Pandas multiply DataFrames with element-wise match of index and column

Question

I have two pandas DataFrames, with one of them having index and columns that are subsets of the other. For example:

DF1 =

date        a     b    c
20170101    1.0   2.2  3
20170102    2.1   5.2  -3.0
20170103    4.2   1.8  10.0
...
20170331    9.8   5.1  4.5

DF2 =

date        a     c
20170101    NaN   2.1
20170103    4     NaN

What I want is element-wise multiplication by matching both the index and column. i.e. only DF1[20170103]['c'] will be multiplied with DF2[20170103]['c'], etc.

The resulting DF should have the same dimension as the bigger one (DF1), with missing values in DF2 set as the original DF1 value:

result DF =

date        a     b    c
20170101    1.0   2.2  6.3
20170102    2.1   5.2  -3.0
20170103    16.8  1.8  10.0
...
20170331    9.8   5.1  4.5

What's the best/fastest way to do this? The real-life matrices are huge, and DF2 is relatively sparse.

Answer 1

I think you need vectorized function mul:

df = DF1.mul(DF2, fill_value=1)
print (df)
             a    b     c
date                     
20170101   1.0  2.2   6.3
20170102   2.1  5.2  -3.0
20170103  16.8  1.8  10.0
20170331   9.8  5.1   4.5