This method is used to 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.
Syntax: pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name=’All’, dropna=True, normalize=False)
Arguments :
- 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.
Below is the implementation of the above method with some examples :
Example 1 :
Python3
# importing packagesimport pandasimport numpy # creating some dataa = numpy.array(["foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar", "foo", "foo", "foo"], dtype=object) b = numpy.array(["one", "one", "one", "two", "one", "one", "one", "two", "two", "two", "one"], dtype=object) c = numpy.array(["dull", "dull", "shiny", "dull", "dull", "shiny", "shiny", "dull", "shiny", "shiny", "shiny"], dtype=object) # form the cross tabpandas.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c']) |
Output :
Example 2 :
Python3
# importing packageimport pandas # create some datafoo = pandas.Categorical(['a', 'b'], categories=['a', 'b', 'c']) bar = pandas.Categorical(['d', 'e'], categories=['d', 'e', 'f']) # form crosstab with dropna=True (default)pandas.crosstab(foo, bar) # form crosstab with dropna=Falsepandas.crosstab(foo, bar, dropna=False) |
Output :
