Python – Replaces nan values based on row conditions

Here is my raw data frame df_:

 index_label,id_label,morning,evening,night
 a,x,nan,eating,sleep
 b,x,shower,eating,nan
 c,x,nan,nan,nan
 d,y,work,reading,travel
 e,y,nan,reading,nan
 f,y,work,nan,nan
 g,z,shower,nan,travel
 h,z,shower,eating,nan

I tried replacing the nan value with a non-value taken from the same dataframe df based on the same id_labels. Each column “morning”, “evening” needs to be cleared from the NAN. The Night column should remain unchanged.

For example, I wrote this for the “Morning” column

crit_nan_ = pd.isna(df_[['morning']])
df_nan_ = df_.loc[crit_nan_]
df_clean_ = df_.loc[~crit_nan_]

But how do I get the result data frame:

 index_label,id_label,morning,evening,night
 a,x,shower,eating,sleep
 b,x,shower,eating,nan
 c,x,shower,eating,nan
 d,y,work,reading,travel
 e,y,work,reading,nan
 f,y,work,reading,nan
 g,z,shower,eating,travel
 h,z,shower,eating,nan

The resulting data frame can use df.groupby Get & > df.fillna :

def fill_na(x):
    return x.fillna(method="ffill").fillna(method="bfill")

for col in ("morning", "evening", ):
    d[col] = d.groupby("id_label")[col].transform(fill_na)