广播两个pandas DataFrames的乘法

Question

I have two DataFrames, for example:

df1 = pn.DataFrame(np.arange(6).reshape(3, 2), columns=['A1', 'B1'])
df2 = pn.DataFrame(np.arange(1,7).reshape(3, 2), columns=['A2', 'B2'])

  A1 B1
0  0  1
1  2  3
2  4  5

  A2 B2
0  1  2
1  3  4
2  5  6

I need multiply df1 and df2 by columns to get a DataFrame with following result:

  A1*A2  A1*B2  B1*A2  B1*B2
0     0      0      1      2
1     6      8      9     12
2    20     24     25     30

Sizes of df1 and df2 in real task are (1000 columns x 90 000 rows).

I don't want to use double "for" cycle across columns of these DataFrames.

Is there a built-in function or some easy way to calculate it?

Answer 1

You can use df.multiply() to multiply df with a series and then concat the resulting dataframes like this:

df3 = pd.concat([df1[["A1", "B1"]].multiply(df2["A2"], axis="index"),
df1[["A1", "B1"]].multiply(df2["B2"], axis="index")], axis = 1)

df3.columns = ['A1*A2', "B1*A2", "A1*B2", "B1*B2"]

You get:

     A1*A2  B1*A2   A1*B2   B1*B2
0     0      1      0       2
1     6      9      8       12
2     20     25     24      30

Answer 2

Use broadcasting for efficient performance gain:

import itertools

df = pd.DataFrame((df1.values[..., None] * df2.values[:, None]).reshape(df1.shape[0],-1))
df.columns = ["*".join(i) for i in itertools.product(*[df1.columns, df2.columns])]

---

The purpose of incorporating df1.values[..., None] is to create an extra dimension to the right having shape (3, 2, 1) from earlier (3, 2) shape of df1.values .

Furthermore, df2.values[:, None] adds an extra dimension towards the center axis so that it's shape becomes (3, 1, 2) from initial (3,2) to aid in the multiplication process.

Finally, reshape them to take on the same number of rows as that of the original df1 (or) df2
( since both share the same shape in the question mentioned ).