How to fill null values in a column in a csv file?

Question

I need read a csv file and fill the empty/null values in column "Phone and Email" based on the person's address and write to a new csv file. Ex: if a person "Jonas Kahnwald" doesn't have the phone number or an email address but has the same address as the person above or below, say "Hannah Kahnwald", then we should fill the empty/null values with those person's details.

I won't be able to use python pandas as the rest of the code/programs are purely based on python 2.7 (unfortunately), so I just need to write a function or a logic to capture this information alone.

Input format/table looks like below with empty cells (csv file):

FirstName,LastName,Phone,Email,Address
Hannah,Kahnwald,1457871452,hannkahn@gmail.com,145han street
Micheal,Kahnwald,6231897383,,145han street
Jonas,Kahnwald,,,145han street
Mikkel,Nielsen,4509213887,mikneil@yahoo.com,887neil ave
Magnus,Nielsen,,magnusneil@kyle.co,887neil ave
Ulrich,Nielsen,,,887neil ave
katharina,Nielsen,,,887neil ave
Elisabeth,Doppler,5439001211,elsisop@amaz.com,211elis park
Peter,Doppler,,,211elis park
bartosz,Tiedmannn,6263172828,tiedman@skype.com,828alex street
Alexander,washington,,,321notsame street
claudia,Tiedamann,,,828alex street

Output format should be like below:

Hannah,Kahnwald,1457871452,hannkahn@gmail.com,145han street
Micheal,Kahnwald,6231897383,hannkahn@gmail.com,145han street
Jonas,Kahnwald,1457871452,hannkahn@gmail.com,145han street
Mikkel,Nielsen,4509213887,mikneil@yahoo.com,887neil ave
Magnus,Nielsen,4509213887,magnusneil@kyle.co,887neil ave
Ulrich,Nielsen,4509213887,mikneil@yahoo.com,887neil ave
katharina,Nielsen,4509213887,mikneil@yahoo.com,887neil ave
Elisabeth,Doppler,5439001211,elsisop@amaz.com,211elis park
Peter,Doppler,5439001212,elsisop@amaz.com,211elis park
bartosz,Tiedmannn,6263172828,tiedman@skype.com,828alex street
Alexander,washington,,,321notsame street
claudia,Tiedamann,6263172828,tiedman@skype.com,828alex street

import csv,os

def get_info(file path):
    data = []
    with open(file, 'rb') as fin:
        csv_reader =  csv.reader(fin)
        next(reader)
        for each in csv_reader:

            FirstName = each[0]
            LN = each[1]
            Phone =  "some function or logic"
            email = " some function or logic"
            Address = each[4]
            login = ""
            logout = ""

            data.append([FirstName,LN,Phone,email,Address,login,logout])

   f.close()
   return data

Answer 1

Here's a significantly updated version that attempts to fill in missing data from other entries in the file, but only if they have the same Address field. To make the searching faster it builds a dictionary for internal use called attr_dict which contains all the records with a particular address. It also uses namedtuples internally to make the code a little more readable.

Note that when retrieving missing information, it will use the data from the first entry it finds stored in this internal dictionary at the Address. In addition, I don't think the sample data you provided contains every possible case, so will need to do additional testing.

import csv
from collections import namedtuple


def get_info(file_path):

    # Read data from file and convert to list of namedtuples, also create address
    # dictionary to use to fill in missing information from others at same address.
    with open(file_path, 'rb') as fin:
        csv_reader =  csv.reader(fin, skipinitialspace=True)

        header = next(csv_reader)
        Record = namedtuple('Record', header)

        newheader = header + ['Login', 'Logout'] # Add names of new columns.
        NewRecord = namedtuple('NewRecord', newheader)

        addr_dict = {}
        data = [newheader]

        for rec in (Record._make(row) for row in csv_reader):
            if rec.Email or rec.Phone:  # Worth saving?
                addr_dict.setdefault(rec.Address, []).append(rec)  # Remember it.

            login, logout = "",  ""  # Values for new columns.
            data.append(NewRecord._make(rec + (login, logout)))

    # Try to fill in missing data from any other records with same Address.
    for i, row in enumerate(data[1:], 1):
        if not (row.Phone and row.Email):  # Info missing?
            # Try to copy it from others at same address.
            updated = False
            for other in addr_dict.get(row.Address, []):
                if not row.Phone and other.Phone:
                    row = row._replace(Phone=other.Phone)
                    updated = True
                if not row.Email and other.Email:
                    row = row._replace(Email=other.Email)
                    updated = True
                if row.Phone and row.Email:  # Info now filled in?
                    break

            if updated:
                data[i] = row

    return data


INPUT_FILE = 'null_cols.csv'
OUTPUT_FILE = 'fill_cols.csv'

data = get_info(INPUT_FILE)

with open(OUTPUT_FILE, 'wb') as fout:
    writer = csv.DictWriter(fout, data[0])  # First elem has column names.
    writer.writeheader()
    for row in data[1:]:
        writer.writerow(row._asdict())

print('Done')

Screenshot of results in Excel: