# pandas.crosstab

pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', 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.

    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