pandas.crosstab

pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False) [source]

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) margins_name : string, default ‘All’ Name of the row / column that will contain the totals when margins is True. New in version 0.21.0. 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 = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
>>> b = np.array(["one", "one", "one", "two", "one", "one",
...               "one", "two", "two", "two", "one"], dtype=object)
>>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
...               "shiny", "dull", "shiny", "shiny", "shiny"],
...               dtype=object)

>>> pd.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