split-apply-combine — pandas 0.18.1 documentation (original) (raw)

Group By: split-apply-combine

By “group by” we are referring to a process involving one or more of the following steps

Of these, the split step is the most straightforward. In fact, in many situations you may wish to split the data set into groups and do something with those groups yourself. In the apply step, we might wish to one of the following:

Since the set of object instance method on pandas data structures are generally rich and expressive, we often simply want to invoke, say, a DataFrame function on each group. The name GroupBy should be quite familiar to those who have used a SQL-based tool (or itertools), in which you can write code like:

SELECT Column1, Column2, mean(Column3), sum(Column4) FROM SomeTable GROUP BY Column1, Column2

We aim to make operations like this natural and easy to express using pandas. We’ll address each area of GroupBy functionality then provide some non-trivial examples / use cases.

See the cookbook for some advanced strategies

Splitting an object into groups

pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names. To create a GroupBy object (more on what the GroupBy object is later), you do the following:

grouped = obj.groupby(key) grouped = obj.groupby(key, axis=1) grouped = obj.groupby([key1, key2])

The mapping can be specified many different ways:

Collectively we refer to the grouping objects as the keys. For example, consider the following DataFrame:

In [1]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ...: 'foo', 'bar', 'foo', 'foo'], ...: 'B' : ['one', 'one', 'two', 'three', ...: 'two', 'two', 'one', 'three'], ...: 'C' : np.random.randn(8), ...: 'D' : np.random.randn(8)}) ...:

In [2]: df Out[2]: A B C D 0 foo one 0.469112 -0.861849 1 bar one -0.282863 -2.104569 2 foo two -1.509059 -0.494929 3 bar three -1.135632 1.071804 4 foo two 1.212112 0.721555 5 bar two -0.173215 -0.706771 6 foo one 0.119209 -1.039575 7 foo three -1.044236 0.271860

We could naturally group by either the A or B columns or both:

In [3]: grouped = df.groupby('A')

In [4]: grouped = df.groupby(['A', 'B'])

These will split the DataFrame on its index (rows). We could also split by the columns:

In [5]: def get_letter_type(letter): ...: if letter.lower() in 'aeiou': ...: return 'vowel' ...: else: ...: return 'consonant' ...:

In [6]: grouped = df.groupby(get_letter_type, axis=1)

Starting with 0.8, pandas Index objects now supports duplicate values. If a non-unique index is used as the group key in a groupby operation, all values for the same index value will be considered to be in one group and thus the output of aggregation functions will only contain unique index values:

In [7]: lst = [1, 2, 3, 1, 2, 3]

In [8]: s = pd.Series([1, 2, 3, 10, 20, 30], lst)

In [9]: grouped = s.groupby(level=0)

In [10]: grouped.first() Out[10]: 1 1 2 2 3 3 dtype: int64

In [11]: grouped.last() Out[11]: 1 10 2 20 3 30 dtype: int64

In [12]: grouped.sum() Out[12]: 1 11 2 22 3 33 dtype: int64

Note that no splitting occurs until it’s needed. Creating the GroupBy object only verifies that you’ve passed a valid mapping.

Note

Many kinds of complicated data manipulations can be expressed in terms of GroupBy operations (though can’t be guaranteed to be the most efficient). You can get quite creative with the label mapping functions.

GroupBy sorting

By default the group keys are sorted during the groupby operation. You may however pass sort=False for potential speedups:

In [13]: df2 = pd.DataFrame({'X' : ['B', 'B', 'A', 'A'], 'Y' : [1, 2, 3, 4]})

In [14]: df2.groupby(['X']).sum() Out[14]: Y X
A 7 B 3

In [15]: df2.groupby(['X'], sort=False).sum() Out[15]: Y X
B 3 A 7

Note that groupby will preserve the order in which observations are sorted within each group. For example, the groups created by groupby() below are in the order the appeared in the original DataFrame:

In [16]: df3 = pd.DataFrame({'X' : ['A', 'B', 'A', 'B'], 'Y' : [1, 4, 3, 2]})

In [17]: df3.groupby(['X']).get_group('A') Out[17]: X Y 0 A 1 2 A 3

In [18]: df3.groupby(['X']).get_group('B') Out[18]: X Y 1 B 4 3 B 2

GroupBy object attributes

The groups attribute is a dict whose keys are the computed unique groups and corresponding values being the axis labels belonging to each group. In the above example we have:

In [19]: df.groupby('A').groups Out[19]: {'bar': [1, 3, 5], 'foo': [0, 2, 4, 6, 7]}

In [20]: df.groupby(get_letter_type, axis=1).groups Out[20]: {'consonant': ['B', 'C', 'D'], 'vowel': ['A']}

Calling the standard Python len function on the GroupBy object just returns the length of the groups dict, so it is largely just a convenience:

In [21]: grouped = df.groupby(['A', 'B'])

In [22]: grouped.groups Out[22]: {('bar', 'one'): [1], ('bar', 'three'): [3], ('bar', 'two'): [5], ('foo', 'one'): [0, 6], ('foo', 'three'): [7], ('foo', 'two'): [2, 4]}

In [23]: len(grouped) Out[23]: 6

GroupBy will tab complete column names (and other attributes)

In [24]: df Out[24]: gender height weight 2000-01-01 male 42.849980 157.500553 2000-01-02 male 49.607315 177.340407 2000-01-03 male 56.293531 171.524640 2000-01-04 female 48.421077 144.251986 2000-01-05 male 46.556882 152.526206 2000-01-06 female 68.448851 168.272968 2000-01-07 male 70.757698 136.431469 2000-01-08 female 58.909500 176.499753 2000-01-09 female 76.435631 174.094104 2000-01-10 male 45.306120 177.540920

In [25]: gb = df.groupby('gender')

In [26]: gb. gb.agg gb.boxplot gb.cummin gb.describe gb.filter gb.get_group gb.height gb.last gb.median gb.ngroups gb.plot gb.rank gb.std gb.transform gb.aggregate gb.count gb.cumprod gb.dtype gb.first gb.groups gb.hist gb.max gb.min gb.nth gb.prod gb.resample gb.sum gb.var gb.apply gb.cummax gb.cumsum gb.fillna gb.gender gb.head gb.indices gb.mean gb.name gb.ohlc gb.quantile gb.size gb.tail gb.weight

GroupBy with MultiIndex

With hierarchically-indexed data, it’s quite natural to group by one of the levels of the hierarchy.

Let’s create a series with a two-level MultiIndex.

In [27]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ....: ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] ....:

In [28]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])

In [29]: s = pd.Series(np.random.randn(8), index=index)

In [30]: s Out[30]: first second bar one -0.575247 two 0.254161 baz one -1.143704 two 0.215897 foo one 1.193555 two -0.077118 qux one -0.408530 two -0.862495 dtype: float64

We can then group by one of the levels in s.

In [31]: grouped = s.groupby(level=0)

In [32]: grouped.sum() Out[32]: first bar -0.321085 baz -0.927807 foo 1.116437 qux -1.271025 dtype: float64

If the MultiIndex has names specified, these can be passed instead of the level number:

In [33]: s.groupby(level='second').sum() Out[33]: second one -0.933926 two -0.469555 dtype: float64

The aggregation functions such as sum will take the level parameter directly. Additionally, the resulting index will be named according to the chosen level:

In [34]: s.sum(level='second') Out[34]: second one -0.933926 two -0.469555 dtype: float64

Also as of v0.6, grouping with multiple levels is supported.

In [35]: s Out[35]: first second third bar doo one 1.346061 two 1.511763 baz bee one 1.627081 two -0.990582 foo bop one -0.441652 two 1.211526 qux bop one 0.268520 two 0.024580 dtype: float64

In [36]: s.groupby(level=['first', 'second']).sum() Out[36]: first second bar doo 2.857824 baz bee 0.636499 foo bop 0.769873 qux bop 0.293100 dtype: float64

More on the sum function and aggregation later.

DataFrame column selection in GroupBy

Once you have created the GroupBy object from a DataFrame, for example, you might want to do something different for each of the columns. Thus, using[] similar to getting a column from a DataFrame, you can do:

In [37]: grouped = df.groupby(['A'])

In [38]: grouped_C = grouped['C']

In [39]: grouped_D = grouped['D']

This is mainly syntactic sugar for the alternative and much more verbose:

In [40]: df['C'].groupby(df['A']) Out[40]: <pandas.core.groupby.SeriesGroupBy object at 0x129fce310>

Additionally this method avoids recomputing the internal grouping information derived from the passed key.

Iterating through groups

With the GroupBy object in hand, iterating through the grouped data is very natural and functions similarly to itertools.groupby:

In [41]: grouped = df.groupby('A')

In [42]: for name, group in grouped: ....: print(name) ....: print(group) ....: bar A B C D 1 bar one -0.042379 -0.089329 3 bar three -0.009920 -0.945867 5 bar two 0.495767 1.956030 foo A B C D 0 foo one -0.919854 -1.131345 2 foo two 1.247642 0.337863 4 foo two 0.290213 -0.932132 6 foo one 0.362949 0.017587 7 foo three 1.548106 -0.016692

In the case of grouping by multiple keys, the group name will be a tuple:

In [43]: for name, group in df.groupby(['A', 'B']): ....: print(name) ....: print(group) ....: ('bar', 'one') A B C D 1 bar one -0.042379 -0.089329 ('bar', 'three') A B C D 3 bar three -0.00992 -0.945867 ('bar', 'two') A B C D 5 bar two 0.495767 1.95603 ('foo', 'one') A B C D 0 foo one -0.919854 -1.131345 6 foo one 0.362949 0.017587 ('foo', 'three') A B C D 7 foo three 1.548106 -0.016692 ('foo', 'two') A B C D 2 foo two 1.247642 0.337863 4 foo two 0.290213 -0.932132

It’s standard Python-fu but remember you can unpack the tuple in the for loop statement if you wish: for (k1, k2), group in grouped:.

Selecting a group

A single group can be selected using GroupBy.get_group():

In [44]: grouped.get_group('bar') Out[44]: A B C D 1 bar one -0.042379 -0.089329 3 bar three -0.009920 -0.945867 5 bar two 0.495767 1.956030

Or for an object grouped on multiple columns:

In [45]: df.groupby(['A', 'B']).get_group(('bar', 'one')) Out[45]: A B C D 1 bar one -0.042379 -0.089329

Aggregation

Once the GroupBy object has been created, several methods are available to perform a computation on the grouped data.

An obvious one is aggregation via the aggregate or equivalently agg method:

In [46]: grouped = df.groupby('A')

In [47]: grouped.aggregate(np.sum) Out[47]: C D A
bar 0.443469 0.920834 foo 2.529056 -1.724719

In [48]: grouped = df.groupby(['A', 'B'])

In [49]: grouped.aggregate(np.sum) Out[49]: C D A B
bar one -0.042379 -0.089329 three -0.009920 -0.945867 two 0.495767 1.956030 foo one -0.556905 -1.113758 three 1.548106 -0.016692 two 1.537855 -0.594269

As you can see, the result of the aggregation will have the group names as the new index along the grouped axis. In the case of multiple keys, the result is aMultiIndex by default, though this can be changed by using the as_index option:

In [50]: grouped = df.groupby(['A', 'B'], as_index=False)

In [51]: grouped.aggregate(np.sum) Out[51]: A B C D 0 bar one -0.042379 -0.089329 1 bar three -0.009920 -0.945867 2 bar two 0.495767 1.956030 3 foo one -0.556905 -1.113758 4 foo three 1.548106 -0.016692 5 foo two 1.537855 -0.594269

In [52]: df.groupby('A', as_index=False).sum() Out[52]: A C D 0 bar 0.443469 0.920834 1 foo 2.529056 -1.724719

Note that you could use the reset_index DataFrame function to achieve the same result as the column names are stored in the resulting MultiIndex:

In [53]: df.groupby(['A', 'B']).sum().reset_index() Out[53]: A B C D 0 bar one -0.042379 -0.089329 1 bar three -0.009920 -0.945867 2 bar two 0.495767 1.956030 3 foo one -0.556905 -1.113758 4 foo three 1.548106 -0.016692 5 foo two 1.537855 -0.594269

Another simple aggregation example is to compute the size of each group. This is included in GroupBy as the size method. It returns a Series whose index are the group names and whose values are the sizes of each group.

In [54]: grouped.size() Out[54]: A B
bar one 1 three 1 two 1 foo one 2 three 1 two 2 dtype: int64

In [55]: grouped.describe() Out[55]: C D 0 count 1.000000 1.000000 mean -0.042379 -0.089329 std NaN NaN min -0.042379 -0.089329 25% -0.042379 -0.089329 50% -0.042379 -0.089329 75% -0.042379 -0.089329 ... ... ... 5 mean 0.768928 -0.297134 std 0.677005 0.898022 min 0.290213 -0.932132 25% 0.529570 -0.614633 50% 0.768928 -0.297134 75% 1.008285 0.020364 max 1.247642 0.337863

[48 rows x 2 columns]

Note

Aggregation functions will not return the groups that you are aggregating over if they are named columns, when as_index=True, the default. The grouped columns will be the indices of the returned object.

Passing as_index=False will return the groups that you are aggregating over, if they are named columns.

Aggregating functions are ones that reduce the dimension of the returned objects, for example: mean, sum, size, count, std, var, sem, describe, first, last, nth, min, max. This is what happens when you do for example DataFrame.sum() and get back a Series.

nth can act as a reducer or a filter, see here

Applying multiple functions at once

With grouped Series you can also pass a list or dict of functions to do aggregation with, outputting a DataFrame:

In [56]: grouped = df.groupby('A')

In [57]: grouped['C'].agg([np.sum, np.mean, np.std]) Out[57]: sum mean std A
bar 0.443469 0.147823 0.301765 foo 2.529056 0.505811 0.966450

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.

In [58]: grouped['D'].agg({'result1' : np.sum, ....: 'result2' : np.mean}) ....: Out[58]: result2 result1 A
bar 0.306945 0.920834 foo -0.344944 -1.724719

On a grouped DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index:

In [59]: grouped.agg([np.sum, np.mean, np.std]) Out[59]: C D
sum mean std sum mean std A
bar 0.443469 0.147823 0.301765 0.920834 0.306945 1.490982 foo 2.529056 0.505811 0.966450 -1.724719 -0.344944 0.645875

Passing a dict of functions has different behavior by default, see the next section.

Applying different functions to DataFrame columns

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

In [60]: grouped.agg({'C' : np.sum, ....: 'D' : lambda x: np.std(x, ddof=1)}) ....: Out[60]: C D A
bar 0.443469 1.490982 foo 2.529056 0.645875

The function names can also be strings. In order for a string to be valid it must be either implemented on GroupBy or available via dispatching:

In [61]: grouped.agg({'C' : 'sum', 'D' : 'std'}) Out[61]: C D A
bar 0.443469 1.490982 foo 2.529056 0.645875

Cython-optimized aggregation functions

Some common aggregations, currently only sum, mean, std, and sem, have optimized Cython implementations:

In [62]: df.groupby('A').sum() Out[62]: C D A
bar 0.443469 0.920834 foo 2.529056 -1.724719

In [63]: df.groupby(['A', 'B']).mean() Out[63]: C D A B
bar one -0.042379 -0.089329 three -0.009920 -0.945867 two 0.495767 1.956030 foo one -0.278452 -0.556879 three 1.548106 -0.016692 two 0.768928 -0.297134

Of course sum and mean are implemented on pandas objects, so the above code would work even without the special versions via dispatching (see below).

Transformation

The transform method returns an object that is indexed the same (same size) as the one being grouped. Thus, the passed transform function should return a result that is the same size as the group chunk. For example, suppose we wished to standardize the data within each group:

In [64]: index = pd.date_range('10/1/1999', periods=1100)

In [65]: ts = pd.Series(np.random.normal(0.5, 2, 1100), index)

In [66]: ts = ts.rolling(window=100,min_periods=100).mean().dropna()

In [67]: ts.head() Out[67]: 2000-01-08 0.779333 2000-01-09 0.778852 2000-01-10 0.786476 2000-01-11 0.782797 2000-01-12 0.798110 Freq: D, dtype: float64

In [68]: ts.tail() Out[68]: 2002-09-30 0.660294 2002-10-01 0.631095 2002-10-02 0.673601 2002-10-03 0.709213 2002-10-04 0.719369 Freq: D, dtype: float64

In [69]: key = lambda x: x.year

In [70]: zscore = lambda x: (x - x.mean()) / x.std()

In [71]: transformed = ts.groupby(key).transform(zscore)

We would expect the result to now have mean 0 and standard deviation 1 within each group, which we can easily check:

Original Data

In [72]: grouped = ts.groupby(key)

In [73]: grouped.mean() Out[73]: 2000 0.442441 2001 0.526246 2002 0.459365 dtype: float64

In [74]: grouped.std() Out[74]: 2000 0.131752 2001 0.210945 2002 0.128753 dtype: float64

Transformed Data

In [75]: grouped_trans = transformed.groupby(key)

In [76]: grouped_trans.mean() Out[76]: 2000 -1.668427e-16 2001 2.609785e-16 2002 2.853714e-16 dtype: float64

In [77]: grouped_trans.std() Out[77]: 2000 1.0 2001 1.0 2002 1.0 dtype: float64

We can also visually compare the original and transformed data sets.

In [78]: compare = pd.DataFrame({'Original': ts, 'Transformed': transformed})

In [79]: compare.plot() Out[79]: <matplotlib.axes._subplots.AxesSubplot at 0x129f9d150>

_images/groupby_transform_plot.png

Another common data transform is to replace missing data with the group mean.

In [80]: data_df Out[80]: A B C 0 1.539708 -1.166480 0.533026 1 1.302092 -0.505754 NaN 2 -0.371983 1.104803 -0.651520 3 -1.309622 1.118697 -1.161657 4 -1.924296 0.396437 0.812436 5 0.815643 0.367816 -0.469478 6 -0.030651 1.376106 -0.645129 .. ... ... ... 993 0.012359 0.554602 -1.976159 994 0.042312 -1.628835 1.013822 995 -0.093110 0.683847 -0.774753 996 -0.185043 1.438572 NaN 997 -0.394469 -0.642343 0.011374 998 -1.174126 1.857148 NaN 999 0.234564 0.517098 0.393534

[1000 rows x 3 columns]

In [81]: countries = np.array(['US', 'UK', 'GR', 'JP'])

In [82]: key = countries[np.random.randint(0, 4, 1000)]

In [83]: grouped = data_df.groupby(key)

Non-NA count in each group

In [84]: grouped.count() Out[84]: A B C GR 209 217 189 JP 240 255 217 UK 216 231 193 US 239 250 217

In [85]: f = lambda x: x.fillna(x.mean())

In [86]: transformed = grouped.transform(f)

We can verify that the group means have not changed in the transformed data and that the transformed data contains no NAs.

In [87]: grouped_trans = transformed.groupby(key)

In [88]: grouped.mean() # original group means Out[88]: A B C GR -0.098371 -0.015420 0.068053 JP 0.069025 0.023100 -0.077324 UK 0.034069 -0.052580 -0.116525 US 0.058664 -0.020399 0.028603

In [89]: grouped_trans.mean() # transformation did not change group means Out[89]: A B C GR -0.098371 -0.015420 0.068053 JP 0.069025 0.023100 -0.077324 UK 0.034069 -0.052580 -0.116525 US 0.058664 -0.020399 0.028603

In [90]: grouped.count() # original has some missing data points Out[90]: A B C GR 209 217 189 JP 240 255 217 UK 216 231 193 US 239 250 217

In [91]: grouped_trans.count() # counts after transformation Out[91]: A B C GR 228 228 228 JP 267 267 267 UK 247 247 247 US 258 258 258

In [92]: grouped_trans.size() # Verify non-NA count equals group size Out[92]: GR 228 JP 267 UK 247 US 258 dtype: int64

Note

Some functions when applied to a groupby object will automatically transform the input, returning an object of the same shape as the original. Passing as_index=False will not affect these transformation methods.

For example: fillna, ffill, bfill, shift.

In [93]: grouped.ffill() Out[93]: A B C 0 1.539708 -1.166480 0.533026 1 1.302092 -0.505754 0.533026 2 -0.371983 1.104803 -0.651520 3 -1.309622 1.118697 -1.161657 4 -1.924296 0.396437 0.812436 5 0.815643 0.367816 -0.469478 6 -0.030651 1.376106 -0.645129 .. ... ... ... 993 0.012359 0.554602 -1.976159 994 0.042312 -1.628835 1.013822 995 -0.093110 0.683847 -0.774753 996 -0.185043 1.438572 -0.774753 997 -0.394469 -0.642343 0.011374 998 -1.174126 1.857148 -0.774753 999 0.234564 0.517098 0.393534

[1000 rows x 3 columns]

Filtration

New in version 0.12.

The filter method returns a subset of the original object. Suppose we want to take only elements that belong to groups with a group sum greater than 2.

In [94]: sf = pd.Series([1, 1, 2, 3, 3, 3])

In [95]: sf.groupby(sf).filter(lambda x: x.sum() > 2) Out[95]: 3 3 4 3 5 3 dtype: int64

The argument of filter must be a function that, applied to the group as a whole, returns True or False.

Another useful operation is filtering out elements that belong to groups with only a couple members.

In [96]: dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})

In [97]: dff.groupby('B').filter(lambda x: len(x) > 2) Out[97]: A B 2 2 b 3 3 b 4 4 b 5 5 b

Alternatively, instead of dropping the offending groups, we can return a like-indexed objects where the groups that do not pass the filter are filled with NaNs.

In [98]: dff.groupby('B').filter(lambda x: len(x) > 2, dropna=False) Out[98]: A B 0 NaN NaN 1 NaN NaN 2 2.0 b 3 3.0 b 4 4.0 b 5 5.0 b 6 NaN NaN 7 NaN NaN

For dataframes with multiple columns, filters should explicitly specify a column as the filter criterion.

In [99]: dff['C'] = np.arange(8)

In [100]: dff.groupby('B').filter(lambda x: len(x['C']) > 2) Out[100]: A B C 2 2 b 2 3 3 b 3 4 4 b 4 5 5 b 5

Note

Some functions when applied to a groupby object will act as a filter on the input, returning a reduced shape of the original (and potentially eliminating groups), but with the index unchanged. Passing as_index=False will not affect these transformation methods.

For example: head, tail.

In [101]: dff.groupby('B').head(2) Out[101]: A B C 0 0 a 0 1 1 a 1 2 2 b 2 3 3 b 3 6 6 c 6 7 7 c 7

Dispatching to instance methods

When doing an aggregation or transformation, you might just want to call an instance method on each data group. This is pretty easy to do by passing lambda functions:

In [102]: grouped = df.groupby('A')

In [103]: grouped.agg(lambda x: x.std()) Out[103]: C D A
bar 0.301765 1.490982 foo 0.966450 0.645875

But, it’s rather verbose and can be untidy if you need to pass additional arguments. Using a bit of metaprogramming cleverness, GroupBy now has the ability to “dispatch” method calls to the groups:

In [104]: grouped.std() Out[104]: C D A
bar 0.301765 1.490982 foo 0.966450 0.645875

What is actually happening here is that a function wrapper is being generated. When invoked, it takes any passed arguments and invokes the function with any arguments on each group (in the above example, the stdfunction). The results are then combined together much in the style of aggand transform (it actually uses apply to infer the gluing, documented next). This enables some operations to be carried out rather succinctly:

In [105]: tsdf = pd.DataFrame(np.random.randn(1000, 3), .....: index=pd.date_range('1/1/2000', periods=1000), .....: columns=['A', 'B', 'C']) .....:

In [106]: tsdf.ix[::2] = np.nan

In [107]: grouped = tsdf.groupby(lambda x: x.year)

In [108]: grouped.fillna(method='pad') Out[108]: A B C 2000-01-01 NaN NaN NaN 2000-01-02 -0.353501 -0.080957 -0.876864 2000-01-03 -0.353501 -0.080957 -0.876864 2000-01-04 0.050976 0.044273 -0.559849 2000-01-05 0.050976 0.044273 -0.559849 2000-01-06 0.030091 0.186460 -0.680149 2000-01-07 0.030091 0.186460 -0.680149 ... ... ... ... 2002-09-20 2.310215 0.157482 -0.064476 2002-09-21 2.310215 0.157482 -0.064476 2002-09-22 0.005011 0.053897 -1.026922 2002-09-23 0.005011 0.053897 -1.026922 2002-09-24 -0.456542 -1.849051 1.559856 2002-09-25 -0.456542 -1.849051 1.559856 2002-09-26 1.123162 0.354660 1.128135

[1000 rows x 3 columns]

In this example, we chopped the collection of time series into yearly chunks then independently called fillna on the groups.

New in version 0.14.1.

The nlargest and nsmallest methods work on Series style groupbys:

In [109]: s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3])

In [110]: g = pd.Series(list('abababab'))

In [111]: gb = s.groupby(g)

In [112]: gb.nlargest(3) Out[112]: a 4 19.0 0 9.0 2 7.0 b 1 8.0 3 5.0 7 3.3 dtype: float64

In [113]: gb.nsmallest(3) Out[113]: a 6 4.2 2 7.0 0 9.0 b 5 1.0 7 3.3 3 5.0 dtype: float64

Flexible apply

Some operations on the grouped data might not fit into either the aggregate or transform categories. Or, you may simply want GroupBy to infer how to combine the results. For these, use the apply function, which can be substituted for both aggregate and transform in many standard use cases. However,apply can handle some exceptional use cases, for example:

In [114]: df Out[114]: A B C D 0 foo one -0.919854 -1.131345 1 bar one -0.042379 -0.089329 2 foo two 1.247642 0.337863 3 bar three -0.009920 -0.945867 4 foo two 0.290213 -0.932132 5 bar two 0.495767 1.956030 6 foo one 0.362949 0.017587 7 foo three 1.548106 -0.016692

In [115]: grouped = df.groupby('A')

could also just call .describe()

In [116]: grouped['C'].apply(lambda x: x.describe()) Out[116]: A
bar count 3.000000 mean 0.147823 std 0.301765 min -0.042379 25% -0.026149 50% -0.009920 75% 0.242924 ...
foo mean 0.505811 std 0.966450 min -0.919854 25% 0.290213 50% 0.362949 75% 1.247642 max 1.548106 Name: C, dtype: float64

The dimension of the returned result can also change:

In [117]: grouped = df.groupby('A')['C']

In [118]: def f(group): .....: return pd.DataFrame({'original' : group, .....: 'demeaned' : group - group.mean()}) .....:

In [119]: grouped.apply(f) Out[119]: demeaned original 0 -1.425665 -0.919854 1 -0.190202 -0.042379 2 0.741831 1.247642 3 -0.157743 -0.009920 4 -0.215598 0.290213 5 0.347944 0.495767 6 -0.142862 0.362949 7 1.042295 1.548106

apply on a Series can operate on a returned value from the applied function, that is itself a series, and possibly upcast the result to a DataFrame

In [120]: def f(x): .....: return pd.Series([ x, x**2 ], index = ['x', 'x^2']) .....:

In [121]: s Out[121]: 0 9.0 1 8.0 2 7.0 3 5.0 4 19.0 5 1.0 6 4.2 7 3.3 dtype: float64

In [122]: s.apply(f) Out[122]: x x^2 0 9.0 81.00 1 8.0 64.00 2 7.0 49.00 3 5.0 25.00 4 19.0 361.00 5 1.0 1.00 6 4.2 17.64 7 3.3 10.89

Note

apply can act as a reducer, transformer, or filter function, depending on exactly what is passed to apply. So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in the output as well as set the indices.

Warning

In the current implementation apply calls func twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first group.

In [123]: d = pd.DataFrame({"a":["x", "y"], "b":[1,2]})

In [124]: def identity(df): .....: print df .....: return df .....:

In [125]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[125]: a b 0 x 1 1 y 2

Other useful features

Automatic exclusion of “nuisance” columns

Again consider the example DataFrame we’ve been looking at:

In [126]: df Out[126]: A B C D 0 foo one -0.919854 -1.131345 1 bar one -0.042379 -0.089329 2 foo two 1.247642 0.337863 3 bar three -0.009920 -0.945867 4 foo two 0.290213 -0.932132 5 bar two 0.495767 1.956030 6 foo one 0.362949 0.017587 7 foo three 1.548106 -0.016692

Supposed we wished to compute the standard deviation grouped by the Acolumn. There is a slight problem, namely that we don’t care about the data in column B. We refer to this as a “nuisance” column. If the passed aggregation function can’t be applied to some columns, the troublesome columns will be (silently) dropped. Thus, this does not pose any problems:

In [127]: df.groupby('A').std() Out[127]: C D A
bar 0.301765 1.490982 foo 0.966450 0.645875

NA and NaT group handling

If there are any NaN or NaT values in the grouping key, these will be automatically excluded. So there will never be an “NA group” or “NaT group”. This was not the case in older versions of pandas, but users were generally discarding the NA group anyway (and supporting it was an implementation headache).

Grouping with ordered factors

Categorical variables represented as instance of pandas’s Categorical class can be used as group keys. If so, the order of the levels will be preserved:

In [128]: data = pd.Series(np.random.randn(100))

In [129]: factor = pd.qcut(data, [0, .25, .5, .75, 1.])

In [130]: data.groupby(factor).mean() Out[130]: [-2.617, -0.684] -1.331461 (-0.684, -0.0232] -0.272816 (-0.0232, 0.541] 0.263607 (0.541, 2.369] 1.166038 dtype: float64

Grouping with a Grouper specification

You may need to specify a bit more data to properly group. You can use the pd.Grouper to provide this local control.

In [131]: import datetime

In [132]: df = pd.DataFrame({ .....: 'Branch' : 'A A A A A A A B'.split(), .....: 'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(), .....: 'Quantity': [1,3,5,1,8,1,9,3], .....: 'Date' : [ .....: datetime.datetime(2013,1,1,13,0), .....: datetime.datetime(2013,1,1,13,5), .....: datetime.datetime(2013,10,1,20,0), .....: datetime.datetime(2013,10,2,10,0), .....: datetime.datetime(2013,10,1,20,0), .....: datetime.datetime(2013,10,2,10,0), .....: datetime.datetime(2013,12,2,12,0), .....: datetime.datetime(2013,12,2,14,0), .....: ] .....: }) .....:

In [133]: df Out[133]: Branch Buyer Date Quantity 0 A Carl 2013-01-01 13:00:00 1 1 A Mark 2013-01-01 13:05:00 3 2 A Carl 2013-10-01 20:00:00 5 3 A Carl 2013-10-02 10:00:00 1 4 A Joe 2013-10-01 20:00:00 8 5 A Joe 2013-10-02 10:00:00 1 6 A Joe 2013-12-02 12:00:00 9 7 B Carl 2013-12-02 14:00:00 3

Groupby a specific column with the desired frequency. This is like resampling.

In [134]: df.groupby([pd.Grouper(freq='1M',key='Date'),'Buyer']).sum() Out[134]: Quantity Date Buyer
2013-01-31 Carl 1 Mark 3 2013-10-31 Carl 6 Joe 9 2013-12-31 Carl 3 Joe 9

You have an ambiguous specification in that you have a named index and a column that could be potential groupers.

In [135]: df = df.set_index('Date')

In [136]: df['Date'] = df.index + pd.offsets.MonthEnd(2)

In [137]: df.groupby([pd.Grouper(freq='6M',key='Date'),'Buyer']).sum() Out[137]: Quantity Date Buyer
2013-02-28 Carl 1 Mark 3 2014-02-28 Carl 9 Joe 18

In [138]: df.groupby([pd.Grouper(freq='6M',level='Date'),'Buyer']).sum() Out[138]: Quantity Date Buyer
2013-01-31 Carl 1 Mark 3 2014-01-31 Carl 9 Joe 18

Taking the first rows of each group

Just like for a DataFrame or Series you can call head and tail on a groupby:

In [139]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])

In [140]: df Out[140]: A B 0 1 2 1 1 4 2 5 6

In [141]: g = df.groupby('A')

In [142]: g.head(1) Out[142]: A B 0 1 2 2 5 6

In [143]: g.tail(1) Out[143]: A B 1 1 4 2 5 6

This shows the first or last n rows from each group.

Warning

Before 0.14.0 this was implemented with a fall-through apply, so the result would incorrectly respect the as_index flag:

g.head(1): # was equivalent to g.apply(lambda x: x.head(1)) A B A 1 0 1 2 5 2 5 6

Taking the nth row of each group

To select from a DataFrame or Series the nth item, use the nth method. This is a reduction method, and will return a single row (or no row) per group if you pass an int for n:

In [144]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])

In [145]: g = df.groupby('A')

In [146]: g.nth(0) Out[146]: B A
1 NaN 5 6.0

In [147]: g.nth(-1) Out[147]: B A
1 4.0 5 6.0

In [148]: g.nth(1) Out[148]: B A
1 4.0

If you want to select the nth not-null item, use the dropna kwarg. For a DataFrame this should be either 'any' or 'all' just like you would pass to dropna, for a Series this just needs to be truthy.

nth(0) is the same as g.first()

In [149]: g.nth(0, dropna='any') Out[149]: B A
1 4.0 5 6.0

In [150]: g.first() Out[150]: B A
1 4.0 5 6.0

nth(-1) is the same as g.last()

In [151]: g.nth(-1, dropna='any') # NaNs denote group exhausted when using dropna Out[151]: B A
1 4.0 5 6.0

In [152]: g.last() Out[152]: B A
1 4.0 5 6.0

In [153]: g.B.nth(0, dropna=True) Out[153]: A 1 4.0 5 6.0 Name: B, dtype: float64

As with other methods, passing as_index=False, will achieve a filtration, which returns the grouped row.

In [154]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])

In [155]: g = df.groupby('A',as_index=False)

In [156]: g.nth(0) Out[156]: A B 0 1 NaN 2 5 6.0

In [157]: g.nth(-1) Out[157]: A B 1 1 4.0 2 5 6.0

You can also select multiple rows from each group by specifying multiple nth values as a list of ints.

In [158]: business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B')

In [159]: df = pd.DataFrame(1, index=business_dates, columns=['a', 'b'])

get the first, 4th, and last date index for each month

In [160]: df.groupby((df.index.year, df.index.month)).nth([0, 3, -1]) Out[160]: a b 2014 4 1 1 4 1 1 4 1 1 5 1 1 5 1 1 5 1 1 6 1 1 6 1 1 6 1 1

Enumerate group items

New in version 0.13.0.

To see the order in which each row appears within its group, use thecumcount method:

In [161]: df = pd.DataFrame(list('aaabba'), columns=['A'])

In [162]: df Out[162]: A 0 a 1 a 2 a 3 b 4 b 5 a

In [163]: df.groupby('A').cumcount() Out[163]: 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64

In [164]: df.groupby('A').cumcount(ascending=False) # kwarg only Out[164]: 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64

Plotting

Groupby also works with some plotting methods. For example, suppose we suspect that some features in a DataFrame may differ by group, in this case, the values in column 1 where the group is “B” are 3 higher on average.

In [165]: np.random.seed(1234)

In [166]: df = pd.DataFrame(np.random.randn(50, 2))

In [167]: df['g'] = np.random.choice(['A', 'B'], size=50)

In [168]: df.loc[df['g'] == 'B', 1] += 3

We can easily visualize this with a boxplot:

In [169]: df.groupby('g').boxplot() Out[169]: OrderedDict([('A', {'boxes': [<matplotlib.lines.Line2D at 0x129abe210>, <matplotlib.lines.Line2D at 0x10ef39090>], 'caps': [<matplotlib.lines.Line2D at 0x129727350>, <matplotlib.lines.Line2D at 0x1214858d0>, <matplotlib.lines.Line2D at 0x11291c810>, <matplotlib.lines.Line2D at 0x129a7e290>], 'fliers': [<matplotlib.lines.Line2D at 0x129795150>, <matplotlib.lines.Line2D at 0x113a77c10>], 'means': [], 'medians': [<matplotlib.lines.Line2D at 0x1131b7610>, <matplotlib.lines.Line2D at 0x1213be590>], 'whiskers': [<matplotlib.lines.Line2D at 0x129abe150>, <matplotlib.lines.Line2D at 0x129924750>, <matplotlib.lines.Line2D at 0x12134ff90>, <matplotlib.lines.Line2D at 0x120cd4f10>]}), ('B', {'boxes': [<matplotlib.lines.Line2D at 0x129abe9d0>, <matplotlib.lines.Line2D at 0x129af1d90>], 'caps': [<matplotlib.lines.Line2D at 0x129c98110>, <matplotlib.lines.Line2D at 0x129c988d0>, <matplotlib.lines.Line2D at 0x1299ec510>, <matplotlib.lines.Line2D at 0x1299ecf50>], 'fliers': [<matplotlib.lines.Line2D at 0x1128ee850>, <matplotlib.lines.Line2D at 0x129d6ca90>], 'means': [], 'medians': [<matplotlib.lines.Line2D at 0x12138a790>, <matplotlib.lines.Line2D at 0x129961250>], 'whiskers': [<matplotlib.lines.Line2D at 0x129f56450>, <matplotlib.lines.Line2D at 0x129767750>, <matplotlib.lines.Line2D at 0x129a68410>, <matplotlib.lines.Line2D at 0x129a68e90>]})])

_images/groupby_boxplot.png

The result of calling boxplot is a dictionary whose keys are the values of our grouping column g (“A” and “B”). The values of the resulting dictionary can be controlled by the return_type keyword of boxplot. See the visualization documentation for more.

Warning

For historical reasons, df.groupby("g").boxplot() is not equivalent to df.boxplot(by="g"). See here for an explanation.

Examples

Regrouping by factor

Regroup columns of a DataFrame according to their sum, and sum the aggregated ones.

In [170]: df = pd.DataFrame({'a':[1,0,0], 'b':[0,1,0], 'c':[1,0,0], 'd':[2,3,4]})

In [171]: df Out[171]: a b c d 0 1 0 1 2 1 0 1 0 3 2 0 0 0 4

In [172]: df.groupby(df.sum(), axis=1).sum() Out[172]: 1 9 0 2 2 1 1 3 2 0 4

Returning a Series to propagate names

Group DataFrame columns, compute a set of metrics and return a named Series. The Series name is used as the name for the column index. This is especially useful in conjunction with reshaping operations such as stacking in which the column index name will be used as the name of the inserted column:

In [173]: df = pd.DataFrame({ .....: 'a': [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], .....: 'b': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1], .....: 'c': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], .....: 'd': [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], .....: }) .....:

In [174]: def compute_metrics(x): .....: result = {'b_sum': x['b'].sum(), 'c_mean': x['c'].mean()} .....: return pd.Series(result, name='metrics') .....:

In [175]: result = df.groupby('a').apply(compute_metrics)

In [176]: result Out[176]: metrics b_sum c_mean a
0 2.0 0.5 1 2.0 0.5 2 2.0 0.5

In [177]: result.stack() Out[177]: a metrics 0 b_sum 2.0 c_mean 0.5 1 b_sum 2.0 c_mean 0.5 2 b_sum 2.0 c_mean 0.5 dtype: float64