change a column based on other column values, possibly using other column values as result

filter_by_condition.py

import pandas as pd
df = pd.read_excel('soxl.xlsx')
df=df[df['IDs of phase known'].notnull() | df['Ids of distance known'].notnull()]

#If you wanted to remove any row with any missing data you can use the builtin :

df = df.dropna()

#which removes any row with missing values (which otherwise get imported as Nan values).

#If you want to replace the 2nd col. with master column values when they are missing, you can do

df = df.where((pd.notnull(df)), None)

#to replace the NaN's with None (useful in the next step) and then

df['IDs of phase known']= df.apply(lambda r:r['IDs of phase known'] if r['IDs of phase known'] else r['Set of Ids known for transformers'],axis=1)

#And of course you can do the same with the 3rd col. If you want to replace only in the case where both col2 and 3 values are missing, you can do similarly, but check both columns for None:

df['IDs of phase known']= df.apply(lambda r:r['Set of Ids known for transformers'] if not (r['IDs of phase known'] or r['Ids of distance known']) else r['IDs of phase known'] ,axis=1 )

#You can also do this, actually a bit easier, with numpy:

df['IDs of phase known'] = np.where(np.isnan(df['IDs of phase known']), df['Set of Ids known for transformers'], df['IDs of phase known'])

#If you want to just replace missing values with master column vals, or

df['IDs of phase known'] = np.where( (np.isnan(df['IDs of phase known']) & (np.isnan(df['Ids of distance known']))),df['Set of Ids known for transformers'],df['IDs of phase known'])