Data loss while extracting the rows from large csv file

This is in continuation from my previous question. I have 2 files, file1.csv and a large csv called master_file.csv. They have several columns and have a common column name called EMP_Code.

File 1 example:

EMP_name	EMP_Code	EMP_dept
b	f367	abc
a	c264	xyz
c	d264	abc

master_file example:

EMP_name EMP_age EMP_Service EMP_Code EMP_dept
a         30       6           c264     xyz
b         29       3           f367     abc
r         27       1           g364     lmn
d         45       10          c264     abc
t         50       25          t453     lmn

I want to extract similar rows from master_file using all the EMP_Code values in file1. I tried the following code and I am loosing a lot of data. I cannot read the complete master csv file as it is around 20gb, has millions of rows and running out of memory. I want to read the master_file in chunks and extract the complete rows for each of the EMP_Code present in file1 and save it into new file Employee_full_data.

import csv
import pandas as pd

df = pd.read_csv(r"master_file.csv")
li = [c264,f367]
full_data = df[df.EMP_Code.isin(li)]
full_data.to_csv(r"Employee_full_data.csv", index=False)

I also tried the following code. I receive an empty file whenever I use EMP_Code column and works fine when I use columns like Emp_name or EMP_dept. I want to extract the data using EMP_Code.

import csv
import pandas as pd

df = pd.read_csv(r"file1.csv")

list_codes = list(df.EMP_Code)
selected_rows = []
with open(r"master_file.csv") as csv_file:
    reader = csv.DictReader(csv_file)
    for row in reader:
        if row['EMP_Code'] in list_codes:
            selected_rows.append(row)`

article_usage = pd.DataFrame.from_records(selected_rows)
article_usage.to_csv(r"Employee_full_data.csv", index=False)

Is there any other way that I can extract the data without loss? I have heard about join and reading data in chunks but not sure how to use it here. Any help is appreciated

Answer

I ran the code from your 2nd example (using csv.DictReader) on your small example and it worked. I’m guessing your problem might have to do with the real-life scale of master_file as you’ve alluded to.

The problem might be that despite using csv.DictReader to stream information in, you’re still using a Pandas dataframe to aggregate everything before writing it out, and maybe the output is breaking your memory budget.

If that’s true, then use csv.DictWriter to stream out. The only tricky bit is getting the writer set up because it needs to know the fieldnames, which can’t be known till we’ve read the first row, so we’ll set up the writer in the first iteration of the read loop.

(I’ve removed the with open(... contexts because I think they add too much indentation)

df = pd.read_csv(r"file1.csv")
list_codes = list(df.EMP_Code)

f_in = open(r"master_file.csv", newline="")
reader = csv.DictReader(f_in)

f_out = open(r"output.csv", "w", newline="")

init_writer = True
for row in reader:
    if init_writer:
        writer = csv.DictWriter(f_out, fieldnames=row)
        writer.writeheader()
        init_writer = False

    if row["EMP_Code"] in list_codes:
        writer.writerow(row)

f_out.close()
f_in.close()

EMP_name	EMP_age	EMP_Service	EMP_Code	EMP_dept
a	30	6	c264	xyz
b	29	3	f367	abc
d	45	10	c264	abc

And if you’d like to get rid of Pandas altogether:

list_codes = set()
with open(r"file1.csv", newline="") as f:
    reader = csv.DictReader(f)
    for row in reader:
        list_codes.add(row["EMP_Code"])