0 votes

There are two methods of going ahead with this, one is by hardcoding the data frame, which might get a little tedious when it comes to large datasets. The other way around is to use the functions provided by pandas.

Here is an explanation for both the methods:

You can try hardcoding it like this

df.values array([[nan, 0.7, nan], [nan, 0.65, 0.5], [nan, 0.2, 0.5], [0.1, 0.2, nan], [0.1, 0.2, 0.9], [0.6, nan, 0.5], [0.1, nan, nan]])

The other way is to use a pandas function for it. These functions work on converting numPy arrays from al sorts of panda objects. This came out with pandas v0.24.0

**to_numpy(**): Works for index, series and data frame**array**: works with index and series only