contact_script.py

contact_script.py

import pandas as pd
pd.set_option('display.max_rows', None)



'''
    cleans google contacts data
    input: 
        contacts csv
    output: 
        cleaned contacts csv
            [first_name, last_name, phone, email]
'''
def clean_google_contacts(gdata):
    # select & rename
    gdata = gdata[['Given Name', 'Family Name', 'E-mail 1 - Value', 'Phone 1 - Value']]
    gdata = gdata.rename(
            columns={
                'Given Name': 'first_name', 
                'Family Name': 'last_name', 
                'E-mail 1 - Value': 'email', 
                'Phone 1 - Value': 'phone'
            }
        )

    return gdata



'''
    joins current relationships sheet with google contacts
    input:
        relationships csv
            [first_name, last_name, phone, email]
        cleaned contacts csv
            [first_name, last_name, phone, email]
    output:
        final csv coalesced and de-duplicated
            [first_name, last_name, phone, email]
'''
def contacts_join(r, c):
    # remove companies & establishments
    r = r[(r['first_name'].notnull()) & (r['last_name'].notnull())]
    c = c[(c['first_name'].notnull()) & (c['last_name'].notnull())]
    
    # combine
    f = r.merge(c, on=['first_name', 'last_name'], how='outer')
    
    # coalesce information
    f['email'] = f['email_y'].combine_first(f['email_x'])
    f['phone'] = f['phone_y'].combine_first(f['phone_x'])
    
    # select rows
    f = f[['first_name', 'last_name', 'email', 'phone']]
    
    # de-duplicate, but should do this in Google
    f = f.drop_duplicates()
    
    # save
    f.to_csv('connection_fox_upload.csv', index=False)
    print('saved final csv')

    return f.head()



if __name__ == '__main__':
    r = pd.read_csv('relationships.csv')
    g = pd.read_csv('contacts.csv')
    print(g.columns)  # file structure of google contacts export
    '''
    ['Name', 'Given Name', 'Additional Name', 'Family Name', 'Yomi Name',
    'Given Name Yomi', 'Additional Name Yomi', 'Family Name Yomi',
    'Name Prefix', 'Name Suffix', 'Initials', 'Nickname', 'Short Name',
    'Maiden Name', 'Birthday', 'Gender', 'Location', 'Billing Information',
    'Directory Server', 'Mileage', 'Occupation', 'Hobby', 'Sensitivity',
    'Priority', 'Subject', 'Notes', 'Language', 'Photo', 'Group Membership',
    'E-mail 1 - Type', 'E-mail 1 - Value', 'E-mail 2 - Type',
    'E-mail 2 - Value', 'E-mail 3 - Type', 'E-mail 3 - Value',
    'Phone 1 - Type', 'Phone 1 - Value', 'Address 1 - Type',
    'Address 1 - Formatted', 'Address 1 - Street', 'Address 1 - City',
    'Address 1 - PO Box', 'Address 1 - Region', 'Address 1 - Postal Code',
    'Address 1 - Country', 'Address 1 - Extended Address',
    'Organization 1 - Type', 'Organization 1 - Name',
    'Organization 1 - Yomi Name', 'Organization 1 - Title',
    'Organization 1 - Department', 'Organization 1 - Symbol',
    'Organization 1 - Location', 'Organization 1 - Job Description',
    'Website 1 - Type', 'Website 1 - Value']
    '''
    c = clean_google_contacts(g)
    f = contacts_join(r, c)