Parsing and searching a CSV file

Question

I want to parse through a contacts list CSV file that looks like this:

First Name  Last Name  Full Name                            Short Name  Phone Number
Jenny       Smith      CN=Jenny Smith/OU=CORP/O=COMPANY     jesmi       6468675309
Mary        Poppin     CN=Mary Poppins/OU=STORE/O=COMPANY   mapop       7005555578
Tony        Stark      CN=Tony Stark/OU=STORE/O=COMPANY     tostar      6007777798
Peter       Parker     CN=Peter Parker/OU=NEWS/O=COMPANY    pepar       5008889090

I want to be able to search through column "Full Name" and select string "OU=STORE" and move all rows that contain "OU=STORE" aside, and then move it to it's own csv file called "store.csv". Then repeat the same process for "OU=CORP" and "OU=NEWS".

---

This is what I want my output to look like:

Store.csv should contain only this information once the process is done.

First Name  Last Name  Full Name                            Short Name  Phone Number
Mary        Poppin     CN=Mary Poppins/OU=STORE/O=COMPANY   mapop       7005555578
Tony        Stark      CN=Tony Stark/OU=STORE/O=COMPANY     tostar      6007777798

corp.csv

First Name  Last Name  Full Name                            Short Name  Phone Number
Jenny       Smith      CN=Jenny Smith/OU=CORP/O=COMPANY     jesmi       6468675309

news.csv

First Name  Last Name  Full Name                            Short Name  Phone Number    
Peter       Parker     CN=Peter Parker/OU=NEWS/O=COMPANY    pepar       5008889090

I have a small script of what I've done so far but I'm not sure what to do in the end:

import pandas as pd
import csv

#this is the source folder    
source_dir = 'C:/Users/username/documents/contacts/contactslist.csv'

#this is the folder where I want to move the parsed data.
store_target_dir = 'C:/Users/username/documents/contacts/store/'
corp_target_dir = 'C:/Users/username/documents/contacts/corp/'
news_target_dir = 'C:/Users/username/documents/contacts/news/'

col_list = ["Full Name"]

store = 'OU=STORE'
corp = 'OU=CORP'
news = 'OU=NEWS'

#When it comes time to move the data to their folders with their csv name
csvName = store_target_dir + "/" + "store.csv"
csvName2 = corp_target_dir + "/" + "corp.csv"
csvName3 = news_target_dir + "/" +"news.csv"

#opening the file
file = open(source_dir)

#reading the csv file
df = pd.read_csv(file)

Answer 1

To filter you DataFrame, you could do something like this:

# key is the value you are looking for, e.g. 'OU=STORE'    
indices = [key in value for value in df['File Name']]
subset = df[indices]

indices is a bool list indicating whether a line contains key or not

Answer 2

You could extract the OU= value and add it as another column. .unique() could then be used to determine the 3 possible values and then each CSV created based on the that value. For example:

import pandas as pd

df = pd.read_csv('contactslist.csv', dtype={'Phone Number': str})
df['file'] = df['Full Name'].str.extract(r'OU=(\S+)/')

for key in df['file'].unique():
    df_filtered = df.loc[df['file'] == key]
    df_filtered = df_filtered.drop(['file'], axis=1)
    df_filtered.to_csv(f"{key}.csv", index=False)      

Answer 3

Trying to not do too much modifications to your code a solution could look like this:

import os, os.path
import csv

#this is the source folder
original_contacts_filename = r'C:\Users\username\documents\contacts\contactslist.csv'

#this is the folder where I want to move the parsed data.
store_target_dir = r'C:\Users\username\documents\contacts\store'
corp_target_dir = r'C:\Users\username\documents\contacts\corp'
news_target_dir = r'C:\Users\username\documents\contacts\news'

os.makedirs(store_target_dir, exist_ok=True)
os.makedirs(corp_target_dir, exist_ok=True)
os.makedirs(news_target_dir, exist_ok=True)

store = 'OU=STORE'
corp = 'OU=CORP'
news = 'OU=NEWS'

#When it comes time to move the data to their folders with their csv name
csv_name = os.path.join(store_target_dir, "store.csv")
csv_name2 = os.path.join(corp_target_dir, "corp.csv")
csv_name3 = os.path.join(news_target_dir, "news.csv")

with (
    open(original_contacts_filename, newline='') as original_contacts_file,
    open(csv_name, mode='w', newline='') as csv_file, 
    open(csv_name2, mode='w', newline='') as csv_file2, 
    open(csv_name3, mode='w', newline='') as csv_file3):

    original_contacts = csv.DictReader(original_contacts_file)
    
    store_destination = csv.writer(csv_file)
    corp_destination = csv.writer(csv_file2)
    news_destionation = csv.writer(csv_file3)

    output_headers = ('First Name', 'Last Name', 'Full Name', 'Short Name', 'Phone Number')
    store_destination.writerow(output_headers)
    corp_destination.writerow(output_headers)
    news_destionation.writerow(output_headers)

    for current_contact in original_contacts:
        if store in current_contact['Full Name']:
            output_destination = store_destination
        elif corp in current_contact['Full Name']:
            output_destination = corp_destination
        elif news in current_contact['Full Name']:
            output_destination = news_destionation
        else:
            output_destination = None
        
        if output_destination is not None:
            output_destination.writerow(current_contact[column] for column in output_headers)

But we can see a lot of repetitions which usually smells. We could simplyfy the code like this:

import os, os.path
import csv
import re

original_contacts_filename = 'contactslist.csv'

source_directory = r'C:\Users\username\documents\contacts'

corporate_units_expected = ('store', 'corp', 'news')

target_directory = r'C:\Users\username\documents\contacts'
target_files_info = {
    current_unit: (
        current_name := os.path.join(target_directory, current_unit, f'{current_unit}2.csv'),
        open(current_name, 'w', newline='')
    )
    for current_unit in corporate_units_expected
}

for current_target, _ in target_files_info.values():
    os.makedirs(os.path.dirname(current_target), exist_ok=True)
    
matcher = re.compile(r'OU=([ 0-1a-zA-Z]+)')

with (
    open(original_contacts_filename, newline='') as original_contacts_file,
    target_files_info['store'][1], target_files_info['corp'][1], target_files_info['news'][1]
):
    original_contacts = csv.DictReader(original_contacts_file)

    writers = { 
        current_unit: csv.writer(current_target[1])
        for (current_unit, current_target) in target_files_info.items()
    }

    output_headers = ('First Name', 'Last Name', 'Full Name', 'Short Name', 'Phone Number')

    for current_writer in writers.values():
        current_writer.writerow(output_headers)
    
    for current_contact in original_contacts:
        if match_found := matcher.search(current_contact['Full Name']):
            current_writer = writers[match_found[1].lower()]
            current_writer.writerow(current_contact[column] for column in output_headers)

We could also have an example where we don't know in advance how many files we which to sort these entries into but it get a lot more complicated because we cannot use the with statement directly.