<>第4章 变形
import numpy as np import pandas as pd df = pd.read_csv('data/table.csv') df.
head()
School Class ID Gender Address Height Weight Math Physics
0 S_1 C_1 1101 M street_1 173 63 34.0 A+
1 S_1 C_1 1102 F street_2 192 73 32.5 B+
2 S_1 C_1 1103 M street_2 186 82 87.2 B+
3 S_1 C_1 1104 F street_2 167 81 80.4 B-
4 S_1 C_1 1105 F street_4 159 64 84.8 B+
<>一、透视表

<>1. pivot

<>
一般状态下,数据在DataFrame会以压缩(stacked)状态存放,例如上面的Gender,两个类别被叠在一列中,pivot函数可将某一列作为新的cols:
df.pivot(index='ID',columns='Gender',values='Height').head()
Gender F M
ID
1101 NaN 173.0
1102 192.0 NaN
1103 NaN 186.0
1104 167.0 NaN
1105 159.0 NaN
<>然而pivot函数具有很强的局限性,除了功能上较少之外,还不允许values中出现重复的行列索引对(pair),例如下面的语句就会报错:
#df.pivot(index='School',columns='Gender',values='Height').head()
<>因此,更多的时候会选择使用强大的pivot_table函数

<>2. pivot_table

<>首先,再现上面的操作:
pd.pivot_table(df,index='ID',columns='Gender',values='Height').head()
Gender F M
ID
1101 NaN 173.0
1102 192.0 NaN
1103 NaN 186.0
1104 167.0 NaN
1105 159.0 NaN
<>由于功能更多,速度上自然是比不上原来的pivot函数:
%timeit df.pivot(index='ID',columns='Gender',values='Height') %timeit pd.
pivot_table(df,index='ID',columns='Gender',values='Height') 2.28 ms ± 74.8 µs
per loop (mean ± std. dev. of 7 runs, 100 loops each) 9.77 ms ± 498 µs per loop
(mean ± std. dev. of 7 runs, 100 loops each)
<>Pandas中提供了各种选项,下面介绍常用参数:

<>① aggfunc:对组内进行聚合统计,可传入各类函数,默认为’mean’
pd.pivot_table(df,index='School',columns='Gender',values='Height',aggfunc=[
'mean','sum']).head()
mean sum
Gender F M F M
School
S_1 173.125000 178.714286 1385 1251
S_2 173.727273 172.000000 1911 1548
<>② margins:汇总边际状态
pd.pivot_table(df,index='School',columns='Gender',values='Height',aggfunc=[
'mean','sum'],margins=True).head() #margins_name可以设置名字,默认为'All'
mean sum
Gender F M All F M All
School
S_1 173.125000 178.714286 175.733333 1385 1251 2636
S_2 173.727273 172.000000 172.950000 1911 1548 3459
All 173.473684 174.937500 174.142857 3296 2799 6095
<>③ 行、列、值都可以为多级
pd.pivot_table(df,index=['School','Class'], columns=['Gender','Address'],
values=['Height','Weight'])
Height ... Weight
Gender F M ... F M
Address street_1 street_2 street_4 street_5 street_6 street_7 street_1 street_2
street_4 street_5 ... street_4 street_5 street_6 street_7 street_1 street_2
street_4 street_5 street_6 street_7
School Class
S_1 C_1 NaN 179.5 159.0 NaN NaN NaN 173.0 186.0 NaN NaN ... 64.0 NaN NaN NaN
63.0 82.0 NaN NaN NaN NaN
C_2 NaN NaN 176.0 162.0 167.0 NaN NaN NaN NaN 188.0 ... 94.0 63.0 63.0 NaN NaN
NaN NaN 68.0 53.0 NaN
C_3 175.0 NaN NaN 187.0 NaN NaN NaN 195.0 161.0 NaN ... NaN 69.0 NaN NaN NaN
70.0 68.0 NaN NaN 82.0
S_2 C_1 NaN NaN NaN 159.0 161.0 NaN NaN NaN 163.5 NaN ... NaN 97.0 61.0 NaN NaN
NaN 71.0 NaN NaN 84.0
C_2 NaN NaN NaN NaN NaN 188.5 175.0 NaN 155.0 193.0 ... NaN NaN NaN 76.5 74.0
NaN 91.0 100.0 NaN NaN
C_3 NaN NaN 157.0 NaN 164.0 190.0 NaN NaN 187.0 171.0 ... 78.0 NaN 81.0 99.0
NaN NaN 73.0 88.0 NaN NaN
C_4 NaN 176.0 NaN NaN 175.5 NaN NaN NaN NaN NaN ... NaN NaN 57.0 NaN NaN NaN
NaN NaN NaN 82.0
7 rows × 24 columns

<>3. crosstab(交叉表)

<>交叉表是一种特殊的透视表,典型的用途如分组统计,如现在想要统计关于街道和性别分组的频数:
pd.crosstab(index=df['Address'],columns=df['Gender'])
Gender F M
Address
street_1 1 2
street_2 4 2
street_4 3 5
street_5 3 3
street_6 5 1
street_7 3 3
<>交叉表的功能也很强大(但目前还不支持多级分组),下面说明一些重要参数:

<>① values和aggfunc:分组对某些数据进行聚合操作,这两个参数必须成对出现
pd.crosstab(index=df['Address'],columns=df['Gender'], values=np.random.randint(
1,20,df.shape[0]),aggfunc='min') #默认参数等于如下方法:
#pd.crosstab(index=df['Address'],columns=df['Gender'],values=1,aggfunc='count')
Gender F M
Address
street_1 6 4
street_2 10 5
street_4 6 2
street_5 10 8
street_6 9 4
street_7 8 4
<>② 除了边际参数margins外,还引入了normalize参数,可选’all’,‘index’,'columns’参数值
pd.crosstab(index=df['Address'],columns=df['Gender'],normalize='all',margins=
True)
Gender F M All
Address
street_1 0.028571 0.057143 0.085714
street_2 0.114286 0.057143 0.171429
street_4 0.085714 0.142857 0.228571
street_5 0.085714 0.085714 0.171429
street_6 0.142857 0.028571 0.171429
street_7 0.085714 0.085714 0.171429
All 0.542857 0.457143 1.000000
<>二、其他变形方法

<>1. melt

<>melt函数可以认为是pivot函数的逆操作,将unstacked状态的数据,压缩成stacked,使“宽”的DataFrame变“窄”
df_m = df[['ID','Gender','Math']] df_m.head()
ID Gender Math
0 1101 M 34.0
1 1102 F 32.5
2 1103 M 87.2
3 1104 F 80.4
4 1105 F 84.8 df.pivot(index='ID',columns='Gender',values='Math').head()
Gender F M
ID
1101 NaN 34.0
1102 32.5 NaN
1103 NaN 87.2
1104 80.4 NaN
1105 84.8 NaN
<>melt函数中的id_vars表示需要保留的列,value_vars表示需要stack的一组列
pivoted = df.pivot(index='ID',columns='Gender',values='Math') result = pivoted.
reset_index().melt(id_vars=['ID'],value_vars=['F','M'],value_name='Math')\ .
dropna().set_index('ID').sort_index() #检验是否与展开前的df相同,可以分别将这些链式方法的中间步骤展开,看看是什么结果
result.equals(df_m.set_index('ID')) True
<>2. 压缩与展开

<>(1)stack:这是最基础的变形函数,总共只有两个参数:level和dropna
df_s = pd.pivot_table(df,index=['Class','ID'],columns='Gender',values=['Height'
,'Weight']) df_s.groupby('Class').head(2)
Height Weight
Gender F M F M
Class ID
C_1 1101 NaN 173.0 NaN 63.0
1102 192.0 NaN 73.0 NaN
C_2 1201 NaN 188.0 NaN 68.0
1202 176.0 NaN 94.0 NaN
C_3 1301 NaN 161.0 NaN 68.0
1302 175.0 NaN 57.0 NaN
C_4 2401 192.0 NaN 62.0 NaN
2402 NaN 166.0 NaN 82.0 df_stacked = df_s.stack() df_stacked.groupby('Class').
head(2)
Height Weight
Class ID Gender
C_1 1101 M 173.0 63.0
1102 F 192.0 73.0
C_2 1201 M 188.0 68.0
1202 F 176.0 94.0
C_3 1301 M 161.0 68.0
1302 F 175.0 57.0
C_4 2401 F 192.0 62.0
2402 M 166.0 82.0
<>stack函数可以看做将横向的索引放到纵向,因此功能类似与melt,参数level可指定变化的列索引是哪一层(或哪几层,需要列表)
df_stacked = df_s.stack(0) df_stacked.groupby('Class').head(2)
Gender F M
Class ID
C_1 1101 Height NaN 173.0
Weight NaN 63.0
C_2 1201 Height NaN 188.0
Weight NaN 68.0
C_3 1301 Height NaN 161.0
Weight NaN 68.0
C_4 2401 Height 192.0 NaN
Weight 62.0 NaN
<>(2) unstack:stack的逆函数,功能上类似于pivot_table
df_stacked.head()
Gender F M
Class ID
C_1 1101 Height NaN 173.0
Weight NaN 63.0
1102 Height 192.0 NaN
Weight 73.0 NaN
1103 Height NaN 186.0 result = df_stacked.unstack().swaplevel(1,0,axis=1).
sort_index(axis=1) result.equals(df_s) #同样在unstack中可以指定level参数 True
<>三、哑变量与因子化

<>1. Dummy Variable(哑变量)

<>这里主要介绍get_dummies函数,其功能主要是进行one-hot编码:
df_d = df[['Class','Gender','Weight']] df_d.head()
Class Gender Weight
0 C_1 M 63
1 C_1 F 73
2 C_1 M 82
3 C_1 F 81
4 C_1 F 64
<>现在希望将上面的表格前两列转化为哑变量,并加入第三列Weight数值:
pd.get_dummies(df_d[['Class','Gender']]).join(df_d['Weight']).head()
#可选prefix参数添加前缀,prefix_sep添加分隔符
Class_C_1 Class_C_2 Class_C_3 Class_C_4 Gender_F Gender_M Weight
0 1 0 0 0 0 1 63
1 1 0 0 0 1 0 73
2 1 0 0 0 0 1 82
3 1 0 0 0 1 0 81
4 1 0 0 0 1 0 64
<>2. factorize方法

<>该方法主要用于自然数编码,并且缺失值会被记做-1,其中sort参数表示是否排序后赋值
codes, uniques = pd.factorize(['b', None, 'a', 'c', 'b'], sort=True) display(
codes) display(uniques) array([ 1, -1, 0, 2, 1]) array(['a', 'b', 'c'],
dtype=object)

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