Timedeltas are differences in times, expressed in difference units, e.g. days, hours, minutes, seconds. They can be both positive and negative.
Timedelta is a subclass of datetime.timedelta, and behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes.
Timedelta
datetime.timedelta
np.timedelta64
You can construct a Timedelta scalar through various arguments:
In [1]: import datetime # strings In [2]: pd.Timedelta('1 days') Out[2]: Timedelta('1 days 00:00:00') In [3]: pd.Timedelta('1 days 00:00:00') Out[3]: Timedelta('1 days 00:00:00') In [4]: pd.Timedelta('1 days 2 hours') Out[4]: Timedelta('1 days 02:00:00') In [5]: pd.Timedelta('-1 days 2 min 3us') Out[5]: Timedelta('-2 days +23:57:59.999997') # like datetime.timedelta # note: these MUST be specified as keyword arguments In [6]: pd.Timedelta(days=1, seconds=1) Out[6]: Timedelta('1 days 00:00:01') # integers with a unit In [7]: pd.Timedelta(1, unit='d') Out[7]: Timedelta('1 days 00:00:00') # from a datetime.timedelta/np.timedelta64 In [8]: pd.Timedelta(datetime.timedelta(days=1, seconds=1)) Out[8]: Timedelta('1 days 00:00:01') In [9]: pd.Timedelta(np.timedelta64(1, 'ms')) Out[9]: Timedelta('0 days 00:00:00.001000') # negative Timedeltas have this string repr # to be more consistent with datetime.timedelta conventions In [10]: pd.Timedelta('-1us') Out[10]: Timedelta('-1 days +23:59:59.999999') # a NaT In [11]: pd.Timedelta('nan') Out[11]: NaT In [12]: pd.Timedelta('nat') Out[12]: NaT # ISO 8601 Duration strings In [13]: pd.Timedelta('P0DT0H1M0S') Out[13]: Timedelta('0 days 00:01:00') In [14]: pd.Timedelta('P0DT0H0M0.000000123S') Out[14]: Timedelta('0 days 00:00:00.000000123')
New in version 0.23.0: Added constructor for ISO 8601 Duration strings
DateOffsets (Day, Hour, Minute, Second, Milli, Micro, Nano) can also be used in construction.
Day, Hour, Minute, Second, Milli, Micro, Nano
In [15]: pd.Timedelta(pd.offsets.Second(2)) Out[15]: Timedelta('0 days 00:00:02')
Further, operations among the scalars yield another scalar Timedelta.
In [16]: pd.Timedelta(pd.offsets.Day(2)) + pd.Timedelta(pd.offsets.Second(2)) +\ ....: pd.Timedelta('00:00:00.000123') ....: Out[16]: Timedelta('2 days 00:00:02.000123')
Using the top-level pd.to_timedelta, you can convert a scalar, array, list, or Series from a recognized timedelta format / value into a Timedelta type. It will construct Series if the input is a Series, a scalar if the input is scalar-like, otherwise it will output a TimedeltaIndex.
pd.to_timedelta
TimedeltaIndex
You can parse a single string to a Timedelta:
In [17]: pd.to_timedelta('1 days 06:05:01.00003') Out[17]: Timedelta('1 days 06:05:01.000030') In [18]: pd.to_timedelta('15.5us') Out[18]: Timedelta('0 days 00:00:00.000015500')
or a list/array of strings:
In [19]: pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan']) Out[19]: TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT], dtype='timedelta64[ns]', freq=None)
The unit keyword argument specifies the unit of the Timedelta:
unit
In [20]: pd.to_timedelta(np.arange(5), unit='s') Out[20]: TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03', '0 days 00:00:04'], dtype='timedelta64[ns]', freq=None) In [21]: pd.to_timedelta(np.arange(5), unit='d') Out[21]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
Pandas represents Timedeltas in nanosecond resolution using 64 bit integers. As such, the 64 bit integer limits determine the Timedelta limits.
Timedeltas
In [22]: pd.Timedelta.min Out[22]: Timedelta('-106752 days +00:12:43.145224193') In [23]: pd.Timedelta.max Out[23]: Timedelta('106751 days 23:47:16.854775807')
You can operate on Series/DataFrames and construct timedelta64[ns] Series through subtraction operations on datetime64[ns] Series, or Timestamps.
timedelta64[ns]
datetime64[ns]
Timestamps
In [24]: s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D')) In [25]: td = pd.Series([pd.Timedelta(days=i) for i in range(3)]) In [26]: df = pd.DataFrame({'A': s, 'B': td}) In [27]: df Out[27]: A B 0 2012-01-01 0 days 1 2012-01-02 1 days 2 2012-01-03 2 days In [28]: df['C'] = df['A'] + df['B'] In [29]: df Out[29]: A B C 0 2012-01-01 0 days 2012-01-01 1 2012-01-02 1 days 2012-01-03 2 2012-01-03 2 days 2012-01-05 In [30]: df.dtypes Out[30]: A datetime64[ns] B timedelta64[ns] C datetime64[ns] dtype: object In [31]: s - s.max() Out[31]: 0 -2 days 1 -1 days 2 0 days dtype: timedelta64[ns] In [32]: s - datetime.datetime(2011, 1, 1, 3, 5) Out[32]: 0 364 days 20:55:00 1 365 days 20:55:00 2 366 days 20:55:00 dtype: timedelta64[ns] In [33]: s + datetime.timedelta(minutes=5) Out[33]: 0 2012-01-01 00:05:00 1 2012-01-02 00:05:00 2 2012-01-03 00:05:00 dtype: datetime64[ns] In [34]: s + pd.offsets.Minute(5) Out[34]: 0 2012-01-01 00:05:00 1 2012-01-02 00:05:00 2 2012-01-03 00:05:00 dtype: datetime64[ns] In [35]: s + pd.offsets.Minute(5) + pd.offsets.Milli(5) Out[35]: 0 2012-01-01 00:05:00.005 1 2012-01-02 00:05:00.005 2 2012-01-03 00:05:00.005 dtype: datetime64[ns]
Operations with scalars from a timedelta64[ns] series:
In [36]: y = s - s[0] In [37]: y Out[37]: 0 0 days 1 1 days 2 2 days dtype: timedelta64[ns]
Series of timedeltas with NaT values are supported:
NaT
In [38]: y = s - s.shift() In [39]: y Out[39]: 0 NaT 1 1 days 2 1 days dtype: timedelta64[ns]
Elements can be set to NaT using np.nan analogously to datetimes:
np.nan
In [40]: y[1] = np.nan In [41]: y Out[41]: 0 NaT 1 NaT 2 1 days dtype: timedelta64[ns]
Operands can also appear in a reversed order (a singular object operated with a Series):
In [42]: s.max() - s Out[42]: 0 2 days 1 1 days 2 0 days dtype: timedelta64[ns] In [43]: datetime.datetime(2011, 1, 1, 3, 5) - s Out[43]: 0 -365 days +03:05:00 1 -366 days +03:05:00 2 -367 days +03:05:00 dtype: timedelta64[ns] In [44]: datetime.timedelta(minutes=5) + s Out[44]: 0 2012-01-01 00:05:00 1 2012-01-02 00:05:00 2 2012-01-03 00:05:00 dtype: datetime64[ns]
min, max and the corresponding idxmin, idxmax operations are supported on frames:
min, max
idxmin, idxmax
In [45]: A = s - pd.Timestamp('20120101') - pd.Timedelta('00:05:05') In [46]: B = s - pd.Series(pd.date_range('2012-1-2', periods=3, freq='D')) In [47]: df = pd.DataFrame({'A': A, 'B': B}) In [48]: df Out[48]: A B 0 -1 days +23:54:55 -1 days 1 0 days 23:54:55 -1 days 2 1 days 23:54:55 -1 days In [49]: df.min() Out[49]: A -1 days +23:54:55 B -1 days +00:00:00 dtype: timedelta64[ns] In [50]: df.min(axis=1) Out[50]: 0 -1 days 1 -1 days 2 -1 days dtype: timedelta64[ns] In [51]: df.idxmin() Out[51]: A 0 B 0 dtype: int64 In [52]: df.idxmax() Out[52]: A 2 B 0 dtype: int64
min, max, idxmin, idxmax operations are supported on Series as well. A scalar result will be a Timedelta.
min, max, idxmin, idxmax
In [53]: df.min().max() Out[53]: Timedelta('-1 days +23:54:55') In [54]: df.min(axis=1).min() Out[54]: Timedelta('-1 days +00:00:00') In [55]: df.min().idxmax() Out[55]: 'A' In [56]: df.min(axis=1).idxmin() Out[56]: 0
You can fillna on timedeltas, passing a timedelta to get a particular value.
In [57]: y.fillna(pd.Timedelta(0)) Out[57]: 0 0 days 1 0 days 2 1 days dtype: timedelta64[ns] In [58]: y.fillna(pd.Timedelta(10, unit='s')) Out[58]: 0 0 days 00:00:10 1 0 days 00:00:10 2 1 days 00:00:00 dtype: timedelta64[ns] In [59]: y.fillna(pd.Timedelta('-1 days, 00:00:05')) Out[59]: 0 -1 days +00:00:05 1 -1 days +00:00:05 2 1 days 00:00:00 dtype: timedelta64[ns]
You can also negate, multiply and use abs on Timedeltas:
abs
In [60]: td1 = pd.Timedelta('-1 days 2 hours 3 seconds') In [61]: td1 Out[61]: Timedelta('-2 days +21:59:57') In [62]: -1 * td1 Out[62]: Timedelta('1 days 02:00:03') In [63]: - td1 Out[63]: Timedelta('1 days 02:00:03') In [64]: abs(td1) Out[64]: Timedelta('1 days 02:00:03')
Numeric reduction operation for timedelta64[ns] will return Timedelta objects. As usual NaT are skipped during evaluation.
In [65]: y2 = pd.Series(pd.to_timedelta(['-1 days +00:00:05', 'nat', ....: '-1 days +00:00:05', '1 days'])) ....: In [66]: y2 Out[66]: 0 -1 days +00:00:05 1 NaT 2 -1 days +00:00:05 3 1 days 00:00:00 dtype: timedelta64[ns] In [67]: y2.mean() Out[67]: Timedelta('-1 days +16:00:03.333333334') In [68]: y2.median() Out[68]: Timedelta('-1 days +00:00:05') In [69]: y2.quantile(.1) Out[69]: Timedelta('-1 days +00:00:05') In [70]: y2.sum() Out[70]: Timedelta('-1 days +00:00:10')
Timedelta Series, TimedeltaIndex, and Timedelta scalars can be converted to other ‘frequencies’ by dividing by another timedelta, or by astyping to a specific timedelta type. These operations yield Series and propagate NaT -> nan. Note that division by the NumPy scalar is true division, while astyping is equivalent of floor division.
nan
In [71]: december = pd.Series(pd.date_range('20121201', periods=4)) In [72]: january = pd.Series(pd.date_range('20130101', periods=4)) In [73]: td = january - december In [74]: td[2] += datetime.timedelta(minutes=5, seconds=3) In [75]: td[3] = np.nan In [76]: td Out[76]: 0 31 days 00:00:00 1 31 days 00:00:00 2 31 days 00:05:03 3 NaT dtype: timedelta64[ns] # to days In [77]: td / np.timedelta64(1, 'D') Out[77]: 0 31.000000 1 31.000000 2 31.003507 3 NaN dtype: float64 In [78]: td.astype('timedelta64[D]') Out[78]: 0 31.0 1 31.0 2 31.0 3 NaN dtype: float64 # to seconds In [79]: td / np.timedelta64(1, 's') Out[79]: 0 2678400.0 1 2678400.0 2 2678703.0 3 NaN dtype: float64 In [80]: td.astype('timedelta64[s]') Out[80]: 0 2678400.0 1 2678400.0 2 2678703.0 3 NaN dtype: float64 # to months (these are constant months) In [81]: td / np.timedelta64(1, 'M') Out[81]: 0 1.018501 1 1.018501 2 1.018617 3 NaN dtype: float64
Dividing or multiplying a timedelta64[ns] Series by an integer or integer Series yields another timedelta64[ns] dtypes Series.
In [82]: td * -1 Out[82]: 0 -31 days +00:00:00 1 -31 days +00:00:00 2 -32 days +23:54:57 3 NaT dtype: timedelta64[ns] In [83]: td * pd.Series([1, 2, 3, 4]) Out[83]: 0 31 days 00:00:00 1 62 days 00:00:00 2 93 days 00:15:09 3 NaT dtype: timedelta64[ns]
Rounded division (floor-division) of a timedelta64[ns] Series by a scalar Timedelta gives a series of integers.
In [84]: td // pd.Timedelta(days=3, hours=4) Out[84]: 0 9.0 1 9.0 2 9.0 3 NaN dtype: float64 In [85]: pd.Timedelta(days=3, hours=4) // td Out[85]: 0 0.0 1 0.0 2 0.0 3 NaN dtype: float64
The mod (%) and divmod operations are defined for Timedelta when operating with another timedelta-like or with a numeric argument.
In [86]: pd.Timedelta(hours=37) % datetime.timedelta(hours=2) Out[86]: Timedelta('0 days 01:00:00') # divmod against a timedelta-like returns a pair (int, Timedelta) In [87]: divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11)) Out[87]: (10, Timedelta('0 days 00:10:00')) # divmod against a numeric returns a pair (Timedelta, Timedelta) In [88]: divmod(pd.Timedelta(hours=25), 86400000000000) Out[88]: (Timedelta('0 days 00:00:00.000000001'), Timedelta('0 days 01:00:00'))
You can access various components of the Timedelta or TimedeltaIndex directly using the attributes days,seconds,microseconds,nanoseconds. These are identical to the values returned by datetime.timedelta, in that, for example, the .seconds attribute represents the number of seconds >= 0 and < 1 day. These are signed according to whether the Timedelta is signed.
days,seconds,microseconds,nanoseconds
.seconds
These operations can also be directly accessed via the .dt property of the Series as well.
.dt
Series
Note
Note that the attributes are NOT the displayed values of the Timedelta. Use .components to retrieve the displayed values.
.components
For a Series:
In [89]: td.dt.days Out[89]: 0 31.0 1 31.0 2 31.0 3 NaN dtype: float64 In [90]: td.dt.seconds Out[90]: 0 0.0 1 0.0 2 303.0 3 NaN dtype: float64
You can access the value of the fields for a scalar Timedelta directly.
In [91]: tds = pd.Timedelta('31 days 5 min 3 sec') In [92]: tds.days Out[92]: 31 In [93]: tds.seconds Out[93]: 303 In [94]: (-tds).seconds Out[94]: 86097
You can use the .components property to access a reduced form of the timedelta. This returns a DataFrame indexed similarly to the Series. These are the displayed values of the Timedelta.
DataFrame
In [95]: td.dt.components Out[95]: days hours minutes seconds milliseconds microseconds nanoseconds 0 31.0 0.0 0.0 0.0 0.0 0.0 0.0 1 31.0 0.0 0.0 0.0 0.0 0.0 0.0 2 31.0 0.0 5.0 3.0 0.0 0.0 0.0 3 NaN NaN NaN NaN NaN NaN NaN In [96]: td.dt.components.seconds Out[96]: 0 0.0 1 0.0 2 3.0 3 NaN Name: seconds, dtype: float64
You can convert a Timedelta to an ISO 8601 Duration string with the .isoformat method
.isoformat
In [97]: pd.Timedelta(days=6, minutes=50, seconds=3, ....: milliseconds=10, microseconds=10, ....: nanoseconds=12).isoformat() ....: Out[97]: 'P6DT0H50M3.010010012S'
To generate an index with time delta, you can use either the TimedeltaIndex or the timedelta_range() constructor.
timedelta_range()
Using TimedeltaIndex you can pass string-like, Timedelta, timedelta, or np.timedelta64 objects. Passing np.nan/pd.NaT/nat will represent missing values.
timedelta
np.nan/pd.NaT/nat
In [98]: pd.TimedeltaIndex(['1 days', '1 days, 00:00:05', np.timedelta64(2, 'D'), ....: datetime.timedelta(days=2, seconds=2)]) ....: Out[98]: TimedeltaIndex(['1 days 00:00:00', '1 days 00:00:05', '2 days 00:00:00', '2 days 00:00:02'], dtype='timedelta64[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 [99]: pd.TimedeltaIndex(['0 days', '10 days', '20 days'], freq='infer') Out[99]: TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq='10D')
Similar to date_range(), you can construct regular ranges of a TimedeltaIndex using timedelta_range(). The default frequency for timedelta_range is calendar day:
date_range()
timedelta_range
In [100]: pd.timedelta_range(start='1 days', periods=5) Out[100]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
Various combinations of start, end, and periods can be used with timedelta_range:
start
end
periods
In [101]: pd.timedelta_range(start='1 days', end='5 days') Out[101]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D') In [102]: pd.timedelta_range(end='10 days', periods=4) Out[102]: TimedeltaIndex(['7 days', '8 days', '9 days', '10 days'], dtype='timedelta64[ns]', freq='D')
The freq parameter can passed a variety of frequency aliases:
freq
In [103]: pd.timedelta_range(start='1 days', end='2 days', freq='30T') Out[103]: TimedeltaIndex(['1 days 00:00:00', '1 days 00:30:00', '1 days 01:00:00', '1 days 01:30:00', '1 days 02:00:00', '1 days 02:30:00', '1 days 03:00:00', '1 days 03:30:00', '1 days 04:00:00', '1 days 04:30:00', '1 days 05:00:00', '1 days 05:30:00', '1 days 06:00:00', '1 days 06:30:00', '1 days 07:00:00', '1 days 07:30:00', '1 days 08:00:00', '1 days 08:30:00', '1 days 09:00:00', '1 days 09:30:00', '1 days 10:00:00', '1 days 10:30:00', '1 days 11:00:00', '1 days 11:30:00', '1 days 12:00:00', '1 days 12:30:00', '1 days 13:00:00', '1 days 13:30:00', '1 days 14:00:00', '1 days 14:30:00', '1 days 15:00:00', '1 days 15:30:00', '1 days 16:00:00', '1 days 16:30:00', '1 days 17:00:00', '1 days 17:30:00', '1 days 18:00:00', '1 days 18:30:00', '1 days 19:00:00', '1 days 19:30:00', '1 days 20:00:00', '1 days 20:30:00', '1 days 21:00:00', '1 days 21:30:00', '1 days 22:00:00', '1 days 22:30:00', '1 days 23:00:00', '1 days 23:30:00', '2 days 00:00:00'], dtype='timedelta64[ns]', freq='30T') In [104]: pd.timedelta_range(start='1 days', periods=5, freq='2D5H') Out[104]: TimedeltaIndex(['1 days 00:00:00', '3 days 05:00:00', '5 days 10:00:00', '7 days 15:00:00', '9 days 20:00:00'], dtype='timedelta64[ns]', freq='53H')
New in version 0.23.0.
Specifying start, end, and periods will generate a range of evenly spaced timedeltas from start to end inclusively, with periods number of elements in the resulting TimedeltaIndex:
In [105]: pd.timedelta_range('0 days', '4 days', periods=5) Out[105]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') In [106]: pd.timedelta_range('0 days', '4 days', periods=10) Out[106]: TimedeltaIndex(['0 days 00:00:00', '0 days 10:40:00', '0 days 21:20:00', '1 days 08:00:00', '1 days 18:40:00', '2 days 05:20:00', '2 days 16:00:00', '3 days 02:40:00', '3 days 13:20:00', '4 days 00:00:00'], dtype='timedelta64[ns]', freq='640T')
Similarly to other of the datetime-like indices, DatetimeIndex and PeriodIndex, you can use TimedeltaIndex as the index of pandas objects.
DatetimeIndex
PeriodIndex
In [107]: s = pd.Series(np.arange(100), .....: index=pd.timedelta_range('1 days', periods=100, freq='h')) .....: In [108]: s Out[108]: 1 days 00:00:00 0 1 days 01:00:00 1 1 days 02:00:00 2 1 days 03:00:00 3 1 days 04:00:00 4 .. 4 days 23:00:00 95 5 days 00:00:00 96 5 days 01:00:00 97 5 days 02:00:00 98 5 days 03:00:00 99 Freq: H, Length: 100, dtype: int64
Selections work similarly, with coercion on string-likes and slices:
In [109]: s['1 day':'2 day'] Out[109]: 1 days 00:00:00 0 1 days 01:00:00 1 1 days 02:00:00 2 1 days 03:00:00 3 1 days 04:00:00 4 .. 2 days 19:00:00 43 2 days 20:00:00 44 2 days 21:00:00 45 2 days 22:00:00 46 2 days 23:00:00 47 Freq: H, Length: 48, dtype: int64 In [110]: s['1 day 01:00:00'] Out[110]: 1 In [111]: s[pd.Timedelta('1 day 1h')] Out[111]: 1
Furthermore you can use partial string selection and the range will be inferred:
In [112]: s['1 day':'1 day 5 hours'] Out[112]: 1 days 00:00:00 0 1 days 01:00:00 1 1 days 02:00:00 2 1 days 03:00:00 3 1 days 04:00:00 4 1 days 05:00:00 5 Freq: H, dtype: int64
Finally, the combination of TimedeltaIndex with DatetimeIndex allow certain combination operations that are NaT preserving:
In [113]: tdi = pd.TimedeltaIndex(['1 days', pd.NaT, '2 days']) In [114]: tdi.to_list() Out[114]: [Timedelta('1 days 00:00:00'), NaT, Timedelta('2 days 00:00:00')] In [115]: dti = pd.date_range('20130101', periods=3) In [116]: dti.to_list() Out[116]: [Timestamp('2013-01-01 00:00:00', freq='D'), Timestamp('2013-01-02 00:00:00', freq='D'), Timestamp('2013-01-03 00:00:00', freq='D')] In [117]: (dti + tdi).to_list() Out[117]: [Timestamp('2013-01-02 00:00:00'), NaT, Timestamp('2013-01-05 00:00:00')] In [118]: (dti - tdi).to_list() Out[118]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')]
Similarly to frequency conversion on a Series above, you can convert these indices to yield another Index.
In [119]: tdi / np.timedelta64(1, 's') Out[119]: Float64Index([86400.0, nan, 172800.0], dtype='float64') In [120]: tdi.astype('timedelta64[s]') Out[120]: Float64Index([86400.0, nan, 172800.0], dtype='float64')
Scalars type ops work as well. These can potentially return a different type of index.
# adding or timedelta and date -> datelike In [121]: tdi + pd.Timestamp('20130101') Out[121]: DatetimeIndex(['2013-01-02', 'NaT', '2013-01-03'], dtype='datetime64[ns]', freq=None) # subtraction of a date and a timedelta -> datelike # note that trying to subtract a date from a Timedelta will raise an exception In [122]: (pd.Timestamp('20130101') - tdi).to_list() Out[122]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2012-12-30 00:00:00')] # timedelta + timedelta -> timedelta In [123]: tdi + pd.Timedelta('10 days') Out[123]: TimedeltaIndex(['11 days', NaT, '12 days'], dtype='timedelta64[ns]', freq=None) # division can result in a Timedelta if the divisor is an integer In [124]: tdi / 2 Out[124]: TimedeltaIndex(['0 days 12:00:00', NaT, '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None) # or a Float64Index if the divisor is a Timedelta In [125]: tdi / tdi[0] Out[125]: Float64Index([1.0, nan, 2.0], dtype='float64')
Similar to timeseries resampling, we can resample with a TimedeltaIndex.
In [126]: s.resample('D').mean() Out[126]: 1 days 11.5 2 days 35.5 3 days 59.5 4 days 83.5 5 days 97.5 Freq: D, dtype: float64