Extract values from two columns of a dataframe and put it in a list

user_14698158 0
Viewed 86

I have a dataframe as shown below:

df = 

 A  col_1   col_45   col_3
1.0   4.0    45.0    [1, 9]
2.0   4.0    NaN     [9, 10]
3.0   49.2   10.8    [1, 10]

The values in col_1 are of type float and the values in col_3 are in a list. For every row, I want to extract the values in col_1 and col_3 and put it together in a list.

I tried the following:

df[['col_1','col_3']].astype(float).values.tolist()

But it threw me a Value error: ValueError: setting an array element with a sequence..

I would like to have a list as follows:

[[4.0,1.0,9.0],
 [4.0,9.0,10.0],
 [49.2,1.0,10.0]]

Is there a way to do this? Thanks.

pandas python
3 Answers

Convert one element in col_1 to list then use merge two list like list_1 + list_2, You can use pandas.apply with axis=1 for iterate over each row:

>>> df.apply(lambda row: [row['col_1']] + row['col_3'], axis=1)
0      [4.0, 1, 9]
1     [4.0, 9, 10]
2    [49.2, 1, 10]
dtype: object

>>> df.apply(lambda row: [row['col_1']] + row['col_3'], axis=1).to_list()
[
    [4.0, 1, 9], 
    [4.0, 9, 10], 
    [49.2, 1, 10]
]
user_17405770

Use apply functions to cast the col_1 to list and then concatenate by + operator -

df['col_1'].apply(lambda x: [x]) + df['col_3']

Output

0      [4.0, 1, 9]
1     [4.0, 9, 10]
2    [49.2, 1, 10]
dtype: object
user_40980130

The best IMO, might be to use underlying numpy array:

out = np.c_[df['col_1'].to_numpy(), df['col_3'].to_list()].tolist()

output:

[[4.0, 1.0, 9.0],
 [4.0, 9.0, 10.0],
 [49.2, 1.0, 10.0]]

If you want to keep a DataFrame:

pd.concat([df['col_1'], pd.DataFrame(df['col_3'].to_list())], axis=1)

output:

   col_1  0   1
0    4.0  1   9
1    4.0  9  10
2   49.2  1  10
user_163434640
Related