Adding multiple rows to newly created columns in a pandas dataframe

I'm using pandas to store the results of a machine learning model, and I have a dataframe that stores the input data. I want to extend that dataframe with the two outputs that the model returns, but I don't know how to do it.

I've tried doing somethin like this:

import pandas
df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':[1,2,3,4,5]})
df[['col3', 'col4']] = [[1,2,3,4,5],[1,2,3,4,5]]

But it throws an error

Exception has occurred: ValueError
Columns must be same length as key
  File "D:\InSilicoOP-FUAM\In Silico OP\src\pruebas.py", line 20, in <module>
    df[['col3', 'col4']] = [[1,2,3,4,5],[1,2,3,4,5]]
ValueError: Columns must be same length as key

I've also tried with
df['col3', 'col4'] = [1,2,3,4,5],[1,2,3,4,5] and df['col3', 'col4'] = [[1,2,3,4,5],[1,2,3,4,5]] and those throw ValueError: Length of values (2) does not match length of index (5)

I know I can assign each column separatedly, like so

df['col3'] = [1,2,3,4,5]

But then I'd have to separate the results from the model (which is a big problem on it's own...)

Is there a way to assign multiple

Assuming your results are stored in a list of lists, you could convert that to pandas DataFrame and join to the original:

results = [[1,2,3,4,5],[1,2,3,4,5]]

>>> df.join(pd.DataFrame(results, index=["col_3","col_4"]).T)

   col1  col2  col_3  col_4
0     1     1      1      1
1     2     2      2      2
2     3     3      3      3
3     4     4      4      4
4     5     5      5      5

In your specific example you could use the loc property. Make sure you insert the array in the right shape though:

df.loc[:,["col3", "col4"]] = [[1,1],[2,2],[3,3],[4,4],[5,5]]

Alternatively you can use numpy's transpose to create the right shape from the array that you had in your example.

df.loc[:,["col3", "col4"]] = np.transpose([[1,2,3,4,5],[1,2,3,4,5]])

You could write the outputs of col3 and col4 to another dataframe and then join them. I'm not sure if this is still separating your results out from the model?

df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':[1,2,3,4,5]})
df2 = pd.DataFrame({'col3':[1,2,3,4,5],'col4':[1,2,3,4,5]})
df3 = df.join(df2)
print(df3)
   col1  col2  col3  col4
0     1     1     1     1
1     2     2     2     2
2     3     3     3     3
3     4     4     4     4
4     5     5     5     5

You can unpack the values from the results list and assign them to the columns col3 and col4:

import pandas as pd

df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':[1,2,3,4,5]})

results = [[1,2,3,4,5],[1,2,3,4,5]]

df['col3'], df['col4'] = results