pandas的corsstab

 

pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, dropna=True, normalize=False)

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.

aggfunc : function, optional

If specified, requires values be specified as well

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

margins : boolean, default False

Add row/column margins (subtotals)

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.

In [1]:

import numpy as np
a = np.array(["foo", "foo", "foo", "foo", "bar", "bar","bar", "bar", "foo", "foo", "foo"], dtype=object)
a

In [2]:

b = np.array(["one", "one", "one", "two", "one", "one", "one", "two", "two", "two", "one"], dtype=object)
b

In [3]: 

pd.crosstab(a,b)

Out[3]:

col_0	one	two
row_0
bar	3	1
foo	4	3

In [4]:

 pd.crosstab(a, b, rownames=['a'], colnames=['b'])

 

Out[4]:

b	one	two
a
bar	3	1
foo	4	3

In [5] 

c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny","shiny", "dull", "shiny", "shiny", "shiny"],
               dtype=object)
c

In [6]:

import pandas as pd
pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])

Out[6]:

b	one		two
c	dull	shiny	dull	shiny
a
bar	1	2	1	0
foo	2	2	1	2

In [7]:

foo1 = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
bar1= pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
pd.crosstab(foo1, bar1,dropna='true')
# '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 

 

Out[7]:

col_0	d	e	f
row_0
a	1	0	0
b	0	1	0
c	0	0	0