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.

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.

aggfunc:function, optional

If specified, requires values be specified as well.

margins:bool, default False

Add row/column margins (subtotals).

margins_name:str, default ‘All’

Name of the row/column that will contain the totals when margins is True.

dropna:bool, default True

Do not include columns whose entries are all NaN.

normalize:bool, {‘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.

Returns

DataFrame

Cross tabulation of the data.

See also

DataFrame.pivot

Reshape data based on column values.

pivot_table

Create a pivot table as a 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

Here ‘c’ and ‘f’ are not represented in the data and will not be shown in the output because dropna is True by default. Set dropna=False to preserve categories with no data.

>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
>>> pd.crosstab(foo, bar)
col_0  d  e
row_0
a      1  0
b      0  1
>>> pd.crosstab(foo, bar, dropna=False)
col_0  d  e  f
row_0
a      1  0  0
b      0  1  0
c      0  0  0