 23:47:16.854775807')

See also

Representing Out-of-Bounds Spans

## Indexing

One of the main uses for `DatetimeIndex` is as an index for pandas objects. The `DatetimeIndex` class contains many time series 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 have all the basic functionality of regular `Index` objects, and a smorgasbord of advanced time series 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.

`DatetimeIndex` can be used like a regular index and offers all of its intelligent functionality like selection, slicing, etc.

    In [68]: rng = pd.date_range(start, end, freq='BM')

    In [69]: ts = pd.Series(np.random.randn(len(rng)), index=rng)

    In [70]: ts.index
    Out[70]:
    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 [71]: ts[:5].index
    Out[71]:
    DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
                   '2011-05-31'],
                  dtype='datetime64[ns]', freq='BM')

    In [72]: ts[::2].index
    Out[72]:
    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

Dates and strings that parse to timestamps can be passed as indexing parameters:

    In [73]: ts['1/31/2011']
    Out[73]: -1.2812473076599531

    In [74]: ts[datetime(2011, 12, 25):]
    Out[74]:
    2011-12-30    0.687738
    Freq: BM, dtype: float64

    In [75]: ts['10/31/2011':'12/31/2011']
    Out[75]:
    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 [76]: ts['2011']
    Out[76]:
    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 [77]: ts['2011-6']
    Out[77]:
    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:

    In [78]: dft = pd.DataFrame(randn(100000,1),
       ....:                    columns=['A'],
       ....:                    index=pd.date_range('20130101',periods=100000,freq='T'))
       ....:

    In [79]: dft
    Out[79]:
                                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 [80]: dft['2013']
    Out[80]:
                                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 [81]: dft['2013-1':'2013-2']
    Out[81]:
                                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 [82]: dft['2013-1':'2013-2-28']
    Out[82]:
                                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 [83]: dft['2013-1':'2013-2-28 00:00:00']
    Out[83]:
                                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 [84]: dft['2013-1-15':'2013-1-15 12:30:00']
    Out[84]:
                                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 a `DataFrame` with a `MultiIndex`:

    In [85]: 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 [86]: dft2
    Out[86]:
                                  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 [87]: dft2.loc['2013-01-05']
    Out[87]:
                                  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 [88]: idx = pd.IndexSlice

    In [89]: dft2 = dft2.swaplevel(0, 1).sort_index()

    In [90]: dft2.loc[idx[:, '2013-01-05'], :]
    Out[90]:
                                  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 the index. If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match.

Consider a `Series` object with a minute resolution index:

    In [91]: 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 [92]: series_minute.index.resolution
    Out[92]: 'minute'

A timestamp string less accurate than a minute gives a `Series` object.

    In [93]: series_minute['2011-12-31 23']
    Out[93]:
    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 [94]: series_minute['2011-12-31 23:59']
    Out[94]: 1

    In [95]: series_minute['2011-12-31 23:59:00']
    Out[95]: 1

If index resolution is second, then, the minute-accurate timestamp gives a `Series`.

    In [96]: 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 [97]: series_second.index.resolution
    Out[97]: 'second'

    In [98]: series_second['2011-12-31 23:59']
    Out[98]:
    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 [99]: dft_minute = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
       ....:                            index=series_minute.index)
       ....:

    In [100]: dft_minute['2011-12-31 23']
    Out[100]:
                         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 the 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 [101]: dft_minute.loc['2011-12-31 23:59']
    Out[101]:
    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 [102]: series_monthly = pd.Series([1, 2, 3],
       .....:                           pd.DatetimeIndex(['2011-12',
       .....:                                             '2012-01',
       .....:                                             '2012-02']))
       .....:

    In [103]: series_monthly.index.resolution
    Out[103]: 'day'

    In [104]: series_monthly['2011-12'] # returns Series
    Out[104]:
    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 [105]: dft[datetime(2013, 1, 1):datetime(2013,2,28)]
    Out[105]:
                                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 [106]: dft[datetime(2013, 1, 1, 10, 12, 0):datetime(2013, 2, 28, 10, 12, 0)]
    Out[106]:
                                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 similar to slicing. Note that `truncate` assumes a 0 value for any unspecified date component in a `DatetimeIndex` in contrast to slicing which returns any partially matching dates:

    In [107]: rng2 = pd.date_range('2011-01-01', '2012-01-01', freq='W')

    In [108]: ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2)

    In [109]: ts2.truncate(before='2011-11', after='2011-12')
    Out[109]:
    2011-11-06   -0.773743
    2011-11-13    0.247216
    2011-11-20    0.591308
    2011-11-27    2.228500
    Freq: W-SUN, dtype: float64

    In [110]: ts2['2011-11':'2011-12']
    Out[110]:
    2011-11-06   -0.773743
    2011-11-13    0.247216
    2011-11-20    0.591308
    2011-11-27    2.228500
    2011-12-04    0.838769
    2011-12-11    0.658538
    2011-12-18    0.567353
    2011-12-25   -1.076735
    Freq: W-SUN, dtype: float64

Even complicated fancy indexing that breaks the `DatetimeIndex` frequency regularity will result in a `DatetimeIndex`, although frequency is lost:

    In [111]: ts2[[0, 2, 6]].index
    Out[111]: DatetimeIndex(['2011-01-02', '2011-01-16', '2011-02-13'], 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
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 [112]: d = datetime(2008, 8, 18, 9, 0)

    In [113]: d + relativedelta(months=4, days=5)
    Out[113]: datetime.datetime(2008, 12, 23, 9, 0)

We could have done the same thing with `DateOffset`:

    In [114]: from pandas.tseries.offsets import *

    In [115]: d + DateOffset(months=4, days=5)
    Out[115]: 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 [116]: d - 5 * BDay()
    Out[116]: Timestamp('2008-08-11 09:00:00')

    In [117]: d + BMonthEnd()
    Out[117]: Timestamp('2008-08-29 09:00:00')

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

    In [118]: d
    Out[118]: datetime.datetime(2008, 8, 18, 9, 0)

    In [119]: offset = BMonthEnd()

    In [120]: offset.rollforward(d)
    Out[120]: Timestamp('2008-08-29 09:00:00')

    In [121]: offset.rollback(d)
    Out[121]: 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 [122]: day = Day()

    In [123]: day.apply(pd.Timestamp('2014-01-01 09:00'))
    Out[123]: Timestamp('2014-01-02 09:00:00')

    In [124]: day = Day(normalize=True)

    In [125]: day.apply(pd.Timestamp('2014-01-01 09:00'))
    Out[125]: Timestamp('2014-01-02 00:00:00')

    In [126]: hour = Hour()

    In [127]: hour.apply(pd.Timestamp('2014-01-01 22:00'))
    Out[127]: Timestamp('2014-01-01 23:00:00')

    In [128]: hour = Hour(normalize=True)

    In [129]: hour.apply(pd.Timestamp('2014-01-01 22:00'))
    Out[129]: Timestamp('2014-01-01 00:00:00')

    In [130]: hour.apply(pd.Timestamp('2014-01-01 23:00'))
    Out[130]: 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 [131]: d
    Out[131]: datetime.datetime(2008, 8, 18, 9, 0)

    In [132]: d + Week()
    Out[132]: Timestamp('2008-08-25 09:00:00')

    In [133]: d + Week(weekday=4)
    Out[133]: Timestamp('2008-08-22 09:00:00')

    In [134]: (d + Week(weekday=4)).weekday()
    Out[134]: 4

    In [135]: d - Week()
    Out[135]: Timestamp('2008-08-11 09:00:00')

`normalize` option will be effective for addition and subtraction.

    In [136]: d + Week(normalize=True)
    Out[136]: Timestamp('2008-08-25 00:00:00')

    In [137]: d - Week(normalize=True)
    Out[137]: Timestamp('2008-08-11 00:00:00')

Another example is parameterizing `YearEnd` with the specific ending month:

    In [138]: d + YearEnd()
    Out[138]: Timestamp('2008-12-31 09:00:00')

    In [139]: d + YearEnd(month=6)
    Out[139]: 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 [140]: rng = pd.date_range('2012-01-01', '2012-01-03')

    In [141]: s = pd.Series(rng)

    In [142]: rng
    Out[142]: DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03'], dtype='datetime64[ns]', freq='D')

    In [143]: rng + DateOffset(months=2)
    Out[143]: DatetimeIndex(['2012-03-01', '2012-03-02', '2012-03-03'], dtype='datetime64[ns]', freq='D')

    In [144]: s + DateOffset(months=2)
    Out[144]:
    0   2012-03-01
    1   2012-03-02
    2   2012-03-03
    dtype: datetime64[ns]

    In [145]: s - DateOffset(months=2)
    Out[145]:
    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 [146]: s - Day(2)
    Out[146]:
    0   2011-12-30
    1   2011-12-31
    2   2012-01-01
    dtype: datetime64[ns]

    In [147]: td = s - pd.Series(pd.date_range('2011-12-29', '2011-12-31'))

    In [148]: td
    Out[148]:
    0   3 days
    1   3 days
    2   3 days
    dtype: timedelta64[ns]

    In [149]: td + Minute(15)
    Out[149]:
    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 [150]: rng + BQuarterEnd()
    Out[150]: 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 [151]: from pandas.tseries.offsets import CustomBusinessDay

    In [152]: weekmask_egypt = 'Sun Mon Tue Wed Thu'

    # They also observe International Workers' Day so let's
    # add that for a couple of years
    In [153]: holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]

    In [154]: bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)

    In [155]: dt = datetime(2013, 4, 30)

    In [156]: dt + 2 * bday_egypt
    Out[156]: Timestamp('2013-05-05 00:00:00')

Let’s map to the weekday names

    In [157]: dts = pd.date_range(dt, periods=5, freq=bday_egypt)

    In [158]: pd.Series(dts.weekday, dts).map(pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split()))
    Out[158]:
    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 [159]: from pandas.tseries.holiday import USFederalHolidayCalendar

    In [160]: bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())

    # Friday before MLK Day
    In [161]: dt = datetime(2014, 1, 17)

    # Tuesday after MLK Day (Monday is skipped because it's a holiday)
    In [162]: dt + bday_us
    Out[162]: Timestamp('2014-01-21 00:00:00')

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

    In [163]: from pandas.tseries.offsets import CustomBusinessMonthBegin

    In [164]: bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())

    # Skip new years
    In [165]: dt = datetime(2013, 12, 17)

    In [166]: dt + bmth_us
    Out[166]: Timestamp('2014-01-02 00:00:00')

    # Define date index with custom offset
    In [167]: pd.DatetimeIndex(start='20100101',end='20120101',freq=bmth_us)
    Out[167]:
    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 [168]: bh = BusinessHour()

    In [169]: bh
    Out[169]: <BusinessHour: BH=09:00-17:00>

    # 2014-08-01 is Friday
    In [170]: pd.Timestamp('2014-08-01 10:00').weekday()
    Out[170]: 4

    In [171]: pd.Timestamp('2014-08-01 10:00') + bh
    Out[171]: Timestamp('2014-08-01 11:00:00')

    # Below example is the same as: pd.Timestamp('2014-08-01 09:00') + bh
    In [172]: pd.Timestamp('2014-08-01 08:00') + bh
    Out[172]: Timestamp('2014-08-01 10:00:00')

    # If the results is on the end time, move to the next business day
    In [173]: pd.Timestamp('2014-08-01 16:00') + bh
    Out[173]: Timestamp('2014-08-04 09:00:00')

    # Remainings are added to the next day
    In [174]: pd.Timestamp('2014-08-01 16:30') + bh
    Out[174]: Timestamp('2014-08-04 09:30:00')

    # Adding 2 business hours
    In [175]: pd.Timestamp('2014-08-01 10:00') + BusinessHour(2)
    Out[175]: Timestamp('2014-08-01 12:00:00')

    # Subtracting 3 business hours
    In [176]: pd.Timestamp('2014-08-01 10:00') + BusinessHour(-3)
    Out[176]: 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 [177]: bh = BusinessHour(start='11:00', end=time(20, 0))

    In [178]: bh
    Out[178]: <BusinessHour: BH=11:00-20:00>

    In [179]: pd.Timestamp('2014-08-01 13:00') + bh
    Out[179]: Timestamp('2014-08-01 14:00:00')

    In [180]: pd.Timestamp('2014-08-01 09:00') + bh
    Out[180]: Timestamp('2014-08-01 12:00:00')

    In [181]: pd.Timestamp('2014-08-01 18:00') + bh
    Out[181]: 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 [182]: bh = BusinessHour(start='17:00', end='09:00')

    In [183]: bh
    Out[183]: <BusinessHour: BH=17:00-09:00>

    In [184]: pd.Timestamp('2014-08-01 17:00') + bh
    Out[184]: Timestamp('2014-08-01 18:00:00')

    In [185]: pd.Timestamp('2014-08-01 23:00') + bh
    Out[185]: Timestamp('2014-08-02 00:00:00')

    # Although 2014-08-02 is Satuaday,
    # it is valid because it starts from 08-01 (Friday).
    In [186]: pd.Timestamp('2014-08-02 04:00') + bh
    Out[186]: 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 [187]: pd.Timestamp('2014-08-04 04:00') + bh
    Out[187]: 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 [188]: BusinessHour().rollback(pd.Timestamp('2014-08-02 15:00'))
    Out[188]: Timestamp('2014-08-01 17:00:00')

    In [189]: BusinessHour().rollforward(pd.Timestamp('2014-08-02 15:00'))
    Out[189]: 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 [190]: BusinessHour().apply(pd.Timestamp('2014-08-02 15:00'))
    Out[190]: Timestamp('2014-08-04 10:00:00')

    # BusinessDay results (for reference)
    In [191]: BusinessHour().rollforward(pd.Timestamp('2014-08-02'))
    Out[191]: 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 [192]: BusinessHour().apply(pd.Timestamp('2014-08-02'))
    Out[192]: 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 [193]: from pandas.tseries.holiday import USFederalHolidayCalendar

    In [194]: bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())

    # Friday before MLK Day
    In [195]: dt = datetime(2014, 1, 17, 15)

    In [196]: dt + bhour_us
    Out[196]: Timestamp('2014-01-17 16:00:00')

    # Tuesday after MLK Day (Monday is skipped because it's a holiday)
    In [197]: dt + bhour_us * 2
    Out[197]: Timestamp('2014-01-21 09:00:00')

You can use keyword arguments supported by either `BusinessHour` and `CustomBusinessDay`.

    In [198]: 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 [199]: dt + bhour_mon * 2
    Out[199]: 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.

Alias | Description
--- | ---
B | business day frequency
C | custom business day frequency
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 end frequency
QS | quarter start frequency
BQS | business quarter start frequency
A, Y | year end frequency
BA, BY | business year end frequency
AS, YS | year start frequency
BAS, BYS | 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 [200]: pd.date_range(start, periods=5, freq='B')
    Out[200]:
    DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
                   '2011-01-07'],
                  dtype='datetime64[ns]', freq='B')

    In [201]: pd.date_range(start, periods=5, freq=BDay())
    Out[201]:
    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 [202]: pd.date_range(start, periods=10, freq='2h20min')
    Out[202]:
    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 [203]: pd.date_range(start, periods=10, freq='1D10U')
    Out[203]:
    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 [204]: pd.Timestamp('2014-01-02') + MonthBegin(n=1)
    Out[204]: Timestamp('2014-02-01 00:00:00')

    In [205]: pd.Timestamp('2014-01-02') + MonthEnd(n=1)
    Out[205]: Timestamp('2014-01-31 00:00:00')

    In [206]: pd.Timestamp('2014-01-02') - MonthBegin(n=1)
    Out[206]: Timestamp('2014-01-01 00:00:00')

    In [207]: pd.Timestamp('2014-01-02') - MonthEnd(n=1)
    Out[207]: Timestamp('2013-12-31 00:00:00')

    In [208]: pd.Timestamp('2014-01-02') + MonthBegin(n=4)
    Out[208]: Timestamp('2014-05-01 00:00:00')

    In [209]: pd.Timestamp('2014-01-02') - MonthBegin(n=4)
    Out[209]: 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 [210]: pd.Timestamp('2014-01-01') + MonthBegin(n=1)
    Out[210]: Timestamp('2014-02-01 00:00:00')

    In [211]: pd.Timestamp('2014-01-31') + MonthEnd(n=1)
    Out[211]: Timestamp('2014-02-28 00:00:00')

    In [212]: pd.Timestamp('2014-01-01') - MonthBegin(n=1)
    Out[212]: Timestamp('2013-12-01 00:00:00')

    In [213]: pd.Timestamp('2014-01-31') - MonthEnd(n=1)
    Out[213]: Timestamp('2013-12-31 00:00:00')

    In [214]: pd.Timestamp('2014-01-01') + MonthBegin(n=4)
    Out[214]: Timestamp('2014-05-01 00:00:00')

    In [215]: pd.Timestamp('2014-01-31') - MonthBegin(n=4)
    Out[215]: 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 [216]: pd.Timestamp('2014-01-02') + MonthBegin(n=0)
    Out[216]: Timestamp('2014-02-01 00:00:00')

    In [217]: pd.Timestamp('2014-01-02') + MonthEnd(n=0)
    Out[217]: Timestamp('2014-01-31 00:00:00')

    In [218]: pd.Timestamp('2014-01-01') + MonthBegin(n=0)
    Out[218]: Timestamp('2014-01-01 00:00:00')

    In [219]: pd.Timestamp('2014-01-31') + MonthEnd(n=0)
    Out[219]: 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 [220]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
       .....:     AbstractHolidayCalendar, nearest_workday, MO
       .....:

    In [221]: 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 [222]: cal = ExampleCalendar()

    In [223]: cal.holidays(datetime(2012, 1, 1), datetime(2012, 12, 31))
    Out[223]: 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 [224]: from pandas.tseries.offsets import CDay

    In [225]: pd.DatetimeIndex(start='7/1/2012', end='7/10/2012',
       .....:     freq=CDay(calendar=cal)).to_pydatetime()
       .....:
    Out[225]:
    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 [226]: offset = CustomBusinessDay(calendar=cal)

    In [227]: datetime(2012, 5, 25) + offset
    Out[227]: Timestamp('2012-05-29 00:00:00')

    In [228]: datetime(2012, 7, 3) + offset
    Out[228]: Timestamp('2012-07-05 00:00:00')

    In [229]: datetime(2012, 7, 3) + 2 * offset
    Out[229]: Timestamp('2012-07-06 00:00:00')

    In [230]: datetime(2012, 7, 6) + offset
    Out[230]: 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 [231]: AbstractHolidayCalendar.start_date
    Out[231]: Timestamp('1970-01-01 00:00:00')

    In [232]: AbstractHolidayCalendar.end_date
    Out[232]: Timestamp('2030-12-31 00:00:00')

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

    In [233]: AbstractHolidayCalendar.start_date = datetime(2012, 1, 1)

    In [234]: AbstractHolidayCalendar.end_date = datetime(2012, 12, 31)

    In [235]: cal.holidays()
    Out[235]: 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 [236]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
       .....:     USLaborDay
       .....:

    In [237]: cal = get_calendar('ExampleCalendar')

    In [238]: cal.rules
    Out[238]:
    [Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>),
     Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7faad3eafb18>),
     Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>)]

    In [239]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)

    In [240]: new_cal.rules
    Out[240]:
    [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 0x7faad3eafb18>),
     Holiday: MemorialDay (month=5, day=31, offset=<DateOffset: kwds={'weekday': MO(-1)}>)]

## Time Series-Related Instance Methods

### Shifting / Lagging

One may want to shift or lag the values in a time series back and forward in time. The method for this is `shift`, which is available on all of the pandas objects.

    In [241]: ts = ts[:5]

    In [242]: ts.shift(1)
    Out[242]:
    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 [243]: ts.shift(5, freq=offsets.BDay())
    Out[243]:
    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 [244]: ts.shift(5, freq='BM')
    Out[244]:
    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 `Series` objects also have a `tshift` convenience method that changes all the dates in the index by a specified number of offsets:

    In [245]: ts.tshift(5, freq='D')
    Out[245]:
    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 [246]: dr = pd.date_range('1/1/2010', periods=3, freq=3 * offsets.BDay())

    In [247]: ts = pd.Series(randn(3), index=dr)

    In [248]: ts
    Out[248]:
    2010-01-01    0.155932
    2010-01-06    1.486218
    2010-01-11   -2.148675
    Freq: 3B, dtype: float64

    In [249]: ts.asfreq(BDay())
    Out[249]:
    2010-01-01    0.155932
    2010-01-04         NaN
    2010-01-05         NaN
    2010-01-06    1.486218
    2010-01-07         NaN
    2010-01-08         NaN
    2010-01-11   -2.148675
    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 [250]: ts.asfreq(BDay(), method='pad')
    Out[250]:
    2010-01-01    0.155932
    2010-01-04    0.155932
    2010-01-05    0.155932
    2010-01-06    1.486218
    2010-01-07    1.486218
    2010-01-08    1.486218
    2010-01-11   -2.148675
    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.

## 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 [251]: rng = pd.date_range('1/1/2012', periods=100, freq='S')

    In [252]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)

    In [253]: ts.resample('5Min').sum()
    Out[253]:
    2012-01-01    25653
    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.

Any function available via dispatching is available as a method of the returned object, including `sum`, `mean`, `std`, `sem`, `max`, `min`, `median`, `first`, `last`, `ohlc`:

    In [254]: ts.resample('5Min').mean()
    Out[254]:
    2012-01-01    256.53
    Freq: 5T, dtype: float64

    In [255]: ts.resample('5Min').ohlc()
    Out[255]:
                open  high  low  close
    2012-01-01   296   496    6    449

    In [256]: ts.resample('5Min').max()
    Out[256]:
    2012-01-01    496
    Freq: 5T, dtype: int64

For downsampling, `closed` can be set to ‘left’ or ‘right’ to specify which end of the interval is closed:

    In [257]: ts.resample('5Min', closed='right').mean()
    Out[257]:
    2011-12-31 23:55:00    296.000000
    2012-01-01 00:00:00    256.131313
    Freq: 5T, dtype: float64

    In [258]: ts.resample('5Min', closed='left').mean()
    Out[258]:
    2012-01-01    256.53
    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 [259]: ts.resample('5Min').mean() # by default label='left'
    Out[259]:
    2012-01-01    256.53
    Freq: 5T, dtype: float64

    In [260]: ts.resample('5Min', label='left').mean()
    Out[260]:
    2012-01-01    256.53
    Freq: 5T, dtype: float64

    In [261]: ts.resample('5Min', label='left', loffset='1s').mean()
    Out[261]:
    2012-01-01 00:00:01    256.53
    dtype: float64

Note

The default values for `label` and `closed` is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

    In [262]: rng2 = pd.date_range('1/1/2012', end='3/31/2012', freq='D')

    In [263]: ts2 = pd.Series(range(len(rng2)), index=rng2)

    # default: label='right', closed='right'
    In [264]: ts2.resample('M').max()
    Out[264]:
    2012-01-31    30
    2012-02-29    59
    2012-03-31    90
    Freq: M, dtype: int64

    # default: label='left', closed='left'
    In [265]: ts2.resample('SM').max()
    Out[265]:
    2011-12-31    13
    2012-01-15    29
    2012-01-31    44
    2012-02-15    58
    2012-02-29    73
    2012-03-15    89
    2012-03-31    90
    Freq: SM-15, dtype: int64

    In [266]: ts2.resample('SM', label='right', closed='right').max()
    Out[266]:
    2012-01-15    14.0
    2012-01-31    30.0
    2012-02-15    45.0
    2012-02-29    59.0
    2012-03-15    74.0
    2012-03-31    90.0
    2012-04-15     NaN
    Freq: SM-15, 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 timestamp 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.

### Upsampling

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 [267]: ts[:2].resample('250L').asfreq()
    Out[267]:
    2012-01-01 00:00:00.000    296.0
    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 [268]: ts[:2].resample('250L').ffill()
    Out[268]:
    2012-01-01 00:00:00.000    296
    2012-01-01 00:00:00.250    296
    2012-01-01 00:00:00.500    296
    2012-01-01 00:00:00.750    296
    2012-01-01 00:00:01.000    199
    Freq: 250L, dtype: int64

    In [269]: ts[:2].resample('250L').ffill(limit=2)
    Out[269]:
    2012-01-01 00:00:00.000    296.0
    2012-01-01 00:00:00.250    296.0
    2012-01-01 00:00:00.500    296.0
    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 [270]: rng = pd.date_range('2014-1-1', periods=100, freq='D') + pd.Timedelta('1s')

    In [271]: ts = pd.Series(range(100), index=rng)

If we want to resample to the full range of the series

    In [272]: ts.resample('3T').sum()
    Out[272]:
    2014-01-01 00:00:00     0
    2014-01-01 00:03:00     0
    2014-01-01 00:06:00     0
    2014-01-01 00:09:00     0
    2014-01-01 00:12:00     0
    2014-01-01 00:15:00     0
    2014-01-01 00:18:00     0
                           ..
    2014-04-09 23:42:00     0
    2014-04-09 23:45:00     0
    2014-04-09 23:48:00     0
    2014-04-09 23:51:00     0
    2014-04-09 23:54:00     0
    2014-04-09 23:57:00     0
    2014-04-10 00:00:00    99
    Freq: 3T, Length: 47521, dtype: int64

We can instead only resample those groups where we have points as follows:

    In [273]: from functools import partial

    In [274]: from pandas.tseries.frequencies import to_offset

    In [275]: def round(t, freq):
       .....:     freq = to_offset(freq)
       .....:     return pd.Timestamp((t.value // freq.delta.value) * freq.delta.value)
       .....:

    In [276]: ts.groupby(partial(round, freq='3T')).sum()
    Out[276]:
    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 [277]: df = pd.DataFrame(np.random.randn(1000, 3),
       .....:                   index=pd.date_range('1/1/2012', freq='S', periods=1000),
       .....:                   columns=['A', 'B', 'C'])
       .....:

    In [278]: r = df.resample('3T')

    In [279]: r.mean()
    Out[279]:
                                A         B         C
    2012-01-01 00:00:00 -0.038580 -0.085117 -0.024750
    2012-01-01 00:03:00  0.052387 -0.061477  0.029548
    2012-01-01 00:06:00  0.121377 -0.010630 -0.043691
    2012-01-01 00:09:00 -0.106814 -0.053819  0.097222
    2012-01-01 00:12:00  0.032560  0.080543  0.167380
    2012-01-01 00:15:00  0.060486 -0.057602 -0.106213

We can select a specific column or columns using standard getitem.

    In [280]: r['A'].mean()
    Out[280]:
    2012-01-01 00:00:00   -0.038580
    2012-01-01 00:03:00    0.052387
    2012-01-01 00:06:00    0.121377
    2012-01-01 00:09:00   -0.106814
    2012-01-01 00:12:00    0.032560
    2012-01-01 00:15:00    0.060486
    Freq: 3T, Name: A, dtype: float64

    In [281]: r[['A','B']].mean()
    Out[281]:
                                A         B
    2012-01-01 00:00:00 -0.038580 -0.085117
    2012-01-01 00:03:00  0.052387 -0.061477
    2012-01-01 00:06:00  0.121377 -0.010630
    2012-01-01 00:09:00 -0.106814 -0.053819
    2012-01-01 00:12:00  0.032560  0.080543
    2012-01-01 00:15:00  0.060486 -0.057602

You can pass a list or dict of functions to do aggregation with, outputting a `DataFrame`:

    In [282]: r['A'].agg([np.sum, np.mean, np.std])
    Out[282]:
                               sum      mean       std
    2012-01-01 00:00:00  -6.944481 -0.038580  0.985150
    2012-01-01 00:03:00   9.429707  0.052387  1.078022
    2012-01-01 00:06:00  21.847876  0.121377  0.996365
    2012-01-01 00:09:00 -19.226593 -0.106814  0.914070
    2012-01-01 00:12:00   5.860874  0.032560  1.100055
    2012-01-01 00:15:00   6.048588  0.060486  1.001532

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 [283]: r.agg([np.sum, np.mean])
    Out[283]:
                                 A                    B                    C  \
                               sum      mean        sum      mean        sum
    2012-01-01 00:00:00  -6.944481 -0.038580 -15.320993 -0.085117  -4.454941
    2012-01-01 00:03:00   9.429707  0.052387 -11.065916 -0.061477   5.318688
    2012-01-01 00:06:00  21.847876  0.121377  -1.913420 -0.010630  -7.864429
    2012-01-01 00:09:00 -19.226593 -0.106814  -9.687468 -0.053819  17.499920
    2012-01-01 00:12:00   5.860874  0.032560  14.497725  0.080543  30.128432
    2012-01-01 00:15:00   6.048588  0.060486  -5.760208 -0.057602 -10.621260

                             mean
    2012-01-01 00:00:00 -0.024750
    2012-01-01 00:03:00  0.029548
    2012-01-01 00:06:00 -0.043691
    2012-01-01 00:09:00  0.097222
    2012-01-01 00:12:00  0.167380
    2012-01-01 00:15:00 -0.106213

By passing a dict to `aggregate` you can apply a different aggregation to the columns of a `DataFrame`:

    In [284]: r.agg({'A' : np.sum,
       .....:        'B' : lambda x: np.std(x, ddof=1)})
       .....:
    Out[284]:
                                 A         B
    2012-01-01 00:00:00  -6.944481  1.087752
    2012-01-01 00:03:00   9.429707  1.014552
    2012-01-01 00:06:00  21.847876  0.954588
    2012-01-01 00:09:00 -19.226593  1.027990
    2012-01-01 00:12:00   5.860874  1.021503
    2012-01-01 00:15:00   6.048588  1.004984

The function names can also be strings. In order for a string to be valid it must be implemented on the Resampled object

    In [285]: r.agg({'A' : 'sum', 'B' : 'std'})
    Out[285]:
                                 A         B
    2012-01-01 00:00:00  -6.944481  1.087752
    2012-01-01 00:03:00   9.429707  1.014552
    2012-01-01 00:06:00  21.847876  0.954588
    2012-01-01 00:09:00 -19.226593  1.027990
    2012-01-01 00:12:00   5.860874  1.021503
    2012-01-01 00:15:00   6.048588  1.004984

Furthermore, you can also specify multiple aggregation functions for each column separately.

    In [286]: r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })
    Out[286]:
                                 A                   B
                               sum       std      mean       std
    2012-01-01 00:00:00  -6.944481  0.985150 -0.085117  1.087752
    2012-01-01 00:03:00   9.429707  1.078022 -0.061477  1.014552
    2012-01-01 00:06:00  21.847876  0.996365 -0.010630  0.954588
    2012-01-01 00:09:00 -19.226593  0.914070 -0.053819  1.027990
    2012-01-01 00:12:00   5.860874  1.100055  0.080543  1.021503
    2012-01-01 00:15:00   6.048588  1.001532 -0.057602  1.004984

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 [287]: 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 [288]: df
    Out[288]:
                  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 [289]: df.resample('M', on='date').sum()
    Out[289]:
                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 [290]: df.resample('M', level='d').sum()
    Out[290]:
                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 [291]: pd.Period('2012', freq='A-DEC')
    Out[291]: Period('2012', 'A-DEC')

    In [292]: pd.Period('2012-1-1', freq='D')
    Out[292]: Period('2012-01-01', 'D')

    In [293]: pd.Period('2012-1-1 19:00', freq='H')
    Out[293]: Period('2012-01-01 19:00', 'H')

    In [294]: pd.Period('2012-1-1 19:00', freq='5H')
    Out[294]: 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 [295]: p = pd.Period('2012', freq='A-DEC')

    In [296]: p + 1
    Out[296]: Period('2013', 'A-DEC')

    In [297]: p - 3
    Out[297]: Period('2009', 'A-DEC')

    In [298]: p = pd.Period('2012-01', freq='2M')

    In [299]: p + 2
    Out[299]: Period('2012-05', '2M')

    In [300]: p - 1
    Out[300]: Period('2011-11', '2M')

    In [301]: p == pd.Period('2012-01', freq='3M')

    IncompatibleFrequencyTraceback (most recent call last)
    <ipython-input-301-4b67dc0b596c> in <module>()
    ----> 1 p == pd.Period('2012-01', freq='3M')

    pandas/_libs/period.pyx in pandas._libs.period._Period.__richcmp__()

    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 [302]: p = pd.Period('2014-07-01 09:00', freq='H')

    In [303]: p + Hour(2)
    Out[303]: Period('2014-07-01 11:00', 'H')

    In [304]: p + timedelta(minutes=120)
    Out[304]: Period('2014-07-01 11:00', 'H')

    In [305]: p + np.timedelta64(7200, 's')
    Out[305]: 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 [306]: p = pd.Period('2014-07', freq='M')

    In [307]: p + MonthEnd(3)
    Out[307]: 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 [308]: pd.Period('2012', freq='A-DEC') - pd.Period('2002', freq='A-DEC')
    Out[308]: 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 [309]: prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')

    In [310]: prng
    Out[310]:
    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 [311]: pd.PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
    Out[311]: 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 [312]: pd.PeriodIndex(start='2014-01', freq='3M', periods=4)
    Out[312]: PeriodIndex(['2014-01', '2014-04', '2014-07', '2014-10'], dtype='period[3M]', freq='3M')

If `start` or `end` are `Period` objects, they will be used as anchor endpoints for a `PeriodIndex` with frequency matching that of the `PeriodIndex` constructor.

    In [313]: pd.PeriodIndex(start=pd.Period('2017Q1', freq='Q'),
       .....:                end=pd.Period('2017Q2', freq='Q'), freq='M')
       .....:
    Out[313]: PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'], dtype='period[M]', freq='M')

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

    In [314]: ps = pd.Series(np.random.randn(len(prng)), prng)

    In [315]: ps
    Out[315]:
    2011-01    0.258318
    2011-02   -2.503700
    2011-03   -0.303053
    2011-04    0.270509
    2011-05    1.004841
    2011-06   -0.129044
    2011-07   -1.406335
    2011-08   -1.310412
    2011-09    0.769439
    2011-10   -0.542325
    2011-11    2.010541
    2011-12    1.001558
    2012-01   -0.087453
    Freq: M, dtype: float64

`PeriodIndex` supports addition and subtraction with the same rule as `Period`.

    In [316]: idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')

    In [317]: idx
    Out[317]:
    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 [318]: idx + Hour(2)
    Out[318]:
    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 [319]: idx = pd.period_range('2014-07', periods=5, freq='M')

    In [320]: idx
    Out[320]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]', freq='M')

    In [321]: idx + MonthEnd(3)
    Out[321]: 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 [322]: pi = pd.period_range('2016-01-01', periods=3, freq='M')

    In [323]: pi
    Out[323]: PeriodIndex(['2016-01', '2016-02', '2016-03'], dtype='period[M]', freq='M')

    In [324]: pi.dtype
    Out[324]: 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 [325]: pi.astype('period[D]')
    Out[325]: PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]', freq='D')

    # convert to DatetimeIndex
    In [326]: pi.astype('datetime64[ns]')
    Out[326]: DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='datetime64[ns]', freq='MS')

    # convert to PeriodIndex
    In [327]: dti = pd.date_range('2011-01-01', freq='M', periods=3)

    In [328]: dti
    Out[328]: DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], dtype='datetime64[ns]', freq='M')

    In [329]: dti.astype('period[M]')
    Out[329]: 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 [330]: ps['2011-01']
    Out[330]: 0.25831819727391592

    In [331]: ps[datetime(2011, 12, 25):]
    Out[331]:
    2011-12    1.001558
    2012-01   -0.087453
    Freq: M, dtype: float64

    In [332]: ps['10/31/2011':'12/31/2011']
    Out[332]:
    2011-10   -0.542325
    2011-11    2.010541
    2011-12    1.001558
    Freq: M, dtype: float64

Passing a string representing a lower frequency than `PeriodIndex` returns partial sliced data.

    In [333]: ps['2011']
    Out[333]:
    2011-01    0.258318
    2011-02   -2.503700
    2011-03   -0.303053
    2011-04    0.270509
    2011-05    1.004841
    2011-06   -0.129044
    2011-07   -1.406335
    2011-08   -1.310412
    2011-09    0.769439
    2011-10   -0.542325
    2011-11    2.010541
    2011-12    1.001558
    Freq: M, dtype: float64

    In [334]: dfp = pd.DataFrame(np.random.randn(600,1),
       .....:                    columns=['A'],
       .....:                    index=pd.period_range('2013-01-01 9:00', periods=600, freq='T'))
       .....:

    In [335]: dfp
    Out[335]:
                             A
    2013-01-01 09:00  0.005210
    2013-01-01 09:01 -0.014385
    2013-01-01 09:02 -0.212404
    2013-01-01 09:03 -1.227760
    2013-01-01 09:04 -0.809722
    2013-01-01 09:05 -1.719723
    2013-01-01 09:06 -0.808486
    ...                    ...
    2013-01-01 18:53 -0.783098
    2013-01-01 18:54  0.755005
    2013-01-01 18:55 -1.116732
    2013-01-01 18:56 -0.940692
    2013-01-01 18:57  0.228536
    2013-01-01 18:58  0.109472
    2013-01-01 18:59  0.235414

    [600 rows x 1 columns]

    In [336]: dfp['2013-01-01 10H']
    Out[336]:
                             A
    2013-01-01 10:00 -0.148998
    2013-01-01 10:01  2.154810
    2013-01-01 10:02 -1.605646
    2013-01-01 10:03  0.021024
    2013-01-01 10:04 -0.623737
    2013-01-01 10:05  1.451612
    2013-01-01 10:06  1.062463
    ...                    ...
    2013-01-01 10:53  0.273119
    2013-01-01 10:54 -0.994071
    2013-01-01 10:55 -1.222179
    2013-01-01 10:56 -1.167118
    2013-01-01 10:57  0.262822
    2013-01-01 10:58 -0.283786
    2013-01-01 10:59  1.190726

    [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 [337]: dfp['2013-01-01 10H':'2013-01-01 11H']
    Out[337]:
                             A
    2013-01-01 10:00 -0.148998
    2013-01-01 10:01  2.154810
    2013-01-01 10:02 -1.605646
    2013-01-01 10:03  0.021024
    2013-01-01 10:04 -0.623737
    2013-01-01 10:05  1.451612
    2013-01-01 10:06  1.062463
    ...                    ...
    2013-01-01 11:53 -1.477914
    2013-01-01 11:54  0.594465
    2013-01-01 11:55 -0.903243
    2013-01-01 11:56  1.182131
    2013-01-01 11:57  0.621345
    2013-01-01 11:58 -0.996113
    2013-01-01 11:59 -0.191659

    [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 [338]: p = pd.Period('2011', freq='A-DEC')

    In [339]: p
    Out[339]: 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 [340]: p.asfreq('M', how='start')
    Out[340]: Period('2011-01', 'M')

    In [341]: p.asfreq('M', how='end')
    Out[341]: Period('2011-12', 'M')

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

    In [342]: p.asfreq('M', 's')
    Out[342]: Period('2011-01', 'M')

    In [343]: p.asfreq('M', 'e')
    Out[343]: 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 [344]: p = pd.Period('2011-12', freq='M')

    In [345]: p.asfreq('A-NOV')
    Out[345]: 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 [346]: p = pd.Period('2012Q1', freq='Q-DEC')

    In [347]: p.asfreq('D', 's')
    Out[347]: Period('2012-01-01', 'D')

    In [348]: p.asfreq('D', 'e')
    Out[348]: Period('2012-03-31', 'D')

`Q-MAR` defines fiscal year end in March:

    In [349]: p = pd.Period('2011Q4', freq='Q-MAR')

    In [350]: p.asfreq('D', 's')
    Out[350]: Period('2011-01-01', 'D')

    In [351]: p.asfreq('D', 'e')
    Out[351]: 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 [352]: rng = pd.date_range('1/1/2012', periods=5, freq='M')

    In [353]: ts = pd.Series(np.random.randn(len(rng)), index=rng)

    In [354]: ts
    Out[354]:
    2012-01-31   -0.898547
    2012-02-29   -1.332247
    2012-03-31   -0.741645
    2012-04-30    0.094321
    2012-05-31   -0.438813
    Freq: M, dtype: float64

    In [355]: ps = ts.to_period()

    In [356]: ps
    Out[356]:
    2012-01   -0.898547
    2012-02   -1.332247
    2012-03   -0.741645
    2012-04    0.094321
    2012-05   -0.438813
    Freq: M, dtype: float64

    In [357]: ps.to_timestamp()
    Out[357]:
    2012-01-01   -0.898547
    2012-02-01   -1.332247
    2012-03-01   -0.741645
    2012-04-01    0.094321
    2012-05-01   -0.438813
    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 [358]: ps.to_timestamp('D', how='s')
    Out[358]:
    2012-01-01   -0.898547
    2012-02-01   -1.332247
    2012-03-01   -0.741645
    2012-04-01    0.094321
    2012-05-01   -0.438813
    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 [359]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')

    In [360]: ts = pd.Series(np.random.randn(len(prng)), prng)

    In [361]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9

    In [362]: ts.head()
    Out[362]:
    1990-03-01 09:00   -0.564874
    1990-06-01 09:00   -1.426510
    1990-09-01 09:00    1.295437
    1990-12-01 09:00    1.124017
    1991-03-01 09:00    0.840428
    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 [363]: span = pd.period_range('1215-01-01', '1381-01-01', freq='D')

    In [364]: span
    Out[364]:
    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 an `int64` based YYYYMMDD representation.

    In [365]: s = pd.Series([20121231, 20141130, 99991231])

    In [366]: s
    Out[366]:
    0    20121231
    1    20141130
    2    99991231
    dtype: int64

    In [367]: def conv(x):
       .....:     return pd.Period(year = x // 10000, month = x//100 % 100, day = x%100, freq='D')
       .....:

    In [368]: s.apply(conv)
    Out[368]:
    0   2012-12-31
    1   2014-11-30
    2   9999-12-31
    dtype: object

    In [369]: s.apply(conv)[2]
    Out[369]: Period('9999-12-31', 'D')

These can easily be converted to a `PeriodIndex`

    In [370]: span = pd.PeriodIndex(s.apply(conv))

    In [371]: span
    Out[371]: 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` currently is 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 [372]: rng = pd.date_range('3/6/2012 00:00', periods=15, freq='D')

    In [373]: rng.tz is None
    Out[373]: 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 [374]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
       .....:                          tz='Europe/London')
       .....:

    In [375]: rng_pytz.tz
    Out[375]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>

    # dateutil
    In [376]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
       .....:                              tz='dateutil/Europe/London')
       .....:

    In [377]: rng_dateutil.tz
    Out[377]: tzfile('/usr/share/zoneinfo/Europe/London')

    # dateutil - utc special case
    In [378]: rng_utc = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
       .....:                         tz=dateutil.tz.tzutc())
       .....:

    In [379]: rng_utc.tz
    Out[379]: 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 [380]: tz_pytz = pytz.timezone('Europe/London')

    In [381]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
       .....:                          tz=tz_pytz)
       .....:

    In [382]: rng_pytz.tz == tz_pytz
    Out[382]: True

    # dateutil
    In [383]: tz_dateutil = dateutil.tz.gettz('Europe/London')

    In [384]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
       .....:                              tz=tz_dateutil)
       .....:

    In [385]: rng_dateutil.tz == tz_dateutil
    Out[385]: 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 [386]: ts = pd.Series(np.random.randn(len(rng)), rng)

    In [387]: ts_utc = ts.tz_localize('UTC')

    In [388]: ts_utc
    Out[388]:
    2012-03-06 00:00:00+00:00    0.037206
    2012-03-07 00:00:00+00:00    2.313998
    2012-03-08 00:00:00+00:00    1.458296
    2012-03-09 00:00:00+00:00   -0.620431
    2012-03-10 00:00:00+00:00   -0.000111
    2012-03-11 00:00:00+00:00   -0.342783
    2012-03-12 00:00:00+00:00   -0.664322
    2012-03-13 00:00:00+00:00    0.654814
    2012-03-14 00:00:00+00:00    1.550680
    2012-03-15 00:00:00+00:00    0.174511
    2012-03-16 00:00:00+00:00    1.360491
    2012-03-17 00:00:00+00:00    0.799737
    2012-03-18 00:00:00+00:00    0.449149
    2012-03-19 00:00:00+00:00    0.111346
    2012-03-20 00:00:00+00:00   -0.435531
    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 [389]: ts_utc.tz_convert('US/Eastern')
    Out[389]:
    2012-03-05 19:00:00-05:00    0.037206
    2012-03-06 19:00:00-05:00    2.313998
    2012-03-07 19:00:00-05:00    1.458296
    2012-03-08 19:00:00-05:00   -0.620431
    2012-03-09 19:00:00-05:00   -0.000111
    2012-03-10 19:00:00-05:00   -0.342783
    2012-03-11 20:00:00-04:00   -0.664322
    2012-03-12 20:00:00-04:00    0.654814
    2012-03-13 20:00:00-04:00    1.550680
    2012-03-14 20:00:00-04:00    0.174511
    2012-03-15 20:00:00-04:00    1.360491
    2012-03-16 20:00:00-04:00    0.799737
    2012-03-17 20:00:00-04:00    0.449149
    2012-03-18 20:00:00-04:00    0.111346
    2012-03-19 20:00:00-04:00   -0.435531
    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 [390]: rng_eastern = rng_utc.tz_convert('US/Eastern')

    In [391]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')

    In [392]: rng_eastern[5]
    Out[392]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', freq='D')

    In [393]: rng_berlin[5]
    Out[393]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', freq='D')

    In [394]: rng_eastern[5] == rng_berlin[5]
    Out[394]: True

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

    In [395]: rng_eastern[5]
    Out[395]: Timestamp('2012-03-10 19:00:00-0500', tz='US/Eastern', freq='D')

    In [396]: rng_berlin[5]
    Out[396]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin', freq='D')

    In [397]: rng_eastern[5].tz_convert('Europe/Berlin')
    Out[397]: Timestamp('2012-03-11 01:00:00+0100', tz='Europe/Berlin')

Localization of `Timestamp` functions just like `DatetimeIndex` and `Series`:

    In [398]: rng[5]
    Out[398]: Timestamp('2012-03-11 00:00:00', freq='D')

    In [399]: rng[5].tz_localize('Asia/Shanghai')
    Out[399]: 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 [400]: eastern = ts_utc.tz_convert('US/Eastern')

    In [401]: berlin = ts_utc.tz_convert('Europe/Berlin')

    In [402]: result = eastern + berlin

    In [403]: result
    Out[403]:
    2012-03-06 00:00:00+00:00    0.074412
    2012-03-07 00:00:00+00:00    4.627997
    2012-03-08 00:00:00+00:00    2.916592
    2012-03-09 00:00:00+00:00   -1.240863
    2012-03-10 00:00:00+00:00   -0.000221
    2012-03-11 00:00:00+00:00   -0.685566
    2012-03-12 00:00:00+00:00   -1.328643
    2012-03-13 00:00:00+00:00    1.309628
    2012-03-14 00:00:00+00:00    3.101359
    2012-03-15 00:00:00+00:00    0.349022
    2012-03-16 00:00:00+00:00    2.720983
    2012-03-17 00:00:00+00:00    1.599475
    2012-03-18 00:00:00+00:00    0.898297
    2012-03-19 00:00:00+00:00    0.222691
    2012-03-20 00:00:00+00:00   -0.871062
    Freq: D, dtype: float64

    In [404]: result.index
    Out[404]:
    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 [405]: didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')

    In [406]: didx
    Out[406]:
    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 [407]: didx.tz_localize(None)
    Out[407]:
    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 [408]: didx.tz_convert(None)
    Out[408]:
    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 [409]: didx.tz_convert('UCT').tz_localize(None)
    Out[409]:
    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 [410]: 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 [411]: rng_hourly_eastern = rng_hourly.tz_localize('US/Eastern', ambiguous='infer')

    In [412]: rng_hourly_eastern.tolist()
    Out[412]:
    [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 [413]: rng_hourly_dst = np.array([1, 1, 0, 0, 0])

    In [414]: rng_hourly.tz_localize('US/Eastern', ambiguous=rng_hourly_dst).tolist()
    Out[414]:
    [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 [415]: rng_hourly.tz_localize('US/Eastern', ambiguous='NaT').tolist()
    Out[415]:
    [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 [416]: didx = pd.DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')

    In [417]: didx
    Out[417]:
    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 [418]: didx.tz_localize(None)
    Out[418]:
    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 [419]: didx.tz_convert(None)
    Out[419]:
    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 [420]: didx.tz_convert('UCT').tz_localize(None)
    Out[420]:
    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 [421]: s_naive = pd.Series(pd.date_range('20130101',periods=3))

    In [422]: s_naive
    Out[422]:
    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 [423]: s_aware = pd.Series(pd.date_range('20130101',periods=3,tz='US/Eastern'))

    In [424]: s_aware
    Out[424]:
    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 [425]: s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
    Out[425]:
    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 [426]: s_naive.astype('datetime64[ns, US/Eastern]')
    Out[426]:
    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 [427]: s_aware.astype('datetime64[ns]')
    Out[427]:
    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 [428]: s_aware.astype('datetime64[ns, CET]')
    Out[428]:
    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 [429]: s_naive.values
    Out[429]:
    array(['2013-01-01T00:00:00.000000000', '2013-01-02T00:00:00.000000000',
           '2013-01-03T00:00:00.000000000'], dtype='datetime64[ns]')

    In [430]: s_aware.values
    Out[430]:
    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 [431]: pd.Series(s_aware.values)
    Out[431]:
    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 [432]: pd.Series(s_aware.values).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
    Out[432]:
    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]

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