[英]Python DataFrame: replace or combine selected values into main DataFrame
I have two pandas DataFrame as below. 我有两个熊猫DataFrame如下。 It contains strings and np.nan values.
它包含字符串和np.nan值。 df =
df =
A B C D E F
0 aaa abx fwe dcs NaN gsx
1 bbb daf dxs fsx NaN ewe
2 ccc NaN NaN NaN NaN dfw
3 ddd NaN NaN asc NaN NaN
4 eee NaN NaN cse NaN NaN
5 fff NaN NaN wer xer NaN
df_result = df_result =
A C E F
2 sfa NaN NaN wes
4 web NaN NaN NaN
5 NaN wwc wew NaN
What I want is copy entire df_result DataFrame to df DataFrame with corresponding columns and index. 我想要的是将整个df_result DataFrame复制到具有相应列和索引的df DataFrame。 so my output would be =
所以我的输出是=
A B C D E F
0 aaa abx fwe dcs NaN gsx
1 bbb dxs fsx fsx NaN ewe
2 sfa NaN NaN NaN NaN wes
3 wen NaN NaN asc NaN NaN
4 web NaN NaN cse NaN NaN
5 NaN NaN wwc wer wew NaN
So basically I want to copy exact values of df_result to df even thought ther are np.nan values like A:5 (changed from fff to NaN). 所以基本上我想将df_result的精确值复制到df,即使认为这是np.nan值,如A:5(从fff更改为NaN)。 Also, I need to keep the order of columns as it is.
另外,我需要保持列的顺序不变。 Please let me know efficient way to do this.
请让我知道执行此操作的有效方法。 Thank you!
谢谢!
df.update(dfr.fillna('NaN'))
df.replace('NaN',np.nan)
Out[501]:
A B C D E F
0 aaa abx fwe dcs NaN gsx
1 bbb daf dxs fsx NaN ewe
2 sfa NaN NaN NaN NaN wes
3 ddd NaN NaN asc NaN NaN
4 web NaN NaN cse NaN NaN
5 NaN NaN wwc wer wew NaN
Assuming your columns and indices are setup properly, you can just say. 假设您的列和索引设置正确,您可以说。
df.loc[df_result.index,df_result.columns] = df_result
example of it working: 工作示例:
import pandas as pd
import numpy as np
df=pd.DataFrame(data=[ [1 for y in range(5)] for i in range(5)] , columns=list(range(5)))
df.loc[0::2,2]=np.nan
print(df)
df2 = pd.DataFrame(data=[ [2 for y in range(3)] for i in range(2)] , columns=list(range(2,5)),index=range(1,3))
df2.loc[:,3] = np.nan
print(df2)
df.loc[df2.index,df2.columns] = df2
print(df)
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