Selective Pandas Dropna So That The Dataframe Will Be Different Lengths

I am attempting to drop the nan values in my DataFrame df, however I am having difficulty in dropping the for each column without effecting the entire row. An example of my df can

Solution 1:

Use justify with DataFrame.dropna:

df = pd.DataFrame(justify(df.values, invalid_val=np.nan, axis=0, side='up'), 
                  index=df.index, 
                  columns=df.columns).dropna(how='all')
print (df)
    Advertising  No Advertising
0          71.0             7.0
1          65.0            36.0
2          14.0             9.0
3          76.0           103.0
4          73.0             NaN
5          85.0             NaN
6          17.0             NaN

Another slowier solution is use DataFrame.apply with Series.dropna:

df = df.apply(lambda x: pd.Series(x.dropna().values))
print (df)

   Advertising  No Advertising
0         71.0             7.0
1         65.0            36.0
2         14.0             9.0
3         76.0           103.0
4         73.0             NaN
5         85.0             NaN
6         17.0             NaN

Mixing numeric with strings (empty strings) is not good idea, because if need processes number later pandas functions failed, so rather dont do it.

But is is possible by :

df = df.fillna('')