pandas.crosstab

pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, dropna=True, normalize=False)

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

Parameters:

index : array-like, Series, or list of arrays/Series

Values to group by in the rows

columns : array-like, Series, or list of arrays/Series

Values to group by in the columns

values : array-like, optional

Array of values to aggregate according to the factors. Requires aggfunc be specified.

aggfunc : function, optional

If specified, requires values be specified as well

rownames : sequence, default None

If passed, 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)

dropna : boolean, default True

Do not include columns whose entries are all NaN

normalize : boolean, {‘all’, ‘index’, ‘columns’}, or {0,1}, default False

Normalize by dividing all values by the sum of values.

• If passed ‘all’ or True, will normalize over all values.
• If passed ‘index’ will normalize over each row.
• If passed ‘columns’ will normalize over each column.
• If margins is True, will also normalize margin values.

New in version 0.18.1.

Returns:

crosstab : DataFrame

Notes

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

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 the event that there aren’t overlapping indexes an empty DataFrame will be returned.

Examples

>>> a
array([foo, foo, foo, foo, bar, bar,
       bar, bar, foo, foo, foo], dtype=object)
>>> b
array([one, one, one, two, one, one,
       one, two, two, two, one], dtype=object)
>>> c
array([dull, dull, shiny, dull, dull, shiny,
       shiny, dull, shiny, shiny, shiny], dtype=object)

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

>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
>>> crosstab(foo, bar)  # 'c' and 'f' are not represented in the data,
                        # but they still will be counted in the output
col_0  d  e  f
row_0
a      1  0  0
b      0  1  0
c      0  0  0