Pandas中DataFrame的基本操作之重新索引讲解

目录

• Pandas DataFrame之重新索引
• 1.reindex可以对行和列索引
• 2.reindex插值处理
• Pandas DataFrame重置索引案例

import pandas as pd
data=[[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4]]
df = pd.DataFrame(data,index=["d","b","c","a"])
print(df)

df=df.reindex(["a","b","c","d","e"])
print(df)

df=df.reindex(columns=[2,1,3,4,0])
print(df)

import pandas as pd 
data=[[1,1,1,1],[2,2,2,2],[3,3,3,3]]
df = pd.DataFrame(data,index=range(3))
print(df)
df=df.reindex([0,1,2,3,4,5],method="ffill")
print("--------------")
print(df)

import pandas as pd
import numpy as np
a=pd.DataFrame(np.random.randint(1,10,20).reshape(4,5))
print(a)
   0  1  2  3  4
0  1  3  2  7  6
1  8  2  2  7  2
2  2  6  6  2  5
3  4  1  6  8  9
b=a.sort_values(by=4)
print(b)
   0  1  2  3  4
1  8  2  2  7  2
2  2  6  6  2  5
0  1  3  2  7  6
3  4  1  6  8  9
### 重置索引:方法1
c=a.sort_values(by=4,ignore_index=True)
print(c)
   0  1  2  3  4
0  8  2  2  7  2
1  2  6  6  2  5
2  1  3  2  7  6
3  4  1  6  8  9
### 重置索引：方法2
d=b.reset_index(drop=True)
print(d)
   0  1  2  3  4
0  8  2  2  7  2
1  2  6  6  2  5
2  1  3  2  7  6
3  4  1  6  8  9