pandas create new columns from tuple values in one column-CodePudding

I have a dataframe that looks like

    RMSE    SELECTED DATA   information
0   100 [12, 15, 19, 13]    (arr1, str1, fl1)
1   200 [7, 12, 3]          (arr2, str2, fl2)
2   300 [5, 9, 3, 3, 3, 3]  (arr3, str3, fl3)

Here, I want to break up the information column into three distinct columns: the first column containing the arrays , the second column containing the string and the last column containing the float Thus the new dataframe would look like

    RMSE    SELECTED DATA   ARRAYS  STRING  FLOAT
0   100 [12, 15, 19, 13]    arr1    str1    fl1
1   200 [7, 12, 3]          arr2    str2    fl2
2   300 [5, 9, 3, 3, 3, 3]  arr3    str3    fl3

I thought one way would be to isolate the information column and then slice it using .apply like so:

df['arrays'] = df['information'].apply(lambda row : row[0])

and do this for each entry. But I was curious if there is a better way to do this as if there are many more entries it may become tedious or slow with a for loop

CodePudding user response：

Let us recreate the dataframe

tojoin = pd.DataFrame(df.pop('information').to_numpy().tolist(), 
                      index = df.index, 
                      columns = ['ARRAYS', 'STRING', 'FLOAT'])
df = df.join(tojoin)