pandas 之 交叉表-透视表

import numpy as np 
import pandas as pd 

认识

A pivot table is a data summarization tool(数据汇总工具) frequently found in spreadsheet programs and other data analysis software(广泛应用于数据分析中). It aggregates a table of data by one or more keys, arranging the data in a rectangle(矩形) with some of the group keys along the rows and some along the columns.
Pivot tables in Python with pandas are made possible through the groupby facility(促进) described in this chapter combined with reshape operations utilizing hierarchical indexing.
DataFrame has a pivot_table method, and there is also a top-level pandas.pivot_table function. In addition to providing a convenience interface to groupby, pivot_table can add partial totals , also known as margins.

Returning to the tipping dataset, suppose you wanted to compute a table of group means(the default pivot_table aggregation type) arranged by day and smoker on the rows: (对分组计算组内平均)

tips = pd.read_csv('../examples/tips.csv')

"新增一列 tip_pct"

tips['tip_pct'] = tips['tip'] / tips['total_bill']

tips[:6]

'新增一列 tip_pct'

	total_bill	tip	smoker	day	time	size	tip_pct
0	16.99	1.01	No	Sun	Dinner	2	0.059447
1	10.34	1.66	No	Sun	Dinner	3	0.160542
2	21.01	3.50	No	Sun	Dinner	3	0.166587
3	23.68	3.31	No	Sun	Dinner	2	0.139780
4	24.59	3.61	No	Sun	Dinner	4	0.146808
5	25.29	4.71	No	Sun	Dinner	4	0.186240

"默认的aggregation 是 mean"
tips.pivot_table(index=['day', 'smoker'])

'默认的aggregation 是 mean'

		size	tip	tip_pct	total_bill
day	smoker
Fri	No	2.250000	2.812500	0.151650	18.420000
	Yes	2.066667	2.714000	0.174783	16.813333
Sat	No	2.555556	3.102889	0.158048	19.661778
	Yes	2.476190	2.875476	0.147906	21.276667
Sun	No	2.929825	3.167895	0.160113	20.506667
	Yes	2.578947	3.516842	0.187250	24.120000
Thur	No	2.488889	2.673778	0.160298	17.113111
	Yes	2.352941	3.030000	0.163863	19.190588

This could have been produced with groupby directly. Now, suppose we want to aggregate only tip_pct and size, and additionally group by time. I'll put smoker in the table columns and day in the rows:

tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],
                 columns='smoker')

		size		tip_pct
	smoker	No	Yes	No	Yes
time	day
Dinner	Fri	2.000000	2.222222	0.139622	0.165347
	Sat	2.555556	2.476190	0.158048	0.147906
	Sun	2.929825	2.578947	0.160113	0.187250
	Thur	2.000000	NaN	0.159744	NaN
Lunch	Fri	3.000000	1.833333	0.187735	0.188937
	Thur	2.500000	2.352941	0.160311	0.163863

We could augment this table to include partial totals by passing margins=True. This has the effect of adding all row and column labels, with corresponding values being the group statistics for all the data within a single tier:

tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],
                columns='smoker', margins=True)

		size			tip_pct
	smoker	No	Yes	All	No	Yes	All
time	day
Dinner	Fri	2.000000	2.222222	2.166667	0.139622	0.165347	0.158916
	Sat	2.555556	2.476190	2.517241	0.158048	0.147906	0.153152
	Sun	2.929825	2.578947	2.842105	0.160113	0.187250	0.166897
	Thur	2.000000	NaN	2.000000	0.159744	NaN	0.159744
Lunch	Fri	3.000000	1.833333	2.000000	0.187735	0.188937	0.188765
	Thur	2.500000	2.352941	2.459016	0.160311	0.163863	0.161301
All		2.668874	2.408602	2.569672	0.159328	0.163196	0.160803

Here, the All values are means without taking into account smoker versus non-smoker or any of the two levels of grouping on the rows.

To use a different aggregation function, pass it to aggfunc. For example, count or len will give you a cross-tabulation of group sizes:

tips.pivot_table('tip_pct', index=['time', 'smoker'],
                columns='day', aggfunc=len, margins=True)

	day	Fri	Sat	Sun	Thur	All
time	smoker
Dinner	No	3.0	45.0	57.0	1.0	106.0
	Yes	9.0	42.0	19.0	NaN	70.0
Lunch	No	1.0	NaN	NaN	44.0	45.0
	Yes	6.0	NaN	NaN	17.0	23.0
All		19.0	87.0	76.0	62.0	244.0

If some combinations are empty, you may wish to pass a fill_value

tips.pivot_table('tip_pct', index=['time', 'size', 'smoker'],
                columns='day', aggfunc='mean', fill_value=0)

		day	Fri	Sat	Sun	Thur
time	size	smoker
Dinner	1	No	0.000000	0.137931	0.000000	0.000000
		Yes	0.000000	0.325733	0.000000	0.000000
	2	No	0.139622	0.162705	0.168859	0.159744
		Yes	0.171297	0.148668	0.207893	0.000000
	3	No	0.000000	0.154661	0.152663	0.000000
		Yes	0.000000	0.144995	0.152660	0.000000
	4	No	0.000000	0.150096	0.148143	0.000000
		Yes	0.117750	0.124515	0.193370	0.000000
	5	No	0.000000	0.000000	0.206928	0.000000
		Yes	0.000000	0.106572	0.065660	0.000000
	6	No	0.000000	0.000000	0.103799	0.000000
Lunch	1	No	0.000000	0.000000	0.000000	0.181728
		Yes	0.223776	0.000000	0.000000	0.000000
	2	No	0.000000	0.000000	0.000000	0.166005
		Yes	0.181969	0.000000	0.000000	0.158843
	3	No	0.187735	0.000000	0.000000	0.084246
		Yes	0.000000	0.000000	0.000000	0.204952
	4	No	0.000000	0.000000	0.000000	0.138919
		Yes	0.000000	0.000000	0.000000	0.155410
	5	No	0.000000	0.000000	0.000000	0.121389
	6	No	0.000000	0.000000	0.000000	0.173706

See Table 10-2 for a summary of pivot_table methods.

function anme	Description
values	Column name or names to aggregate; 默认聚合所有的数值列
index	Column names or other group keys to group on the rows of the resulting pivot table
columns	Column names or other group keys to group on the columns of the result pivot table
aggfunc	Aggregation function or list of function(默认是mean); can be any function valid in a groupby context
fill_value	Replace missing values in result table
dropna	If True, do not include columns whose entries are all NA
margins	Add row/column subtotals and grand total

交叉表: Crosstab

• 是透视表的一部分, aggfunc=count而已
  A cross-tabulation (or crosstab for short) is a special case of a pivot table that computes group frequencies.Here is an example:

As part of some survey analysis, we might want to summarize this data nationality and handedness. You could use pivot_table to do this, but the pandas.crosstab function can be more convenient:

pd.crosstab(data.Nationality, data.Handedness, margins=True)

The first two arguments to crosstab can each either be an array or Series or a list of arrays. As in the tips data:

"根据 day, time 对 smoker 进行统计"
pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)

'根据 day, time 对 smoker 进行统计'

	smoker	No	Yes	All
time	day
Dinner	Fri	3	9	12
	Sat	45	42	87
	Sun	57	19	76
	Thur	1	0	1
Lunch	Fri	1	6	7
	Thur	44	17	61
All		151	93	244

小结

Mastering pandas's data grouping tools can help both with data cleaning as well as modeling or statistical analysis work.
(熟练掌握 groupby 对 数据清洗, 建模统计等都是有认识和实操方面的帮助的.)