I have a .pkl
file that has looks something like this but has at least 300 rows:
X | Y | Z | M |
---|---|---|---|
-0.522 | 3 | 0.55 | Yes |
0.44 | 5 | NaN | No |
NaN | NaN | 0.241 | Maybe |
0.325 | 3 | Nan | Yes |
I want to get a list of values for Y and M [3 = Yes, 5 = No, 3 = Yes ] but in some of the rows there are NaNs.
Currently I am able to get Y without the NaNs But there are no NaNs in M. I need to remove the M values that do not have a Y value. (Y = NaN)
Then print(Y_no_nans together with M_no_Y_nans)
You can do the following (df is your dataframe):
df2 = df[(pd.notna(df['Y']) & (pd.notna(df['M']))]
result = list(zip(df2['Y'], df2['M']))
print(result)
Output
[(3, 'Yes'), (5, 'No'), (3, 'Yes')]