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I've had issues finding a concise way to append a series to each row of a dataframe, with the series labels becoming new columns in the df. All the values will be the same on each of the dataframes' rows, which is desired.

I can get the effect by doing the following:

df["new_col_A"] = ser["new_col_A"]
.....
df["new_col_Z"] = ser["new_col_Z"]

But this is so tedious there must be a better way, right?

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  • Hello friend, mind explaining what exactly you want to do, you just want a quick code that can append multiple series at once? Commented Oct 21, 2022 at 0:56
  • pd.concat([df, ser], axis=1)? Commented Oct 21, 2022 at 1:01
  • Hey, sorry if it was vague. What I'm hoping for is something which takes a dataframe like this: df = pd.DataFrame([[1, 2], [1, 3], [4, 6]], columns=['A', 'B']) which when "concated" with a series like this: ser = pd.Series(["a","b"],index=["C",D"]) will give a result like this: df = pd.DataFrame([[1, 2,"a","b"], [1, 3,"a","b"], [4, 6,"a","b"]], columns=['A', 'B','C','D']) Commented Oct 21, 2022 at 1:23
  • I found the suggestion from @BeRT2me, simply adds it to the bottom and introduces NaNs all over the table. Commented Oct 21, 2022 at 1:27
  • 1
    Add all of that as an Edit to your question, not as a comment~ Commented Oct 21, 2022 at 1:28

1 Answer 1

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Given:

# df

   A  B
0  1  2
1  1  3
2  4  6

# ser

C    a
D    b
dtype: object

Doing:

df[ser.index] = ser
print(df)

Output:

   A  B  C  D
0  1  2  a  b
1  1  3  a  b
2  4  6  a  b
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1 Comment

Thanks heaps, and also thanks for the tip about further comments

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