Python中的 pandas.crosstab()函数

此方法用于计算两个（或更多）因素的简单交叉表。默认情况下，除非传递值数组和聚合函数，否则计算因子的频率表。

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.

以下是上述方法的实现以及一些示例：

示例 1：

Python3

# importing packages
import pandas
import numpy
  
# creating some data
a = 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 tab
pandas.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])

输出 ：

示例 2：

Python3

# importing package
import pandas
  
# create some data
foo = 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=False
pandas.crosstab(foo, bar, dropna=False)

输出 ：