pandas索引和复合索引

一、获取索引 index

import pandas as ps
import numpy as npa1 = ps.DataFrame(np.arange(8).reshape((2,4)),columns=["A","B","C","D"],index=["a","b"])
print(a1,"\n","#"*50)#    A  B  C  D
# a  0  1  2  3
# b  4  5  6  7print(a1.index,"\n","#"*50) # 获取a1的行索引# Index(['a', 'b'], dtype='object')

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二、设置索引 index

import pandas as ps
import numpy as npa1 = ps.DataFrame(np.arange(8).reshape((2,4)),columns=["A","B","C","D"],index=["a","b"])
print(a1,"\n","#"*50)#    A  B  C  D
# a  0  1  2  3
# b  4  5  6  7a1.index = ["x","z"] # 指定a1的行索引
print(a1,"\n","#"*50)#    A  B  C  D
# x  0  1  2  3
# z  4  5  6  7

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三、重设索引 reindex()

import pandas as ps
import numpy as npa1 = ps.DataFrame(np.arange(8).reshape((2,4)),columns=["A","B","C","D"],index=["a","b"])
print(a1,"\n","#"*50)#    A  B  C  D
# a  0  1  2  3
# b  4  5  6  7print(a1.reindex(["x","y"]),"\n","#"*50) # 重新设置index(因为本身没有y索引的一行，故全为NaN)#      A    B    C    D
# x  0.0  1.0  2.0  3.0
# y  NaN  NaN  NaN  NaN

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四、将列索引下的元素设置为行索引 set_index()

import pandas as ps
import numpy as npa1 = ps.DataFrame(np.arange(8).reshape((2,4)),columns=["A","B","C","D"],index=["a","b"])
print(a1,"\n","#"*50)#    A  B  C  D
# a  0  1  2  3
# b  4  5  6  7print(a1.set_index("A",drop=False),"\n","#"*50) # 将某列索引下的元素设置为行索引(内部参数drop等于假时被设置为索引的那一列元素将不会消失，为真时将会消失)
print(a1.set_index("A").index,"\n","#"*50) # 输出设定后的索引#    B  C  D
# A
# 0  1  2  3
# 4  5  6  7

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五、去除重复的元素 unique()

import pandas as ps
import numpy as npa2 = ps.DataFrame(np.ones(8).reshape((2,4)),columns=["A","B","C","D"]) # 设置一个全为1的2*4的二维数组
print(a2,"\n","#"*50)#      A    B    C    D
# 0  1.0  1.0  1.0  1.0
# 1  1.0  1.0  1.0  1.0a2 = a2.set_index("A",drop=False) # 将A索引下的元素设置为行索引（索引可为相同的值）
print(a2,"\n","#"*50)print(a2.loc[1.0,"A"].unique(),"\n","#"*50) # unique()方法过掉重复的元素

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六、复合索引 set_index([])

import pandas as ps
import numpy as npa2 = ps.DataFrame(np.ones(8).reshape((2,4)),columns=["A","B","C","D"]) # 设置一个全为1的2*4的二维数组(重新赋值)
a2 = a2.set_index(["A","B"],drop=False) # 复合索引，可以将多列设置为索引
print(a2,"\n","#"*50,"\n",a2.index) # 输出a2和它的索引

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1、复合索引查找数值 loc

import pandas as ps
import numpy as npa3 = ps.DataFrame(np.arange(8).reshape((2,4)),columns=["A","B","C","D"])
print(a3,"\n","*"*50)
a3 = a3.set_index(["A","B"],drop=False) # 复合索引，可以将多列设置为索引
print(a3,"\n","*"*50)#      A  B  C  D
# A B
# 0 1  0  1  2  3
# 4 5  4  5  6  7print(a3.loc[0].loc[1,"C"],"\n","*"*50) # 通过loc方法一层一层索引到需要的数值

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1、复合索引查找数值 swaplevel() + loc

import pandas as ps
import numpy as npa3 = ps.DataFrame(np.arange(8).reshape((2,4)),columns=["A","B","C","D"])
print(a3,"\n","*"*50)
a3 = a3.set_index(["A","B"],drop=False) # 复合索引，可以将多列设置为索引
print(a3,"\n","*"*50)#      A  B  C  D
# A B
# 0 1  0  1  2  3
# 4 5  4  5  6  7print(a3.swaplevel(),"\n","*"*50) # swaplevel() 将内索引和外索引调换位置#      A  B  C  D
# B A
# 1 0  0  1  2  3
# 5 4  4  5  6  7print(a3.swaplevel().loc[1,"C"]) # 用swaplevel() 方法调换了内外索引后就可以直接使用loc索引到需要的数值了