3

I have a dataframe with columns consisting of lists of numbers:

idx codes       new_column
0   [12,18,5]
1   [22,15]
2   [4]
3   [15,1]

How can I add a new column to the dataframe consisting of the first list entry of the codes column:

idx codes     new_column
0   [12,18,5]  12
1   [22,15]    22
2   [4]         4
3   [15,1]     15

I tried:
    df['new_column']=df['codes'][0]

However, that didn't work.
cs95
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afshin
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1 Answers1

8

The easiest way is using str.get 

# df['new_column'] = df['codes'].str.get(0)
df['new_column'] = df['codes'].str[0]

However, I would suggest a list comprehension for speed, if there are no NaNs:

df['new_column'] = [l[0] for l in df['codes']]

If lists can be empty, you can do something like:

df['new_column'] = [l[0] if len(l) > 0 else np.nan for l in df['codes']]

To handle NaNs with the list comprehension, you can use loc to subset and assign back.

m = df['codes'].notna()
df.loc[m, 'new_column'] = [
    l[0] if len(l) > 0 else np.nan for l in df.loc[m, 'codes']]

Obligatory why-is-list-comp-worth-it link: For loops with pandas - When should I care?

cs95
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