Reshaping and Pivot Tables

Reshaping by pivoting DataFrame objects

Data is often stored in CSV files or databases in so-called “stacked” or “record” format:

In [1]: df
Out[1]: 
         date variable     value
0  2000-01-03        A  0.469112
1  2000-01-04        A -0.282863
2  2000-01-05        A -1.509059
3  2000-01-03        B -1.135632
4  2000-01-04        B  1.212112
5  2000-01-05        B -0.173215
6  2000-01-03        C  0.119209
7  2000-01-04        C -1.044236
8  2000-01-05        C -0.861849
9  2000-01-03        D -2.104569
10 2000-01-04        D -0.494929
11 2000-01-05        D  1.071804

For the curious here is how the above DataFrame was created:

import pandas.util.testing as tm; tm.N = 3
def unpivot(frame):
    N, K = frame.shape
    data = {'value' : frame.values.ravel('F'),
            'variable' : np.asarray(frame.columns).repeat(N),
            'date' : np.tile(np.asarray(frame.index), K)}
    return pd.DataFrame(data, columns=['date', 'variable', 'value'])
df = unpivot(tm.makeTimeDataFrame())

To select out everything for variable A we could do:

In [2]: df[df['variable'] == 'A']
Out[2]: 
        date variable     value
0 2000-01-03        A  0.469112
1 2000-01-04        A -0.282863
2 2000-01-05        A -1.509059

But suppose we wish to do time series operations with the variables. A better representation would be where the columns are the unique variables and an index of dates identifies individual observations. To reshape the data into this form, use the pivot function:

In [3]: df.pivot(index='date', columns='variable', values='value')
Out[3]: 
variable           A         B         C         D
date                                              
2000-01-03  0.469112 -1.135632  0.119209 -2.104569
2000-01-04 -0.282863  1.212112 -1.044236 -0.494929
2000-01-05 -1.509059 -0.173215 -0.861849  1.071804

If the values argument is omitted, and the input DataFrame has more than one column of values which are not used as column or index inputs to pivot, then the resulting “pivoted” DataFrame will have hierarchical columns whose topmost level indicates the respective value column:

In [4]: df['value2'] = df['value'] * 2

In [5]: pivoted = df.pivot('date', 'variable')

In [6]: pivoted
Out[6]: 
               value                                  value2            \
variable           A         B         C         D         A         B   
date                                                                     
2000-01-03  0.469112 -1.135632  0.119209 -2.104569  0.938225 -2.271265   
2000-01-04 -0.282863  1.212112 -1.044236 -0.494929 -0.565727  2.424224   
2000-01-05 -1.509059 -0.173215 -0.861849  1.071804 -3.018117 -0.346429   

                                
variable           C         D  
date                            
2000-01-03  0.238417 -4.209138  
2000-01-04 -2.088472 -0.989859  
2000-01-05 -1.723698  2.143608  

You of course can then select subsets from the pivoted DataFrame:

In [7]: pivoted['value2']
Out[7]: 
variable           A         B         C         D
date                                              
2000-01-03  0.938225 -2.271265  0.238417 -4.209138
2000-01-04 -0.565727  2.424224 -2.088472 -0.989859
2000-01-05 -3.018117 -0.346429 -1.723698  2.143608

Note that this returns a view on the underlying data in the case where the data are homogeneously-typed.

Reshaping by stacking and unstacking

Closely related to the pivot function are the related stack and unstack functions currently available on Series and DataFrame. These functions are designed to work together with MultiIndex objects (see the section on hierarchical indexing). Here are essentially what these functions do:

  • stack: “pivot” a level of the (possibly hierarchical) column labels, returning a DataFrame with an index with a new inner-most level of row labels.
  • unstack: inverse operation from stack: “pivot” a level of the (possibly hierarchical) row index to the column axis, producing a reshaped DataFrame with a new inner-most level of column labels.

The clearest way to explain is by example. Let’s take a prior example data set from the hierarchical indexing section:

In [8]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
   ...:                      'foo', 'foo', 'qux', 'qux'],
   ...:                     ['one', 'two', 'one', 'two',
   ...:                      'one', 'two', 'one', 'two']]))
   ...: 

In [9]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])

In [10]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])

In [11]: df2 = df[:4]

In [12]: df2
Out[12]: 
                     A         B
first second                    
bar   one     0.721555 -0.706771
      two    -1.039575  0.271860
baz   one    -0.424972  0.567020
      two     0.276232 -1.087401

The stack function “compresses” a level in the DataFrame’s columns to produce either:

  • A Series, in the case of a simple column Index
  • A DataFrame, in the case of a MultiIndex in the columns

If the columns have a MultiIndex, you can choose which level to stack. The stacked level becomes the new lowest level in a MultiIndex on the columns:

In [13]: stacked = df2.stack()

In [14]: stacked
Out[14]: 
first  second   
bar    one     A    0.721555
               B   -0.706771
       two     A   -1.039575
               B    0.271860
baz    one     A   -0.424972
               B    0.567020
       two     A    0.276232
               B   -1.087401
dtype: float64

With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack is unstack, which by default unstacks the last level:

In [15]: stacked.unstack()
Out[15]: 
                     A         B
first second                    
bar   one     0.721555 -0.706771
      two    -1.039575  0.271860
baz   one    -0.424972  0.567020
      two     0.276232 -1.087401

In [16]: stacked.unstack(1)
Out[16]: 
second        one       two
first                      
bar   A  0.721555 -1.039575
      B -0.706771  0.271860
baz   A -0.424972  0.276232
      B  0.567020 -1.087401

In [17]: stacked.unstack(0)
Out[17]: 
first          bar       baz
second                      
one    A  0.721555 -0.424972
       B -0.706771  0.567020
two    A -1.039575  0.276232
       B  0.271860 -1.087401

If the indexes have names, you can use the level names instead of specifying the level numbers:

In [18]: stacked.unstack('second')
Out[18]: 
second        one       two
first                      
bar   A  0.721555 -1.039575
      B -0.706771  0.271860
baz   A -0.424972  0.276232
      B  0.567020 -1.087401

Notice that the stack and unstack methods implicitly sort the index levels involved. Hence a call to stack and then unstack, or viceversa, will result in a sorted copy of the original DataFrame or Series:

In [19]: index = pd.MultiIndex.from_product([[2,1], ['a', 'b']])

In [20]: df = pd.DataFrame(np.random.randn(4), index=index, columns=['A'])

In [21]: df
Out[21]: 
            A
2 a -0.370647
  b -1.157892
1 a -1.344312
  b  0.844885

In [22]: all(df.unstack().stack() == df.sort_index())
Out[22]: True

while the above code will raise a TypeError if the call to sort_index is removed.

Multiple Levels

You may also stack or unstack more than one level at a time by passing a list of levels, in which case the end result is as if each level in the list were processed individually.

In [23]: columns = pd.MultiIndex.from_tuples([
   ....:         ('A', 'cat', 'long'), ('B', 'cat', 'long'),
   ....:         ('A', 'dog', 'short'), ('B', 'dog', 'short')
   ....:     ],
   ....:     names=['exp', 'animal', 'hair_length']
   ....: )
   ....: 

In [24]: df = pd.DataFrame(np.random.randn(4, 4), columns=columns)

In [25]: df
Out[25]: 
exp                 A         B         A         B
animal            cat       cat       dog       dog
hair_length      long      long     short     short
0            1.075770 -0.109050  1.643563 -1.469388
1            0.357021 -0.674600 -1.776904 -0.968914
2           -1.294524  0.413738  0.276662 -0.472035
3           -0.013960 -0.362543 -0.006154 -0.923061

In [26]: df.stack(level=['animal', 'hair_length'])
Out[26]: 
exp                          A         B
  animal hair_length                    
0 cat    long         1.075770 -0.109050
  dog    short        1.643563 -1.469388
1 cat    long         0.357021 -0.674600
  dog    short       -1.776904 -0.968914
2 cat    long        -1.294524  0.413738
  dog    short        0.276662 -0.472035
3 cat    long        -0.013960 -0.362543
  dog    short       -0.006154 -0.923061

The list of levels can contain either level names or level numbers (but not a mixture of the two).

# df.stack(level=['animal', 'hair_length'])
# from above is equivalent to:
In [27]: df.stack(level=[1, 2])
Out[27]: 
exp                          A         B
  animal hair_length                    
0 cat    long         1.075770 -0.109050
  dog    short        1.643563 -1.469388
1 cat    long         0.357021 -0.674600
  dog    short       -1.776904 -0.968914
2 cat    long        -1.294524  0.413738
  dog    short        0.276662 -0.472035
3 cat    long        -0.013960 -0.362543
  dog    short       -0.006154 -0.923061

Missing Data

These functions are intelligent about handling missing data and do not expect each subgroup within the hierarchical index to have the same set of labels. They also can handle the index being unsorted (but you can make it sorted by calling sort_index, of course). Here is a more complex example:

In [28]: columns = pd.MultiIndex.from_tuples([('A', 'cat'), ('B', 'dog'),
   ....:                                      ('B', 'cat'), ('A', 'dog')],
   ....:                                     names=['exp', 'animal'])
   ....: 

In [29]: index = pd.MultiIndex.from_product([('bar', 'baz', 'foo', 'qux'),
   ....:                                     ('one', 'two')],
   ....:                                    names=['first', 'second'])
   ....: 

In [30]: df = pd.DataFrame(np.random.randn(8, 4), index=index, columns=columns)

In [31]: df2 = df.iloc[[0, 1, 2, 4, 5, 7]]

In [32]: df2
Out[32]: 
exp                  A         B                   A
animal             cat       dog       cat       dog
first second                                        
bar   one     0.895717  0.805244 -1.206412  2.565646
      two     1.431256  1.340309 -1.170299 -0.226169
baz   one     0.410835  0.813850  0.132003 -0.827317
foo   one    -1.413681  1.607920  1.024180  0.569605
      two     0.875906 -2.211372  0.974466 -2.006747
qux   two    -1.226825  0.769804 -1.281247 -0.727707

As mentioned above, stack can be called with a level argument to select which level in the columns to stack:

In [33]: df2.stack('exp')
Out[33]: 
animal                 cat       dog
first second exp                    
bar   one    A    0.895717  2.565646
             B   -1.206412  0.805244
      two    A    1.431256 -0.226169
             B   -1.170299  1.340309
baz   one    A    0.410835 -0.827317
             B    0.132003  0.813850
foo   one    A   -1.413681  0.569605
             B    1.024180  1.607920
      two    A    0.875906 -2.006747
             B    0.974466 -2.211372
qux   two    A   -1.226825 -0.727707
             B   -1.281247  0.769804

In [34]: df2.stack('animal')
Out[34]: 
exp                         A         B
first second animal                    
bar   one    cat     0.895717 -1.206412
             dog     2.565646  0.805244
      two    cat     1.431256 -1.170299
             dog    -0.226169  1.340309
baz   one    cat     0.410835  0.132003
             dog    -0.827317  0.813850
foo   one    cat    -1.413681  1.024180
             dog     0.569605  1.607920
      two    cat     0.875906  0.974466
             dog    -2.006747 -2.211372
qux   two    cat    -1.226825 -1.281247
             dog    -0.727707  0.769804

Unstacking can result in missing values if subgroups do not have the same set of labels. By default, missing values will be replaced with the default fill value for that data type, NaN for float, NaT for datetimelike, etc. For integer types, by default data will converted to float and missing values will be set to NaN.

In [35]: df3 = df.iloc[[0, 1, 4, 7], [1, 2]]

In [36]: df3
Out[36]: 
exp                  B          
animal             dog       cat
first second                    
bar   one     0.805244 -1.206412
      two     1.340309 -1.170299
foo   one     1.607920  1.024180
qux   two     0.769804 -1.281247

In [37]: df3.unstack()
Out[37]: 
exp            B                              
animal       dog                 cat          
second       one       two       one       two
first                                         
bar     0.805244  1.340309 -1.206412 -1.170299
foo     1.607920       NaN  1.024180       NaN
qux          NaN  0.769804       NaN -1.281247

Alternatively, unstack takes an optional fill_value argument, for specifying the value of missing data.

In [38]: df3.unstack(fill_value=-1e9)
Out[38]: 
exp                B                                          
animal           dog                         cat              
second           one           two           one           two
first                                                         
bar     8.052440e-01  1.340309e+00 -1.206412e+00 -1.170299e+00
foo     1.607920e+00 -1.000000e+09  1.024180e+00 -1.000000e+09
qux    -1.000000e+09  7.698036e-01 -1.000000e+09 -1.281247e+00

With a MultiIndex

Unstacking when the columns are a MultiIndex is also careful about doing the right thing:

In [39]: df[:3].unstack(0)
Out[39]: 
exp            A                   B                                      A  \
animal       cat                 dog                cat                 dog   
first        bar       baz       bar      baz       bar       baz       bar   
second                                                                        
one     0.895717  0.410835  0.805244  0.81385 -1.206412  0.132003  2.565646   
two     1.431256       NaN  1.340309      NaN -1.170299       NaN -0.226169   

exp               
animal            
first        baz  
second            
one    -0.827317  
two          NaN  

In [40]: df2.unstack(1)
Out[40]: 
exp            A                   B                                       A  \
animal       cat                 dog                 cat                 dog   
second       one       two       one       two       one       two       one   
first                                                                          
bar     0.895717  1.431256  0.805244  1.340309 -1.206412 -1.170299  2.565646   
baz     0.410835       NaN  0.813850       NaN  0.132003       NaN -0.827317   
foo    -1.413681  0.875906  1.607920 -2.211372  1.024180  0.974466  0.569605   
qux          NaN -1.226825       NaN  0.769804       NaN -1.281247       NaN   

exp               
animal            
second       two  
first             
bar    -0.226169  
baz          NaN  
foo    -2.006747  
qux    -0.727707  

Reshaping by Melt

The top-level melt() and melt() functions are useful to massage a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are “unpivoted” to the row axis, leaving just two non-identifier columns, “variable” and “value”. The names of those columns can be customized by supplying the var_name and value_name parameters.

For instance,

In [41]: cheese = pd.DataFrame({'first' : ['John', 'Mary'],
   ....:                        'last' : ['Doe', 'Bo'],
   ....:                        'height' : [5.5, 6.0],
   ....:                        'weight' : [130, 150]})
   ....: 

In [42]: cheese
Out[42]: 
  first  height last  weight
0  John     5.5  Doe     130
1  Mary     6.0   Bo     150

In [43]: cheese.melt(id_vars=['first', 'last'])
Out[43]: 
  first last variable  value
0  John  Doe   height    5.5
1  Mary   Bo   height    6.0
2  John  Doe   weight  130.0
3  Mary   Bo   weight  150.0

In [44]: cheese.melt(id_vars=['first', 'last'], var_name='quantity')
Out[44]: 
  first last quantity  value
0  John  Doe   height    5.5
1  Mary   Bo   height    6.0
2  John  Doe   weight  130.0
3  Mary   Bo   weight  150.0

Another way to transform is to use the wide_to_long panel data convenience function.

In [45]: dft = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
   ....:                     "A1980" : {0 : "d", 1 : "e", 2 : "f"},
   ....:                     "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
   ....:                     "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
   ....:                     "X"     : dict(zip(range(3), np.random.randn(3)))
   ....:                    })
   ....: 

In [46]: dft["id"] = dft.index

In [47]: dft
Out[47]: 
  A1970 A1980  B1970  B1980         X  id
0     a     d    2.5    3.2 -0.121306   0
1     b     e    1.2    1.3 -0.097883   1
2     c     f    0.7    0.1  0.695775   2

In [48]: pd.wide_to_long(dft, ["A", "B"], i="id", j="year")
Out[48]: 
                X  A    B
id year                  
0  1970 -0.121306  a  2.5
1  1970 -0.097883  b  1.2
2  1970  0.695775  c  0.7
0  1980 -0.121306  d  3.2
1  1980 -0.097883  e  1.3
2  1980  0.695775  f  0.1

Combining with stats and GroupBy

It should be no shock that combining pivot / stack / unstack with GroupBy and the basic Series and DataFrame statistical functions can produce some very expressive and fast data manipulations.

In [49]: df
Out[49]: 
exp                  A         B                   A
animal             cat       dog       cat       dog
first second                                        
bar   one     0.895717  0.805244 -1.206412  2.565646
      two     1.431256  1.340309 -1.170299 -0.226169
baz   one     0.410835  0.813850  0.132003 -0.827317
      two    -0.076467 -1.187678  1.130127 -1.436737
foo   one    -1.413681  1.607920  1.024180  0.569605
      two     0.875906 -2.211372  0.974466 -2.006747
qux   one    -0.410001 -0.078638  0.545952 -1.219217
      two    -1.226825  0.769804 -1.281247 -0.727707

In [50]: df.stack().mean(1).unstack()
Out[50]: 
animal             cat       dog
first second                    
bar   one    -0.155347  1.685445
      two     0.130479  0.557070
baz   one     0.271419 -0.006733
      two     0.526830 -1.312207
foo   one    -0.194750  1.088763
      two     0.925186 -2.109060
qux   one     0.067976 -0.648927
      two    -1.254036  0.021048

# same result, another way
In [51]: df.groupby(level=1, axis=1).mean()
Out[51]: 
animal             cat       dog
first second                    
bar   one    -0.155347  1.685445
      two     0.130479  0.557070
baz   one     0.271419 -0.006733
      two     0.526830 -1.312207
foo   one    -0.194750  1.088763
      two     0.925186 -2.109060
qux   one     0.067976 -0.648927
      two    -1.254036  0.021048

In [52]: df.stack().groupby(level=1).mean()
Out[52]: 
exp            A         B
second                    
one     0.071448  0.455513
two    -0.424186 -0.204486

In [53]: df.mean().unstack(0)
Out[53]: 
exp            A         B
animal                    
cat     0.060843  0.018596
dog    -0.413580  0.232430

Pivot tables

While pivot provides general purpose pivoting of DataFrames with various data types (strings, numerics, etc.), Pandas also provides the pivot_table function for pivoting with aggregation of numeric data.

The function pandas.pivot_table can be used to create spreadsheet-style pivot tables. See the cookbook for some advanced strategies

It takes a number of arguments

  • data: A DataFrame object
  • values: a column or a list of columns to aggregate
  • index: a column, Grouper, array which has the same length as data, or list of them. Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.
  • columns: a column, Grouper, array which has the same length as data, or list of them. Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.
  • aggfunc: function to use for aggregation, defaulting to numpy.mean

Consider a data set like this:

In [54]: import datetime

In [55]: df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 6,
   ....:                    'B': ['A', 'B', 'C'] * 8,
   ....:                    'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
   ....:                    'D': np.random.randn(24),
   ....:                    'E': np.random.randn(24),
   ....:                    'F': [datetime.datetime(2013, i, 1) for i in range(1, 13)] +
   ....:                         [datetime.datetime(2013, i, 15) for i in range(1, 13)]})
   ....: 

In [56]: df
Out[56]: 
        A  B    C         D         E          F
0     one  A  foo  0.341734 -0.317441 2013-01-01
1     one  B  foo  0.959726 -1.236269 2013-02-01
2     two  C  foo -1.110336  0.896171 2013-03-01
3   three  A  bar -0.619976 -0.487602 2013-04-01
4     one  B  bar  0.149748 -0.082240 2013-05-01
5     one  C  bar -0.732339 -2.182937 2013-06-01
6     two  A  foo  0.687738  0.380396 2013-07-01
..    ... ..  ...       ...       ...        ...
17    one  C  bar -0.345352  0.206053 2013-06-15
18    two  A  foo  1.314232 -0.251905 2013-07-15
19  three  B  foo  0.690579 -2.213588 2013-08-15
20    one  C  foo  0.995761  1.063327 2013-09-15
21    one  A  bar  2.396780  1.266143 2013-10-15
22    two  B  bar  0.014871  0.299368 2013-11-15
23  three  C  bar  3.357427 -0.863838 2013-12-15

[24 rows x 6 columns]

We can produce pivot tables from this data very easily:

In [57]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
Out[57]: 
C             bar       foo
A     B                    
one   A  1.120915 -0.514058
      B -0.338421  0.002759
      C -0.538846  0.699535
three A -1.181568       NaN
      B       NaN  0.433512
      C  0.588783       NaN
two   A       NaN  1.000985
      B  0.158248       NaN
      C       NaN  0.176180

In [58]: pd.pivot_table(df, values='D', index=['B'], columns=['A', 'C'], aggfunc=np.sum)
Out[58]: 
A       one               three                 two          
C       bar       foo       bar       foo       bar       foo
B                                                            
A  2.241830 -1.028115 -2.363137       NaN       NaN  2.001971
B -0.676843  0.005518       NaN  0.867024  0.316495       NaN
C -1.077692  1.399070  1.177566       NaN       NaN  0.352360

In [59]: pd.pivot_table(df, values=['D','E'], index=['B'], columns=['A', 'C'], aggfunc=np.sum)
Out[59]: 
          D                                                           E  \
A       one               three                 two                 one   
C       bar       foo       bar       foo       bar       foo       bar   
B                                                                         
A  2.241830 -1.028115 -2.363137       NaN       NaN  2.001971  2.786113   
B -0.676843  0.005518       NaN  0.867024  0.316495       NaN  1.368280   
C -1.077692  1.399070  1.177566       NaN       NaN  0.352360 -1.976883   

                                                     
A               three                 two            
C       foo       bar       foo       bar       foo  
B                                                    
A -0.043211  1.922577       NaN       NaN  0.128491  
B -1.103384       NaN -2.128743 -0.194294       NaN  
C  1.495717 -0.263660       NaN       NaN  0.872482  

The result object is a DataFrame having potentially hierarchical indexes on the rows and columns. If the values column name is not given, the pivot table will include all of the data that can be aggregated in an additional level of hierarchy in the columns:

In [60]: pd.pivot_table(df, index=['A', 'B'], columns=['C'])
Out[60]: 
                D                   E          
C             bar       foo       bar       foo
A     B                                        
one   A  1.120915 -0.514058  1.393057 -0.021605
      B -0.338421  0.002759  0.684140 -0.551692
      C -0.538846  0.699535 -0.988442  0.747859
three A -1.181568       NaN  0.961289       NaN
      B       NaN  0.433512       NaN -1.064372
      C  0.588783       NaN -0.131830       NaN
two   A       NaN  1.000985       NaN  0.064245
      B  0.158248       NaN -0.097147       NaN
      C       NaN  0.176180       NaN  0.436241

Also, you can use Grouper for index and columns keywords. For detail of Grouper, see Grouping with a Grouper specification.

In [61]: pd.pivot_table(df, values='D', index=pd.Grouper(freq='M', key='F'), columns='C')
Out[61]: 
C                bar       foo
F                             
2013-01-31       NaN -0.514058
2013-02-28       NaN  0.002759
2013-03-31       NaN  0.176180
2013-04-30 -1.181568       NaN
2013-05-31 -0.338421       NaN
2013-06-30 -0.538846       NaN
2013-07-31       NaN  1.000985
2013-08-31       NaN  0.433512
2013-09-30       NaN  0.699535
2013-10-31  1.120915       NaN
2013-11-30  0.158248       NaN
2013-12-31  0.588783       NaN

You can render a nice output of the table omitting the missing values by calling to_string if you wish:

In [62]: table = pd.pivot_table(df, index=['A', 'B'], columns=['C'])

In [63]: print(table.to_string(na_rep=''))
                D                   E          
C             bar       foo       bar       foo
A     B                                        
one   A  1.120915 -0.514058  1.393057 -0.021605
      B -0.338421  0.002759  0.684140 -0.551692
      C -0.538846  0.699535 -0.988442  0.747859
three A -1.181568            0.961289          
      B            0.433512           -1.064372
      C  0.588783           -0.131830          
two   A            1.000985            0.064245
      B  0.158248           -0.097147          
      C            0.176180            0.436241

Note that pivot_table is also available as an instance method on DataFrame.

Adding margins

If you pass margins=True to pivot_table, special All columns and rows will be added with partial group aggregates across the categories on the rows and columns:

In [64]: df.pivot_table(index=['A', 'B'], columns='C', margins=True, aggfunc=np.std)
Out[64]: 
                D                             E                    
C             bar       foo       All       bar       foo       All
A     B                                                            
one   A  1.804346  1.210272  1.569879  0.179483  0.418374  0.858005
      B  0.690376  1.353355  0.898998  1.083825  0.968138  1.101401
      C  0.273641  0.418926  0.771139  1.689271  0.446140  1.422136
three A  0.794212       NaN  0.794212  2.049040       NaN  2.049040
      B       NaN  0.363548  0.363548       NaN  1.625237  1.625237
      C  3.915454       NaN  3.915454  1.035215       NaN  1.035215
two   A       NaN  0.442998  0.442998       NaN  0.447104  0.447104
      B  0.202765       NaN  0.202765  0.560757       NaN  0.560757
      C       NaN  1.819408  1.819408       NaN  0.650439  0.650439
All      1.556686  0.952552  1.246608  1.250924  0.899904  1.059389

Cross tabulations

Use the crosstab function to compute a cross-tabulation of two (or more) factors. By default crosstab computes a frequency table of the factors unless an array of values and an aggregation function are passed.

It takes a number of arguments

  • index: array-like, values to group by in the rows
  • columns: array-like, values to group by in the columns
  • values: array-like, optional, array of values to aggregate according to the factors
  • aggfunc: function, optional, If no values array is passed, computes a frequency table
  • rownames: sequence, default None, must match number of row arrays passed
  • colnames: sequence, default None, if passed, must match number of column arrays passed
  • margins: boolean, default False, Add row/column margins (subtotals)
  • normalize: boolean, {‘all’, ‘index’, ‘columns’}, or {0,1}, default False. Normalize by dividing all values by the sum of values.

Any Series passed will have their name attributes used unless row or column names for the cross-tabulation are specified

For example:

In [65]: foo, bar, dull, shiny, one, two = 'foo', 'bar', 'dull', 'shiny', 'one', 'two'

In [66]: a = np.array([foo, foo, bar, bar, foo, foo], dtype=object)

In [67]: b = np.array([one, one, two, one, two, one], dtype=object)

In [68]: c = np.array([dull, dull, shiny, dull, dull, shiny], dtype=object)

In [69]: pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
Out[69]: 
b    one        two      
c   dull shiny dull shiny
a                        
bar    1     0    0     1
foo    2     1    1     0

If crosstab receives only two Series, it will provide a frequency table.

In [70]: df = pd.DataFrame({'A': [1, 2, 2, 2, 2], 'B': [3, 3, 4, 4, 4],
   ....:                    'C': [1, 1, np.nan, 1, 1]})
   ....: 

In [71]: df
Out[71]: 
   A  B    C
0  1  3  1.0
1  2  3  1.0
2  2  4  NaN
3  2  4  1.0
4  2  4  1.0

In [72]: pd.crosstab(df.A, df.B)
Out[72]: 
B  3  4
A      
1  1  0
2  1  3

Any input passed containing Categorical data will have all of its categories included in the cross-tabulation, even if the actual data does not contain any instances of a particular category.

In [73]: foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])

In [74]: bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])

In [75]: pd.crosstab(foo, bar)
Out[75]: 
col_0  d  e  f
row_0         
a      1  0  0
b      0  1  0
c      0  0  0

Normalization

New in version 0.18.1.

Frequency tables can also be normalized to show percentages rather than counts using the normalize argument:

In [76]: pd.crosstab(df.A, df.B, normalize=True)
Out[76]: 
B    3    4
A          
1  0.2  0.0
2  0.2  0.6

normalize can also normalize values within each row or within each column:

In [77]: pd.crosstab(df.A, df.B, normalize='columns')
Out[77]: 
B    3    4
A          
1  0.5  0.0
2  0.5  1.0

crosstab can also be passed a third Series and an aggregation function (aggfunc) that will be applied to the values of the third Series within each group defined by the first two Series:

In [78]: pd.crosstab(df.A, df.B, values=df.C, aggfunc=np.sum)
Out[78]: 
B    3    4
A          
1  1.0  NaN
2  1.0  2.0

Adding Margins

Finally, one can also add margins or normalize this output.

In [79]: pd.crosstab(df.A, df.B, values=df.C, aggfunc=np.sum, normalize=True,
   ....:             margins=True)
   ....: 
Out[79]: 
B       3    4   All
A                   
1    0.25  0.0  0.25
2    0.25  0.5  0.75
All  0.50  0.5  1.00

Tiling

The cut function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables:

In [80]: ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])

In [81]: pd.cut(ages, bins=3)
Out[81]: 
[(9.95, 26.667], (9.95, 26.667], (9.95, 26.667], (9.95, 26.667], (9.95, 26.667], (9.95, 26.667], (26.667, 43.333], (43.333, 60.0], (43.333, 60.0]]
Categories (3, interval[float64]): [(9.95, 26.667] < (26.667, 43.333] < (43.333, 60.0]]

If the bins keyword is an integer, then equal-width bins are formed. Alternatively we can specify custom bin-edges:

In [82]: c = pd.cut(ages, bins=[0, 18, 35, 70])

In [83]: c
Out[83]: 
[(0, 18], (0, 18], (0, 18], (0, 18], (18, 35], (18, 35], (18, 35], (35, 70], (35, 70]]
Categories (3, interval[int64]): [(0, 18] < (18, 35] < (35, 70]]

New in version 0.20.0.

If the bins keyword is an IntervalIndex, then these will be used to bin the passed data.

pd.cut([25, 20, 50], bins=c.categories)

Computing indicator / dummy variables

To convert a categorical variable into a “dummy” or “indicator” DataFrame, for example a column in a DataFrame (a Series) which has k distinct values, can derive a DataFrame containing k columns of 1s and 0s:

In [84]: df = pd.DataFrame({'key': list('bbacab'), 'data1': range(6)})

In [85]: pd.get_dummies(df['key'])
Out[85]: 
   a  b  c
0  0  1  0
1  0  1  0
2  1  0  0
3  0  0  1
4  1  0  0
5  0  1  0

Sometimes it’s useful to prefix the column names, for example when merging the result with the original DataFrame:

In [86]: dummies = pd.get_dummies(df['key'], prefix='key')

In [87]: dummies
Out[87]: 
   key_a  key_b  key_c
0      0      1      0
1      0      1      0
2      1      0      0
3      0      0      1
4      1      0      0
5      0      1      0

In [88]: df[['data1']].join(dummies)
Out[88]: 
   data1  key_a  key_b  key_c
0      0      0      1      0
1      1      0      1      0
2      2      1      0      0
3      3      0      0      1
4      4      1      0      0
5      5      0      1      0

This function is often used along with discretization functions like cut:

In [89]: values = np.random.randn(10)

In [90]: values
Out[90]: 
array([ 0.4082, -1.0481, -0.0257, -0.9884,  0.0941,  1.2627,  1.29  ,
        0.0824, -0.0558,  0.5366])

In [91]: bins = [0, 0.2, 0.4, 0.6, 0.8, 1]

In [92]: pd.get_dummies(pd.cut(values, bins))
Out[92]: 
   (0.0, 0.2]  (0.2, 0.4]  (0.4, 0.6]  (0.6, 0.8]  (0.8, 1.0]
0           0           0           1           0           0
1           0           0           0           0           0
2           0           0           0           0           0
3           0           0           0           0           0
4           1           0           0           0           0
5           0           0           0           0           0
6           0           0           0           0           0
7           1           0           0           0           0
8           0           0           0           0           0
9           0           0           1           0           0

See also Series.str.get_dummies.

New in version 0.15.0.

get_dummies() also accepts a DataFrame. By default all categorical variables (categorical in the statistical sense, those with object or categorical dtype) are encoded as dummy variables.

In [93]: df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
   ....:                    'C': [1, 2, 3]})
   ....: 

In [94]: pd.get_dummies(df)
Out[94]: 
   C  A_a  A_b  B_b  B_c
0  1    1    0    0    1
1  2    0    1    0    1
2  3    1    0    1    0

All non-object columns are included untouched in the output.

You can control the columns that are encoded with the columns keyword.

In [95]: pd.get_dummies(df, columns=['A'])
Out[95]: 
   B  C  A_a  A_b
0  c  1    1    0
1  c  2    0    1
2  b  3    1    0

Notice that the B column is still included in the output, it just hasn’t been encoded. You can drop B before calling get_dummies if you don’t want to include it in the output.

As with the Series version, you can pass values for the prefix and prefix_sep. By default the column name is used as the prefix, and ‘_’ as the prefix separator. You can specify prefix and prefix_sep in 3 ways

  • string: Use the same value for prefix or prefix_sep for each column to be encoded
  • list: Must be the same length as the number of columns being encoded.
  • dict: Mapping column name to prefix
In [96]: simple = pd.get_dummies(df, prefix='new_prefix')

In [97]: simple
Out[97]: 
   C  new_prefix_a  new_prefix_b  new_prefix_b  new_prefix_c
0  1             1             0             0             1
1  2             0             1             0             1
2  3             1             0             1             0

In [98]: from_list = pd.get_dummies(df, prefix=['from_A', 'from_B'])

In [99]: from_list
Out[99]: 
   C  from_A_a  from_A_b  from_B_b  from_B_c
0  1         1         0         0         1
1  2         0         1         0         1
2  3         1         0         1         0

In [100]: from_dict = pd.get_dummies(df, prefix={'B': 'from_B', 'A': 'from_A'})

In [101]: from_dict
Out[101]: 
   C  from_A_a  from_A_b  from_B_b  from_B_c
0  1         1         0         0         1
1  2         0         1         0         1
2  3         1         0         1         0

New in version 0.18.0.

Sometimes it will be useful to only keep k-1 levels of a categorical variable to avoid collinearity when feeding the result to statistical models. You can switch to this mode by turn on drop_first.

In [102]: s = pd.Series(list('abcaa'))

In [103]: pd.get_dummies(s)
Out[103]: 
   a  b  c
0  1  0  0
1  0  1  0
2  0  0  1
3  1  0  0
4  1  0  0

In [104]: pd.get_dummies(s, drop_first=True)
Out[104]: 
   b  c
0  0  0
1  1  0
2  0  1
3  0  0
4  0  0

When a column contains only one level, it will be omitted in the result.

In [105]: df = pd.DataFrame({'A':list('aaaaa'),'B':list('ababc')})

In [106]: pd.get_dummies(df)
Out[106]: 
   A_a  B_a  B_b  B_c
0    1    1    0    0
1    1    0    1    0
2    1    1    0    0
3    1    0    1    0
4    1    0    0    1

In [107]: pd.get_dummies(df, drop_first=True)
Out[107]: 
   B_b  B_c
0    0    0
1    1    0
2    0    0
3    1    0
4    0    1

Factorizing values

To encode 1-d values as an enumerated type use factorize:

In [108]: x = pd.Series(['A', 'A', np.nan, 'B', 3.14, np.inf])

In [109]: x
Out[109]: 
0       A
1       A
2     NaN
3       B
4    3.14
5     inf
dtype: object

In [110]: labels, uniques = pd.factorize(x)

In [111]: labels
Out[111]: array([ 0,  0, -1,  1,  2,  3])

In [112]: uniques
Out[112]: Index(['A', 'B', 3.14, inf], dtype='object')

Note that factorize is similar to numpy.unique, but differs in its handling of NaN:

Note

The following numpy.unique will fail under Python 3 with a TypeError because of an ordering bug. See also Here

In [2]: pd.factorize(x, sort=True)
Out[2]:
(array([ 2,  2, -1,  3,  0,  1]),
 Index([3.14, inf, u'A', u'B'], dtype='object'))

In [3]: np.unique(x, return_inverse=True)[::-1]
Out[3]: (array([3, 3, 0, 4, 1, 2]), array([nan, 3.14, inf, 'A', 'B'], dtype=object))

Note

If you just want to handle one column as a categorical variable (like R’s factor), you can use df["cat_col"] = pd.Categorical(df["col"]) or df["cat_col"] = df["col"].astype("category"). For full docs on Categorical, see the Categorical introduction and the API documentation. This feature was introduced in version 0.15.

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