[英]Replace values in pandas dataframe based on other dataframe
I have two dataframes, one in the form of: 我有两个数据框,其中一个的形式为:
# X Y
1 2 0.0
2 5 0.0
3 10 0.0
4 15 0.0
5 17 0.0
6 21 0.0
and one in the form of: 一种形式为:
A B C
1 4 2
2 5 3
3 6 4
I want to replace all the ABC values from the second dataframe, with the X values; 我想用X值替换第二个数据帧中的所有ABC值; so I want go over the ABC df and if the number matches the # of df1 to replace it with the X value
所以我想遍历ABC df,如果该数字与df1的#匹配,则将其替换为X值
the end table should look: 茶几应该看起来像:
A B C
2 15 5
5 17 10
10 21 15
is there a way I can do it? 有办法吗?
IIUC replace
IIUC
replace
df1.replace(df.set_index('#').X)
Out[382]:
A B C
0 2 15 5
1 5 17 10
2 10 21 15
say your first DataFrame is a
and your second is b
, you can map b
columns to ax
values like this: 假设您的第一个DataFrame是
a
,第二个是b
,您可以将b
列映射到ax
值,如下所示:
b.apply(lambda y: a.x[(y -1).tolist()].values)
The result is: 结果是:
A B C
0 2 15 5
1 5 17 10
2 10 21 15
Only you should use: 只有您应该使用:
df1.set_index('#',inplace = True)
df=df.apply(lambda x: x.replace(df1.loc[x,'X']))
Example: 例:
import pandas as pd
import numpy as np
df1=pd.DataFrame()
df1['#']=[1,2,3,4,5,6]
df1['X']=[2,5,10,15,17,21]
df1['Y']=[0,0,0,0,0,0]
df=pd.DataFrame()
df['A']=[1,2,3]
df['B']=[4,5,6]
df['C']=[2,3,4]
df1.set_index('#',inplace = True)
df=df.apply(lambda x: x.replace(df1.loc[x,'X']))
print(df)
Output: 输出:
A B C
0 2 15 5
1 5 17 10
2 10 21 15
Note df1.set_index('#',inplace = True)
set '#'
column like index . 注意
df1.set_index('#',inplace = True)
将'#'
列设置为index。 if this column was already the index it is not necessary to execute it 如果此列已经是索引,则无需执行它
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