Python/Numpy(CSV): Finding values, appending another csv

If I understand your problem correctly; You need to read from 2 input files:

• 1 containing the users IDs you are looking for

• 2 containing the project data related to users

In that fashion something like this would find all the users you specify in 1 in file 2 and write them out to result.csv

Sepicify your search IDs in search_for.csv. Keep in mind that this will revrite your result.csv every time you run it.

import csv
import sys
import os


inputPatterns = open(os.curdir + '/search_for.csv', 'rt')

# Reader for the IDs (users) you are looking to find (key)
reader = csv.reader(inputPatterns)

ids = []

# reading the IDs you are looking for from search_for.csv
for row in reader:
    ids.append(row[0])
inputPatterns.close()

# Let's see if any of the user IDs we are looking for has any project related info
# if so write them to your output CSV
for userID in ids:
    # Organization list with names and Company ID and reader
    userList = open(os.curdir + '/users.csv', 'rt')
    reader = csv.reader(userList)

    # This will be the output file
    result_f = open(os.curdir + "/" + userID + ".csv", 'w')
    w = csv.writer(result_f)
    # Writing header information
    w.writerow(['Date', 'Prj1_Assigned', 'Prj1_closed', 'Prj2_assigned', 'Prj2_solved'])

    # Scanning for projects for user and appending them
    for row in reader:
        if userID == row[1]:
            w.writerow([row[3], row[4], row[5], row[6], row[7]])
    result_f.close()
    userList.close()

For example, search_for.csv looks like this

This is an ideal use-case for pandas:

import pandas as pd

id_list = ['KTD684']

df = pd.read_csv('input.csv')
# Only keep values that are in 'id_list'
df = df[df['ID'].isin(id_list)]

gb = df.groupby('ID')
for name, group in gb:
    with open('{}.csv'.format(name), 'a') as f:
        group.to_csv(f, header=False, index=False,
                     columns=["Date", "Prj1_Assigned", "Prj1_closed",
                             "Prj2_assigned", "Prj2_solved"])

This will open the CSV, only select rows that are in your list (id_list), group by the values in the ID column and save individual CSV files for each unique ID. You just need to expand id_list to have the ids you are interested in.

Extended example:

Reading in the CSV results in a DataFrame object like this:

df = pd.read_csv('input.csv')
               Name      ID      Title          Date  Prj1_Assigned  \
0    Joshua Morales  MF6B9X   Tech_Rep   08-Nov-2016            948
1      Betty García  ERTW77        SME   08-Nov-2016            965
2  Kathleen Marrero  KTD684  Probation   08-Nov-2016            946
3         Mark León  GSL89D   Tech_Rep   08-Nov-2016            951

   Prj1_closed Prj2_assigned Prj2_solved
0          740             8           8
1          854            15          12
2          948            na          na
3          844             6           4

If you just select KTD684 and GSL89D:

id_list = ['KTD684', 'GSL89D']
df = df[df['ID'].isin(id_list)]
               Name      ID      Title          Date  Prj1_Assigned  \
2  Kathleen Marrero  KTD684  Probation   08-Nov-2016            946
3         Mark León  GSL89D   Tech_Rep   08-Nov-2016            951

   Prj1_closed Prj2_assigned Prj2_solved
2          948            na          na
3          844             6           4

The groupby operation groups on ID and export each unique ID to a CSV file resulting in:

KTD684.csv
Date,Prj1_Assigned,Prj1_closed,Prj2_assigned,Prj2_solved
08-Nov-2016,946,948,na,na

GSL89D.csv
Date,Prj1_Assigned,Prj1_closed,Prj2_assigned,Prj2_solved
08-Nov-2016,951,844,6,4

Here's a pure python approach which reads the master .csv file with csv.DictReader, matches the ids, and appends the file data into a new or existing .csv file with csv.DictWriter():

from csv import DictReader
from csv import DictWriter

from os.path import isfile

def export_csv(user_id, master_csv, fieldnames, key_id, extension=".csv"):
    filename = user_id + extension
    file_exists = isfile(filename)

    with open(file=master_csv) as in_file, open(
        file=filename, mode="a", newline=""
    ) as out_file:

        # Create reading and writing objects
        csv_reader = DictReader(in_file)
        csv_writer = DictWriter(out_file, fieldnames=fieldnames)

        # Only write header once
        if not file_exists:
            csv_writer.writeheader()

        # Go through lines and match ids
        for line in csv_reader:
            if line[key_id] == user_id:

                 # Modify line and append to file
                line = {k: v.strip() for k, v in line.items() if k in fieldnames}
                csv_writer.writerow(line)

Which can be called like this:

export_csv(
    user_id="KTD684",
    master_csv="master.csv",
    fieldnames=["Date", "Prj1_Assigned", "Prj1_closed", "Prj2_assigned", "Prj2_solved"],
    key_id="ID",
)

And produces the following KTD684.csv:

Date,Prj1_Assigned,Prj1_closed,Prj2_assigned,Prj2_solved
08-Nov-2016,946,948,na,na