Resolved: Drop rows that contains the same value in pandas DataFrame

In this post, we will see how to resolve Drop rows that contains the same value in pandas DataFrame

Question:

I’m currently working on a data frame like the one below:
artist week1 week2 week3 week4
Drake 2 2 3 1
Muse NA NA NA NA
Bruno Mars 3 3 4 2
Imagine Dragons NA NA NA NA
Justin Timberlake 2 2 NA 1

What I want to do is to drop the rows that only contain “NA” values. The result should be something like this:
artist week1 week2 week3 week4
Drake 2 2 3 1
Bruno Mars 3 3 4 2
Justin Timberlake 2 2 NA 1

I’ve tried using the pandas drop() function but drops every row with at least one “NA” value. In that case, the row for Justin Timberlake would be dropped but that’s not what I need.

Best Answer:

Use df.dropna() and set how='all' meaning If all values are NA, drop that row or column. then set the subset columns.
Or Keep only the rows with at least 2 non-NA values.

If you have better answer, please add a comment about this, thank you!

Source: Stackoverflow.com