Generate multiple columns based on conditions from other columns

Question

I've searched quite a bit for a solution, but almost all questions are related to the creation of a single column. So, here is my problem.

Given an example DataFrame:

df = pd.DataFrame({
    "blue": [5, 5, 4], 
    "red": [1, 7, 5],
    "yellow": [3, 9, 0],
    "orange": [9, 7, 3],
    "config": ["north", "south", "north"]
})

   blue config  orange  red  yellow
0     5  north       9    1       3
1     5  south       7    7       9
2     4  north       3    5       0

What I would like to achieve is to create additional columns based on multiple conditions (a mapping to be specific). Here is an example of what I have tried:

def gen_col(row):

    if row["config"] == "north":
        new_blue = row["blue"]
        new_red = row["red"]
        new_yellow = row["yellow"]
        new_orange = row["orange"]
        return new_blue, new_red, new_yellow, new_orange
    elif row["config"] == "south":
        new_blue = row["orange"]
        new_red = row["yellow"]
        new_yellow = row["red"]
        new_orange = row["blue"]
        return new_blue, new_red, new_yellow, new_orange

df["new_blue", "new_red", "new_yellow", "new_orange"] = df.apply(gen_col, axis=1)

However, this returns the following:

   blue config  orange  red  yellow (new_blue, new_red, new_yellow, new_orange)
0     5  north       9    1       3             (5, 1, 3, 9)
1     5  south       7    7       9             (7, 9, 7, 5)
2     4  north       3    5       0             (4, 5, 0, 3)

Any ideas on how to create separate new columns?

jezrael · Accepted Answer · 2020-05-29 06:55:58Z

4

Use result_type='expand' parameter in DataFrame.apply and also add nested lists for assigned columns:

df[["new_blue", "new_red", "new_yellow", "new_orange"]] = df.apply(gen_col, axis=1, result_type='expand')
print (df)
   blue  red  yellow  orange config  new_blue  new_red  new_yellow  new_orange
0     5    1       3       9  north         5        1           3           9
1     5    7       9       7  south         7        9           7           5
2     4    5       0       3  north         4        5           0           3

edited May 29, 2020 at 6:55

answered May 29, 2020 at 6:50

jezrael

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2 Comments

gplt Over a year ago

thanks for the lightning fast reply! And note to self, take a (closer) look at the docs :)

ds_Abc Over a year ago

@jezrael You seem to be very knowledgeable in pandas. Could you please help me by providing your views on this? stackoverflow.com/questions/62069465/… Thanks.

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