iterate over pandas columns based on conditions

Question

want to calculate C based on values of count, A and B

sample df:

count	A	B	C
yes	23	2	nan
nan	23	1	nan
yes	41	6	nan

result I want

count	A	B	C
yes	23	2	46
nan	23	1	0
yes	41	6	246

calculate C = A*B only when count value = yes otherwise C values =0 that is, it should skip nan values of count

Any help is appreciable

I am trying this

for ind, row in df.iterrows():
    if df['count'] == 'yes':
        df.loc[ ind, 'C'] =row['A'] *row['B']
    else:
        df.loc[ ind, 'C'] =0

But it's giving error : ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

Another option:

df.C = df.A.mul(df.B).where(df['count'].eq('yes')).fillna(0)

df
#  count   A  B      C
#0   yes  23  2   46.0
#1   NaN  23  1    0.0
#2   yes  41  6  246.0

Or if you prefer operators: df.C = (df.A * df.B).where(df['count'] == 'yes').fillna(0)

Answer 2

pandas overloads * for this operation, provided you correctly specify the indices you want to set:

mask = df["count"].notna()
df.loc[mask, "C"] = df["A"]*df["B"]
df.C.fillna(0, inplace=True)

or a slightly more concise version that would annoy your coworkers:

df["C"] = df["A"]*df["B"]*(df["count"].notna())

In the last, df["count"].notna() returns a boolean column, which is converted to a numeric type when multiplied by numerical columns. Concise but as clear.

output for either:

  count   A  B      C
0   yes  23  2   46.0
1   NaN  23  1      0
2   yes  41  6  246.0

This will be more performant than .apply and much more performant than iterrows.

Answer 3

Just use this:-

df['C']=df[df['count']=='yes']['C'].fillna(value=df['A']*df['B'])
df['C']=df['C'].fillna(0)

Try this:-

for ind, row in df.iterrows():
    if row['count'] == 'yes':
        df.loc[ ind, 'C'] =row['A'] *row['B']
    else:
        df.loc[ ind, 'C'] =0

You are getting error because you write df['count']=='yes' instead of row['count'] == 'yes'