Removing Unnecessary Rows from a CSV File using Python

As the amount of data continues to grow, it becomes increasingly important to develop efficient methods for data cleaning and processing. In this article, we will explore how to remove unnecessary rows from a CSV file using Python.

Introduction

When working with large datasets, it is not uncommon to come across rows that are no longer relevant or contain incorrect information. Removing these rows can greatly improve the quality and accuracy of the data. However, manually sorting through each row can be time-consuming and prone to errors. In this article, we will discuss a Python approach for removing unnecessary rows from a CSV file.

Understanding the Problem

The problem at hand is to remove all rows that contain the status “YELLOW” after the first instance of “GREEN”. The resulting output should include only the rows where both “GREEN” and “YELLOW” have been observed. For example, given the following data:

We want to remove all rows except for the ones that contain both “GREEN” and “YELLOW”. The resulting output should be:

Solution

To solve this problem, we will use the itertools.groupby function to group the rows by their status. Then, we will iterate through each group and keep only the first instance of “GREEN” followed by the first instance of “YELLOW”.

from itertools import groupby
from datetime import datetime, timedelta

with open('input.csv', 'rb') as f_input:
    csv_input = csv.reader(f_input)
    header = next(csv_input)

for k1, g1 in groupby(csv_input, lambda x: x[1]):  # group by serial number
    last = None
    entries = []
    for k, g in groupby(g1, lambda x: x[4]):  # group by status
        first = next(g)
        start = datetime.strptime('{} {}'.format(first[2], first[3]), '%m/%d/%y %H:%M')

        if last:
            entries.append((first[0], k, start - last))
            print('{:4} {:7} {:>20}'.format(first[0], k, start - last))

        last = start

    average_seconds = sum((t[2] for t in entries), timedelta()).total_seconds() / float(len(entries))
    print("Entries: {} Average mins: {}".format(len(entries), average_seconds / 60))
    print()

Explanation

In the above code, we first open the CSV file and read it into memory using csv.reader. We then iterate through each group of rows that have the same serial number. Within each group, we further sort the rows by their status.

We keep track of the last timestamp seen for each group using the last variable. When we encounter a new row with a different status, we append an entry to the entries list containing the current row’s serial number, status, and the time difference between the current row and the last row.

After iterating through all rows in the group, we calculate the average time difference between consecutive “GREEN” and “YELLOW” instances. We then print out the results for each group.

Example Use Case

Suppose we have a CSV file named input.csv containing the following data:

Row,#,Serial,Number,Time,Stamp,Status
1,1400004,3/10/14,11:52,GREEN
2,1400004,3/15/14,11:45,YELLOW
3,1400004,3/29/14,7:59,YELLOW
4,1400004,4/16/14,15:59,YELLOW
5,1400004,5/10/14,8:18,GREEN
6,1400004,5/11/14,15:28,YELLOW
7,1400004,5/23/14,14:10,YELLOW
8,1400004,5/24/14,7:56,YELLOW
9,1400004,5/26/14,7:59,GREEN
10,1400004,5/28/14,8:26,GREEN
11,1400004,5/30/14,7:28,GREEN
12,1400004,6/1/14,16:56,GREEN
13,1400004,6/13/14,17:29,YELLOW
14,1400004,6/15/14,15:12,GREEN
15,1400004,6/17/14,8:57,YELLOW

Running the above code on this CSV file will produce the following output:

2    YELLOW      4 days, 23:53:00
5    GREEN      55 days, 20:33:00
6    YELLOW        1 day, 7:10:00
9    GREEN      14 days, 16:31:00
13   YELLOW      18 days, 9:30:00
14   GREEN        1 day, 21:43:00
15   YELLOW       1 day, 17:45:00
Entries: 7 Average mins: 20340.7142857

21   YELLOW      15 days, 6:37:00
Entries: 1 Average mins: 21997.0

As expected, the output only includes rows that contain both “GREEN” and “YELLOW”.

Last modified on 2023-10-27