pandas-app.py

import pandas as pd
from time import time
from datetime import datetime

df = pd.read_excel(r'./cvs.xlsx')

accepted_keywords       = ( 'python', )
accepted_regions        = ( 'jbeil', 'beirut', 'batroun' )
accepted_mail_domains   = ( 'gmail', 'outlook' )
accepted_age            = ( 20, 40 )

accepted_cvs = []

for index, row in df.iterrows() :

    accepted = False

    experience  = row['Experience'].lower()
    skills      = row['Skills'].lower()
    region      = row['Region'].lower()
    contact     = row['Contact Information'].lower()
    age         = int( ( time() - row['Birth Date'].timestamp() ) / 31556926 )

    for accepted_keyword in accepted_keywords :

        if ( accepted_keyword in ( skills + experience ) ) :

            accepted = True
            break

    if ( accepted ) :

        accepted = False

        for accepted_region in accepted_regions :

            if ( accepted_region in ( region ) ) :

                accepted = True
                break

    if ( accepted ) :

        accepted = False

        for accepted_mail_domain in accepted_mail_domains :

            if ( accepted_mail_domain in ( contact ) ) :

                accepted = True
                break

    if ( accepted ) :

        accepted = False

        if ( accepted_age[0] <= age <= accepted_age[1] ) :

            accepted = True

    if ( accepted ) :

        accepted_cvs.append([

            row['First Name'],
            row['Last Name'],
            row['Experience'],
            row['Region'],
            row['Hobbies'],
            row['Education'],
            row['Contact Information'],
            row['Birth Date'].strftime("%d/%m/%Y"),
            row['Skills'],

        ])

df_to_store = pd.DataFrame(
    
    accepted_cvs,
    columns = [
        'First Name',
        'Last Name',
        'Experience',
        'Region',
        'Hobbies',
        'Education',
        'Contact Information',
        'Birth Date',
        'Skills',
    ]
    
)s

df_to_store.to_excel( r'./accepted_cvs.xlsx' )