# Group By: split-apply-combine

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

> - Splitting the data into groups based on some criteria
> - Applying a function to each group independently
> - Combining the results into a data structure

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:

> - Aggregation: computing a summary statistic (or statistics) about each group. Some examples:

>   > - Compute group sums or means
>   > - Compute group sizes / counts

> - Transformation: perform some group-specific computations and return a like-indexed. Some examples:

>   > - Standardizing data (zscore) within group
>   > - Filling NAs within groups with a value derived from each group

> - Filtration: discard some groups, according to a group-wise computation that evaluates True or False. Some examples:

>   > - Discarding data that belongs to groups with only a few members
>   > - Filtering out data based on the group sum or mean

> - Some combination of the above: GroupBy will examine the results of the apply step and try to return a sensibly combined result if it doesn’t fit into either of the above two categories

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:

> - A Python function, to be called on each of the axis labels
> - A list or NumPy array of the same length as the selected axis
> - A dict or Series, providing a `label -> group name` mapping
> - For DataFrame objects, a string indicating a column to be used to group. Of course `df.groupby('A')` is just syntactic sugar for `df.groupby(df['A'])`, but it makes life simpler
> - A list of any of the above things

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.<TAB>
    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 0x7f0952125a90>

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 a MultiIndex 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 = pd.rolling_mean(ts, 100, 100).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   -2.460118e-16
    2001    2.609785e-16
    2002    2.853714e-16
    dtype: float64

    In [77]: grouped_trans.std()
    Out[77]:
    2000    1
    2001    1
    2002    1
    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 0x7f0952102a50>

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    b
    3   3    b
    4   4    b
    5   5    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 potentitally 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 `std` function). The results are then combined together much in the style of `agg` and `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
    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^s'])
       .....:

    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^s
    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 `A` column. 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

    In [147]: g.nth(-1)
    Out[147]:
       B
    A
    1  4
    5  6

    In [148]: g.nth(1)
    Out[148]:
       B
    A
    1  4

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
    5  6

    In [150]: g.first()
    Out[150]:
       B
    A
    1  4
    5  6

    # 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
    5  6

    In [152]: g.last()
    Out[152]:
       B
    A
    1  4
    5  6

    In [153]: g.B.nth(0, dropna=True)
    Out[153]:
    A
    1    4
    5    6
    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

    In [157]: g.nth(-1)
    Out[157]:
       A  B
    1  1  4
    2  5  6

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
    2014-04-01  1  1
    2014-04-04  1  1
    2014-04-30  1  1
    2014-05-01  1  1
    2014-05-06  1  1
    2014-05-30  1  1
    2014-06-02  1  1
    2014-06-05  1  1
    2014-06-30  1  1

### Enumerate group items

New in version 0.13.0.

To see the order in which each row appears within its group, use the `cumcount` 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 my 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 object at 0x7f094e7d3d90>, <matplotlib.lines.Line2D object at 0x7f094eceb1d0>], 'fliers': [<matplotlib.lines.Line2D object at 0x7f094e88b950>, <matplotlib.lines.Line2D object at 0x7f095e1d31d0>], 'medians': [<matplotlib.lines.Line2D object at 0x7f095d8eccd0>, <matplotlib.lines.Line2D object at 0x7f095e123f50>], 'means': [], 'whiskers': [<matplotlib.lines.Line2D object at 0x7f094e7d3210>, <matplotlib.lines.Line2D object at 0x7f095e1e9750>, <matplotlib.lines.Line2D object at 0x7f094ed92f90>, <matplotlib.lines.Line2D object at 0x7f095e141fd0>], 'caps': [<matplotlib.lines.Line2D object at 0x7f094e7a3f50>, <matplotlib.lines.Line2D object at 0x7f095354c790>, <matplotlib.lines.Line2D object at 0x7f094e965450>, <matplotlib.lines.Line2D object at 0x7f094f266e90>]}), ('B', {'boxes': [<matplotlib.lines.Line2D object at 0x7f094e657ad0>, <matplotlib.lines.Line2D object at 0x7f094ebf14d0>], 'fliers': [<matplotlib.lines.Line2D object at 0x7f094f1e3650>, <matplotlib.lines.Line2D object at 0x7f094eb26ed0>], 'medians': [<matplotlib.lines.Line2D object at 0x7f0970e54310>, <matplotlib.lines.Line2D object at 0x7f094e99c710>], 'means': [], 'whiskers': [<matplotlib.lines.Line2D object at 0x7f094ebbbcd0>, <matplotlib.lines.Line2D object at 0x7f094e8b9b50>, <matplotlib.lines.Line2D object at 0x7f094ec08990>, <matplotlib.lines.Line2D object at 0x7f094e9dc090>], 'caps': [<matplotlib.lines.Line2D object at 0x7f094e8b9cd0>, <matplotlib.lines.Line2D object at 0x7f095e1c8a10>, <matplotlib.lines.Line2D object at 0x7f094ea3b5d0>, <matplotlib.lines.Line2D object at 0x7f095e1d1f50>]})])

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     0.5
    1            2     0.5
    2            2     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