Creating multiple additional calculated columns to a dataframe in pandas using a for statement

Question

I am trying to create an additional column to my dataframe using a loop, where the additional columns would be a multiple of a current column, but that multiple will change. I understand that this can be solved relatively easily by just creating a new column for each, but this is part of a larger project that I cant do that for.

Starting with this dataframe:

   Time  Amount
0    20      10
1    10       5
2    15      25

Hoping for the following outcome:

       Time  Amount  Amount i=2  Amount i=3  Amount i=4
0    20      10          20          30          40
1    10       5          10          15          20
2    15      25          50          75          75

Think there should be an easy answer, but cant find anything online. So far I have this:

data = {'Time':  [20,10,15],
     'Amount': [10,5,25]}    
df = pd.DataFrame(data)

for i in range(2,5):
    df = df.append(df['Amount']*i)
    print(df)

Thanks

I think your expect outcome is wrong

Corralien
– Corralien

2021-09-23 20:38:32 +00:00
Commented Sep 23, 2021 at 20:38 — Corralien
– Corralien, Commented Sep 23, 2021 at 20:38

1sixunhuit · Accepted Answer · 2021-09-23 20:35:09Z

2

Do you want something like this ?

for i in range(2,5): 
    df["Amout i={}".format(i)] = df['Amount']*i

Output :

   Time  Amount  Amout i=2  Amout i=3  Amout i=4
0    20      10         20         30         40
1    10       5         10         15         20
2    15      25         50         75        100

answered Sep 23, 2021 at 20:35

1sixunhuit

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Comments

Renaud · Accepted Answer · 2021-09-23 20:37:44Z

1

Are you looking for:

import pandas as pd


data={'Time':[20,10,15],'Amount':[10,5,25]}

df=pd.DataFrame(data)

for i in range(2,5):
    df['Amount i='+str(i)]=df['Amount']*i
print(df)

Result:

   Time  Amount  Amount i=2  Amount i=3  Amount i=4
0    20      10          20          30          40
1    10       5          10          15          20
2    15      25          50          75         100

answered Sep 23, 2021 at 20:37

Renaud

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Comments

Corralien · Accepted Answer · 2021-09-23 20:39:44Z

0

Try:

df = df.join(pd.concat([df['Amount'].mul(i).rename(f'Amount i={i}')
                            for i in range(2, 5)], axis=1))

>>> df
   Time  Amount  Amount i=2  Amount i=3  Amount i=4
0    20      10          20          30          40
1    10       5          10          15          20
2    15      25          50          75         100

answered Sep 23, 2021 at 20:39

Corralien

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