【Pandas学习笔记Task05】：变形

import numpy as np
import pandas as pd

一、长宽表的变形

长表可以理解为某个特征单独作为列索引；而宽表可以以分组的思想去理解，值是其他特征的，但是这个值还包含着某个特征的影响在里面。

1.pivot

df = pd.DataFrame({
     'Class':[1,1,2,2],
                    'Name':['San Zhang','San Zhang','Si Li','Si Li'],
                     'Subject':['Chinese','Math','Chinese','Math'],
                      'Grade':[80,75,90,85]})# 从列索引名到数据的映射来构造数据框
df

	Class	Name	Subject	Grade
0	1	San Zhang	Chinese	80
1	1	San Zhang	Math	75
2	2	Si Li	Chinese	90
3	2	Si Li	Math	85

目标是将语文和数学分数作为列来展示。其实也就是将subject列的值作为索引然后来将grade进行对应填值。对于长变宽，要有变形后的行索引、需要转到列索引的列，以及行列索引对应的某个特征（Grade）的值。

df.pivot(index='Name', columns='Subject', values='Grade')

Subject	Chinese	Math
Name
San Zhang	80	75
Si Li	90	85

# df.iloc[1, 2] = 'Chinese'
df.pivot(index='Name', columns='Subject', values='Grade')
# 利用pivot要保证pivot后的行列索引对应的值都是唯一的。

Subject	Chinese	Math
Name
San Zhang	80	75
Si Li	90	85

df = pd.DataFrame({
     'Class':[1, 1, 2, 2, 1, 1, 2, 2],
                     'Name':['San Zhang', 'San Zhang', 'Si Li', 'Si Li',
                             'San Zhang', 'San Zhang', 'Si Li', 'Si Li'],
                     'Examination': ['Mid', 'Final', 'Mid', 'Final',
                                     'Mid', 'Final', 'Mid', 'Final'],
                      'Subject':['Chinese', 'Chinese', 'Chinese', 'Chinese',
                                'Math', 'Math', 'Math', 'Math'],
                     'Grade':[80, 75, 85, 65, 90, 85, 92, 88],
                     'rank':[10, 15, 21, 15, 20, 7, 6, 2]})
pivot_multi = df.pivot(index = ['Class','Name'],
                      columns = ['Subject','Examination'],
                      values = ['Grade', 'rank'])
pivot_multi

		Grade				rank
	Subject	Chinese		Math		Chinese		Math
	Examination	Mid	Final	Mid	Final	Mid	Final	Mid	Final
Class	Name
1	San Zhang	80	75	90	85	10	15	20	7
2	Si Li	85	65	92	88	21	15	6	2

2. pivot_table

pivot_table的使用是用于解决pivot只能对唯一性做变形。这个可以通过聚合将行列组合的多个值变为一个值。

df = pd.DataFrame({
     'Name':['San Zhang', 'San Zhang',
                             'San Zhang', 'San Zhang',
                             'Si Li', 'Si Li', 'Si Li', 'Si Li'],
                      'Subject':['Chinese', 'Chinese', 'Math', 'Math',
                                 'Chinese', 'Chinese', 'Math', 'Math'],
                     'Grade':[80, 90, 100, 90, 70, 80, 85, 95]})
df

	Name	Subject	Grade
0	San Zhang	Chinese	80
1	San Zhang	Chinese	90
2	San Zhang	Math	100
3	San Zhang	Math	90
4	Si Li	Chinese	70
5	Si Li	Chinese	80
6	Si Li	Math	85
7	Si Li	Math	95

df.pivot_table(index = 'Name',
              columns='Subject',
              values = 'Grade',
              aggfunc = 'mean')
# 这里aggfunc代表的是要使用的聚合函数
# 如果有重复值，而且未指定aggfunc，pivot_table默认就取平均

Subject	Chinese	Math
Name
San Zhang	85	95
Si Li	75	90

pivot_table还具有边际汇总，利用margins=True来实现。也就是将每一行再执行一次aggfunc

df.pivot_table(index = 'Name',
              columns='Subject',
              values = 'Grade',
              aggfunc = 'median',
              margins = True)

Subject	Chinese	Math	All
Name
San Zhang	85	95.0	90.0
Si Li	75	90.0	82.5
All	80	92.5	87.5

总体的汇总为新表中四个元素的平均值.如果是sum或者median，那么sum或者median的是原表的四个值

3.melt

melt可以理解为pivot的相应的逆操作，把宽表转为长表。

df = pd.DataFrame({
     'Class':[1,2],
                     'Name':['San Zhang', 'Si Li'],
                      'Chinese':[80, 90],
                      'Math':[80, 75]})
df_melted = df.melt(id_vars= ['Class','Name'],
                   value_vars= ['Chinese','Math'],
                   var_name='Subject',
                   value_name='Grade')
df_melted

	Class	Name	Subject	Grade
0	1	San Zhang	Chinese	80
1	2	Si Li	Chinese	90
2	1	San Zhang	Math	80
3	2	Si Li	Math	75

df_unmelted = df_melted.pivot(index = ['Class', 'Name'],
                                 columns='Subject',
                                 values='Grade')
df_unmelted

	Subject	Chinese	Math
Class	Name
1	San Zhang	80	80
2	Si Li	90	75

df_unmelted.reset_index().rename_axis(columns={
     'Subject':''})

	Class	Name	Chinese	Math
0	1	San Zhang	80	80
1	2	Si Li	90	75

4.wide_to_long

二、索引的变形

1.stack与unstack

将行列索引进行交换，维度会发生变化，这就属于变形。

df = pd.DataFrame(np.ones((4,2)),
                     index =pd.Index([('A', 'cat', 'big'),
                                        ('A', 'dog', 'small'),
                                        ('B', 'cat', 'big'),
                                        ('B', 'dog', 'small')]),
                      columns=['col_1', 'col_2'])
df.unstack()# 默认转化最内层，移动到列索引的最内层

		col_1		col_2
		big	small	big	small
A	cat	1.0	NaN	1.0	NaN
	dog	NaN	1.0	NaN	1.0
B	cat	1.0	NaN	1.0	NaN
	dog	NaN	1.0	NaN	1.0

# unstack也支持多层的转化
df.unstack([0,2]) # 这里不支持传切片

	col_1				col_2
	A		B		A		B
	big	small	big	small	big	small	big	small
cat	1.0	NaN	1.0	NaN	1.0	NaN	1.0	NaN
dog	NaN	1.0	NaN	1.0	NaN	1.0	NaN	1.0

三、其他变形函数

1.crosstab

对于crosstab，感觉还是用pivot_table方便。

df = pd.read_csv('data/learn_pandas.csv')
df.pivot_table(index = 'School',
              columns = 'Transfer',
              values = 'Name',
              aggfunc = 'count')

Transfer	N	Y
School
Fudan University	38.0	1.0
Peking University	28.0	2.0
Shanghai Jiao Tong University	53.0	NaN
Tsinghua University	62.0	4.0

2.explode

df_ex = pd.DataFrame({
     'A': [[1, 2],
                             'my_str',
                             {
     1, 2},
                             pd.Series([3, 4])],
                          'B': 1})
df_ex

	A	B
0	[1, 2]	1
1	my_str	1
2	{1, 2}	1
3	0 3 1 4 dtype: int64	1

df_ex.explode('A') # 对某一列纵向展开

	A	B
0	1	1
0	2	1
1	my_str	1
2	{1, 2}	1
3	3	1
3	4	1

pd.get_dummies(df.Grade).head()# 这是一个DataFrame，将某列的唯一值作为列索引，值0与1代表特征

	Freshman	Junior	Senior	Sophomore
0	1	0	0	0
1	1	0	0	0
2	0	0	1	0
3	0	0	0	1
4	0	0	0	1

四、练习

1.EX1：美国非法药物数据集

df = pd.read_csv('data/drugs.csv').sort_values(['State','COUNTY','SubstanceName'],ignore_index=True)
df.head()

	YYYY	State	COUNTY	SubstanceName	DrugReports
0	2011	KY	ADAIR	Buprenorphine	3
1	2012	KY	ADAIR	Buprenorphine	5
2	2013	KY	ADAIR	Buprenorphine	4
3	2014	KY	ADAIR	Buprenorphine	27
4	2015	KY	ADAIR	Buprenorphine	5

res = df.pivot(index=['State','COUNTY','SubstanceName'],
              columns='YYYY',
              values='DrugReports')
res = res.reset_index().rename_axis(columns={
     'YYYY':''})
res.head()

转换数据格式这里直接想到前面的pivot方法。（虽然想到了，但是应用起来还是不熟练，不得不返回去再看一遍）。而且还包含着要去对索引层名字的修改与重置。

	State	COUNTY	SubstanceName	2010	2011	2012	2013	2014	2015	2016	2017
0	KY	ADAIR	Buprenorphine	NaN	3.0	5.0	4.0	27.0	5.0	7.0	10.0
1	KY	ADAIR	Codeine	NaN	NaN	1.0	NaN	NaN	NaN	NaN	1.0
2	KY	ADAIR	Fentanyl	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN
3	KY	ADAIR	Heroin	NaN	NaN	1.0	2.0	NaN	1.0	NaN	2.0
4	KY	ADAIR	Hydrocodone	6.0	9.0	10.0	10.0	9.0	7.0	11.0	3.0

res_melted = res.melt(id_vars = ['State','COUNTY','SubstanceName'],
                         value_vars = res.columns[-8:],
                         var_name = 'YYYY',
                         value_name = 'DrugReports').dropna(
                         subset=['DrugReports'])
res_melted.head()

	State	COUNTY	SubstanceName	YYYY	DrugReports
4	KY	ADAIR	Hydrocodone	2010	6.0
6	KY	ADAIR	Methadone	2010	1.0
13	KY	ALLEN	Hydrocodone	2010	10.0
15	KY	ALLEN	Methadone	2010	4.0
17	KY	ALLEN	Oxycodone	2010	15.0

res_melted = res_melted[df.columns].sort_values([
                  'State','COUNTY','SubstanceName'],ignore_index=True
                  ).astype({
     'YYYY':'int64', 'DrugReports':'int64'})
res_melted.head()

	YYYY	State	COUNTY	SubstanceName	DrugReports
0	2011	KY	ADAIR	Buprenorphine	3
1	2012	KY	ADAIR	Buprenorphine	5
2	2013	KY	ADAIR	Buprenorphine	4
3	2014	KY	ADAIR	Buprenorphine	27
4	2015	KY	ADAIR	Buprenorphine	5

恢复的过程也就是对melt方法的应用。（这里对melt的参数运用还没有理解到位，于是就只能照猫画虎将实例的参数设置对应着搬过来用）。因为第1问的DrugReports格式被改为了float64，所以答案用astype方法将类型重置为int64.