Filtering for different conditions in different columns pandas

Python 3.9 and Pandas 1.3.4

So here’s the df:

1   First Name  Last Name  fullname
2   Freddie     Mercury    Freddie Mercury
3   John        Lennon     John Lennon
4   David       Bowie      David Bowie
5                          John Doe
6   Joseph                 Joseph
7               Jovi       Jovi

My piece of code currently just finds the fullname column is just First Name + Last Name.

I’m currently trying to filter for blank entries in the First Name column, Last Name column, and any "John Does" in the fullname column.

Current code:

import pandas as pd

df = pd.read_csv('file.csv', dtype=str, header=0)

df2 = pd.DataFrame(df, columns=['First Name', 'Last Name', 'fullname'])

df['fullname'] = (df[['First Name', 'Last Name']].fillna('').agg(' '.join, axis=1).str.strip().replace('', 'John Doe'))

df_sort = df2.loc[df2['First Name'] == " "] | df2.loc[df2['Last Name'] == " "] | df2.loc[df2['fullname'] == "John Doe"]


df.to_csv('file.csv', index=False)
df_sort.to_csv('missing names.csv', index=False)

Currently I am having the missing names write to a new file and outputs only this:

First Name   Last Name   fullname

Everything is empty under.

I would like for the output to be:

First Name   Last Name   fullname
                         John Doe
Joseph
             Jovi

>Solution :

Replace possible missing values to empty string, compare and test if at least one value match in DataFrame.any:

df_sort = df2[df2[['First Name', 'Last Name']].fillna('').eq('').any(axis=1)]

Or if there are missing values use:

df_sort = df2[df2[['First Name', 'Last Name']].isna().any(axis=1)]