pd crosstab 사용

In [1]:

from IPython.core.display import display, HTML
display(HTML("<style>.container {width:90% !important;}</style>"))

In [2]:

import seaborn as sns
import pandas as pd
pd.crosstab?

Signature:
pd.crosstab(
    index,
    columns,
    values=None,
    rownames=None,
    colnames=None,
    aggfunc=None,
    margins=False,
    margins_name='All',
    dropna=True,
    normalize=False,
)
Docstring:
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.

    .. versionadded:: 0.21.0

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.

    .. versionadded:: 0.18.1

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
File:      c:\users\user\.conda\envs\py38_64\envs\ds_37\lib\site-packages\pandas\core\reshape\pivot.py
Type:      function

In [3]:

df = sns.load_dataset('titanic')
df

Out[3]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

In [4]:

pd.crosstab(index = df['sex'], columns= df['alive'])

Out[4]:

alive	no	yes
sex
female	81	233
male	468	109

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