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I made a for loop that creates a different pandas dataframe on each iteration. Something like this -> First iteration:

index Letter Value
0 A 1
1 B 2
2 C 3

Second iteration:

index Letter Value
0 C 5
1 D 3
2 E 1
3 F 2

Third iteration:

index Letter Value
0 A 2
1 F 1

I want to save each dataframe to a new one that looks like this:

index Letter Value Value Value
0 A 1 2
1 B 2
2 C 3 5
3 D 3
4 E 1
5 F 2 1

Also, new letters can appear on each iteration, so for example if 'G' appears for the first time on interation 'n', a new row would need to be created on the desired consolidated dataframe.

Zehtre
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1 Answers1

0

You can make Letter the index for each dataframe, and then use pd.concat with axis=1:

dataframes = [df1, df2, df3]
new_df = pd.concat([d.set_index('Letter') for d in dataframes], axis=1)

Output:

>>> new_df
        Value  Value  Value
Letter                     
A       1.0   NaN     2.0  
B       2.0   NaN    NaN   
C       3.0    5.0   NaN   
D      NaN     3.0   NaN   
E      NaN     1.0   NaN   
F      NaN     2.0    1.0