'Sum of list values in a df, new column, values are objects

I have a df made of values from a dictionary. I can get rid of [], ',' and split it all in different cols (one col per number). But can't make the transfer to float, tried different ways. I'm looking for new cols with sum of values for each []. Used (df1['all'][0]) and got the numbers like in column 'all'.

import pandas as pd
data = {'all':['[0.75, 0.34, 0.91, 0.12, 0.5],[0.54, 0.65, 0.5, 0.79, 0.91],[0.81, 0.77, 0.82, 0.66, 0.38],[0.0, 0.78, 0.87, 0.81, 0.67],[0.0, 0.0, 0.56, 0.44, 0.0]']
        }
 
df1 = pd.DataFrame(data)
 
print(df1)

Preferred output

    all                all1    all2   all3    all4    all5
0  [0.75, 0.34.....    2.62    3.39   3.44    3.13    1.0
python-3.xpandaslistdataframe


Solution 1:[1]

Try:

from ast import literal_eval

df_out = pd.DataFrame(
    [
        {"all": orig} | {f"all{i}": sum(l) for i, l in enumerate(row, 1)}
        for orig, row in zip(df1["all"], df1["all"].apply(literal_eval))
    ]
)
print(df_out)

Prints:

                                                                                                                                                    all  all1  all2  all3  all4  all5
0  [0.75, 0.34, 0.91, 0.12, 0.5],[0.54, 0.65, 0.5, 0.79, 0.91],[0.81, 0.77, 0.82, 0.66, 0.38],[0.0, 0.78, 0.87, 0.81, 0.67],[0.0, 0.0, 0.56, 0.44, 0.0]  2.62  3.39  3.44  3.13   1.0

Solution 2:[2]

This is a modification of @Andrej Kesely answer. This modification works for Python <3.9 and does not drop other columns that are contained in df1.

from ast import literal_eval 

df_out = pd.concat(
    [
        df1,
        pd.DataFrame(
            [
                {**{f"all{i}": sum(l) for i, l in enumerate(row, 1)}}
                for row in df1["all"].apply(literal_eval)
            ]
        )
    ],
    axis=1        
)
print(df_out)

  col1 col2                                                all  all1  all2  all3  all4  all5 
0    A    C  [0.75, 0.34, 0.91, 0.12, 0.5],[0.54, 0.65, 0.5...  2.62  3.39  3.44  3.13   1.0 
1    B    D  [0.75, 0.34, 0.91, 0.12, 0.5],[0.54, 0.65, 0.5...  2.62  3.39  3.44  3.13   1.0

Sources

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Source: Stack Overflow

Solution Source
Solution 1 Andrej Kesely
Solution 2

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