[Python] Pandas: How to sum columns based on conditional of other column values?

I have the following pandas DataFrame.

import pandas as pd
df = pd.read_csv('filename.csv')

print(df)

dog A B C
0 dog1 0.787575 0.159330 0.053095
1 dog10 0.770698 0.169487 0.059815
2 dog11 0.792689 0.152043 0.055268
3 dog12 0.785066 0.160361 0.054573
4 dog13 0.795455 0.150464 0.054081
5 dog14 0.794873 0.150700 0.054426
.. ....
8 dog19 0.811585 0.140207 0.048208
9 dog2 0.797202 0.152033 0.050765
10 dog20 0.801607 0.145137 0.053256
11 dog21 0.792689 0.152043 0.055268
....


I create a new column by summing columns "A", "B", "C" as follows:

df['total_ABC'] = df[["A", "B", "B"]].sum(axis=1)


Now I would like to do this based on a conditional, i.e. if "A" < 0.78 then create a new summed column df['smallA_sum'] = df[["A", "B", "B"]].sum(axis=1). Otherwise, the value should be zero.

How does one create conditional statements like this?

My thought would be to use

df['smallA_sum'] = df1.apply(lambda row: (row['A']+row['B']+row['C']) if row['A'] < 0.78))


However, this doesn't work and I'm not able to specify axis.

How do you create a column based on the values of other columns?

You could also do something like for each df['dog'] == 'dog2', create column dog2_sum, i.e.

df['dog2_sum'] = df1.apply(lambda row: (row['A']+row['B']+row['C']) if df['dog'] == 'dog2'))


but my approach is incorrect.

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