CODEWITHSUNDEEP
pythonpandas
Tiago Duque
Tiago Duque

Reputation: 2079

Combining columns and joining non-missing values in Pandas

Imagine that I have a single Dataframe as such:

df = pd.DataFrame([[1,2,3,None],[1,2,3,None],[1,2,3,None],[None,2,3,1]], columns=["A","B","C","AA"])
A B C AA
1 2 3
1 2 3
2 3 1

Column AA is actually the same as A, but has suffered a typo somewhere in the data processing pipeline precious steps.

How can I actually rename ['AA'] to ['A'] and move the non-missing values? Example:

A B C
1 2 3
1 2 3
1 2 3

I imagine that if I do:

df['A'] = df['AA']

Null values will be copied.

So, any hints here?

Upvotes: 0

Views: 153

Answers (3)

sammywemmy
sammywemmy

Reputation: 28709

You could try combine_first:

In [8]: df.assign(A=df.A.combine_first(df.AA)).drop(columns='AA')
Out[8]: 
     A  B  C
0  1.0  2  3
1  1.0  2  3
2  1.0  2  3
3  1.0  2  3

Upvotes: 1

Utpal Dutt
Utpal Dutt

Reputation: 403

To add to @mullinscr, first sum the columns and then drop the 'AA' column

df['A'] = df[['A','AA']].sum(axis=1)
df.drop('AA', axis=1, inplace=True)

Upvotes: 0

mullinscr
mullinscr

Reputation: 1748

Sum them both together:

df['A'] = df[['A','AA']].sum(axis=1)

Result is:

     A  B  C   AA
0  1.0  2  3  NaN
1  1.0  2  3  NaN
2  1.0  2  3  NaN
3  1.0  2  3  1.0

Upvotes: 0

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