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

[12 rows x 3 columns]

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 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

[3 rows x 3 columns]

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

[3 rows x 4 columns]

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  

[3 rows x 8 columns]

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

[3 rows x 4 columns]

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 = MultiIndex.from_tuples(tuples, names=['first', 'second'])

In [10]: df = DataFrame(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

[4 rows x 2 columns]

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

[4 rows x 2 columns]

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

[4 rows x 2 columns]

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

[4 rows x 2 columns]

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

[4 rows x 2 columns]

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.

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 sortlevel, of course). Here is a more complex example:

In [19]: columns = MultiIndex.from_tuples([('A', 'cat'), ('B', 'dog'),
   ....:                                   ('B', 'cat'), ('A', 'dog')],
   ....:                                  names=['exp', 'animal'])
   ....: 

In [20]: df = DataFrame(randn(8, 4), index=index, columns=columns)

In [21]: df2 = df.ix[[0, 1, 2, 4, 5, 7]]

In [22]: df2
Out[22]: 
exp                  A         B                   A
animal             cat       dog       cat       dog
first second                                        
bar   one    -0.370647 -1.157892 -1.344312  0.844885
      two     1.075770 -0.109050  1.643563 -1.469388
baz   one     0.357021 -0.674600 -1.776904 -0.968914
foo   one    -0.013960 -0.362543 -0.006154 -0.923061
      two     0.895717  0.805244 -1.206412  2.565646
qux   two     0.410835  0.813850  0.132003 -0.827317

[6 rows x 4 columns]

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

In [23]: df2.stack('exp')
Out[23]: 
animal                 cat       dog
first second exp                    
bar   one    A   -0.370647  0.844885
             B   -1.344312 -1.157892
      two    A    1.075770 -1.469388
             B    1.643563 -0.109050
baz   one    A    0.357021 -0.968914
             B   -1.776904 -0.674600
foo   one    A   -0.013960 -0.923061
             B   -0.006154 -0.362543
      two    A    0.895717  2.565646
             B   -1.206412  0.805244
qux   two    A    0.410835 -0.827317
             B    0.132003  0.813850

[12 rows x 2 columns]

In [24]: df2.stack('animal')
Out[24]: 
exp                         A         B
first second animal                    
bar   one    cat    -0.370647 -1.344312
             dog     0.844885 -1.157892
      two    cat     1.075770  1.643563
             dog    -1.469388 -0.109050
baz   one    cat     0.357021 -1.776904
             dog    -0.968914 -0.674600
foo   one    cat    -0.013960 -0.006154
             dog    -0.923061 -0.362543
      two    cat     0.895717 -1.206412
             dog     2.565646  0.805244
qux   two    cat     0.410835  0.132003
             dog    -0.827317  0.813850

[12 rows x 2 columns]

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

In [25]: df[:3].unstack(0)
Out[25]: 
exp            A                   B                                     A  \
animal       cat                 dog               cat                 dog   
first        bar       baz       bar     baz       bar       baz       bar   
second                                                                       
one    -0.370647  0.357021 -1.157892 -0.6746 -1.344312 -1.776904  0.844885   
two     1.075770       NaN -0.109050     NaN  1.643563       NaN -1.469388   

exp               
animal            
first        baz  
second            
one    -0.968914  
two          NaN  

[2 rows x 8 columns]

In [26]: df2.unstack(1)
Out[26]: 
exp            A                   B                                       A  \
animal       cat                 dog                 cat                 dog   
second       one       two       one       two       one       two       one   
first                                                                          
bar    -0.370647  1.075770 -1.157892 -0.109050 -1.344312  1.643563  0.844885   
baz     0.357021       NaN -0.674600       NaN -1.776904       NaN -0.968914   
foo    -0.013960  0.895717 -0.362543  0.805244 -0.006154 -1.206412 -0.923061   
qux          NaN  0.410835       NaN  0.813850       NaN  0.132003       NaN   

exp               
animal            
second       two  
first             
bar    -1.469388  
baz          NaN  
foo     2.565646  
qux    -0.827317  

[4 rows x 8 columns]

Reshaping by Melt

The melt function found in pandas.core.reshape is useful to massage a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are “pivoted” 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 [27]: cheese = DataFrame({'first' : ['John', 'Mary'],
   ....:                     'last' : ['Doe', 'Bo'],
   ....:                     'height' : [5.5, 6.0],
   ....:                     'weight' : [130, 150]})
   ....: 

In [28]: cheese
Out[28]: 
  first  height last  weight
0  John     5.5  Doe     130
1  Mary     6.0   Bo     150

[2 rows x 4 columns]

In [29]: melt(cheese, id_vars=['first', 'last'])
Out[29]: 
  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

[4 rows x 4 columns]

In [30]: melt(cheese, id_vars=['first', 'last'], var_name='quantity')
Out[30]: 
  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

[4 rows x 4 columns]

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

In [31]: 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 [32]: dft["id"] = dft.index

In [33]: dft
Out[33]: 
  A1970 A1980  B1970  B1980         X  id
0     a     d    2.5    3.2 -0.076467   0
1     b     e    1.2    1.3 -1.187678   1
2     c     f    0.7    0.1  1.130127   2

[3 rows x 6 columns]

In [34]: pd.wide_to_long(dft, ["A", "B"], i="id", j="year")
Out[34]: 
                X  A    B
id year                  
0  1970 -0.076467  a  2.5
1  1970 -1.187678  b  1.2
2  1970  1.130127  c  0.7
0  1980 -0.076467  d  3.2
1  1980 -1.187678  e  1.3
2  1980  1.130127  f  0.1

[6 rows x 3 columns]

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 [35]: df
Out[35]: 
exp                  A         B                   A
animal             cat       dog       cat       dog
first second                                        
bar   one    -0.370647 -1.157892 -1.344312  0.844885
      two     1.075770 -0.109050  1.643563 -1.469388
baz   one     0.357021 -0.674600 -1.776904 -0.968914
      two    -1.294524  0.413738  0.276662 -0.472035
foo   one    -0.013960 -0.362543 -0.006154 -0.923061
      two     0.895717  0.805244 -1.206412  2.565646
qux   one     1.431256  1.340309 -1.170299 -0.226169
      two     0.410835  0.813850  0.132003 -0.827317

[8 rows x 4 columns]

In [36]: df.stack().mean(1).unstack()
Out[36]: 
animal             cat       dog
first second                    
bar   one    -0.857479 -0.156504
      two     1.359666 -0.789219
baz   one    -0.709942 -0.821757
      two    -0.508931 -0.029148
foo   one    -0.010057 -0.642802
      two    -0.155347  1.685445
qux   one     0.130479  0.557070
      two     0.271419 -0.006733

[8 rows x 2 columns]

# same result, another way
In [37]: df.groupby(level=1, axis=1).mean()
Out[37]: 
animal             cat       dog
first second                    
bar   one    -0.857479 -0.156504
      two     1.359666 -0.789219
baz   one    -0.709942 -0.821757
      two    -0.508931 -0.029148
foo   one    -0.010057 -0.642802
      two    -0.155347  1.685445
qux   one     0.130479  0.557070
      two     0.271419 -0.006733

[8 rows x 2 columns]

In [38]: df.stack().groupby(level=1).mean()
Out[38]: 
exp            A         B
second                    
one     0.016301 -0.644049
two     0.110588  0.346200

[2 rows x 2 columns]

In [39]: df.mean().unstack(0)
Out[39]: 
exp            A         B
animal                    
cat     0.311433 -0.431481
dog    -0.184544  0.133632

[2 rows x 2 columns]

Pivot tables and cross-tabulations

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
  • rows: list of columns to group by on the table rows
  • cols: list of columns to group by on the table columns
  • aggfunc: function to use for aggregation, defaulting to numpy.mean

Consider a data set like this:

In [40]: df = 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)})
   ....: 

In [41]: df
Out[41]: 
        A  B    C         D         E
0     one  A  foo -1.436737  0.149748
1     one  B  foo -1.413681 -0.732339
2     two  C  foo  1.607920  0.687738
3   three  A  bar  1.024180  0.176444
4     one  B  bar  0.569605  0.403310
5     one  C  bar  0.875906 -0.154951
6     two  A  foo -2.211372  0.301624
7   three  B  foo  0.974466 -2.179861
8     one  C  foo -2.006747 -1.369849
9     one  A  bar -0.410001 -0.954208
10    two  B  bar -0.078638  1.462696
11  three  C  bar  0.545952 -1.743161
12    one  A  foo -1.219217 -0.826591
13    one  B  foo -1.226825 -0.345352
14    two  C  foo  0.769804  1.314232
      ... ..  ...       ...       ...

[24 rows x 5 columns]

We can produce pivot tables from this data very easily:

In [42]: pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
Out[42]: 
C             bar       foo
A     B                    
one   A  0.274863 -1.327977
      B -0.079051 -1.320253
      C  0.377300 -0.832506
three A -0.128534       NaN
      B       NaN  0.835120
      C -0.037012       NaN
two   A       NaN -1.154627
      B -0.594487       NaN
      C       NaN  1.188862

[9 rows x 2 columns]

In [43]: pivot_table(df, values='D', rows=['B'], cols=['A', 'C'], aggfunc=np.sum)
Out[43]: 
A       one               three                 two          
C       bar       foo       bar       foo       bar       foo
B                                                            
A  0.549725 -2.655954 -0.257067       NaN       NaN -2.309255
B -0.158102 -2.640506       NaN  1.670241 -1.188974       NaN
C  0.754600 -1.665013 -0.074024       NaN       NaN  2.377724

[3 rows x 6 columns]

In [44]: pivot_table(df, values=['D','E'], rows=['B'], cols=['A', 'C'], aggfunc=np.sum)
Out[44]: 
          D                                                           E  \
A       one               three                 two                 one   
C       bar       foo       bar       foo       bar       foo       bar   
B                                                                         
A  0.549725 -2.655954 -0.257067       NaN       NaN -2.309255 -2.190477   
B -0.158102 -2.640506       NaN  1.670241 -1.188974       NaN  1.399070   
C  0.754600 -1.665013 -0.074024       NaN       NaN  2.377724  2.241830   

                                                     
A               three                 two            
C       foo       bar       foo       bar       foo  
B                                                    
A -0.676843  0.867024       NaN       NaN  0.316495  
B -1.077692       NaN  1.177566  2.358867       NaN  
C -1.687290 -2.230762       NaN       NaN  2.001971  

[3 rows x 12 columns]

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 [45]: pivot_table(df, rows=['A', 'B'], cols=['C'])
Out[45]: 
                D                   E          
C             bar       foo       bar       foo
A     B                                        
one   A  0.274863 -1.327977 -1.095238 -0.338421
      B -0.079051 -1.320253  0.699535 -0.538846
      C  0.377300 -0.832506  1.120915 -0.843645
three A -0.128534       NaN  0.433512       NaN
      B       NaN  0.835120       NaN  0.588783
      C -0.037012       NaN -1.115381       NaN
two   A       NaN -1.154627       NaN  0.158248
      B -0.594487       NaN  1.179433       NaN
      C       NaN  1.188862       NaN  1.000985

[9 rows x 4 columns]

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

In [46]: table = pivot_table(df, rows=['A', 'B'], cols=['C'])

In [47]: print(table.to_string(na_rep=''))
                D                   E          
C             bar       foo       bar       foo
A     B                                        
one   A  0.274863 -1.327977 -1.095238 -0.338421
      B -0.079051 -1.320253  0.699535 -0.538846
      C  0.377300 -0.832506  1.120915 -0.843645
three A -0.128534            0.433512          
      B            0.835120            0.588783
      C -0.037012           -1.115381          
two   A           -1.154627            0.158248
      B -0.594487            1.179433          
      C            1.188862            1.000985

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

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

  • rows: array-like, values to group by in the rows
  • cols: 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)

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

For example:

In [48]: foo, bar, dull, shiny, one, two = 'foo', 'bar', 'dull', 'shiny', 'one', 'two'

In [49]: a = np.array([foo, foo, bar, bar, foo, foo], dtype=object)

In [50]: b = np.array([one, one, two, one, two, one], dtype=object)

In [51]: c = np.array([dull, dull, shiny, dull, dull, shiny], dtype=object)

In [52]: crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
Out[52]: 
b     one          two       
c    dull  shiny  dull  shiny
a                            
bar     1      0     0      1
foo     2      1     1      0

[2 rows x 4 columns]

Adding margins (partial aggregates)

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 [53]: df.pivot_table(rows=['A', 'B'], cols='C', margins=True, aggfunc=np.std)
Out[53]: 
                D                             E                    
C             bar       foo       All       bar       foo       All
A     B                                                            
one   A  0.968543  0.153810  1.084870  0.199447  0.690376  0.602542
      B  0.917338  0.132127  0.894343  0.418926  0.273641  0.771139
      C  0.705136  1.660627  1.254131  1.804346  0.744165  1.598848
three A  1.630183       NaN  1.630183  0.363548       NaN  0.363548
      B       NaN  0.197065  0.197065       NaN  3.915454  3.915454
      C  0.824435       NaN  0.824435  0.887815       NaN  0.887815
two   A       NaN  1.494463  1.494463       NaN  0.202765  0.202765
      B  0.729521       NaN  0.729521  0.400594       NaN  0.400594
      C       NaN  0.592638  0.592638       NaN  0.442998  0.442998
All      0.816058  1.294620  1.055572  1.190502  1.403041  1.249705

[10 rows x 6 columns]

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

In [55]: cut(ages, bins=3)
Out[55]: 
   (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]
     (43.333, 60]
Levels (3): Index(['(9.95, 26.667]', '(26.667, 43.333]', '(43.333, 60]'], dtype=object)

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

In [56]: cut(ages, bins=[0, 18, 35, 70])
Out[56]: 
  (0, 18]
  (0, 18]
  (0, 18]
  (0, 18]
 (18, 35]
 (18, 35]
 (18, 35]
 (35, 70]
 (35, 70]
Levels (3): Index(['(0, 18]', '(18, 35]', '(35, 70]'], dtype=object)

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 [57]: df = DataFrame({'key': list('bbacab'), 'data1': range(6)})

In [58]: get_dummies(df['key'])
Out[58]: 
   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

[6 rows x 3 columns]

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

In [59]: dummies = get_dummies(df['key'], prefix='key')

In [60]: dummies
Out[60]: 
   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

[6 rows x 3 columns]

In [61]: df[['data1']].join(dummies)
Out[61]: 
   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

[6 rows x 4 columns]

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

In [62]: values = randn(10)

In [63]: values
Out[63]: 
array([-0.0822, -2.1829,  0.3804,  0.0848,  0.4324,  1.52  , -0.4937,
        0.6002,  0.2742,  0.1329])

In [64]: bins = [0, 0.2, 0.4, 0.6, 0.8, 1]

In [65]: get_dummies(cut(values, bins))
Out[65]: 
   (0, 0.2]  (0.2, 0.4]  (0.4, 0.6]  (0.6, 0.8]
0         0           0           0           0
1         0           0           0           0
2         0           1           0           0
3         1           0           0           0
4         0           0           1           0
5         0           0           0           0
6         0           0           0           0
7         0           0           0           1
8         0           1           0           0
9         1           0           0           0

[10 rows x 4 columns]

See also get_dummies().

Factorizing values

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

In [66]: x = pd.Series(['A', 'A', np.nan, 'B', 3.14, np.inf])

In [67]: x
Out[67]: 
0       A
1       A
2     NaN
3       B
4    3.14
5     inf
dtype: object

In [68]: labels, uniques = pd.factorize(x)

In [69]: labels
Out[69]: array([ 0,  0, -1,  1,  2,  3])

In [70]: uniques
Out[70]: array(['A', 'B', 3.14, inf], dtype=object)

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

In [71]: pd.factorize(x, sort=True)
Out[71]: (array([ 2,  2, -1,  3,  0,  1]), array([3.14, inf, 'A', 'B'], dtype=object))

In [72]: np.unique(x, return_inverse=True)[::-1]
Out[72]: (array([3, 3, 0, 4, 1, 2]), array([nan, 3.14, inf, 'A', 'B'], dtype=object))