导入需要使用的库和文件：
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.read_csv('data/table.csv',index_col='ID')
>>> df.head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
一、单级索引
最常用的索引方法可能就是这三类，其中iloc表示位置索引，loc表示标签索引，[]也具有很大的便利性，各有特点。
1. loc方法
（a）单行索引：
>>> df.loc[1103]
School S_1
Class C_1
Gender M
Address street_2
Height 186
Weight 82
Math 87.2
Physics B+
Name: 1103, dtype: object
（b）多行索引：
>>> df.loc[[1102, 2304]]
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192 73 32.5 B+
2304 S_2 C_3 F street_6 164 81 95.5 A-
注：所有在loc中使用的切片全部包含右端点！这是因为如果作为Pandas的使用者，那么肯定不太关心最后一个标签再往后一位是什么，但是如果是左闭右开，那么就很麻烦，先要知道再后面一列的名字是什么，非常不方便，因此Pandas中将loc设计为左右全闭
>>> df.loc[1304:2103].head
School Class Gender Address Height Weight Math Physics
ID
1304 S_1 C_3 M street_2 195 70 85.2 A
1305 S_1 C_3 F street_5 187 69 61.7 B-
2101 S_2 C_1 M street_7 174 84 83.3 C
2102 S_2 C_1 F street_6 161 61 50.6 B+
2103 S_2 C_1 M street_4 157 61 52.5 B->
>>> df.loc[2402::-1].head()
School Class Gender Address Height Weight Math Physics
ID
2402 S_2 C_4 M street_7 166 82 48.7 B
2401 S_2 C_4 F street_2 192 62 45.3 A
2305 S_2 C_3 M street_4 187 73 48.9 B
2304 S_2 C_3 F street_6 164 81 95.5 A-
2303 S_2 C_3 F street_7 190 99 65.9 C
（c）单列索引
>>> df.loc[:,'Height'].head()
ID
1101 173
1102 192
1103 186
1104 167
1105 159
Name: Height, dtype: int64
（d）多列索引
>>> df.loc[:,['Height', 'Math']].head()
Height Math
ID
1101 173 34.0
1102 192 32.5
1103 186 87.2
1104 167 80.4
1105 159 84.8
>>> df.loc[:,'Height':'Math'].head()
Height Weight Math
ID
1101 173 63 34.0
1102 192 73 32.5
1103 186 82 87.2
1104 167 81 80.4
1105 159 64 84.8
（e）联合索引
>>> df.loc[1102:2401:3,'Height':'Math'].head()
Height Weight Math
ID
1102 192 73 32.5
1105 159 64 84.8
1203 160 53 58.8
1301 161 68 31.5
1304 195 70 85.2
（f）函数式索引
>>> df.loc[lambda x:x['Gender']=='M'].head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1201 S_1 C_2 M street_5 188 68 97.0 A-
1203 S_1 C_2 M street_6 160 53 58.8 A+
1301 S_1 C_3 M street_4 161 68 31.5 B+
>>> def f(x):
... return [1101, 1105]
>>> df.loc[f]
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1105 S_1 C_1 F street_4 159 64 84.8 B+
（g）布尔索引
>>> df.loc[df['Address'].isin(['street_7', 'street_4'])].head()
School Class Gender Address Height Weight Math Physics
ID
1105 S_1 C_1 F street_4 159 64 84.8 B+
1202 S_1 C_2 F street_4 176 94 63.5 B-
1301 S_1 C_3 M street_4 161 68 31.5 B+
1303 S_1 C_3 M street_7 188 82 49.7 B
2101 S_2 C_1 M street_7 174 84 83.3 C
>>> df.loc[[True if i[-1]=='4' or i[-1]=='7' else False for i in df['Address'].values]].head()
School Class Gender Address Height Weight Math Physics
ID
1105 S_1 C_1 F street_4 159 64 84.8 B+
1202 S_1 C_2 F street_4 176 94 63.5 B-
1301 S_1 C_3 M street_4 161 68 31.5 B+
1303 S_1 C_3 M street_7 188 82 49.7 B
2101 S_2 C_1 M street_7 174 84 83.3 C
小结：本质上说，loc中能传入的只有布尔列表和索引子集构成的列表，只要把握这个原则就很容易理解上面那些操作
2. iloc方法（注意与loc不同，切片右端点不包含）
（a）单行索引
>>> df.iloc[3]
School S_1
Class C_1
Gender F
Address street_2
Height 167
Weight 81
Math 80.4
Physics B-
Name: 1104, dtype: object
（b）多行索引
>>> df.iloc[3:5]
School Class Gender Address Height Weight Math Physics
ID
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
（c）多列索引
>>> df.iloc[:,3].head()
ID
1101 street_1
1102 street_2
1103 street_2
1104 street_2
1105 street_4
Name: Address, dtype: object
（d）多列索引
>>> df.iloc[:,7::-2].head()
Physics Weight Address Class
ID
1101 A+ 63 street_1 C_1
1102 B+ 73 street_2 C_1
1103 B+ 82 street_2 C_1
1104 B- 81 street_2 C_1
1105 B+ 64 street_4 C_1
（e）混合索引
>>> df.iloc[3::4,7::-2].head()
Physics Weight Address Class
ID
1104 B- 81 street_2 C_1
1203 A+ 53 street_6 C_2
1302 A- 57 street_1 C_3
2101 C 84 street_7 C_1
2105 A 81 street_4 C_1
（f）函数式索引
>>> df.iloc[lambda x:[3]].head()
School Class Gender Address Height Weight Math Physics
ID
1104 S_1 C_1 F street_2 167 81 80.4 B-
小结：iloc中接收的参数只能为整数或整数列表或布尔列表，不能使用布尔Series，如果要用就必须如下把values拿出来
>>> df.iloc[(df['School']=='S_1').values].head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
3. []操作符
3.1 Series的[]操作
（a）单元素索引：
>>> s = pd.Series(df['Math'], index=df.index)
>>> s[1101]
34.0
（b）多行索引
>>> s[0:4]
ID
1101 34.0
1102 32.5
1103 87.2
1104 80.4
Name: Math, dtype: float64
（c）函数式索引
>>> s[lambda x: x.index[16::-6]]
ID
2102 50.6
1301 31.5
1105 84.8
Name: Math, dtype: float64
（d）布尔索引
>>> s[s > 80]
ID
1103 87.2
1104 80.4
1105 84.8
1201 97.0
1302 87.7
1304 85.2
2101 83.3
2205 85.4
2304 95.5
Name: Math, dtype: float64
注：如果不想陷入困境，请不要在行索引为浮点值时使用[]操作符，因为在Series中[]的浮点切片并不是进行位置比较，而是值比较，非常特殊
>>> s_int = pd.Series([1, 2, 3, 4], index=[1, 3, 5, 6])
>>> s_float = pd.Series([1,2,3,4],index=[1.,3.,5.,6.])
>>> s_int
1 1
3 2
5 3
6 4
dtype: int64
>>> s_int[2:]
5 3
6 4
dtype: int64
>>> s_float
1.0 1
3.0 2
5.0 3
6.0 4
dtype: int64
>>> s_float[2:]
3.0 2
5.0 3
6.0 4
dtype: int64
3.2 DataFrame的[]操作
（a）单行索引：
>>> df[1:2]
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192 73 32.5 B+
>>> row = df.index.get_loc(1102)
>>> df[row:row+1]
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192 73 32.5 B+
（b）多行索引
>>> df[3:5]
School Class Gender Address Height Weight Math Physics
ID
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
（c）单列索引
>>> df['School'].head()
ID
1101 S_1
1102 S_1
1103 S_1
1104 S_1
1105 S_1
Name: School, dtype: object
（d）多列索引
>>> df[['School', 'Math']].head()
School Math
ID
1101 S_1 34.0
1102 S_1 32.5
1103 S_1 87.2
1104 S_1 80.4
1105 S_1 84.8
（e）函数式索引
>>> df[lambda x: ['Math', 'Physics']].head()
Math Physics
ID
1101 34.0 A+
1102 32.5 B+
1103 87.2 B+
1104 80.4 B-
1105 84.8 B+
（f）布尔索引
>>> df[df['Gender']=='F'].head()
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192 73 32.5 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
1202 S_1 C_2 F street_4 176 94 63.5 B-
1204 S_1 C_2 F street_5 162 63 33.8 B
小结：一般来说，[]操作符常用于列选择或布尔选择，尽量避免行的选择
（a）布尔符号：’&’,’|’,’~’：分别代表和and，或or，取反not
>>> df[(df['Gender']=='F')&(df['Address']=='street_2')].head()
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192 73 32.5 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
2401 S_2 C_4 F street_2 192 62 45.3 A
2404 S_2 C_4 F street_2 160 84 67.7 B
>>> df[df['Math']>85 | (df['Address']=='street_2')].head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
>>> df[~((df['Math']>75)|(df['Address']=='street_1'))].head()
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192 73 32.5 B+
1202 S_1 C_2 F street_4 176 94 63.5 B-
1203 S_1 C_2 M street_6 160 53 58.8 A+
1204 S_1 C_2 F street_5 162 63 33.8 B
1205 S_1 C_2 F street_6 167 63 68.4 B-
loc和[]中相应位置都能使用布尔列表选择：
>>> df.loc[df['Math']>60, df.columns=='Physics'].head()
Physics
ID
1103 B+
1104 B-
1105 B+
1201 A-
1202 B-
（b）isin方法
>>> df[df['Address'].isin(['street_1', 'street_4']) & df['Physics'].isin(['A', 'A+'])]
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
2105 S_2 C_1 M street_4 170 81 34.2 A
2203 S_2 C_2 M street_4 155 91 73.8 A+
>>> df[df[['Address','Physics']].isin({'Address':['street_1','street_4'],'Physics':['A','A+']}).all(1)]
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
2105 S_2 C_1 M street_4 170 81 34.2 A
2203 S_2 C_2 M street_4 155 91 73.8 A+
当只需要取一个元素时，at和iat方法能够提供更快的实现：
>>> print(df.at)
df.at df.at_time( df.attrs
S_1
df.loc df.lookup(
>>> print(df.loc[1101, 'School'])
S_1
>>> print(df.iat[0, 0])
S_1
>>> print(df.iloc[0, 0])
S_1
此处介绍并不是说只能在单级索引中使用区间索引，只是作为一种特殊类型的索引方式，在此处先行介绍
（a）利用interval_range方法
>>> pd.interval_range(start=0, end=5)
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]], dtype='interval[int64, right]')
（b）利用cut将数值列转为区间为元素的分类变量，例如统计数学成绩的区间情况：
>>> math_interval = pd.cut(df['Math'], bins=[0, 40, 60, 80, 100])
>>> math_interval.head()
ID
1101 (0, 40]
1102 (0, 40]
1103 (80, 100]
1104 (80, 100]
1105 (80, 100]
Name: Math, dtype: category
Categories (4, interval[int64, right]): [(0, 40] < (40, 60] < (60, 80] < (80, 100]]
（c）区间索引的选取
>>> df_i = df.join(math_interval, rsuffix='_interval')[['Math', 'Math_interval']].reset_index().set_index('Math_interval')
>>> df_i.head()
ID Math
Math_interval
(0, 40] 1101 34.0
(0, 40] 1102 32.5
(80, 100] 1103 87.2
(80, 100] 1104 80.4
(80, 100] 1105 84.8
>>> df_i.loc[65].head()
ID Math
Math_interval
(60, 80] 1202 63.5
(60, 80] 1205 68.4
(60, 80] 1305 61.7
(60, 80] 2104 72.2
(60, 80] 2202 68.5
>>> df_i.loc[[65, 90]].head()
ID Math
Math_interval
(60, 80] 1202 63.5
(60, 80] 1205 68.4
(60, 80] 1305 61.7
(60, 80] 2104 72.2
(60, 80] 2202 68.5
如果想要选取某个区间，先要把分类变量转化为区间变量，再使用 overlap方法
>>> df_i[df_i.index.astype('interval').overlaps(pd.Interval(70, 85))].head()
ID Math
Math_interval
(80, 100] 1103 87.2
(80, 100] 1104 80.4
(80, 100] 1105 84.8
(80, 100] 1201 97.0
(60, 80] 1202 63.5
二、多级索引
1. 通过from_tuple或from_arrays
（a）直接创建元组
>>> tuples = [('A','a'),('A','b'),('B','a'),('B','b')]
>>> mul_index = pd.MultiIndex.from_tuples(tuples, names=('Upper', 'Lower'))
>>> mul_index
MultiIndex([('A', 'a'),
('A', 'b'),
('B', 'a'),
('B', 'b')],
names=['Upper', 'Lower'])
>>> pd.DataFrame({'Score':['perfect','good','fair','bad']},index=mul_index)
Score
Upper Lower
A a perfect
b good
B a fair
b bad
（b）利用zip创建元组
>>> L1 = list('AABB')
>>> L2 = list('abab')
>>> tuples = list(zip(L1,L2))
>>> mul_index = pd.MultiIndex.from_tuples(tuples, names=('Upper', 'Lower'))
>>> pd.DataFrame({'Score':['perfect','good','fair','bad']},index=mul_index)
Score
Upper Lower
A a perfect
b good
B a fair
b bad
（c）通过Array创建
>>> arrays = [['A','a'],['A','b'],['B','a'],['B','b']]
>>> mul_index = pd.MultiIndex.from_tuples(arrays, names=('Upper', 'Lower'))
>>> pd.DataFrame({'Score':['perfect','good','fair','bad']},index=mul_index)
Score
Upper Lower
A a perfect
b good
B a fair
b bad
>>> mul_index
MultiIndex([('A', 'a'),
('A', 'b'),
('B', 'a'),
('B', 'b')],
names=['Upper', 'Lower'])
2.通过from_product
>>> L1 = ['A','B']
>>> L2 = ['a','b']
>>> pd.MultiIndex.from_product([L1,L2],names=('Upper', 'Lower'))
MultiIndex([('A', 'a'),
('A', 'b'),
('B', 'a'),
('B', 'b')],
names=['Upper', 'Lower'])
3. 指定df中的列创建（set_index方法）
>>> df_using_mul = df.set_index(['Class','Address'])
>>> df_using_mul.head()
School Gender Height Weight Math Physics
Class Address
C_1 street_1 S_1 M 173 63 34.0 A+
street_2 S_1 F 192 73 32.5 B+
street_2 S_1 M 186 82 87.2 B+
street_2 S_1 F 167 81 80.4 B-
street_4 S_1 F 159 64 84.8 B+
>>> df_using_mul.head()
School Gender Height Weight Math Physics
Class Address
C_1 street_1 S_1 M 173 63 34.0 A+
street_2 S_1 F 192 73 32.5 B+
street_2 S_1 M 186 82 87.2 B+
street_2 S_1 F 167 81 80.4 B-
street_4 S_1 F 159 64 84.8 B+
1. 一般切片
>>> df_using_mul.sort_index().loc['C_2','street_5']
School Gender Height Weight Math Physics
Class Address
C_2 street_5 S_1 M 188 68 97.0 A-
street_5 S_1 F 162 63 33.8 B
street_5 S_2 M 193 100 39.1 B
>>> df_using_mul.sort_index().loc[('C_2','street_6'):('C_3','street_4')]
School Gender Height Weight Math Physics
Class Address
C_2 street_6 S_1 M 160 53 58.8 A+
street_6 S_1 F 167 63 68.4 B-
street_7 S_2 F 194 77 68.5 B+
street_7 S_2 F 183 76 85.4 B
C_3 street_1 S_1 F 175 57 87.7 A-
street_2 S_1 M 195 70 85.2 A
street_4 S_1 M 161 68 31.5 B+
street_4 S_2 F 157 78 72.3 B+
street_4 S_2 M 187 73 48.9 B
>>> df_using_mul.sort_index().loc[('C_2','street_7'):'C_3'].head()
School Gender Height Weight Math Physics
Class Address
C_2 street_7 S_2 F 194 77 68.5 B+
street_7 S_2 F 183 76 85.4 B
C_3 street_1 S_1 F 175 57 87.7 A-
street_2 S_1 M 195 70 85.2 A
street_4 S_1 M 161 68 31.5 B+
2. 第一类特殊情况：由元组构成列表
>>> df_using_mul.sort_index().loc[[('C_2','street_7'),('C_3','street_2')]]
School Gender Height Weight Math Physics
Class Address
C_2 street_7 S_2 F 194 77 68.5 B+
street_7 S_2 F 183 76 85.4 B
C_3 street_2 S_1 M 195 70 85.2 A
3. 第二类特殊情况：由列表构成元组
>>> df_using_mul.sort_index().loc[(['C_2','C_3'],['street_4','street_7']),:]
School Gender Height Weight Math Physics
Class Address
C_2 street_4 S_1 F 176 94 63.5 B-
street_4 S_2 M 155 91 73.8 A+
street_7 S_2 F 194 77 68.5 B+
street_7 S_2 F 183 76 85.4 B
C_3 street_4 S_1 M 161 68 31.5 B+
street_4 S_2 F 157 78 72.3 B+
street_4 S_2 M 187 73 48.9 B
street_7 S_1 M 188 82 49.7 B
street_7 S_2 F 190 99 65.9 C
>>> L1,L2 = ['A','B','C'],['a','b','c']
>>> mul_index1 = pd.MultiIndex.from_product([L1,L2],names=('Upper', 'Lower'))
>>> L3,L4 = ['D','E','F'],['d','e','f']
>>> mul_index2 = pd.MultiIndex.from_product([L3,L4],names=('Big', 'Small'))
>>> df_s = pd.DataFrame(np.random.rand(9,9),index=mul_index1,columns=mul_index2)
>>> df_s
Big D E F
Small d e f d e f d e f
Upper Lower
A a 0.865934 0.752678 0.992263 0.471948 0.101374 0.750520 0.029240 0.841838 0.202736
b 0.358436 0.315506 0.141048 0.179118 0.579804 0.387298 0.731970 0.504881 0.664886
c 0.688227 0.076362 0.447927 0.897414 0.990657 0.577089 0.885058 0.242146 0.551289
B a 0.622251 0.583529 0.970421 0.798430 0.075585 0.453897 0.196744 0.243493 0.407374
b 0.660225 0.383329 0.884619 0.646215 0.251076 0.753128 0.857983 0.240076 0.391556
c 0.336650 0.051452 0.472089 0.750627 0.920971 0.131141 0.160800 0.567003 0.608006
C a 0.875975 0.545787 0.449710 0.062922 0.931482 0.595037 0.124742 0.016393 0.221201
b 0.608213 0.789482 0.744773 0.768816 0.364518 0.787751 0.536297 0.282383 0.828840
c 0.972491 0.477164 0.000541 0.236157 0.951343 0.572702 0.270309 0.225364 0.027862
索引Slice的使用非常灵活：
>>> idx=pd.IndexSlice
>>> df_s.loc[idx['B':,df_s['D']['d']>0.3],idx[df_s.sum()>4]]
Big D E
Small d f d e f
Upper Lower
B a 0.622251 0.970421 0.798430 0.075585 0.453897
b 0.660225 0.884619 0.646215 0.251076 0.753128
c 0.336650 0.472089 0.750627 0.920971 0.131141
C a 0.875975 0.449710 0.062922 0.931482 0.595037
b 0.608213 0.744773 0.768816 0.364518 0.787751
c 0.972491 0.000541 0.236157 0.951343 0.572702
1. swaplevel方法（两层交换）
>>> df_using_mul.head()
School Gender Height Weight Math Physics
Class Address
C_1 street_1 S_1 M 173 63 34.0 A+
street_2 S_1 F 192 73 32.5 B+
street_2 S_1 M 186 82 87.2 B+
street_2 S_1 F 167 81 80.4 B-
street_4 S_1 F 159 64 84.8 B+
>>> df_using_mul.swaplevel(i=1,j=0,axis=0).sort_index().head()
School Gender Height Weight Math Physics
Address Class
street_1 C_1 S_1 M 173 63 34.0 A+
C_2 S_2 M 175 74 47.2 B-
C_3 S_1 F 175 57 87.7 A-
street_2 C_1 S_1 F 192 73 32.5 B+
C_1 S_1 M 186 82 87.2 B+
2. reorder_levels方法（多层交换）
>>> df_muls = df.set_index(['School','Class','Address'])
>>> df_muls.head()
Gender Height Weight Math Physics
School Class Address
S_1 C_1 street_1 M 173 63 34.0 A+
street_2 F 192 73 32.5 B+
street_2 M 186 82 87.2 B+
street_2 F 167 81 80.4 B-
street_4 F 159 64 84.8 B+
>>> df_muls.reorder_levels([2,0,1],axis=0).sort_index().head()
Gender Height Weight Math Physics
Address School Class
street_1 S_1 C_1 M 173 63 34.0 A+
C_3 F 175 57 87.7 A-
S_2 C_2 M 175 74 47.2 B-
street_2 S_1 C_1 F 192 73 32.5 B+
C_1 M 186 82 87.2 B+
>>> df_muls.reorder_levels(['Address','School','Class'],axis=0).sort_index().head()
Gender Height Weight Math Physics
Address School Class
street_1 S_1 C_1 M 173 63 34.0 A+
C_3 F 175 57 87.7 A-
S_2 C_2 M 175 74 47.2 B-
street_2 S_1 C_1 F 192 73 32.5 B+
C_1 M 186 82 87.2 B+
三、索引设定
index_col是read_csv中的一个参数，而不是某一个方法：
>>> pd.read_csv('data/table.csv',index_col=['Address','School']).head()
Class ID Gender Height Weight Math Physics
Address School
street_1 S_1 C_1 1101 M 173 63 34.0 A+
street_2 S_1 C_1 1102 F 192 73 32.5 B+
S_1 C_1 1103 M 186 82 87.2 B+
S_1 C_1 1104 F 167 81 80.4 B-
street_4 S_1 C_1 1105 F 159 64 84.8 B+
reindex是指重新索引，它的重要特性在于索引对齐，很多时候用于重新排序：
>>> df.head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
>>> df.reindex(index=[1101,1203,1206,2402])
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173.0 63.0 34.0 A+
1203 S_1 C_2 M street_6 160.0 53.0 58.8 A+
1206 NaN NaN NaN NaN NaN NaN NaN NaN
2402 S_2 C_4 M street_7 166.0 82.0 48.7 B
>>> df.reindex(columns=['Height','Gender','Average']).head()
Height Gender Average
ID
1101 173 M NaN
1102 192 F NaN
1103 186 M NaN
1104 167 F NaN
1105 159 F NaN
可以选择缺失值的填充方法：fill_value和method（bfill/ffill/nearest），其中method参数必须索引单调：
>>> df.reindex(index=[1101,1203,1206,2402],method='bfill')
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1203 S_1 C_2 M street_6 160 53 58.8 A+
1206 S_1 C_3 M street_4 161 68 31.5 B+
2402 S_2 C_4 M street_7 166 82 48.7 B
>>> df.reindex(index=[1101,1203,1206,2402],method='nearest')
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1203 S_1 C_2 M street_6 160 53 58.8 A+
1206 S_1 C_2 F street_6 167 63 68.4 B-
2402 S_2 C_4 M street_7 166 82 48.7 B
reindex_like的作用为生成一个横纵索引完全与参数列表一致的DataFrame，数据使用被调用的表
>>> df_temp = pd.DataFrame({'Weight':np.zeros(5),
... 'Height':np.zeros(5),
... 'ID':[1101,1104,1103,1106,1102]}).set_index('ID')
>>> df_temp.reindex_like(df[0:5][['Weight','Height']])
Weight Height
ID
1101 0.0 0.0
1102 0.0 0.0
1103 0.0 0.0
1104 0.0 0.0
1105 NaN NaN
如果df_temp单调还可以使用method参数：
>>> df_temp = pd.DataFrame({'Weight':range(5),
... 'Height':range(5),
... 'ID':[1101,1104,1103,1106,1102]}).set_index('ID').sort_index()
>>> df_temp.reindex_like(df[0:5][['Weight','Height']],method='bfill')
Weight Height
ID
1101 0 0
1102 4 4
1103 2 2
1104 1 1
1105 3 3
先介绍set_index：从字面意思看，就是将某些列作为索引
使用表内列作为索引：
>>> df.head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
>>> df.set_index('Class').head()
School Gender Address Height Weight Math Physics
Class
C_1 S_1 M street_1 173 63 34.0 A+
C_1 S_1 F street_2 192 73 32.5 B+
C_1 S_1 M street_2 186 82 87.2 B+
C_1 S_1 F street_2 167 81 80.4 B-
C_1 S_1 F street_4 159 64 84.8 B+
利用append参数可以将当前索引维持不变：
>>> df.set_index('Class', append=True).head()
School Gender Address Height Weight Math Physics
ID Class
1101 C_1 S_1 M street_1 173 63 34.0 A+
1102 C_1 S_1 F street_2 192 73 32.5 B+
1103 C_1 S_1 M street_2 186 82 87.2 B+
1104 C_1 S_1 F street_2 167 81 80.4 B-
1105 C_1 S_1 F street_4 159 64 84.8 B+
当使用与表长相同的列作为索引时间（需要先转化为Series，否则报错）：
>>> df.set_index(pd.Series(range(df.shape[0]))).head()
School Class Gender Address Height Weight Math Physics
0 S_1 C_1 M street_1 173 63 34.0 A+
1 S_1 C_1 F street_2 192 73 32.5 B+
2 S_1 C_1 M street_2 186 82 87.2 B+
3 S_1 C_1 F street_2 167 81 80.4 B-
4 S_1 C_1 F street_4 159 64 84.8 B+
可以直接添加多级索引：
>>> df.set_index([pd.Series(range(df.shape[0])),pd.Series(np.ones(df.shape[0]))]).head()
School Class Gender Address Height Weight Math Physics
0 1.0 S_1 C_1 M street_1 173 63 34.0 A+
1 1.0 S_1 C_1 F street_2 192 73 32.5 B+
2 1.0 S_1 C_1 M street_2 186 82 87.2 B+
3 1.0 S_1 C_1 F street_2 167 81 80.4 B-
4 1.0 S_1 C_1 F street_4 159 64 84.8 B+
下面介绍reset_index方法，它的主要功能是将索引重置
默认状态直接恢复到自然数索引：
>>> df.reset_index().head()
ID School Class Gender Address Height Weight Math Physics
0 1101 S_1 C_1 M street_1 173 63 34.0 A+
1 1102 S_1 C_1 F street_2 192 73 32.5 B+
2 1103 S_1 C_1 M street_2 186 82 87.2 B+
3 1104 S_1 C_1 F street_2 167 81 80.4 B-
4 1105 S_1 C_1 F street_4 159 64 84.8 B+
用level参数指定哪一层被reset，用col_level参数指定set到哪一层：
>>> L1,L2 = ['A','B','C'],['a','b','c']
>>> mul_index1 = pd.MultiIndex.from_product([L1,L2],names=('Upper', 'Lower'))
>>> L3,L4 = ['D','E','F'],['d','e','f']
>>> mul_index2 = pd.MultiIndex.from_product([L3,L4],names=('Big', 'Small'))
>>> df_temp = pd.DataFrame(np.random.rand(9,9),index=mul_index1,columns=mul_index2)
>>> df_temp.head()
Big D E F
Small d e f d e f d e f
Upper Lower
A a 0.996924 0.779796 0.198003 0.876215 0.801679 0.740366 0.072776 0.172737 0.103133
b 0.856929 0.384369 0.988760 0.300426 0.109809 0.445339 0.735657 0.109474 0.632733
c 0.631834 0.748637 0.378666 0.696078 0.404629 0.747714 0.237205 0.988239 0.260963
B a 0.740106 0.995469 0.005640 0.204483 0.958359 0.737188 0.696751 0.900894 0.275091
b 0.026315 0.251426 0.594558 0.313601 0.145479 0.433199 0.704520 0.366411 0.473218
>>> df_temp1 = df_temp.reset_index(level=1,col_level=1)
>>> df_temp1.head()
Big D E F
Small Lower d e f d e f d e f
Upper
A a 0.996924 0.779796 0.198003 0.876215 0.801679 0.740366 0.072776 0.172737 0.103133
A b 0.856929 0.384369 0.988760 0.300426 0.109809 0.445339 0.735657 0.109474 0.632733
A c 0.631834 0.748637 0.378666 0.696078 0.404629 0.747714 0.237205 0.988239 0.260963
B a 0.740106 0.995469 0.005640 0.204483 0.958359 0.737188 0.696751 0.900894 0.275091
B b 0.026315 0.251426 0.594558 0.313601 0.145479 0.433199 0.704520 0.366411 0.473218
>>> df_temp1.columns
MultiIndex([( '', 'Lower'),
('D', 'd'),
('D', 'e'),
('D', 'f'),
('E', 'd'),
('E', 'e'),
('E', 'f'),
('F', 'd'),
('F', 'e'),
('F', 'f')],
names=['Big', 'Small'])
>>> df_temp1.index
Index(['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'], dtype='object', name='Upper')
rename_axis是针对多级索引的方法，作用是修改某一层的索引名，而不是索引标签
>>> df_temp.rename_axis(index={'Lower':'LowerLower'},columns={'Big':'BigBig'})[['D', 'E']]
BigBig D E
Small d e f d e f
Upper LowerLower
A a 0.996924 0.779796 0.198003 0.876215 0.801679 0.740366
b 0.856929 0.384369 0.988760 0.300426 0.109809 0.445339
c 0.631834 0.748637 0.378666 0.696078 0.404629 0.747714
B a 0.740106 0.995469 0.005640 0.204483 0.958359 0.737188
b 0.026315 0.251426 0.594558 0.313601 0.145479 0.433199
c 0.642152 0.803119 0.869278 0.643841 0.933842 0.373142
C a 0.419632 0.187484 0.420311 0.136625 0.512117 0.167024
b 0.123571 0.571580 0.201483 0.788676 0.067141 0.955275
c 0.075575 0.832965 0.934871 0.549695 0.511443 0.286503
rename方法用于修改列或者行索引标签，而不是索引名：
>>> df_temp.rename(index={'A':'T'},columns={'e':'changed_e'}).head()
Big D E F
Small d changed_e f d changed_e f d changed_e f
Upper Lower
T a 0.996924 0.779796 0.198003 0.876215 0.801679 0.740366 0.072776 0.172737 0.103133
b 0.856929 0.384369 0.988760 0.300426 0.109809 0.445339 0.735657 0.109474 0.632733
c 0.631834 0.748637 0.378666 0.696078 0.404629 0.747714 0.237205 0.988239 0.260963
B a 0.740106 0.995469 0.005640 0.204483 0.958359 0.737188 0.696751 0.900894 0.275091
b 0.026315 0.251426 0.594558 0.313601 0.145479 0.433199 0.704520 0.366411 0.473218
四、常用索引型函数
当对条件为False的单元进行填充时：
>>> df.head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
>>> df.where(df['Gender']=='M').head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173.0 63.0 34.0 A+
1102 NaN NaN NaN NaN NaN NaN NaN NaN
1103 S_1 C_1 M street_2 186.0 82.0 87.2 B+
1104 NaN NaN NaN NaN NaN NaN NaN NaN
1105 NaN NaN NaN NaN NaN NaN NaN NaN
通过这种方法筛选结果和[]操作符的结果完全一致：
>>> df.where(df['Gender']=='M').dropna().head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173.0 63.0 34.0 A+
1103 S_1 C_1 M street_2 186.0 82.0 87.2 B+
1201 S_1 C_2 M street_5 188.0 68.0 97.0 A-
1203 S_1 C_2 M street_6 160.0 53.0 58.8 A+
1301 S_1 C_3 M street_4 161.0 68.0 31.5 B+
第一个参数为布尔条件，第二个参数为填充值：
>>> df.where(df['Gender']=='M',np.random.rand(df.shape[0],df.shape[1])).head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173.000000 63.000000 34.000000 A+
1102 0.147943 0.670993 0.93367 0.16424 0.314864 0.121429 0.433781 0.074907
1103 S_1 C_1 M street_2 186.000000 82.000000 87.200000 B+
1104 0.749106 0.9844 0.184485 0.674807 0.738321 0.525289 0.019779 0.19905
1105 0.534726 0.657987 0.370359 0.89066 0.613029 0.456765 0.389943 0.756956
mask函数与where功能上相反，其余完全一致，即对条件为True的单元进行填充：
>>> df.mask(df['Gender']=='M').dropna().head()
School Class Gender Address Height Weight Math Physics
ID
1102 S_1 C_1 F street_2 192.0 73.0 32.5 B+
1104 S_1 C_1 F street_2 167.0 81.0 80.4 B-
1105 S_1 C_1 F street_4 159.0 64.0 84.8 B+
1202 S_1 C_2 F street_4 176.0 94.0 63.5 B-
1204 S_1 C_2 F street_5 162.0 63.0 33.8 B
>>> df.mask(df['Gender']=='M',np.random.rand(df.shape[0],df.shape[1])).head()
School Class Gender Address Height Weight Math Physics
ID
1101 0.532138 0.841576 0.885163 0.169569 0.983056 0.714640 0.820599 0.012835
1102 S_1 C_1 F street_2 192.000000 73.000000 32.500000 B+
1103 0.538961 0.155097 0.401648 0.283565 0.617196 0.260921 0.395324 0.478259
1104 S_1 C_1 F street_2 167.000000 81.000000 80.400000 B-
1105 S_1 C_1 F street_4 159.000000 64.000000 84.800000 B+
>>> df.head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1102 S_1 C_1 F street_2 192 73 32.5 B+
1103 S_1 C_1 M street_2 186 82 87.2 B+
1104 S_1 C_1 F street_2 167 81 80.4 B-
1105 S_1 C_1 F street_4 159 64 84.8 B+
query函数中的布尔表达式中，下面的符号都是合法的： 行列索引名、字符串、and/not/or/&/|/~/not in/in/==/!=、四则运算符
>>> df.query('(Address in ["street_6","street_7"])&(Weight>(70+10))&(ID in [1303,2304,2402])')
School Class Gender Address Height Weight Math Physics
ID
1303 S_1 C_3 M street_7 188 82 49.7 B
2304 S_2 C_3 F street_6 164 81 95.5 A-
2402 S_2 C_4 M street_7 166 82 48.7 B
五、重复元素处理
该方法返回了是否重复的布尔列表：
>>> df.duplicated('Class').head()
ID
1101 False
1102 True
1103 True
1104 True
1105 True
dtype: bool
可选参数keep默认为first，即首次出现设为不重复，若为last，则最后一次设为不重复，若为False，则所有重复项为True
>>> df.duplicated('Class',keep='last').tail()
ID
2401 True
2402 True
2403 True
2404 True
2405 False
dtype: bool
>>> df.duplicated('Class',keep=False).head()
ID
1101 True
1102 True
1103 True
1104 True
1105 True
dtype: bool
从名字上看出为剔除重复项，这在后面章节中的分组操作中可能是有用的，例如需要保留每组的第一个值：
>>> df.drop_duplicates('Class')
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1201 S_1 C_2 M street_5 188 68 97.0 A-
1301 S_1 C_3 M street_4 161 68 31.5 B+
2401 S_2 C_4 F street_2 192 62 45.3 A
参数与duplicate函数类似：
>>> df.drop_duplicates('Class',keep='last')
School Class Gender Address Height Weight Math Physics
ID
2105 S_2 C_1 M street_4 170 81 34.2 A
2205 S_2 C_2 F street_7 183 76 85.4 B
2305 S_2 C_3 M street_4 187 73 48.9 B
2405 S_2 C_4 F street_6 193 54 47.6 B
在传入多列时等价于将多列共同视作一个多级索引，比较重复项：
>>> df.drop_duplicates(['School','Class'])
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
1201 S_1 C_2 M street_5 188 68 97.0 A-
1301 S_1 C_3 M street_4 161 68 31.5 B+
2101 S_2 C_1 M street_7 174 84 83.3 C
2201 S_2 C_2 M street_5 193 100 39.1 B
2301 S_2 C_3 F street_4 157 78 72.3 B+
2401 S_2 C_4 F street_2 192 62 45.3 A
六、抽样函数
这里的抽样函数指的就是sample函数
>>> df.sample(n=5)
School Class Gender Address Height Weight Math Physics
ID
2205 S_2 C_2 F street_7 183 76 85.4 B
2202 S_2 C_2 F street_7 194 77 68.5 B+
2101 S_2 C_1 M street_7 174 84 83.3 C
1103 S_1 C_1 M street_2 186 82 87.2 B+
1301 S_1 C_3 M street_4 161 68 31.5 B+
>>> df.sample(frac=0.05)
School Class Gender Address Height Weight Math Physics
ID
1104 S_1 C_1 F street_2 167 81 80.4 B-
2105 S_2 C_1 M street_4 170 81 34.2 A
>>> df.sample(n=df.shape[0],replace=True).head()
School Class Gender Address Height Weight Math Physics
ID
2302 S_2 C_3 M street_5 171 88 32.7 A
1101 S_1 C_1 M street_1 173 63 34.0 A+
2305 S_2 C_3 M street_4 187 73 48.9 B
2101 S_2 C_1 M street_7 174 84 83.3 C
1304 S_1 C_3 M street_2 195 70 85.2 A
>>> df.sample(n=35,replace=True).index.is_unique
False
>>> df.sample(n=3,axis=1).head()
Address Class Weight
ID
1101 street_1 C_1 63
1102 street_2 C_1 73
1103 street_2 C_1 82
1104 street_2 C_1 81
1105 street_4 C_1 64
>>> df.sample(n=3,weights=np.random.rand(df.shape[0])).head()
School Class Gender Address Height Weight Math Physics
ID
1101 S_1 C_1 M street_1 173 63 34.0 A+
2302 S_2 C_3 M street_5 171 88 32.7 A
1105 S_1 C_1 F street_4 159 64 84.8 B+
>>> df.sample(n=3,weights=df['Math']).head()
School Class Gender Address Height Weight Math Physics
ID
2304 S_2 C_3 F street_6 164 81 95.5 A-
1201 S_1 C_2 M street_5 188 68 97.0 A-
2205 S_2 C_2 F street_7 183 76 85.4 B
Original: https://blog.csdn.net/qq_43300880/article/details/124985663
Author: HiSpring流云
Title: Pandas学习笔记（二）—— Pandas索引
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