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Time series / date functionality ¶
pandas contains extensive capabilities and features for working with time series data for all domains.
Using the NumPy
datetime64
and
timedelta64
dtypes, pandas has 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.
For example, pandas supports:
Parsing time series information from various sources and formats
In [1]: import datetime
In [2]: dti = pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01'),
...: datetime.datetime(2018, 1, 1)])
In [3]: dti
Out[3]: DatetimeIndex(['2018-01-01', '2018-01-01', '2018-01-01'], dtype='datetime64[ns]', freq=None)
Generate sequences of fixed-frequency dates and time spans
In [4]: dti = pd.date_range('2018-01-01', periods=3, freq='H')
In [5]: dti
Out[5]:
DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00',
'2018-01-01 02:00:00'],
dtype='datetime64[ns]', freq='H')
Manipulating and converting date times with timezone information
In [6]: dti = dti.tz_localize('UTC')
In [7]: dti
Out[7]:
DatetimeIndex(['2018-01-01 00:00:00+00:00', '2018-01-01 01:00:00+00:00',
'2018-01-01 02:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq='H')
In [8]: dti.tz_convert('US/Pacific')
Out[8]:
DatetimeIndex(['2017-12-31 16:00:00-08:00', '2017-12-31 17:00:00-08:00',
'2017-12-31 18:00:00-08:00'],
dtype='datetime64[ns, US/Pacific]', freq='H')
Resampling or converting a time series to a particular frequency
In [9]: idx = pd.date_range('2018-01-01', periods=5, freq='H')
In [10]: ts = pd.Series(range(len(idx)), index=idx)
In [11]: ts
Out[11]:
2018-01-01 00:00:00 0
2018-01-01 01:00:00 1
2018-01-01 02:00:00 2
2018-01-01 03:00:00 3
2018-01-01 04:00:00 4
Freq: H, dtype: int64
In [12]: ts.resample('2H').mean()
Out[12]:
2018-01-01 00:00:00 0.5
2018-01-01 02:00:00 2.5
2018-01-01 04:00:00 4.0
Freq: 2H, dtype: float64
Performing date and time arithmetic with absolute or relative time increments
In [13]: friday = pd.Timestamp('2018-01-05')
In [14]: friday.day_name()
Out[14]: 'Friday'
# Add 1 day
In [15]: saturday = friday + pd.Timedelta('1 day')
In [16]: saturday.day_name()
Out[16]: 'Saturday'
# Add 1 business day (Friday --> Monday)
In [17]: monday = friday + pd.offsets.BDay()
In [18]: monday.day_name()
Out[18]: 'Monday'
pandas provides a relatively compact and self-contained set of tools for
performing the above tasks and more.
Overview¶
pandas captures 4 general time related concepts:
Date times: A specific date and time with timezone support. Similar to datetime.datetime from the standard library.
Time deltas: An absolute time duration. Similar to datetime.timedelta from the standard library.
Time spans: A span of time defined by a point in time and its associated frequency.
Date offsets: A relative time duration that respects calendar arithmetic. Similar to dateutil.relativedelta.relativedelta from the dateutil package.
For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame
so manipulations can be performed with respect to the time element.
In [19]: pd.Series(range(3), index=pd.date_range('2000', freq='D', periods=3))
Out[19]:
2000-01-01 0
2000-01-02 1
2000-01-03 2
Freq: D, dtype: int64
However, Series and DataFrame can directly also support the time component as data itself.
In [20]: pd.Series(pd.date_range('2000', freq='D', periods=3))
Out[20]:
0 2000-01-01
1 2000-01-02
2 2000-01-03
dtype: datetime64[ns]
Series and DataFrame have extended data type support and functionality for datetime, timedelta
and Period data when passed into those constructors. DateOffset
data however will be stored as object data.
In [21]: pd.Series(pd.period_range('1/1/2011', freq='M', periods=3))
Out[21]:
0 2011-01
1 2011-02
2 2011-03
dtype: period[M]
In [22]: pd.Series([pd.DateOffset(1), pd.DateOffset(2)])
Out[22]:
0 <DateOffset>
1 <2 * DateOffsets>
dtype: object
In [23]: pd.Series(pd.date_range('1/1/2011', freq='M', periods=3))
Out[23]:
0 2011-01-31
1 2011-02-28
2 2011-03-31
dtype: datetime64[ns]
Lastly, pandas represents null date times, time deltas, and time spans as NaT which
is useful for representing missing or null date like values and behaves similar
as np.nan does for float data.
In [24]: pd.Timestamp(pd.NaT)
Out[24]: NaT
In [25]: pd.Timedelta(pd.NaT)
Out[25]: NaT
In [26]: pd.Period(pd.NaT)
Out[26]: NaT
# Equality acts as np.nan would
In [27]: pd.NaT == pd.NaT
Out[27]: False
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 [28]: pd.Timestamp(datetime.datetime(2012, 5, 1))
Out[28]: Timestamp('2012-05-01 00:00:00')
In [29]: pd.Timestamp('2012-05-01')
Out[29]: Timestamp('2012-05-01 00:00:00')
In [30]: pd.Timestamp(2012, 5, 1)
Out[30]: 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 [31]: pd.Period('2011-01')
Out[31]: Period('2011-01', 'M')
In [32]: pd.Period('2012-05', freq='D')
Out[32]: Period('2012-05-01', 'D')
Timestamp and Period can serve as an index. Lists of
Timestamp and Period are automatically coerced to DatetimeIndex
and PeriodIndex respectively.
In [33]: dates = [pd.Timestamp('2012-05-01'),
....: pd.Timestamp('2012-05-02'),
....: pd.Timestamp('2012-05-03')]
....:
In [34]: ts = pd.Series(np.random.randn(3), dates)
In [35]: type(ts.index)
Out[35]: pandas.core.indexes.datetimes.DatetimeIndex
In [36]: ts.index
Out[36]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
In [37]: ts
Out[37]:
2012-05-01 0.469112
2012-05-02 -0.282863
2012-05-03 -1.509059
dtype: float64
In [38]: periods = [pd.Period('2012-01'), pd.Period('2012-02'), pd.Period('2012-03')]
In [39]: ts = pd.Series(np.random.randn(3), periods)
In [40]: type(ts.index)
Out[40]: pandas.core.indexes.period.PeriodIndex
In [41]: ts.index
Out[41]: PeriodIndex(['2012-01', '2012-02', '2012-03'], dtype='period[M]', freq='M')
In [42]: ts
Out[42]:
2012-01 -1.135632
2012-02 1.212112
2012-03 -0.173215
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 [43]: pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None]))
Out[43]:
0 2009-07-31
1 2010-01-10
2 NaT
dtype: datetime64[ns]
In [44]: pd.to_datetime(['2005/11/23', '2010.12.31'])
Out[44]: 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 [45]: pd.to_datetime(['04-01-2012 10:00'], dayfirst=True)
Out[45]: DatetimeIndex(['2012-01-04 10:00:00'], dtype='datetime64[ns]', freq=None)
In [46]: pd.to_datetime(['14-01-2012', '01-14-2012'], dayfirst=True)
Out[46]: 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 [47]: pd.to_datetime('2010/11/12')
Out[47]: Timestamp('2010-11-12 00:00:00')
In [48]: pd.Timestamp('2010/11/12')
Out[48]: Timestamp('2010-11-12 00:00:00')
You can also use the DatetimeIndex constructor directly:
In [49]: pd.DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'])
Out[49]: DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], dtype='datetime64[ns]', freq=None)
The string ‘infer’ can be passed in order to set the frequency of the index as the
inferred frequency upon creation:
In [50]: pd.DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], freq='infer')
Out[50]: DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], dtype='datetime64[ns]', freq='2D')
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 [51]: pd.to_datetime('2010/11/12', format='%Y/%m/%d')
Out[51]: Timestamp('2010-11-12 00:00:00')
In [52]: pd.to_datetime('12-11-2010 00:00', format='%d-%m-%Y %H:%M')
Out[52]: Timestamp('2010-11-12 00:00:00')
For more information on the choices available when specifying the format
option, see the Python datetime documentation.
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 [53]: df = pd.DataFrame({'year': [2015, 2016],
....: 'month': [2, 3],
....: 'day': [4, 5],
....: 'hour': [2, 3]})
....:
In [54]: pd.to_datetime(df)
Out[54]:
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 [55]: pd.to_datetime(df[['year', 'month', 'day']])
Out[55]:
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¶
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 [56]: pd.to_datetime(['2009/07/31', 'asd'], errors='ignore')
Out[56]: Index(['2009/07/31', 'asd'], dtype='object')
Pass errors='coerce' to convert unparseable data to NaT (not a time):
In [57]: pd.to_datetime(['2009/07/31', 'asd'], errors='coerce')
Out[57]: 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 [58]: pd.to_datetime([1349720105, 1349806505, 1349892905,
....: 1349979305, 1350065705], unit='s')
....:
Out[58]:
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 [59]: pd.to_datetime([1349720105100, 1349720105200, 1349720105300,
....: 1349720105400, 1349720105500], unit='ms')
....:
Out[59]:
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)
Constructing a Timestamp or DatetimeIndex with an epoch timestamp
with the tz argument specified will currently localize the epoch timestamps to UTC
first then convert the result to the specified time zone. However, this behavior
is deprecated, and if you have
epochs in wall time in another timezone, it is recommended to read the epochs
as timezone-naive timestamps and then localize to the appropriate timezone:
In [60]: pd.Timestamp(1262347200000000000).tz_localize('US/Pacific')
Out[60]: Timestamp('2010-01-01 12:00:00-0800', tz='US/Pacific')
In [61]: pd.DatetimeIndex([1262347200000000000]).tz_localize('US/Pacific')
Out[61]: DatetimeIndex(['2010-01-01 12:00:00-08:00'], dtype='datetime64[ns, US/Pacific]', freq=None)
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 [62]: pd.to_datetime([1490195805.433, 1490195805.433502912], unit='s')
Out[62]: DatetimeIndex(['2017-03-22 15:16:45.433000088', '2017-03-22 15:16:45.433502913'], dtype='datetime64[ns]', freq=None)
In [63]: pd.to_datetime(1490195805433502912, unit='ns')
Out[63]: Timestamp('2017-03-22 15:16:45.433502912')
See also
From timestamps to epoch¶
To invert the operation from above, namely, to convert from a Timestamp to a ‘unix’ epoch:
In [64]: stamps = pd.date_range('2012-10-08 18:15:05', periods=4, freq='D')
In [65]: stamps
Out[65]:
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 subtract the epoch (midnight at January 1, 1970 UTC) and then floor divide by the
“unit” (1 second).
In [66]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
Out[66]: Int64Index([1349720105, 1349806505, 1349892905, 1349979305], dtype='int64')
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 [67]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
Out[67]: 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 [68]: pd.to_datetime([1, 2, 3], unit='D')
Out[68]: 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 [69]: dates = [datetime.datetime(2012, 5, 1),
....: datetime.datetime(2012, 5, 2),
....: datetime.datetime(2012, 5, 3)]
....:
# Note the frequency information
In [70]: index = pd.DatetimeIndex(dates)
In [71]: index
Out[71]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
# Automatically converted to DatetimeIndex
In [72]: index = pd.Index(dates)
In [73]: index
Out[73]: 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 [74]: start = datetime.datetime(2011, 1, 1)
In [75]: end = datetime.datetime(2012, 1, 1)
In [76]: index = pd.date_range(start, end)
In [77]: index
Out[77]:
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 [78]: index = pd.bdate_range(start, end)
In [79]: index
Out[79]:
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 [80]: pd.date_range(start, periods=1000, freq='M')
Out[80]:
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 [81]: pd.bdate_range(start, periods=250, freq='BQS')
Out[81]:
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 [82]: pd.date_range(start, end, freq='BM')
Out[82]:
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 [83]: pd.date_range(start, end, freq='W')
Out[83]:
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 [84]: pd.bdate_range(end=end, periods=20)
Out[84]:
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 [85]: pd.bdate_range(start=start, periods=20)
Out[85]:
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')
New in version 0.23.0.
Specifying start, end, and periods will generate a range of evenly spaced
dates from start to end inclusively, with periods number of elements in the
resulting DatetimeIndex:
In [86]: pd.date_range('2018-01-01', '2018-01-05', periods=5)
Out[86]:
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05'],
dtype='datetime64[ns]', freq=None)
In [87]: pd.date_range('2018-01-01', '2018-01-05', periods=10)
Out[87]:
DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 10:40:00',
'2018-01-01 21:20:00', '2018-01-02 08:00:00',
'2018-01-02 18:40:00', '2018-01-03 05:20:00',
'2018-01-03 16:00:00', '2018-01-04 02:40:00',
'2018-01-04 13:20:00', '2018-01-05 00:00:00'],
dtype='datetime64[ns]', freq=None)
Custom frequency ranges¶
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 [88]: weekmask = 'Mon Wed Fri'
In [89]: holidays = [datetime.datetime(2011, 1, 5), datetime.datetime(2011, 3, 14)]
In [90]: pd.bdate_range(start, end, freq='C', weekmask=weekmask, holidays=holidays)
Out[90]:
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 [91]: pd.bdate_range(start, end, freq='CBMS', weekmask=weekmask)
Out[91]:
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
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 [92]: pd.Timestamp.min
Out[92]: Timestamp('1677-09-21 00:12:43.145225')
In [93]: pd.Timestamp.max
Out[93]: 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
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 [94]: rng = pd.date_range(start, end, freq='BM')
In [95]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [96]: ts.index
Out[96]:
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 [97]: ts[:5].index
Out[97]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31'],
dtype='datetime64[ns]', freq='BM')
In [98]: ts[::2].index
Out[98]:
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 [99]: ts['1/31/2011']
Out[99]: 0.11920871129693428
In [100]: ts[datetime.datetime(2011, 12, 25):]
Out[100]:
2011-12-30 0.56702
Freq: BM, dtype: float64
In [101]: ts['10/31/2011':'12/31/2011']
Out[101]:
2011-10-31 0.271860
2011-11-30 -0.424972
2011-12-30 0.567020
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 [102]: ts['2011']
Out[102]:
2011-01-31 0.119209
2011-02-28 -1.044236
2011-03-31 -0.861849
2011-04-29 -2.104569
2011-05-31 -0.494929
2011-06-30 1.071804
2011-07-29 0.721555
2011-08-31 -0.706771
2011-09-30 -1.039575
2011-10-31 0.271860
2011-11-30 -0.424972
2011-12-30 0.567020
Freq: BM, dtype: float64
In [103]: ts['2011-6']
Out[103]:
2011-06-30 1.071804
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 [104]: dft = pd.DataFrame(np.random.randn(100000, 1), columns=['A'],
.....: index=pd.date_range('20130101', periods=100000, freq='T'))
.....:
In [105]: dft
Out[105]:
2013-01-01 00:00:00 0.276232
2013-01-01 00:01:00 -1.087401
2013-01-01 00:02:00 -0.673690
2013-01-01 00:03:00 0.113648
2013-01-01 00:04:00 -1.478427
... ...
2013-03-11 10:35:00 -0.747967
2013-03-11 10:36:00 -0.034523
2013-03-11 10:37:00 -0.201754
2013-03-11 10:38:00 -1.509067
2013-03-11 10:39:00 -1.693043
[100000 rows x 1 columns]
In [106]: dft['2013']
Out[106]:
2013-01-01 00:00:00 0.276232
2013-01-01 00:01:00 -1.087401
2013-01-01 00:02:00 -0.673690
2013-01-01 00:03:00 0.113648
2013-01-01 00:04:00 -1.478427
... ...
2013-03-11 10:35:00 -0.747967
2013-03-11 10:36:00 -0.034523
2013-03-11 10:37:00 -0.201754
2013-03-11 10:38:00 -1.509067
2013-03-11 10:39:00 -1.693043
[100000 rows x 1 columns]
This starts on the very first time in the month, and includes the last date and
time for the month:
In [107]: dft['2013-1':'2013-2']
Out[107]:
2013-01-01 00:00:00 0.276232
2013-01-01 00:01:00 -1.087401
2013-01-01 00:02:00 -0.673690
2013-01-01 00:03:00 0.113648
2013-01-01 00:04:00 -1.478427
... ...
2013-02-28 23:55:00 0.850929
2013-02-28 23:56:00 0.976712
2013-02-28 23:57:00 -2.693884
2013-02-28 23:58:00 -1.575535
2013-02-28 23:59:00 -1.573517
[84960 rows x 1 columns]
This specifies a stop time that includes all of the times on the last day:
In [108]: dft['2013-1':'2013-2-28']
Out[108]:
2013-01-01 00:00:00 0.276232
2013-01-01 00:01:00 -1.087401
2013-01-01 00:02:00 -0.673690
2013-01-01 00:03:00 0.113648
2013-01-01 00:04:00 -1.478427
... ...
2013-02-28 23:55:00 0.850929
2013-02-28 23:56:00 0.976712
2013-02-28 23:57:00 -2.693884
2013-02-28 23:58:00 -1.575535
2013-02-28 23:59:00 -1.573517
[84960 rows x 1 columns]
This specifies an exact stop time (and is not the same as the above):
In [109]: dft['2013-1':'2013-2-28 00:00:00']
Out[109]:
2013-01-01 00:00:00 0.276232
2013-01-01 00:01:00 -1.087401
2013-01-01 00:02:00 -0.673690
2013-01-01 00:03:00 0.113648
2013-01-01 00:04:00 -1.478427
... ...
2013-02-27 23:56:00 1.197749
2013-02-27 23:57:00 0.720521
2013-02-27 23:58:00 -0.072718
2013-02-27 23:59:00 -0.681192
2013-02-28 00:00:00 -0.557501
[83521 rows x 1 columns]
We are stopping on the included end-point as it is part of the index:
In [110]: dft['2013-1-15':'2013-1-15 12:30:00']
Out[110]:
2013-01-15 00:00:00 -0.984810
2013-01-15 00:01:00 0.941451
2013-01-15 00:02:00 1.559365
2013-01-15 00:03:00 1.034374
2013-01-15 00:04:00 -1.480656
... ...
2013-01-15 12:26:00 0.371454
2013-01-15 12:27:00 -0.930806
2013-01-15 12:28:00 -0.069177
2013-01-15 12:29:00 0.066510
2013-01-15 12:30:00 -0.003945
[751 rows x 1 columns]
New in version 0.18.0.
DatetimeIndex partial string indexing also works on a DataFrame with a MultiIndex:
In [111]: 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 [112]: dft2
Out[112]:
2013-01-01 00:00:00 a -0.298694
b 0.823553
2013-01-01 12:00:00 a 0.943285
b -1.479399
2013-01-02 00:00:00 a -1.643342
... ...
2013-01-04 12:00:00 b 0.069036
2013-01-05 00:00:00 a 0.122297
b 1.422060
2013-01-05 12:00:00 a 0.370079
b 1.016331
[20 rows x 1 columns]
In [113]: dft2.loc['2013-01-05']
Out[113]:
2013-01-05 00:00:00 a 0.122297
b 1.422060
2013-01-05 12:00:00 a 0.370079
b 1.016331
In [114]: idx = pd.IndexSlice
In [115]: dft2 = dft2.swaplevel(0, 1).sort_index()
In [116]: dft2.loc[idx[:, '2013-01-05'], :]
Out[116]:
a 2013-01-05 00:00:00 0.122297
2013-01-05 12:00:00 0.370079
b 2013-01-05 00:00:00 1.422060
2013-01-05 12:00:00 1.016331
New in version 0.25.0.
Slicing with string indexing also honors UTC offset.
In [117]: df = pd.DataFrame([0], index=pd.DatetimeIndex(['2019-01-01'], tz='US/Pacific'))
In [118]: df
Out[118]:
2019-01-01 00:00:00-08:00 0
In [119]: df['2019-01-01 12:00:00+04:00':'2019-01-01 13:00:00+04:00']
Out[119]:
2019-01-01 00:00:00-08:00 0
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 [120]: 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 [121]: series_minute.index.resolution
Out[121]: 'minute'
A timestamp string less accurate than a minute gives a Series object.
In [122]: series_minute['2011-12-31 23']
Out[122]:
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 [123]: series_minute['2011-12-31 23:59']
Out[123]: 1
In [124]: series_minute['2011-12-31 23:59:00']
Out[124]: 1
If index resolution is second, then the minute-accurate timestamp gives a
Series.
In [125]: 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 [126]: series_second.index.resolution
Out[126]: 'second'
In [127]: series_second['2011-12-31 23:59']
Out[127]:
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 [128]: dft_minute = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
.....: index=series_minute.index)
.....:
In [129]: dft_minute['2011-12-31 23']
Out[129]:
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 [130]: dft_minute.loc['2011-12-31 23:59']
Out[130]:
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 [131]: series_monthly = pd.Series([1, 2, 3],
.....: pd.DatetimeIndex(['2011-12', '2012-01', '2012-02']))
.....:
In [132]: series_monthly.index.resolution
Out[132]: 'day'
In [133]: series_monthly['2011-12'] # returns Series
Out[133]:
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 [134]: dft[datetime.datetime(2013, 1, 1):datetime.datetime(2013, 2, 28)]
Out[134]:
2013-01-01 00:00:00 0.276232
2013-01-01 00:01:00 -1.087401
2013-01-01 00:02:00 -0.673690
2013-01-01 00:03:00 0.113648
2013-01-01 00:04:00 -1.478427
... ...
2013-02-27 23:56:00 1.197749
2013-02-27 23:57:00 0.720521
2013-02-27 23:58:00 -0.072718
2013-02-27 23:59:00 -0.681192
2013-02-28 00:00:00 -0.557501
[83521 rows x 1 columns]
With no defaults.
In [135]: dft[datetime.datetime(2013, 1, 1, 10, 12, 0):
.....: datetime.datetime(2013, 2, 28, 10, 12, 0)]
.....:
Out[135]:
2013-01-01 10:12:00 0.565375
2013-01-01 10:13:00 0.068184
2013-01-01 10:14:00 0.788871
2013-01-01 10:15:00 -0.280343
2013-01-01 10:16:00 0.931536
... ...
2013-02-28 10:08:00 0.148098
2013-02-28 10:09:00 -0.388138
2013-02-28 10:10:00 0.139348
2013-02-28 10:11:00 0.085288
2013-02-28 10:12:00 0.950146
[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 [136]: rng2 = pd.date_range('2011-01-01', '2012-01-01', freq='W')
In [137]: ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2)
In [138]: ts2.truncate(before='2011-11', after='2011-12')
Out[138]:
2011-11-06 0.437823
2011-11-13 -0.293083
2011-11-20 -0.059881
2011-11-27 1.252450
Freq: W-SUN, dtype: float64
In [139]: ts2['2011-11':'2011-12']
Out[139]:
2011-11-06 0.437823
2011-11-13 -0.293083
2011-11-20 -0.059881
2011-11-27 1.252450
2011-12-04 0.046611
2011-12-11 0.059478
2011-12-18 -0.286539
2011-12-25 0.841669
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 [140]: ts2[[0, 2, 6]].index
Out[140]: DatetimeIndex(['2011-01-02', '2011-01-16', '2011-02-13'], dtype='datetime64[ns]', freq=None)
Furthermore, if you have a Series with datetimelike values, then you can
access these properties via the .dt accessor, as detailed in the section
on .dt accessors.
DateOffset objects¶
In the preceding examples, frequency strings (e.g. 'D') were used to specify
a frequency that defined:
how the date times in DatetimeIndex were spaced when using date_range()
the frequency of a Period or PeriodIndex
These frequency strings map to a DateOffset object and its subclasses. A DateOffset
is similar to a Timedelta that represents a duration of time but follows specific calendar duration rules.
For example, a Timedelta day will always increment datetimes by 24 hours, while a DateOffset day
will increment datetimes to the same time the next day whether a day represents 23, 24 or 25 hours due to daylight
savings time. However, all DateOffset subclasses that are an hour or smaller
(Hour, Minute, Second, Milli, Micro, Nano) behave like
Timedelta and respect absolute time.
The basic DateOffset acts similar to dateutil.relativedelta (relativedelta documentation)
that shifts a date time by the corresponding calendar duration specified. The
arithmetic operator (+) or the apply method can be used to perform the shift.
# This particular day contains a day light savings time transition
In [141]: ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
# Respects absolute time
In [142]: ts + pd.Timedelta(days=1)
Out[142]: Timestamp('2016-10-30 23:00:00+0200', tz='Europe/Helsinki')
# Respects calendar time
In [143]: ts + pd.DateOffset(days=1)
Out[143]: Timestamp('2016-10-31 00:00:00+0200', tz='Europe/Helsinki')
In [144]: friday = pd.Timestamp('2018-01-05')
In [145]: friday.day_name()
Out[145]: 'Friday'
# Add 2 business days (Friday --> Tuesday)
In [146]: two_business_days = 2 * pd.offsets.BDay()
In [147]: two_business_days.apply(friday)
Out[147]: Timestamp('2018-01-09 00:00:00')
In [148]: friday + two_business_days
Out[148]: Timestamp('2018-01-09 00:00:00')
In [149]: (friday + two_business_days).day_name()
Out[149]: 'Tuesday'
Most DateOffsets have associated frequencies strings, or offset aliases, that can be passed
into freq keyword arguments. The available date offsets and associated frequency strings can be found below:
DateOffsets additionally have rollforward() and rollback()
methods for moving a date forward or backward respectively to a valid offset
date relative to the offset. For example, business offsets will roll dates
that land on the weekends (Saturday and Sunday) forward to Monday since
business offsets operate on the weekdays.
In [150]: ts = pd.Timestamp('2018-01-06 00:00:00')
In [151]: ts.day_name()
Out[151]: 'Saturday'
# BusinessHour's valid offset dates are Monday through Friday
In [152]: offset = pd.offsets.BusinessHour(start='09:00')
# Bring the date to the closest offset date (Monday)
In [153]: offset.rollforward(ts)
Out[153]: Timestamp('2018-01-08 09:00:00')
# Date is brought to the closest offset date first and then the hour is added
In [154]: ts + offset
Out[154]: Timestamp('2018-01-08 10:00:00')
These operations preserve time (hour, minute, etc) information by default.
To reset time to midnight, use normalize() before or after applying
the operation (depending on whether you want the time information included
in the operation).
In [155]: ts = pd.Timestamp('2014-01-01 09:00')
In [156]: day = pd.offsets.Day()
In [157]: day.apply(ts)
Out[157]: Timestamp('2014-01-02 09:00:00')
In [158]: day.apply(ts).normalize()
Out[158]: Timestamp('2014-01-02 00:00:00')
In [159]: ts = pd.Timestamp('2014-01-01 22:00')
In [160]: hour = pd.offsets.Hour()
In [161]: hour.apply(ts)
Out[161]: Timestamp('2014-01-01 23:00:00')
In [162]: hour.apply(ts).normalize()
Out[162]: Timestamp('2014-01-01 00:00:00')
In [163]: hour.apply(pd.Timestamp("2014-01-01 23:30")).normalize()
Out[163]: 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 [164]: d = datetime.datetime(2008, 8, 18, 9, 0)
In [165]: d
Out[165]: datetime.datetime(2008, 8, 18, 9, 0)
In [166]: d + pd.offsets.Week()
Out[166]: Timestamp('2008-08-25 09:00:00')
In [167]: d + pd.offsets.Week(weekday=4)
Out[167]: Timestamp('2008-08-22 09:00:00')
In [168]: (d + pd.offsets.Week(weekday=4)).weekday()
Out[168]: 4
In [169]: d - pd.offsets.Week()
Out[169]: Timestamp('2008-08-11 09:00:00')
The normalize option will be effective for addition and subtraction.
In [170]: d + pd.offsets.Week(normalize=True)
Out[170]: Timestamp('2008-08-25 00:00:00')
In [171]: d - pd.offsets.Week(normalize=True)
Out[171]: Timestamp('2008-08-11 00:00:00')
Another example is parameterizing YearEnd with the specific ending month:
In [172]: d + pd.offsets.YearEnd()
Out[172]: Timestamp('2008-12-31 09:00:00')
In [173]: d + pd.offsets.YearEnd(month=6)
Out[173]: 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 [174]: rng = pd.date_range('2012-01-01', '2012-01-03')
In [175]: s = pd.Series(rng)
In [176]: rng
Out[176]: DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03'], dtype='datetime64[ns]', freq='D')
In [177]: rng + pd.DateOffset(months=2)
Out[177]: DatetimeIndex(['2012-03-01', '2012-03-02', '2012-03-03'], dtype='datetime64[ns]', freq='D')
In [178]: s + pd.DateOffset(months=2)
Out[178]:
0 2012-03-01
1 2012-03-02
2 2012-03-03
dtype: datetime64[ns]
In [179]: s - pd.DateOffset(months=2)
Out[179]:
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 [180]: s - pd.offsets.Day(2)
Out[180]:
0 2011-12-30
1 2011-12-31
2 2012-01-01
dtype: datetime64[ns]
In [181]: td = s - pd.Series(pd.date_range('2011-12-29', '2011-12-31'))
In [182]: td
Out[182]:
0 3 days
1 3 days
2 3 days
dtype: timedelta64[ns]
In [183]: td + pd.offsets.Minute(15)
Out[183]:
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 [184]: rng + pd.offsets.BQuarterEnd()
Out[184]: DatetimeIndex(['2012-03-30', '2012-03-30', '2012-03-30'], dtype='datetime64[ns]', freq='D')
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 [185]: weekmask_egypt = 'Sun Mon Tue Wed Thu'
# They also observe International Workers' Day so let's
# add that for a couple of years
In [186]: holidays = ['2012-05-01',
.....: datetime.datetime(2013, 5, 1),
.....: np.datetime64('2014-05-01')]
.....:
In [187]: bday_egypt = pd.offsets.CustomBusinessDay(holidays=holidays,
.....: weekmask=weekmask_egypt)
.....:
In [188]: dt = datetime.datetime(2013, 4, 30)
In [189]: dt + 2 * bday_egypt
Out[189]: Timestamp('2013-05-05 00:00:00')
Let’s map to the weekday names:
In [190]: dts = pd.date_range(dt, periods=5, freq=bday_egypt)
In [191]: pd.Series(dts.weekday, dts).map(
.....: pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split()))
.....:
Out[191]:
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 [192]: from pandas.tseries.holiday import USFederalHolidayCalendar
In [193]: bday_us = pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
In [194]: dt = datetime.datetime(2014, 1, 17)
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
In [195]: dt + bday_us
Out[195]: Timestamp('2014-01-21 00:00:00')
Monthly offsets that respect a certain holiday calendar can be defined
in the usual way.
In [196]: bmth_us = pd.offsets.CustomBusinessMonthBegin(
.....: calendar=USFederalHolidayCalendar())
.....:
# Skip new years
In [197]: dt = datetime.datetime(2013, 12, 17)
In [198]: dt + bmth_us
Out[198]: Timestamp('2014-01-02 00:00:00')
# Define date index with custom offset
In [199]: pd.date_range(start='20100101', end='20120101', freq=bmth_us)
Out[199]:
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')
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 frequency.
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, the remaining
hours are added to the next business day.
In [200]: bh = pd.offsets.BusinessHour()
In [201]: bh
Out[201]: <BusinessHour: BH=09:00-17:00>
# 2014-08-01 is Friday
In [202]: pd.Timestamp('2014-08-01 10:00').weekday()
Out[202]: 4
In [203]: pd.Timestamp('2014-08-01 10:00') + bh
Out[203]: Timestamp('2014-08-01 11:00:00')
# Below example is the same as: pd.Timestamp('2014-08-01 09:00') + bh
In [204]: pd.Timestamp('2014-08-01 08:00') + bh
Out[204]: Timestamp('2014-08-01 10:00:00')
# If the results is on the end time, move to the next business day
In [205]: pd.Timestamp('2014-08-01 16:00') + bh
Out[205]: Timestamp('2014-08-04 09:00:00')
# Remainings are added to the next day
In [206]: pd.Timestamp('2014-08-01 16:30') + bh
Out[206]: Timestamp('2014-08-04 09:30:00')
# Adding 2 business hours
In [207]: pd.Timestamp('2014-08-01 10:00') + pd.offsets.BusinessHour(2)
Out[207]: Timestamp('2014-08-01 12:00:00')
# Subtracting 3 business hours
In [208]: pd.Timestamp('2014-08-01 10:00') + pd.offsets.BusinessHour(-3)
Out[208]: Timestamp('2014-07-31 15:00:00')
You can also specify start and end time by keywords. The argument must
be a str with an hour:minute representation or a datetime.time
instance. Specifying seconds, microseconds and nanoseconds as business hour
results in ValueError.
In [209]: bh = pd.offsets.BusinessHour(start='11:00', end=datetime.time(20, 0))
In [210]: bh
Out[210]: <BusinessHour: BH=11:00-20:00>
In [211]: pd.Timestamp('2014-08-01 13:00') + bh
Out[211]: Timestamp('2014-08-01 14:00:00')
In [212]: pd.Timestamp('2014-08-01 09:00') + bh
Out[212]: Timestamp('2014-08-01 12:00:00')
In [213]: pd.Timestamp('2014-08-01 18:00') + bh
Out[213]: 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 [214]: bh = pd.offsets.BusinessHour(start='17:00', end='09:00')
In [215]: bh
Out[215]: <BusinessHour: BH=17:00-09:00>
In [216]: pd.Timestamp('2014-08-01 17:00') + bh
Out[216]: Timestamp('2014-08-01 18:00:00')
In [217]: pd.Timestamp('2014-08-01 23:00') + bh
Out[217]: Timestamp('2014-08-02 00:00:00')
# Although 2014-08-02 is Saturday,
# it is valid because it starts from 08-01 (Friday).
In [218]: pd.Timestamp('2014-08-02 04:00') + bh
Out[218]: 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 [219]: pd.Timestamp('2014-08-04 04:00') + bh
Out[219]: 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 [220]: pd.offsets.BusinessHour().rollback(pd.Timestamp('2014-08-02 15:00'))
Out[220]: Timestamp('2014-08-01 17:00:00')
In [221]: pd.offsets.BusinessHour().rollforward(pd.Timestamp('2014-08-02 15:00'))
Out[221]: 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 [222]: pd.offsets.BusinessHour().apply(pd.Timestamp('2014-08-02 15:00'))
Out[222]: Timestamp('2014-08-04 10:00:00')
# BusinessDay results (for reference)
In [223]: pd.offsets.BusinessHour().rollforward(pd.Timestamp('2014-08-02'))
Out[223]: 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 [224]: pd.offsets.BusinessHour().apply(pd.Timestamp('2014-08-02'))
Out[224]: Timestamp('2014-08-04 10:00:00')
BusinessHour regards Saturday and Sunday as holidays. To use arbitrary
holidays, you can use CustomBusinessHour offset, as explained in the
following subsection.
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 [225]: from pandas.tseries.holiday import USFederalHolidayCalendar
In [226]: bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
In [227]: dt = datetime.datetime(2014, 1, 17, 15)
In [228]: dt + bhour_us
Out[228]: Timestamp('2014-01-17 16:00:00')
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
In [229]: dt + bhour_us * 2
Out[229]: Timestamp('2014-01-21 09:00:00')
You can use keyword arguments supported by either BusinessHour and CustomBusinessDay.
In [230]: bhour_mon = pd.offsets.CustomBusinessHour(start='10:00',
.....: weekmask='Tue Wed Thu Fri')
.....:
# Monday is skipped because it's a holiday, business hour starts from 10:00
In [231]: dt + bhour_mon * 2
Out[231]: Timestamp('2014-01-21 10:00:00')
Combining aliases¶
As we have seen previously, the alias and the offset instance are fungible in
most functions:
In [232]: pd.date_range(start, periods=5, freq='B')
Out[232]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07'],
dtype='datetime64[ns]', freq='B')
In [233]: pd.date_range(start, periods=5, freq=pd.offsets.BDay())
Out[233]:
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 [234]: pd.date_range(start, periods=10, freq='2h20min')
Out[234]:
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 [235]: pd.date_range(start, periods=10, freq='1D10U')
Out[235]:
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:
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 [236]: pd.Timestamp('2014-01-02') + pd.offsets.MonthBegin(n=1)
Out[236]: Timestamp('2014-02-01 00:00:00')
In [237]: pd.Timestamp('2014-01-02') + pd.offsets.MonthEnd(n=1)
Out[237]: Timestamp('2014-01-31 00:00:00')
In [238]: pd.Timestamp('2014-01-02') - pd.offsets.MonthBegin(n=1)
Out[238]: Timestamp('2014-01-01 00:00:00')
In [239]: pd.Timestamp('2014-01-02') - pd.offsets.MonthEnd(n=1)
Out[239]: Timestamp('2013-12-31 00:00:00')
In [240]: pd.Timestamp('2014-01-02') + pd.offsets.MonthBegin(n=4)
Out[240]: Timestamp('2014-05-01 00:00:00')
In [241]: pd.Timestamp('2014-01-02') - pd.offsets.MonthBegin(n=4)
Out[241]: 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 [242]: pd.Timestamp('2014-01-01') + pd.offsets.MonthBegin(n=1)
Out[242]: Timestamp('2014-02-01 00:00:00')
In [243]: pd.Timestamp('2014-01-31') + pd.offsets.MonthEnd(n=1)
Out[243]: Timestamp('2014-02-28 00:00:00')
In [244]: pd.Timestamp('2014-01-01') - pd.offsets.MonthBegin(n=1)
Out[244]: Timestamp('2013-12-01 00:00:00')
In [245]: pd.Timestamp('2014-01-31') - pd.offsets.MonthEnd(n=1)
Out[245]: Timestamp('2013-12-31 00:00:00')
In [246]: pd.Timestamp('2014-01-01') + pd.offsets.MonthBegin(n=4)
Out[246]: Timestamp('2014-05-01 00:00:00')
In [247]: pd.Timestamp('2014-01-31') - pd.offsets.MonthBegin(n=4)
Out[247]: 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 [248]: pd.Timestamp('2014-01-02') + pd.offsets.MonthBegin(n=0)
Out[248]: Timestamp('2014-02-01 00:00:00')
In [249]: pd.Timestamp('2014-01-02') + pd.offsets.MonthEnd(n=0)
Out[249]: Timestamp('2014-01-31 00:00:00')
In [250]: pd.Timestamp('2014-01-01') + pd.offsets.MonthBegin(n=0)
Out[250]: Timestamp('2014-01-01 00:00:00')
In [251]: pd.Timestamp('2014-01-31') + pd.offsets.MonthEnd(n=0)
Out[251]: 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. Furthermore, the 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:
An example of how holidays and holiday calendars are defined:
In [252]: from pandas.tseries.holiday import Holiday, USMemorialDay,\
.....: AbstractHolidayCalendar, nearest_workday, MO
.....:
In [253]: class ExampleCalendar(AbstractHolidayCalendar):
.....: rules = [
.....: USMemorialDay,
.....: Holiday('July 4th', month=7, day=4, observance=nearest_workday),
.....: Holiday('Columbus Day', month=10, day=1,
.....: offset=pd.DateOffset(weekday=MO(2)))]
.....:
In [254]: cal = ExampleCalendar()
In [255]: cal.holidays(datetime.datetime(2012, 1, 1), datetime.datetime(2012, 12, 31))
Out[255]: 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 [256]: pd.date_range(start='7/1/2012', end='7/10/2012',
.....: freq=pd.offsets.CDay(calendar=cal)).to_pydatetime()
.....:
Out[256]:
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 [257]: offset = pd.offsets.CustomBusinessDay(calendar=cal)
In [258]: datetime.datetime(2012, 5, 25) + offset
Out[258]: Timestamp('2012-05-29 00:00:00')
In [259]: datetime.datetime(2012, 7, 3) + offset
Out[259]: Timestamp('2012-07-05 00:00:00')
In [260]: datetime.datetime(2012, 7, 3) + 2 * offset
Out[260]: Timestamp('2012-07-06 00:00:00')
In [261]: datetime.datetime(2012, 7, 6) + offset
Out[261]: Timestamp('2012-07-09 00:00:00')
Ranges are defined by the start_date and end_date class attributes
of AbstractHolidayCalendar. The defaults are shown below.
In [262]: AbstractHolidayCalendar.start_date
Out[262]: Timestamp('1970-01-01 00:00:00')
In [263]: AbstractHolidayCalendar.end_date
Out[263]: Timestamp('2030-12-31 00:00:00')
These dates can be overwritten by setting the attributes as
datetime/Timestamp/string.
In [264]: AbstractHolidayCalendar.start_date = datetime.datetime(2012, 1, 1)
In [265]: AbstractHolidayCalendar.end_date = datetime.datetime(2012, 12, 31)
In [266]: cal.holidays()
Out[266]: 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 [267]: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory,\
.....: USLaborDay
.....:
In [268]: cal = get_calendar('ExampleCalendar')
In [269]: cal.rules
Out[269]:
[Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7f450611f8c0>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]
In [270]: new_cal = HolidayCalendarFactory('NewExampleCalendar', cal, USLaborDay)
In [271]: new_cal.rules
Out[271]:
[Holiday: Labor Day (month=9, day=1, offset=<DateOffset: weekday=MO(+1)>),
Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>),
Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x7f450611f8c0>),
Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: weekday=MO(+2)>)]
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 [272]: ts = pd.Series(range(len(rng)), index=rng)
In [273]: ts = ts[:5]
In [274]: ts.shift(1)
Out[274]:
2012-01-01 NaN
2012-01-02 0.0
2012-01-03 1.0
Freq: D, dtype: float64
The shift method accepts an freq argument which can accept a
DateOffset class or other timedelta-like object or also an
offset alias:
In [275]: ts.shift(5, freq=pd.offsets.BDay())
Out[275]:
2012-01-06 0
2012-01-09 1
2012-01-10 2
Freq: B, dtype: int64
In [276]: ts.shift(5, freq='BM')
Out[276]:
2012-05-31 0
2012-05-31 1
2012-05-31 2
Freq: D, dtype: int64
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 [277]: ts.tshift(5, freq='D')
Out[277]:
2012-01-06 0
2012-01-07 1
2012-01-08 2
Freq: D, dtype: int64
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()
method. For a DatetimeIndex, this is basically just a thin, but convenient
wrapper around reindex() which generates a date_range and
calls reindex.
In [278]: dr = pd.date_range('1/1/2010', periods=3, freq=3 * pd.offsets.BDay())
In [279]: ts = pd.Series(np.random.randn(3), index=dr)
In [280]: ts
Out[280]:
2010-01-01 1.494522
2010-01-06 -0.778425
2010-01-11 -0.253355
Freq: 3B, dtype: float64
In [281]: ts.asfreq(pd.offsets.BDay())
Out[281]:
2010-01-01 1.494522
2010-01-04 NaN
2010-01-05 NaN
2010-01-06 -0.778425
2010-01-07 NaN
2010-01-08 NaN
2010-01-11 -0.253355
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 [282]: ts.asfreq(pd.offsets.BDay(), method='pad')
Out[282]:
2010-01-01 1.494522
2010-01-04 1.494522
2010-01-05 1.494522
2010-01-06 -0.778425
2010-01-07 -0.778425
2010-01-08 -0.778425
2010-01-11 -0.253355
Freq: B, dtype: float64
Filling forward / backward¶
Related to asfreq and reindex is fillna(), which is
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.
.resample()
is similar to using a rolling() operation with
a time-based offset, see a discussion here.
Basics¶
In [283]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
In [284]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [285]: ts.resample('5Min').sum()
Out[285]:
2012-01-01 25103
Freq: 5T, dtype: int64
The resample function is very flexible and allows you to specify many
different parameters to control the frequency conversion and resampling
operation.
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 [286]: ts.resample('5Min').mean()
Out[286]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [287]: ts.resample('5Min').ohlc()
Out[287]:
open high low close
2012-01-01 308 460 9 205
In [288]: ts.resample('5Min').max()
Out[288]:
2012-01-01 460
Freq: 5T, dtype: int64
For downsampling, closed can be set to ‘left’ or ‘right’ to specify which
end of the interval is closed:
In [289]: ts.resample('5Min', closed='right').mean()
Out[289]:
2011-12-31 23:55:00 308.000000
2012-01-01 00:00:00 250.454545
Freq: 5T, dtype: float64
In [290]: ts.resample('5Min', closed='left').mean()
Out[290]:
2012-01-01 251.03
Freq: 5T, dtype: float64
Parameters like label and loffset are used to manipulate the resulting
labels. label specifies whether the result is labeled with the beginning or
the end of the interval. loffset performs a time adjustment on the output
labels.
In [291]: ts.resample('5Min').mean() # by default label='left'
Out[291]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [292]: ts.resample('5Min', label='left').mean()
Out[292]:
2012-01-01 251.03
Freq: 5T, dtype: float64
In [293]: ts.resample('5Min', label='left', loffset='1s').mean()
Out[293]:
2012-01-01 00:00:01 251.03
dtype: float64
Warning
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’.
This might unintendedly lead to looking ahead, where the value for a later
time is pulled back to a previous time as in the following example with
the BusinessDay frequency:
In [294]: s = pd.date_range('2000-01-01', '2000-01-05').to_series()
In [295]: s.iloc[2] = pd.NaT
In [296]: s.dt.weekday_name
Out[296]:
2000-01-01 Saturday
2000-01-02 Sunday
2000-01-03 NaN
2000-01-04 Tuesday
2000-01-05 Wednesday
Freq: D, dtype: object
# default: label='left', closed='left'
In [297]: s.resample('B').last().dt.weekday_name
Out[297]:
1999-12-31 Sunday
2000-01-03 NaN
2000-01-04 Tuesday
2000-01-05 Wednesday
Freq: B, dtype: object
Notice how the value for Sunday got pulled back to the previous Friday.
To get the behavior where the value for Sunday is pushed to Monday, use
instead
In [298]: s.resample('B', label='right', closed='right').last().dt.weekday_name
Out[298]:
2000-01-03 Sunday
2000-01-04 Tuesday
2000-01-05 Wednesday
Freq: B, dtype: object
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 [299]: ts[:2].resample('250L').asfreq()
Out[299]:
2012-01-01 00:00:00.000 308.0
2012-01-01 00:00:00.250 NaN
2012-01-01 00:00:00.500 NaN
2012-01-01 00:00:00.750 NaN
2012-01-01 00:00:01.000 204.0
Freq: 250L, dtype: float64
In [300]: ts[:2].resample('250L').ffill()
Out[300]:
2012-01-01 00:00:00.000 308
2012-01-01 00:00:00.250 308
2012-01-01 00:00:00.500 308
2012-01-01 00:00:00.750 308
2012-01-01 00:00:01.000 204
Freq: 250L, dtype: int64
In [301]: ts[:2].resample('250L').ffill(limit=2)
Out[301]:
2012-01-01 00:00:00.000 308.0
2012-01-01 00:00:00.250 308.0
2012-01-01 00:00:00.500 308.0
2012-01-01 00:00:00.750 NaN
2012-01-01 00:00:01.000 204.0
Freq: 250L, dtype: float64
Sparse resampling¶
Sparse timeseries are the 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 [302]: rng = pd.date_range('2014-1-1', periods=100, freq='D') + pd.Timedelta('1s')
In [303]: ts = pd.Series(range(100), index=rng)
If we want to resample to the full range of the series:
In [304]: ts.resample('3T').sum()
Out[304]:
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
2014-04-09 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 [305]: from functools import partial
In [306]: from pandas.tseries.frequencies import to_offset
In [307]: def round(t, freq):
.....: freq = to_offset(freq)
.....: return pd.Timestamp((t.value // freq.delta.value) * freq.delta.value)
.....:
In [308]: ts.groupby(partial(round, freq='3T')).sum()
Out[308]:
2014-01-01 0
2014-01-02 1
2014-01-03 2
2014-01-04 3
2014-01-05 4
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 [309]: df = pd.DataFrame(np.random.randn(1000, 3),
.....: index=pd.date_range('1/1/2012', freq='S', periods=1000),
.....: columns=['A', 'B', 'C'])
.....:
In [310]: r = df.resample('3T')
In [311]: r.mean()
Out[311]:
A B C
2012-01-01 00:00:00 -0.033823 -0.121514 -0.081447
2012-01-01 00:03:00 0.056909 0.146731 -0.024320
2012-01-01 00:06:00 -0.058837 0.047046 -0.052021
2012-01-01 00:09:00 0.063123 -0.026158 -0.066533
2012-01-01 00:12:00 0.186340 -0.003144 0.074752
2012-01-01 00:15:00 -0.085954 -0.016287 -0.050046
We can select a specific column or columns using standard getitem.
In [312]: r['A'].mean()
Out[312]:
2012-01-01 00:00:00 -0.033823
2012-01-01 00:03:00 0.056909
2012-01-01 00:06:00 -0.058837
2012-01-01 00:09:00 0.063123
2012-01-01 00:12:00 0.186340
2012-01-01 00:15:00 -0.085954
Freq: 3T, Name: A, dtype: float64
In [313]: r[['A', 'B']].mean()
Out[313]:
A B
2012-01-01 00:00:00 -0.033823 -0.121514
2012-01-01 00:03:00 0.056909 0.146731
2012-01-01 00:06:00 -0.058837 0.047046
2012-01-01 00:09:00 0.063123 -0.026158
2012-01-01 00:12:00 0.186340 -0.003144
2012-01-01 00:15:00 -0.085954 -0.016287
You can pass a list or dict of functions to do aggregation with, outputting a DataFrame
:
In [314]: r['A'].agg([np.sum, np.mean, np.std])
Out[314]:
sum mean std
2012-01-01 00:00:00 -6.088060 -0.033823 1.043263
2012-01-01 00:03:00 10.243678 0.056909 1.058534
2012-01-01 00:06:00 -10.590584 -0.058837 0.949264
2012-01-01 00:09:00 11.362228 0.063123 1.028096
2012-01-01 00:12:00 33.541257 0.186340 0.884586
2012-01-01 00:15:00 -8.595393 -0.085954 1.035476
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 [315]: r.agg([np.sum, np.mean])
Out[315]:
A B C
sum mean sum mean sum mean
2012-01-01 00:00:00 -6.088060 -0.033823 -21.872530 -0.121514 -14.660515 -0.081447
2012-01-01 00:03:00 10.243678 0.056909 26.411633 0.146731 -4.377642 -0.024320
2012-01-01 00:06:00 -10.590584 -0.058837 8.468289 0.047046 -9.363825 -0.052021
2012-01-01 00:09:00 11.362228 0.063123 -4.708526 -0.026158 -11.975895 -0.066533
2012-01-01 00:12:00 33.541257 0.186340 -0.565895 -0.003144 13.455299 0.074752
2012-01-01 00:15:00 -8.595393 -0.085954 -1.628689 -0.016287 -5.004580 -0.050046
By passing a dict to aggregate you can apply a different aggregation to the
columns of a DataFrame:
In [316]: r.agg({'A': np.sum,
.....: 'B': lambda x: np.std(x, ddof=1)})
.....:
Out[316]:
A B
2012-01-01 00:00:00 -6.088060 1.001294
2012-01-01 00:03:00 10.243678 1.074597
2012-01-01 00:06:00 -10.590584 0.987309
2012-01-01 00:09:00 11.362228 0.944953
2012-01-01 00:12:00 33.541257 1.095025
2012-01-01 00:15:00 -8.595393 1.035312
The function names can also be strings. In order for a string to be valid it
must be implemented on the resampled object:
In [317]: r.agg({'A': 'sum', 'B': 'std'})
Out[317]:
A B
2012-01-01 00:00:00 -6.088060 1.001294
2012-01-01 00:03:00 10.243678 1.074597
2012-01-01 00:06:00 -10.590584 0.987309
2012-01-01 00:09:00 11.362228 0.944953
2012-01-01 00:12:00 33.541257 1.095025
2012-01-01 00:15:00 -8.595393 1.035312
Furthermore, you can also specify multiple aggregation functions for each column separately.
In [318]: r.agg({'A': ['sum', 'std'], 'B': ['mean', 'std']})
Out[318]:
A B
sum std mean std
2012-01-01 00:00:00 -6.088060 1.043263 -0.121514 1.001294
2012-01-01 00:03:00 10.243678 1.058534 0.146731 1.074597
2012-01-01 00:06:00 -10.590584 0.949264 0.047046 0.987309
2012-01-01 00:09:00 11.362228 1.028096 -0.026158 0.944953
2012-01-01 00:12:00 33.541257 0.884586 -0.003144 1.095025
2012-01-01 00:15:00 -8.595393 1.035476 -0.016287 1.035312
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 [319]: 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 [320]: df
Out[320]:
date a
1 2015-01-04 2015-01-04 0
2 2015-01-11 2015-01-11 1
3 2015-01-18 2015-01-18 2
4 2015-01-25 2015-01-25 3
5 2015-02-01 2015-02-01 4
In [321]: df.resample('M', on='date').sum()
Out[321]:
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 [322]: df.resample('M', level='d').sum()
Out[322]:
2015-01-31 6
2015-02-28 4
Iterating through groups¶
With the Resampler object in hand, iterating through the grouped data is very
natural and functions similarly to itertools.groupby():
In [323]: small = pd.Series(
.....: range(6),
.....: index=pd.to_datetime(['2017-01-01T00:00:00',
.....: '2017-01-01T00:30:00',
.....: '2017-01-01T00:31:00',
.....: '2017-01-01T01:00:00',
.....: '2017-01-01T03:00:00',
.....: '2017-01-01T03:05:00'])
.....: )
.....:
In [324]: resampled = small.resample('H')
In [325]: for name, group in resampled:
.....: print("Group: ", name)
.....: print("-" * 27)
.....: print(group, end="\n\n")
.....:
Group: 2017-01-01 00:00:00
---------------------------
2017-01-01 00:00:00 0
2017-01-01 00:30:00 1
2017-01-01 00:31:00 2
dtype: int64
Group: 2017-01-01 01:00:00
---------------------------
2017-01-01 01:00:00 3
dtype: int64
Group: 2017-01-01 02:00:00
---------------------------
Series([], dtype: int64)
Group: 2017-01-01 03:00:00
---------------------------
2017-01-01 03:00:00 4
2017-01-01 03:05:00 5
dtype: int64
See Iterating through groups or Resampler.__iter__ for more.
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 [326]: pd.Period('2012', freq='A-DEC')
Out[326]: Period('2012', 'A-DEC')
In [327]: pd.Period('2012-1-1', freq='D')
Out[327]: Period('2012-01-01', 'D')
In [328]: pd.Period('2012-1-1 19:00', freq='H')
Out[328]: Period('2012-01-01 19:00', 'H')
In [329]: pd.Period('2012-1-1 19:00', freq='5H')
Out[329]: 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 [330]: p = pd.Period('2012', freq='A-DEC')
In [331]: p + 1
Out[331]: Period('2013', 'A-DEC')
In [332]: p - 3
Out[332]: Period('2009', 'A-DEC')
In [333]: p = pd.Period('2012-01', freq='2M')
In [334]: p + 2
Out[334]: Period('2012-05', '2M')
In [335]: p - 1
Out[335]: Period('2011-11', '2M')
In [336]: p == pd.Period('2012-01', freq='3M')
---------------------------------------------------------------------------
IncompatibleFrequency Traceback (most recent call last)
<ipython-input-336-4b67dc0b596c> in <module>
----> 1 p == pd.Period('2012-01', freq='3M')
/pandas/pandas/_libs/tslibs/period.pyx in pandas._libs.tslibs.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 [337]: p = pd.Period('2014-07-01 09:00', freq='H')
In [338]: p + pd.offsets.Hour(2)
Out[338]: Period('2014-07-01 11:00', 'H')
In [339]: p + datetime.timedelta(minutes=120)
Out[339]: Period('2014-07-01 11:00', 'H')
In [340]: p + np.timedelta64(7200, 's')
Out[340]: Period('2014-07-01 11:00', 'H')
In [1]: p + pd.offsets.
Minute(5)
Traceback
ValueError: Input has different freq from Period(freq=H)
If Period has other frequencies, only the same offsets can be added. Otherwise, ValueError will be raised.
In [341]: p = pd.Period('2014-07', freq='M')
In [342]: p + pd.offsets.MonthEnd(3)
Out[342]: Period('2014-10', 'M')
In [1]: p + pd.offsets.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 [343]: pd.Period('2012', freq='A-DEC') - pd.Period('2002', freq='A-DEC')
Out[343]: <10 * YearEnds: month=12>
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 [344]: prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')
In [345]: prng
Out[345]:
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 [346]: pd.PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
Out[346]: 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 [347]: pd.period_range(start='2014-01', freq='3M', periods=4)
Out[347]: 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 [348]: pd.period_range(start=pd.Period('2017Q1', freq='Q'),
.....: end=pd.Period('2017Q2', freq='Q'), freq='M')
.....:
Out[348]: 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 [349]: ps = pd.Series(np.random.randn(len(prng)), prng)
In [350]: ps
Out[350]:
2011-01 -2.916901
2011-02 0.514474
2011-03 1.346470
2011-04 0.816397
2011-05 2.258648
2011-06 0.494789
2011-07 0.301239
2011-08 0.464776
2011-09 -1.393581
2011-10 0.056780
2011-11 0.197035
2011-12 2.261385
2012-01 -0.329583
Freq: M, dtype: float64
PeriodIndex supports addition and subtraction with the same rule as Period.
In [351]: idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
In [352]: idx
Out[352]:
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 [353]: idx + pd.offsets.Hour(2)
Out[353]:
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 [354]: idx = pd.period_range('2014-07', periods=5, freq='M')
In [355]: idx
Out[355]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]', freq='M')
In [356]: idx + pd.offsets.MonthEnd(3)
Out[356]: 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 [357]: pi = pd.period_range('2016-01-01', periods=3, freq='M')
In [358]: pi
Out[358]: PeriodIndex(['2016-01', '2016-02', '2016-03'], dtype='period[M]', freq='M')
In [359]: pi.dtype
Out[359]: 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 [360]: pi.astype('period[D]')
Out[360]: PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]', freq='D')
# convert to DatetimeIndex
In [361]: pi.astype('datetime64[ns]')
Out[361]: DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='datetime64[ns]', freq='MS')
# convert to PeriodIndex
In [362]: dti = pd.date_range('2011-01-01', freq='M', periods=3)
In [363]: dti
Out[363]: DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31'], dtype='datetime64[ns]', freq='M')
In [364]: dti.astype('period[M]')
Out[364]: 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 [365]: ps['2011-01']
Out[365]: -2.9169013294054507
In [366]: ps[datetime.datetime(2011, 12, 25):]
Out[366]:
2011-12 2.261385
2012-01 -0.329583
Freq: M, dtype: float64
In [367]: ps['10/31/2011':'12/31/2011']
Out[367]:
2011-10 0.056780
2011-11 0.197035
2011-12 2.261385
Freq: M, dtype: float64
Passing a string representing a lower frequency than PeriodIndex returns partial sliced data.
In [368]: ps['2011']
Out[368]:
2011-01 -2.916901
2011-02 0.514474
2011-03 1.346470
2011-04 0.816397
2011-05 2.258648
2011-06 0.494789
2011-07 0.301239
2011-08 0.464776
2011-09 -1.393581
2011-10 0.056780
2011-11 0.197035
2011-12 2.261385
Freq: M, dtype: float64
In [369]: dfp = pd.DataFrame(np.random.randn(600, 1),
.....: columns=['A'],
.....: index=pd.period_range('2013-01-01 9:00',
.....: periods=600,
.....: freq='T'))
.....:
In [370]: dfp
Out[370]:
2013-01-01 09:00 -0.538468
2013-01-01 09:01 -1.365819
2013-01-01 09:02 -0.969051
2013-01-01 09:03 -0.331152
2013-01-01 09:04 -0.245334
... ...
2013-01-01 18:55 0.522460
2013-01-01 18:56 0.118710
2013-01-01 18:57 0.167517
2013-01-01 18:58 0.922883
2013-01-01 18:59 1.721104
[600 rows x 1 columns]
In [371]: dfp['2013-01-01 10H']
Out[371]:
2013-01-01 10:00 -0.308975
2013-01-01 10:01 0.542520
2013-01-01 10:02 1.061068
2013-01-01 10:03 0.754005
2013-01-01 10:04 0.352933
... ...
2013-01-01 10:55 -0.865621
2013-01-01 10:56 -1.167818
2013-01-01 10:57 -2.081748
2013-01-01 10:58 -0.527146
2013-01-01 10:59 0.802298
[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 [372]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[372]:
2013-01-01 10:00 -0.308975
2013-01-01 10:01 0.542520
2013-01-01 10:02 1.061068
2013-01-01 10:03 0.754005
2013-01-01 10:04 0.352933
... ...
2013-01-01 11:55 -0.590204
2013-01-01 11:56 1.539990
2013-01-01 11:57 -1.224826
2013-01-01 11:58 0.578798
2013-01-01 11:59 -0.685496
[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 [373]: p = pd.Period('2011', freq='A-DEC')
In [374]: p
Out[374]: 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 [375]: p.asfreq('M', how='start')
Out[375]: Period('2011-01', 'M')
In [376]: p.asfreq('M', how='end')
Out[376]: Period('2011-12', 'M')
The shorthands ‘s’ and ‘e’ are provided for convenience:
In [377]: p.asfreq('M', 's')
Out[377]: Period('2011-01', 'M')
In [378]: p.asfreq('M', 'e')
Out[378]: 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 [379]: p = pd.Period('2011-12', freq='M')
In [380]: p.asfreq('A-NOV')
Out[380]: 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 [381]: p = pd.Period('2012Q1', freq='Q-DEC')
In [382]: p.asfreq('D', 's')
Out[382]: Period('2012-01-01', 'D')
In [383]: p.asfreq('D', 'e')
Out[383]: Period('2012-03-31', 'D')
Q-MAR defines fiscal year end in March:
In [384]: p = pd.Period('2011Q4', freq='Q-MAR')
In [385]: p.asfreq('D', 's')
Out[385]: Period('2011-01-01', 'D')
In [386]: p.asfreq('D', 'e')
Out[386]: 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 [387]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
In [388]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [389]: ts
Out[389]:
2012-01-31 1.931253
2012-02-29 -0.184594
2012-03-31 0.249656
2012-04-30 -0.978151
2012-05-31 -0.873389
Freq: M, dtype: float64
In [390]: ps = ts.to_period()
In [391]: ps
Out[391]:
2012-01 1.931253
2012-02 -0.184594
2012-03 0.249656
2012-04 -0.978151
2012-05 -0.873389
Freq: M, dtype: float64
In [392]: ps.to_timestamp()
Out[392]:
2012-01-01 1.931253
2012-02-01 -0.184594
2012-03-01 0.249656
2012-04-01 -0.978151
2012-05-01 -0.873389
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 [393]: ps.to_timestamp('D', how='s')
Out[393]:
2012-01-01 1.931253
2012-02-01 -0.184594
2012-03-01 0.249656
2012-04-01 -0.978151
2012-05-01 -0.873389
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 [394]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
In [395]: ts = pd.Series(np.random.randn(len(prng)), prng)
In [396]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In [397]: ts.head()
Out[397]:
1990-03-01 09:00 -0.109291
1990-06-01 09:00 -0.637235
1990-09-01 09:00 -1.735925
1990-12-01 09:00 2.096946
1991-03-01 09:00 -1.039926
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 [398]: span = pd.period_range('1215-01-01', '1381-01-01', freq='D')
In [399]: span
Out[399]:
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 [400]: s = pd.Series([20121231, 20141130, 99991231])
In [401]: s
Out[401]:
0 20121231
1 20141130
2 99991231
dtype: int64
In [402]: def conv(x):
.....: return pd.Period(year=x // 10000, month=x // 100 % 100,
.....: day=x % 100, freq='D')
.....:
In [403]: s.apply(conv)
Out[403]:
0 2012-12-31
1 2014-11-30
2 9999-12-31
dtype: period[D]
In [404]: s.apply(conv)[2]
Out[404]: Period('9999-12-31', 'D')
These can easily be converted to a PeriodIndex:
In [405]: span = pd.PeriodIndex(s.apply(conv))
In [406]: span
Out[406]: 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 the pytz and dateutil libraries or class:datetime.timezone
objects from the standard library.
Working with time zones¶
By default, pandas objects are time zone unaware:
In [407]: rng = pd.date_range('3/6/2012 00:00', periods=15, freq='D')
In [408]: rng.tz is None
Out[408]: True
To localize these dates to a time zone (assign a particular time zone to a naive date),
you can use the tz_localize method or the tz keyword argument in
date_range(), Timestamp, or DatetimeIndex.
You can either pass pytz or dateutil time zone objects or Olson time zone database strings.
Olson time zone strings will return pytz time zone objects by default.
To return dateutil time zone objects, append dateutil/ before the string.
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 time zones so there isn’t a fixed list available. For
common zones, the names are the same as pytz.In [409]: import dateutil
# pytz
In [410]: rng_pytz = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
.....: tz='Europe/London')
.....:
In [411]: rng_pytz.tz
Out[411]: <DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>
# dateutil
In [412]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=3, freq='D')
In [413]: rng_dateutil = rng_dateutil.tz_localize('dateutil/Europe/London')
In [414]: rng_dateutil.tz
Out[414]: tzfile('/usr/share/zoneinfo/Europe/London')
# dateutil - utc special case
In [415]: rng_utc = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
.....: tz=dateutil.tz.tzutc())
.....:
In [416]: rng_utc.tz
Out[416]: tzutc()
New in version 0.25.0.
# datetime.timezone
In [417]: rng_utc = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
.....: tz=datetime.timezone.utc)
.....:
In [418]: rng_utc.tz
Out[418]: datetime.timezone.utc
Note that the UTC time zone is a special case in dateutil and should be constructed explicitly
as an instance of dateutil.tz.tzutc. You can also construct other time
zones objects explicitly first.
In [419]: import pytz
# pytz
In [420]: tz_pytz = pytz.timezone('Europe/London')
In [421]: rng_pytz = pd.date_range(
'3/6/2012 00:00', periods=3, freq='D')
In [422]: rng_pytz = rng_pytz.tz_localize(tz_pytz)
In [423]: rng_pytz.tz == tz_pytz
Out[423]: True
# dateutil
In [424]: tz_dateutil = dateutil.tz.gettz('Europe/London')
In [425]: rng_dateutil = pd.date_range('3/6/2012 00:00', periods=3, freq='D',
.....: tz=tz_dateutil)
.....:
In [426]: rng_dateutil.tz == tz_dateutil
Out[426]: True
To convert a time zone aware pandas object from one time zone to another,
you can use the tz_convert method.
In [427]: rng_pytz.tz_convert('US/Eastern')
Out[427]:
DatetimeIndex(['2012-03-05 19:00:00-05:00', '2012-03-06 19:00:00-05:00',
'2012-03-07 19:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq='D')
When using pytz time zones, DatetimeIndex will construct a different
time zone object than a Timestamp for the same time zone input. A DatetimeIndex
can hold a collection of Timestamp objects that may have different UTC offsets and cannot be
succinctly represented by one pytz time zone instance while one Timestamp
represents one point in time with a specific UTC offset.
In [428]: dti = pd.date_range('2019-01-01', periods=3, freq='D', tz='US/Pacific')
In [429]: dti.tz
Out[429]: <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>
In [430]: ts = pd.Timestamp('2019-01-01', tz='US/Pacific')
In [431]: ts.tz
Out[431]: <DstTzInfo 'US/Pacific' PST-1 day, 16:00:00 STD>
Warning
Be wary of conversions between libraries. For some time zones, pytz and dateutil have different
definitions of the zone. This is more of a problem for unusual time zones than for
‘standard’ zones like US/Eastern.
Warning
Be aware that a time zone definition across versions of time zone 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
For pytz time zones, it is incorrect to pass a time zone object directly into
the datetime.datetime constructor
(e.g., datetime.datetime(2011, 1, 1, tz=pytz.timezone('US/Eastern')).
Instead, the datetime needs to be localized using the localize method
on the pytz time zone object.
Under the hood, all timestamps are stored in UTC. Values from a time zone aware
DatetimeIndex or Timestamp will have their fields (day, hour, minute, etc.)
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 [432]: rng_eastern = rng_utc.tz_convert('US/Eastern')
In [433]: rng_berlin = rng_utc.tz_convert('Europe/Berlin')
In [434]: rng_eastern[2]
Out[434]: Timestamp('2012-03-07 19:00:00-0500', tz='US/Eastern', freq='D')
In [435]: rng_berlin[2]
Out[435]: Timestamp('2012-03-08 01:00:00+0100', tz='Europe/Berlin', freq='D')
In [436]: rng_eastern[2] == rng_berlin[2]
Out[436]: True
Operations between Series in different time zones will yield UTC
Series, aligning the data on the UTC timestamps:
In [437]: ts_utc = pd.Series(range(3), pd.date_range('20130101', periods=3, tz='UTC'))
In [438]: eastern = ts_utc.tz_convert('US/Eastern')
In [439]: berlin = ts_utc.tz_convert('Europe/Berlin')
In [440]: result = eastern + berlin
In [441]: result
Out[441]:
2013-01-01 00:00:00+00:00 0
2013-01-02 00:00:00+00:00 2
2013-01-03 00:00:00+00:00 4
Freq: D, dtype: int64
In [442]: result.index
Out[442]:
DatetimeIndex(['2013-01-01 00:00:00+00:00', '2013-01-02 00:00:00+00:00',
'2013-01-03 00:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq='D')
To remove time zone information, use tz_localize(None) or tz_convert(None).
tz_localize(None) will remove the time zone yielding the local time representation.
tz_convert(None) will remove the time zone after converting to UTC time.
In [443]: didx = pd.date_range(start='2014-08-01 09:00', freq='H',
.....: periods=3, tz='US/Eastern')
.....:
In [444]: didx
Out[444]:
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'],
dtype='datetime64[ns, US/Eastern]', freq='H')
In [445]: didx.tz_localize(None)
Out[445]:
DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00',
'2014-08-01 11:00:00'],
dtype='datetime64[ns]', freq='H')
In [446]: didx.tz_convert(None)
Out[446]:
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00'],
dtype='datetime64[ns]', freq='H')
# tz_convert(None) is identical to tz_convert('UTC').tz_localize(None)
In [447]: didx.tz_convert('UTC').tz_localize(None)
Out[447]:
DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00',
'2014-08-01 15:00:00'],
dtype='datetime64[ns]', freq='H')
Ambiguous times when localizing¶
tz_localize may not be able to determine the UTC offset of a timestamp
because daylight savings time (DST) in a local time zone causes some times to occur
twice within one day (“clocks fall back”). The following options are available:
'raise': Raises a pytz.AmbiguousTimeError (the default behavior)'infer': Attempt to determine the correct offset base on the monotonicity of the timestamps'NaT': Replaces ambiguous times with NaTbool: True represents a DST time, False represents non-DST time. An array-like of bool values is supported for a sequence of times.In [448]: rng_hourly = pd.DatetimeIndex(['11/06/2011 00:00', '11/06/2011 01:00',
.....: '11/06/2011 01:00', '11/06/2011 02:00'])
.....:
This will fail as there are ambiguous times ('11/06/2011 01:00')
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
Handle these ambiguous times by specifying the following.
In [449]: rng_hourly.tz_localize('US/Eastern', ambiguous='infer')
Out[449]:
DatetimeIndex(['2011-11-06 00:00:00-04:00', '2011-11-06 01:00:00-04:00',
'2011-11-06 01:00:00-05:00', '2011-11-06 02:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq=None)
In [450]: rng_hourly.tz_localize('US/Eastern', ambiguous='NaT')
Out[450]:
DatetimeIndex(['2011-11-06 00:00:00-04:00', 'NaT', 'NaT',
'2011-11-06 02:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq=None)
In [451]: rng_hourly.tz_localize('US/Eastern', ambiguous=[True, True, False, False])
Out[451]:
DatetimeIndex(['2011-11-06 00:00:00-04:00', '2011-11-06 01:00:00-04:00',
'2011-11-06 01:00:00-05:00', '2011-11-06 02:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq=None)
Nonexistent times when localizing¶
A DST transition may also shift the local time ahead by 1 hour creating nonexistent
local times (“clocks spring forward”). The behavior of localizing a timeseries with nonexistent times
can be controlled by the nonexistent argument. The following options are available:
'raise': Raises a pytz.NonExistentTimeError (the default behavior)'NaT': Replaces nonexistent times with NaT'shift_forward': Shifts nonexistent times forward to the closest real time'shift_backward': Shifts nonexistent times backward to the closest real timetimedelta object: Shifts nonexistent times by the timedelta duration
In [452]: dti = pd.date_range(start='2015-03-29 02:30:00', periods=3, freq='H')
# 2:30 is a nonexistent time
Localization of nonexistent times will raise an error by default.
In [2]: dti.tz_localize('Europe/Warsaw')
NonExistentTimeError: 2015-03-29 02:30:00
Transform nonexistent times to NaT or shift the times.
In [453]: dti
Out[453]:
DatetimeIndex(['2015-03-29 02:30:00', '2015-03-29 03:30:00',
'2015-03-29 04:30:00'],
dtype='datetime64[ns]', freq='H')
In [454]: dti.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
Out[454]:
DatetimeIndex(['2015-03-29 03:00:00+02:00', '2015-03-29 03:30:00+02:00',
'2015-03-29 04:30:00+02:00'],
dtype='datetime64[ns, Europe/Warsaw]', freq='H')
In [455]: dti.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
Out[455]:
DatetimeIndex(['2015-03-29 01:59:59.999999999+01:00',
'2015-03-29 03:30:00+02:00',
'2015-03-29 04:30:00+02:00'],
dtype='datetime64[ns, Europe/Warsaw]', freq='H')
In [456]: dti.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta(1, unit='H'))
Out[456]:
DatetimeIndex(['2015-03-29 03:30:00+02:00', '2015-03-29 03:30:00+02:00',
'2015-03-29 04:30:00+02:00'],
dtype='datetime64[ns, Europe/Warsaw]', freq='H')
In [457]: dti.tz_localize('Europe/Warsaw', nonexistent='NaT')
Out[457]:
DatetimeIndex(['NaT', '2015-03-29 03:30:00+02:00',
'2015-03-29 04:30:00+02:00'],
dtype='datetime64[ns, Europe/Warsaw]', freq='H')
Time zone series operations¶
A Series with time zone naive values is
represented with a dtype of datetime64[ns].
In [458]: s_naive = pd.Series(pd.date_range('20130101', periods=3))
In [459]: s_naive
Out[459]:
0 2013-01-01
1 2013-01-02
2 2013-01-03
dtype: datetime64[ns]
A Series with a time zone aware values is
represented with a dtype of datetime64[ns, tz] where tz is the time zone
In [460]: s_aware = pd.Series(pd.date_range('20130101', periods=3, tz='US/Eastern'))
In [461]: s_aware
Out[461]:
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 time zone information
can be manipulated via the .dt accessor, see the dt accessor section.
For example, to localize and convert a naive stamp to time zone aware.
In [462]: s_naive.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[462]:
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]
Time zone information can also be manipulated using the astype method.
This method can localize and convert time zone naive timestamps or
convert time zone aware timestamps.
# localize and convert a naive time zone
In [463]: s_naive.astype('datetime64[ns, US/Eastern]')
Out[463]:
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 [464]: s_aware.astype('datetime64[ns]')
Out[464]:
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 time zone
In [465]: s_aware.astype('datetime64[ns, CET]')
Out[465]:
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]
Using Series.to_numpy() on a Series, returns a NumPy array of the data.
NumPy does not currently support time zones (even though it is printing in the local time zone!),
therefore an object array of Timestamps is returned for time zone aware data:
In [466]: s_naive.to_numpy()
Out[466]:
array(['2013-01-01T00:00:00.000000000', '2013-01-02T00:00:00.000000000',
'2013-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
In [467]: s_aware.to_numpy()
Out[467]:
array([Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern', freq='D'),
Timestamp('2013-01-02 00:00:00-0500', tz='US/Eastern', freq='D'),
Timestamp('2013-01-03 00:00:00-0500', tz='US/Eastern', freq='D')],
dtype=object)
By converting to an object array of Timestamps, it preserves the time zone
information. For example, when converting back to a Series:
In [468]: pd.Series(s_aware.to_numpy())
Out[468]:
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]
However, if you want an actual NumPy datetime64[ns] array (with the values
converted to UTC) instead of an array of objects, you can specify the
dtype argument:
In [469]: s_aware.to_numpy(dtype='datetime64[ns]')
Out[469]:
array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000',
'2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
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