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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)

  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:
    indexarray-like, Series, or list of arrays/Series

      Values to group by in the rows.

    columnsarray-like, Series, or list of arrays/Series

      Values to group by in the columns.

    valuesarray-like, optional

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

    rownamessequence, default None

      If passed, must match number of row arrays passed.

    colnamessequence, default None

      If passed, must match number of column arrays passed.

    aggfuncfunction, optional

      If specified, requires values be specified as well.

    marginsbool, default False

      Add row/column margins (subtotals).

    margins_namestr, default ‘All’

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

    dropnabool, default True

      Do not include columns whose entries are all NaN.

    normalizebool, {‘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.

  Reference the user guide for more examples.

  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