I have two DataFrame objects which I want to apply an element-wise multiplication on each row onto:
df_prob_wc.shape # (3505, 13)
df_prob_c.shape # (13, 1)
I thought I could do it with DataFrame.apply()
df_prob_wc.apply(lambda x: x.multiply(df_prob_c), axis=1)
which gives me:
TypeError: ("'int' object is not iterable", 'occurred at index $')
or with
df_prob_wc.apply(lambda x: x * df_prob_c, axis=1)
which gives me:
TypeError: 'int' object is not iterable
But it's not working. However, I can do this:
df_prob_wc.apply(lambda x: x * np.asarray([1,2,3,4,5,6,7,8,9,10,11,12,13]), axis=1)
What am I doing wrong here?
It seems you need multiple by Series created with df_prob_c by iloc:
df_prob_wc = pd.DataFrame({'A':[1,2,3],
'B':[4,5,6],
'C':[7,8,9],
'D':[1,3,5],
'E':[5,3,6],
'F':[7,4,3]})
print (df_prob_wc)
A B C D E F
0 1 4 7 1 5 7
1 2 5 8 3 3 4
2 3 6 9 5 6 3
df_prob_c = pd.DataFrame([[4,5,6,1,2,3]])
#for align data same columns in both df
df_prob_c.index = df_prob_wc.columns
print (df_prob_c)
0
A 4
B 5
C 6
D 1
E 2
F 3
print (df_prob_wc.shape)
(3, 6)
print (df_prob_c.shape)
(6, 1)
print (df_prob_c.iloc[:,0])
A 4
B 5
C 6
D 1
E 2
F 3
Name: 0, dtype: int64
print (df_prob_wc.mul(df_prob_c.iloc[:,0], axis=1))
A B C D E F
0 4 20 42 1 10 21
1 8 25 48 3 6 12
2 12 30 54 5 12 9
Another solution is multiple by numpy array, only need [:,0] for select:
print (df_prob_wc.mul(df_prob_c.values[:,0], axis=1))
A B C D E F
0 4 20 42 1 10 21
1 8 25 48 3 6 12
2 12 30 54 5 12 9
And another solution with DataFrame.squeeze:
print (df_prob_wc.mul(df_prob_c.squeeze(), axis=1))
A B C D E F
0 4 20 42 1 10 21
1 8 25 48 3 6 12
2 12 30 54 5 12 9