如何使用具有相同索引的另一个dataframe对一个dataframe的值进行索引？

Question

import pandas as pd
import numpy as np

df1 = pd.DataFrame({'a': ['.81', '1.2', '.67', '.78'],
                   'b': ['.2', '-.9', '.7', '.89'],
                   'c': ['.3', '.22', '.4', '.98'],
                   'd': ['.5', '.45', '.34', '.92']},
                   index=[0, 1, 2, 3])
df2 = pd.DataFrame({'a': ['1', '2', '3', '4'],
                   'b': ['9', '7', '6', '5'],
                   'c': ['1', '14', '9', '5'],
                   'd': ['3', '12', '2', '34']},
                   index=[0, 1, 2, 3])
count=0

for i in df1.index:
    d = pd.DataFrame()
    d = df1.iloc[[count]]
    count = count+1
    d = d.T
    d.columns = ['Dates'] 
    try:
        d.sort_values(by=['Dates'], inplace=True)
    except KeyError:
        print(KeyError)
    d.dropna(inplace=True)
    d['Dates'] = d['Dates'][:10]
    print(d)
count = 0
for y in df2.index:
    df = pd.DataFrame()
    df = df2.iloc[[count]]
    count = count+1
    df = df.T
    df.columns = ['Dates'] 
    df.dropna(inplace=True)
    print(df)

     Dates
b    .2
c    .3
d    .5
a   .81
  Dates
b   -.9
c   .22
d   .45
a   1.2
  Dates
d   .34
c    .4
a   .67
b    .7
  Dates
a   .78
b   .89
d   .92
c   .98

   Dates
a     1
b     9
c     1
d     3
  Dates
a     2
b     7
c    14
d    12
  Dates
a     3
b     6
c     9
d     2
  Dates
a     4
b     5
c     5
d    34

Answer 1

使用您的示例dataframes，假设DF1通过drop（）ping具有index=2的行而变小：

df1.drop(2,inplace=True)

DF1现在将：

     a    b    c    d
0  .81   .2   .3   .5
1  1.2  -.9  .22  .45
3  .78  .89  .98  .92

您可以以任何方式修改索引。然后，要从DF2中选择DF1中存在的行，可以执行以下操作：

df2.loc[df1.index]

给你：

   a  b   c   d
0  1  9   1   3
1  2  7  14  12
3  4  5   5  34

如果只需要一些列，我们假设C&D:

df2.loc[df1.index,['c','d']]

给你：

    c   d
0   1   3
1  14  12
3   5  34