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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 : 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'])
... # doctest: +NORMALIZE_WHITESPACE
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,
                        # and will not be shown in the output because
                        # dropna is True by default. Set 'dropna=False'
                        # to preserve categories with no data
... # doctest: +SKIP
col_0  d  e
row_0
a      1  0
b      0  1
>>> crosstab(foo, bar, dropna=False)  # 'c' and 'f' are not represented
                        # in the data, but they still will be counted
                        # and shown in the output
... # doctest: +SKIP
col_0  d  e  f
row_0
a      1  0  0
b      0  1  0
c      0  0  0
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