Derive multiple df from single df such that each df has no NaN values

I think that start of a new target DataFrame should occur not only when the number of NaN values changes (compared to previous row), but also when this number is the same, but NaN values are in different columns.

So I propose the following formula:

dfs = [g.dropna(how='all',axis=1) for _,g in
    df.groupby(df.isna().ne(df.isna().shift()).any(axis=1).cumsum())]

You can print partial DataFrames (any number of them) running:

n = 0
for grp in dfs:
    print(f'\ndf No {n}:\n{grp}')
    n += 1

The advantage of my solution over the other becomes obvious when you add to the source DataFrame another row containing:

5   NaN    NaN    3.0

It contains also 1 non-null value (like two previous rows). The other solution will treat all these rows as one partial DataFrame containing:

  movie  rating
3   mol     NaN
4   lob     NaN
5   NaN     3.0

as you can see, with NaN values, whereas my solution divides these rows into 2 separate DataFrames, without any NaN.

create a list of dfs , with a groupby and dropna:

dfs = [g.dropna(how='all',axis=1) for _,g in df.groupby(df.isna().sum(1))]
print(dfs[0],'\n\n',dfs[1],'\n\n',dfs[2])

Or dict:

d = {f"df{e+1}": g[1].dropna(how='all',axis=1) 
       for e,g in enumerate(df.groupby(df.isna().sum(1)))}
print(d['df1'],'\n\n',d['df2'],'\n\n',d['df3']) #read the keys of d

  movie   name  rating
0   thg   John     3.0
1   thg  James     4.0 

   movie  rating
2   mol     5.0 

   movie
3   mol
4   lob