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pandas: powerful Python data analysis toolkit, Release 0.18.1
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 ofits groups.
See somecookbookexamples forsome advanced strategies
In [223]: rng pd.date_range('1/1/2012', periods=100, freq='S')
In [224]: ts pd.Series(np.random.randint(0500len(rng)), index=rng)
In [225]: ts.resample('5Min').sum()
Out[225]:
2012-01-01
24390
Freq: 5T, dtype: int64
The resample function is very flexible and allows you tospecify manydifferent parameters tocontrol the frequency
conversion and resampling operation.
The how parameter can be a function name or numpy array function that takes an array and produces aggregated
values:
In [226]: ts.resample('5Min').mean()
Out[226]:
2012-01-01
243.9
Freq: 5T, dtype: float64
In [227]: ts.resample('5Min').ohlc()
Out[227]:
open
high
low
close
2012-01-01
161
495
1
245
In [228]: ts.resample('5Min').max()
Out[228]:
2012-01-01
495
Freq: 5T, dtype: int64
Any function available viadispatching canbe given tothe how parameter byname,including sum,mean,std, sem,
max, min, median, first, last, ohlc.
For downsampling, closed can be set to ‘left’or ‘right’ to specify which end of the interval is closed:
In [229]: ts.resample('5Min', closed='right').mean()
Out[229]:
2011-12-31 23:55:00
161.000000
2012-01-01 00:00:00
244.737374
Freq: 5T, dtype: float64
In [230]: ts.resample('5Min', closed='left').mean()
Out[230]:
2012-01-01
243.9
Freq: 5T, dtype: float64
Parameters like label and loffset are usedto manipulate the resultinglabels. label specifies whethertheresult
is labeled with the beginning or the end ofthe interval. loffset performs a time adjustment on the output labels.
In [231]: ts.resample('5Min').mean() # by y default t label='right'
Out[231]:
2012-01-01
243.9
Freq: 5T, dtype: float64
20.9. Resampling
691
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In [232]: ts.resample('5Min', label='left').mean()
Out[232]:
2012-01-01
243.9
Freq: 5T, dtype: float64
In [233]: ts.resample('5Min', label='left', loffset='1s').mean()
Out[233]:
2012-01-01 00:00:01
243.9
dtype: float64
The axis parameter can be set to 0 or1 and allows you to resample the specified axis fora DataFrame.
kind can be set to ‘timestamp’ or‘period’to convert the resulting index to/fromtime-stampand time-span represen-
tations. By default resample retains the input representation.
conventioncan be setto ‘start’or‘end’ whenresamplingperiod data (detailbelow). It specifieshow lowfrequency
periods are converted to higher frequency periods.
20.9.1 Up Sampling
For upsampling, you can specify a way to upsample and the limit parameter to interpolate over the gaps that are
created:
# from secondly to every 250 milliseconds
In [234]: ts[:2].resample('250L').asfreq()
Out[234]:
2012-01-01 00:00:00.000
161.0
2012-01-01 00:00:00.250
NaN
2012-01-01 00:00:00.500
NaN
2012-01-01 00:00:00.750
NaN
2012-01-01 00:00:01.000
199.0
Freq: 250L, dtype: float64
In [235]: ts[:2].resample('250L').ffill()
Out[235]:
2012-01-01 00:00:00.000
161
2012-01-01 00:00:00.250
161
2012-01-01 00:00:00.500
161
2012-01-01 00:00:00.750
161
2012-01-01 00:00:01.000
199
Freq: 250L, dtype: int64
In [236]: ts[:2].resample('250L').ffill(limit=2)
Out[236]:
2012-01-01 00:00:00.000
161.0
2012-01-01 00:00:00.250
161.0
2012-01-01 00:00:00.500
161.0
2012-01-01 00:00:00.750
NaN
2012-01-01 00:00:01.000
199.0
Freq: 250L, dtype: float64
20.9.2 Sparse Resampling
Sparsetimeseries are ones where youhave alot fewerpoints relative to theamountoftime you arelooking to resample.
Naively upsampling a sparse series canpotentiallygenerate lots ofintermediate 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.
692
Chapter 20. Time Series / Date functionality
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Since resample is a time-based groupby, the following is a method to efficiently resample only the groups that are
not all NaN
In [237]: rng pd.date_range('2014-1-1', periods=100, freq='D'pd.Timedelta('1s')
In [238]: ts pd.Series(range(100), index=rng)
If we want to resample to the full range of the series
In [239]: ts.resample('3T').sum()
Out[239]:
2014-01-01 00:00:00
0.0
2014-01-01 00:03:00
NaN
2014-01-01 00:06:00
NaN
2014-01-01 00:09:00
NaN
2014-01-01 00:12:00
NaN
2014-01-01 00:15:00
NaN
2014-01-01 00:18:00
NaN
...
2014-04-09 23:42:00
NaN
2014-04-09 23:45:00
NaN
2014-04-09 23:48:00
NaN
2014-04-09 23:51:00
NaN
2014-04-09 23:54:00
NaN
2014-04-09 23:57:00
NaN
2014-04-10 00:00:00
99.0
Freq: 3T, dtype: float64
We can instead only resample those groups where we have points as follows:
In [240]: from functools import partial
In [241]: from pandas.tseries.frequencies import to_offset
In [242]: def round(t, freq):
.....:
freq = to_offset(freq)
.....:
return pd.Timestamp((t.value // freq.delta.value)
*
freq.delta.value)
.....:
In [243]: ts.groupby(partial(round, freq='3T')).sum()
Out[243]:
2014-01-01
0
2014-01-02
1
2014-01-03
2
2014-01-04
3
2014-01-05
4
2014-01-06
5
2014-01-07
6
..
2014-04-04
93
2014-04-05
94
2014-04-06
95
2014-04-07
96
2014-04-08
97
2014-04-09
98
2014-04-10
99
dtype: int64
20.9. Resampling
693
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20.9.3 Aggregation
Similartogroupbyaggregatesand thewindowfunctions,a Resampler can be selectively resampled.
Resampling a DataFrame,the default will be to act on all columns with the same function.
In [244]: df pd.DataFrame(np.random.randn(10003),
.....:
index=pd.date_range('1/1/2012', freq='S', periods=1000),
.....:
columns=['A''B''C'])
.....:
In [245]: df.resample('3T')
In [246]: r.mean()
Out[246]:
A
B
C
2012-01-01 00:00:00 -0.220339
0.034854 -0.073757
2012-01-01 00:03:00
0.037070
0.040013
0.053754
2012-01-01 00:06:00 -0.041597 -0.144562 -0.007614
2012-01-01 00:09:00
0.043127 -0.076432 -0.032570
2012-01-01 00:12:00 -0.027609
0.054618
0.056878
2012-01-01 00:15:00 -0.014181
0.043958
0.077734
We can select a specific column or columns using standardgetitem.
In [247]: r['A'].mean()
Out[247]:
2012-01-01 00:00:00
-0.220339
2012-01-01 00:03:00
0.037070
2012-01-01 00:06:00
-0.041597
2012-01-01 00:09:00
0.043127
2012-01-01 00:12:00
-0.027609
2012-01-01 00:15:00
-0.014181
Freq: 3T, Name: A, dtype: float64
In [248]: r[['A','B']].mean()
Out[248]:
A
B
2012-01-01 00:00:00 -0.220339
0.034854
2012-01-01 00:03:00
0.037070
0.040013
2012-01-01 00:06:00 -0.041597 -0.144562
2012-01-01 00:09:00
0.043127 -0.076432
2012-01-01 00:12:00 -0.027609
0.054618
2012-01-01 00:15:00 -0.014181
0.043958
You canpass a list or dict offunctions to do aggregation with, outputtinga DataFrame:
In [249]: r['A'].agg([np.sum, np.mean, np.std])
Out[249]:
sum
mean
std
2012-01-01 00:00:00 -39.660974 -0.220339
1.033912
2012-01-01 00:03:00
6.672559
0.037070
0.971503
2012-01-01 00:06:00
-7.487453 -0.041597
1.018418
2012-01-01 00:09:00
7.762901
0.043127
1.025842
2012-01-01 00:12:00
-4.969624 -0.027609
0.961649
2012-01-01 00:15:00
-1.418119 -0.014181
0.978847
If a dict is passed, the keys will be used to name the columns. Otherwise the function’s name (stored in the function
object) will be used.
694
Chapter 20. Time Series / Date functionality
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pandas: powerful Python data analysis toolkit, Release 0.18.1
In [250]: r['A'].agg({'result1' : np.sum,
.....:
'result2' : np.mean})
.....:
Out[250]:
result2
result1
2012-01-01 00:00:00 -0.220339 -39.660974
2012-01-01 00:03:00
0.037070
6.672559
2012-01-01 00:06:00 -0.041597
-7.487453
2012-01-01 00:09:00
0.043127
7.762901
2012-01-01 00:12:00 -0.027609
-4.969624
2012-01-01 00:15:00 -0.014181
-1.418119
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 [251]: r.agg([np.sum, np.mean])
Out[251]:
A
B
C
\
sum
mean
sum
mean
sum
2012-01-01 00:00:00 -39.660974 -0.220339
6.273786
0.034854 -13.276324
2012-01-01 00:03:00
6.672559
0.037070
7.202361
0.040013
9.675632
2012-01-01 00:06:00
-7.487453 -0.041597 -26.021155 -0.144562
-1.370600
2012-01-01 00:09:00
7.762901
0.043127 -13.757837 -0.076432
-5.862640
2012-01-01 00:12:00
-4.969624 -0.027609
9.831208
0.054618
10.237970
2012-01-01 00:15:00
-1.418119 -0.014181
4.395766
0.043958
7.773442
mean
2012-01-01 00:00:00 -0.073757
2012-01-01 00:03:00
0.053754
2012-01-01 00:06:00 -0.007614
2012-01-01 00:09:00 -0.032570
2012-01-01 00:12:00
0.056878
2012-01-01 00:15:00
0.077734
By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:
In [252]: r.agg({'A' : np.sum,
.....:
'B' lambda x: np.std(x, ddof=1)})
.....:
Out[252]:
A
B
2012-01-01 00:00:00 -39.660974
1.004756
2012-01-01 00:03:00
6.672559
0.963559
2012-01-01 00:06:00
-7.487453
0.950766
2012-01-01 00:09:00
7.762901
0.949182
2012-01-01 00:12:00
-4.969624
1.093736
2012-01-01 00:15:00
-1.418119
1.028869
The function names can also be strings. In order for a string to be valid it must be implemented on the Resampled
object
In [253]: r.agg({'A' 'sum''B' 'std'})
Out[253]:
A
B
2012-01-01 00:00:00 -39.660974
1.004756
2012-01-01 00:03:00
6.672559
0.963559
2012-01-01 00:06:00
-7.487453
0.950766
2012-01-01 00:09:00
7.762901
0.949182
20.9. Resampling
695
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2012-01-01 00:12:00
-4.969624
1.093736
2012-01-01 00:15:00
-1.418119
1.028869
Furthermore, you can also specify multiple aggregation functions foreach column separately.
In [254]: r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })
Out[254]:
A
B
sum
std
mean
std
2012-01-01 00:00:00 -39.660974
1.033912
0.034854
1.004756
2012-01-01 00:03:00
6.672559
0.971503
0.040013
0.963559
2012-01-01 00:06:00
-7.487453
1.018418 -0.144562
0.950766
2012-01-01 00:09:00
7.762901
1.025842 -0.076432
0.949182
2012-01-01 00:12:00
-4.969624
0.961649
0.054618
1.093736
2012-01-01 00:15:00
-1.418119
0.978847
0.043958
1.028869
20.10 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 withthe convenience function period_range.
20.10.1 Period
APeriod represents a span of time (e.g.,a day,a month,a quarter, etc). Youcan specifythespan via freq keyword
using a frequency alias like below. Because freq represents a span ofPeriod, it cannot be negative like “-3D”.
In [255]: pd.Period('2012', freq='A-DEC')
Out[255]: Period('2012''A-DEC')
In [256]: pd.Period('2012-1-1', freq='D')
Out[256]: Period('2012-01-01''D')
In [257]: pd.Period('2012-1-1 19:00', freq='H')
Out[257]: Period('2012-01-01 19:00''H')
In [258]: pd.Period('2012-1-1 19:00', freq='5H')
Out[258]: Period('2012-01-01 19:00''5H')
Adding andsubtractingintegers fromperiods shifts theperiodbyits own frequency. Arithmetic is notallowed between
Period with different freq (span).
In [259]: pd.Period('2012', freq='A-DEC')
In [260]: 1
Out[260]: Period('2013''A-DEC')
In [261]: 3
Out[261]: Period('2009''A-DEC')
In [262]: pd.Period('2012-01', freq='2M')
In [263]: 2
Out[263]: Period('2012-05''2M')
In [264]: 1
696
Chapter 20. Time Series / Date functionality
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Out[264]: Period('2011-11''2M')
In [265]: == pd.Period('2012-01', freq='3M')
---------------------------------------------------------------------------
IncompatibleFrequency
Traceback (most recent call last)
<ipython-input-265-ff54ce3238f5> in <module>()
----> 1 p == pd.Period('2012-01', freq='3M')
/Users/tom.augspurger/miniconda3/envs/docs/lib/python2.7/site-packages/pandas/pandas/src/period.pyx in pandas._period.Period.__richcmp__ (pandas/src/period.c:12486)()
769
if other.freq != self.freq:
770
msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr)
--> 771
raise IncompatibleFrequency(msg)
772
if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT:
773
return _nat_scalar_rules[op]
IncompatibleFrequency: Input has different freq=3M from Period(freq=2M)
If Period freqis daily orhigher(D,H, T,S,L, U,N),offsets and timedelta-like can be addedif the result can
have the same freq. Otherwise,ValueError will be raised.
In [266]: pd.Period('2014-07-01 09:00', freq='H')
In [267]: Hour(2)
Out[267]: Period('2014-07-01 11:00''H')
In [268]: timedelta(minutes=120)
Out[268]: Period('2014-07-01 11:00''H')
In [269]: np.timedelta64(7200's')
Out[269]: Period('2014-07-01 11:00''H')
In [1]: Minute(5)
Traceback
...
ValueError: Input has different freq from Period(freq=H)
If Period has other freqs,only the same offsets can be added. Otherwise,ValueError will be raised.
In [270]: pd.Period('2014-07', freq='M')
In [271]: MonthEnd(3)
Out[271]: Period('2014-10''M')
In [1]: MonthBegin(3)
Traceback
...
ValueError: Input has different freq from Period(freq=M)
Takingthedifference ofPeriod instances with the same frequencywill returnthenumber offrequencyunits between
them:
In [272]: pd.Period('2012', freq='A-DEC'pd.Period('2002', freq='A-DEC')
Out[272]: 10
20.10.2 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:
20.10. Time Span Representation
697
pandas: powerful Python data analysis toolkit, Release 0.18.1
In [273]: prng pd.period_range('1/1/2011''1/1/2012', freq='M')
In [274]: prng
Out[274]:
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='int64', freq='M')
The PeriodIndex constructor can also be used directly:
In [275]: pd.PeriodIndex(['2011-1''2011-2''2011-3'], freq='M')
Out[275]: PeriodIndex(['2011-01''2011-02''2011-03'], dtype='int64', freq='M')
Passing multiplied frequency outputs a sequence of Period which has multiplied span.
In [276]: pd.PeriodIndex(start='2014-01', freq='3M', periods=4)
Out[276]: PeriodIndex(['2014-01''2014-04''2014-07''2014-10'], dtype='int64', freq='3M')
Just like DatetimeIndex,a PeriodIndex can also be used to index pandas objects:
In [277]: ps pd.Series(np.random.randn(len(prng)), prng)
In [278]: ps
Out[278]:
2011-01
-1.022670
2011-02
1.371155
2011-03
1.035277
2011-04
1.694400
2011-05
-1.659733
2011-06
0.511432
2011-07
0.433176
2011-08
-0.317955
2011-09
-0.517114
2011-10
-0.310466
2011-11
0.543957
2011-12
0.492003
2012-01
0.193420
Freq: M, dtype: float64
PeriodIndex supports addition and subtraction with the same rule as Period.
In [279]: idx pd.period_range('2014-07-01 09:00', periods=5, freq='H')
In [280]: idx
Out[280]:
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='int64', freq='H')
In [281]: idx Hour(2)
Out[281]:
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='int64', freq='H')
In [282]: idx pd.period_range('2014-07', periods=5, freq='M')
In [283]: idx
698
Chapter 20. Time Series / Date functionality
pandas: powerful Python data analysis toolkit, Release 0.18.1
Out[283]: PeriodIndex(['2014-07''2014-08''2014-09''2014-10''2014-11'], dtype='int64', freq='M')
In [284]: idx MonthEnd(3)
Out[284]: PeriodIndex(['2014-10''2014-11''2014-12''2015-01''2015-02'], dtype='int64', freq='M')
20.10.3 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, refertoDatetimeIndexPartialStringIndexing.
In [285]: ps['2011-01']
Out[285]: -1.022669594890105
In [286]: ps[datetime(20111225):]
Out[286]:
2011-12
0.492003
2012-01
0.193420
Freq: M, dtype: float64
In [287]: ps['10/31/2011':'12/31/2011']
Out[287]:
2011-10
-0.310466
2011-11
0.543957
2011-12
0.492003
Freq: M, dtype: float64
Passing a string representinga lower frequency than PeriodIndex returns partial sliced data.
In [288]: ps['2011']
Out[288]:
2011-01
-1.022670
2011-02
1.371155
2011-03
1.035277
2011-04
1.694400
2011-05
-1.659733
2011-06
0.511432
2011-07
0.433176
2011-08
-0.317955
2011-09
-0.517114
2011-10
-0.310466
2011-11
0.543957
2011-12
0.492003
Freq: M, dtype: float64
In [289]: dfp pd.DataFrame(np.random.randn(600,1),
.....:
columns=['A'],
.....:
index=pd.period_range('2013-01-01 9:00', periods=600, freq='T'))
.....:
In [290]: dfp
Out[290]:
A
2013-01-01 09:00
0.197720
2013-01-01 09:01 -0.284769
2013-01-01 09:02
0.061491
2013-01-01 09:03
1.630257
2013-01-01 09:04
2.042442
20.10. Time Span Representation
699
pandas: powerful Python data analysis toolkit, Release 0.18.1
2013-01-01 09:05 -0.804392
2013-01-01 09:06
0.212760
...
...
2013-01-01 18:53
0.150586
2013-01-01 18:54 -0.679569
2013-01-01 18:55 -0.910216
2013-01-01 18:56 -0.413168
2013-01-01 18:57 -0.247752
2013-01-01 18:58
1.590875
2013-01-01 18:59 -2.005294
[600 rows x 1 columns]
In [291]: dfp['2013-01-01 10H']
Out[291]:
A
2013-01-01 10:00 -0.569936
2013-01-01 10:01 -1.179183
2013-01-01 10:02 -0.838602
2013-01-01 10:03 -1.727539
2013-01-01 10:04
1.334027
2013-01-01 10:05
0.417423
2013-01-01 10:06 -0.221189
...
...
2013-01-01 10:53 -0.375925
2013-01-01 10:54
0.212750
2013-01-01 10:55 -0.592417
2013-01-01 10:56 -0.466064
2013-01-01 10:57 -1.715347
2013-01-01 10:58 -0.634913
2013-01-01 10:59 -0.809471
[60 rows x 1 columns]
As with DatetimeIndex,the endpoints will be included in the result. The example below slices data starting from
10:00 to 11:59.
In [292]: dfp['2013-01-01 10H':'2013-01-01 11H']
Out[292]:
A
2013-01-01 10:00 -0.569936
2013-01-01 10:01 -1.179183
2013-01-01 10:02 -0.838602
2013-01-01 10:03 -1.727539
2013-01-01 10:04
1.334027
2013-01-01 10:05
0.417423
2013-01-01 10:06 -0.221189
...
...
2013-01-01 11:53
0.616198
2013-01-01 11:54
2.843156
2013-01-01 11:55
0.572537
2013-01-01 11:56
1.709706
2013-01-01 11:57 -0.205490
2013-01-01 11:58
1.759719
2013-01-01 11:59 -1.181485
[120 rows x 1 columns]
700
Chapter 20. Time Series / Date functionality
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