I would like to multiply two columns of a df by following a specific pattern without using a loop. I have this df :
num m d
0 8 5
1 2 3
2 17 8
The idea is to multply for each row in 'm' every row in 'd' except the one with the same 'num'. The resulting df would be :
num1 num2 mult
0 1 8x3 = 24
0 2 8x8 = 64
1 2 2x8 = 16
Is there a way to do that ?
Thank for your help
You can try:
df = df.set_index('num')
((df[['m']].rename(columns={'m':'d'}) @ df[['d']].T)
.rename_axis('num2', axis=1)
.stack().reset_index(name='mult')
)
Or use broadcasting:
(pd.DataFrame(df['m'].values * df['d'].values[:,None],
index=df['num'],
columns=df['num'].rename('num2'))
.stack().reset_index(name='mult')
)
num num2 mult
0 0 0 40
1 0 1 24
2 0 2 64
3 1 0 10
4 1 1 6
5 1 2 16
6 2 0 85
7 2 1 51
8 2 2 136
You could use the following:
product = df1['m'][df2['num1']].values*df1['d'][df2['num2']].values
df2['mult'] = pd.Series(product, index=df2.index)
I'd recommend first creating a frame with all possible permutations of the 2 columns, then filtering out the rows which don't correspond to the required pattern.
Something like this
df = df.set_index('num')
((df[['m']].rename(columns={'m':'d'}) @ df[['d']].T)
.rename_axis('num2', axis=1)
.stack().reset_index(name='mult')
)
df[df['num']!=df['num2']]
numcolumn have repeated values?