Dropping all zeros in the start and end of a df column

I have a pandas df as follows:

Date          Price
2020-01-01    0
2020-01-02    0
2020-01-03    0
2020-01-04    5
2020-01-05    5
2020-01-06    0
2020-01-07    5
2020-01-08    5
2020-01-09    0
2020-01-10    0

I want to drop all the zeroes before the first non-zero value and all the zeroes after the last non-zero value.
The resulting df would look like:

Date          Price
2020-01-04    5
2020-01-05    5
2020-01-06    0
2020-01-07    5
2020-01-08    5

Please do notice that the 0 value on 2020-01-06 is preserved as it has a non-zero number before and after.
How can I do this in pandas? Doing via loop seems very inefficient as I have a large df.

>Solution :

You could use ne + cummax twice; once to cutoff the 0s at the top; then reverse the DataFrame and cutoff the 0s at the bottom:

out = df[df['Price'].ne(0).cummax() & df['Price'][::-1].ne(0).cummax()]

Output:

         Date  Price
3  2020-01-04      5
4  2020-01-05      5
5  2020-01-06      0
6  2020-01-07      5
7  2020-01-08      5