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Track Changes in Your CSV Data Using Python and Pandas

So you’ve set up your online shop with your vendors’ data obtained via Grepsr’s extension, and you’re receiving their inventory listings as a CSV file regularly. Now you need to periodically monitor the data for changes on the vendors’ side — new additions, removals, price changes, etc.

While your website automatically updates all this information when you import from the CSV file, you might sometimes want to see for yourself or display to your customers what changes your vendors have made to their stock.

Let’s take the same example of Teva as in the previous blog, and see how you can easily compare the old and new data sets, and track the changes.

Using this tutorial (Thanks, Chris Moffitt, for the awesome post!) as a guide and making a few modifications, you can set up a project to work with CSV files instead of Excel spreadsheets.

For this blog, I’m assuming you have Python and Pandas packages installed on your system and you’re familiar with at least the basics of programming. Now, you can easily follow along and customize the code to suit your situation.


Data to make or break your business
Get high-priority web data for your business, when you want it.

Before we start, let’s get our files ready. If you haven’t already, head over to the project on your Grepsr app dashboard and browse through the calendar to see when your crawler was run. When you click on the highlighted dates, you’ll see the time of the crawl on that day, after which you can go to the Download tab below the calendar to download your data for that particular crawl.

If you want the latest data, simply re-run your crawler by going to the Configure & Run tab, and download the file once the web scraping is complete.

scr_calendar
Project crawl times on the Grepsr calendar

All set? Let’s get started!

Step-1: Make life easier by structuring the files

Our first course of action will be to figure out how we can filter unwanted content and create easily manageable files using Python and Pandas.

Our old and new datasets are tevasale_jan10.csv and tevasale_jan26.csv respectively. Here’s a simple code to structure the files:

import pandas as pd

# Reading content from the CSV files
old = pd.read_csv('Teva_files/tevasale_jan10.csv')  
new = pd.read_csv('Teva_files/tevasale_jan26.csv')

# Replacing newlines in the Colors and Sizes columns with " | " as separator
old['Colors'] = old['Colors'].str.replace('n+', ' | ')
new['Colors'] = new['Colors'].str.replace('n+', ' | ')
old['Sizes'] = old['Sizes'].str.replace('n+', ' | ')
new['Sizes'] = new['Sizes'].str.replace('n+', ' | ')

# Removing "Model: " prefix in the Model column
old['Model'] = old['Model'].str.replace('Model: ', '')
new['Model'] = new['Model'].str.replace('Model: ', '')

# Replacing newlines and white-spaces in the Name column with " | " separating the category and name
old['Name'] = old['Name'].str.replace(''s(ns+)', ''s | ')
new['Name'] = new['Name'].str.replace(''s(ns+)', ''s | ')

# Removing empty rows using the Name column as reference
old = old.dropna(subset=['Name']).reset_index(drop=True)
new = new.dropna(subset=['Name']).reset_index(drop=True)

# Writing the structured data to new CSV files
old.to_csv('Teva_files/tevasale_old.csv', index=False)
new.to_csv('Teva_files/tevasale_new.csv', index=False)

Let’s see what our structured file looks like.

scr_csv_file_edited
Data in tevasale_old.csv

Data to make or break your business
Get high-priority web data for your business, when you want it.

Step-2: Find changes in your data and save to a new file

Now that we’ve refined our data, we can proceed with Python to compare two files.

The code for comparing our two CSV files tevasale_old.csv and tevasale_new.csv, and exporting the changes to another CSV file tevasale_changes.csv is as follows:

import pandas as pd

file1 = 'Teva_files/tevasale_old.csv'
file2 = 'Teva_files/tevasale_new.csv'
file3 = 'Teva_files/tevasale_changes.csv'

cols_to_show = ['Model', 'Price', 'Original Price', 'Colors', 'Sizes']

old = pd.read_csv(file1)
new = pd.read_csv(file2)


def report_diff(x):
    return x[0] if x[1] == x[0] else '{0} --> {1}'.format(*x)


old['version'] = 'old'
new['version'] = 'new'

full_set = pd.concat([old, new], ignore_index=True)

changes = full_set.drop_duplicates(subset=cols_to_show, keep='last')

dupe_names = changes.set_index('Name').index.get_duplicates()

dupes = changes[changes['Name'].isin(dupe_names)]

change_new = dupes[(dupes['version'] == 'new')]
change_old = dupes[(dupes['version'] == 'old')]

change_new = change_new.drop(['version'], axis=1)
change_old = change_old.drop(['version'], axis=1)

change_new.set_index('Name', inplace=True)
change_old.set_index('Name', inplace=True)

diff_panel = pd.Panel(dict(df1=change_old, df2=change_new))
diff_output = diff_panel.apply(report_diff, axis=0)

changes['duplicate'] = changes['Name'].isin(dupe_names)
removed_names = changes[(changes['duplicate'] == False) & (changes['version'] == 'old')]
removed_names.set_index('Name', inplace=True)

new_name_set = full_set.drop_duplicates(subset=cols_to_show)

new_name_set['duplicate'] = new_name_set['Name'].isin(dupe_names)

added_names = new_name_set[(new_name_set['duplicate'] == False) & (new_name_set['version'] == 'new')]
added_names.set_index('Name', inplace=True)

df = pd.concat([diff_output, removed_names, added_names], keys=('changed', 'removed', 'added'))
df[cols_to_show].to_csv(file3)

Let’s see what we’ve done here with the help of Python and its Pandas package:

  • Firstly, we’ve read our files into separate data frames old and new.
  • Created a report_diff function to account for the changes between the files — it prints old and new values wherever a change has been made.
  • Added a version column to both data frames to note the origin of each row when we later combine them.
  • Combined the contents of the two data frames and stored them in another data frame full_set.
  • Removed duplicate rows, i.e. unchanged data, from full_set and stored the remaining data in changes.
  • Used the get_duplicates() function to get a list of all names that are duplicated. We named the list dupe_names.
  • Using isin, got a list of all duplicates, dupes.
  • Split dupes based on version to two new data frames change_old and change_new.
  • Removed the version column.
  • Set Name as our index for both data frames.
  • Into diff_output we called our report_diff function, and stored the rows where data has been changed.
  • Then we found out which item is removed from stock and saved it to removed_names.
  • Now to find all new items, we checked for duplicates again, and filtered each row based on the item’s uniqueness AND presence in the ‘new’ data frame. This list was then saved as added_names.
  • Finally we merged the three data frames with keys to differentiate the type of change — changedremoved or added — and we’ve written everything into a new CSV file.

At Last

Our final CSV file tevasale_changes.csv looks something like this:

scr_csv_file_changes
All changes after comparing the old and new CSV files

We can clearly observe additions, removals, and changes in details for each item.


Although the dataset used here was relatively small (~70 items in each file), the code still works for much larger data.

This is a helpful tool to track what changes your vendors have made to their stock. Hence you can easily implement them on your website and give your customers up-to-date and accurate information.


Once again, a huge thanks and gratitude to Chris Moffitt, on whose tutorial the codes are based.


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400+ million entrepreneurs worldwide are attempting to start 300+ million companies, according to the Global Entrepreneurship Monitor. Approximately a hundred million new businesses start every year around the world, while a similar number also fold. What sets successful firms apart are the innovations and resources they utilize that help them stay healthy and relevant. Grepsr […]

How to Use Grepsr Browser Tool to Scrape the Web for Free

A beginner’s guide to your favorite DIY web scraping tool Just over a year ago, we introduced the all new Grepsr along with a beta launch of Chrome extension to fill the gap that Kimono Labs, a widely popular scraping tool, left since it’s closure. Now after a year of iteration on both the UI and UX along with shipping […]

Our Kimono Labs Replacement (Grepsr for Chrome) Levels Up

We’ve recently made a number of improvements to make Grepsr for Chrome that little bit easier, and more handy to use. We’ve also received tons of feature requests (keep ’em coming!), so we thought we’d share couple of our favorites that have most recently made it into Grepsr for Chrome. Infinite Scrolling and Enhanced Pagination Support From […]

Welcome To The (New) Grepsr Blog

Hello, Grepsr friends and family, and welcome to the next chapter of Grepsr Blog! It may not look much different yet, but we’re ramping up our editorial operation. Over the next few months you’ll see more posts, more announcements and analysis, more writing, and even new forms of content here. We’re still hammering out all the […]

Introducing the All New Grepsr

Chrome Extension, APIs, Better Support & Much More

Importance of Web Scraping in the Age of Big Data

Big Data has become an internet buzz lately. Not a day goes by without a mention of Big Data in many articles published by media or tech companies around the world.

Web Scraping vs API

Every system you come across today has an API already developed for their customers or it is at least in their bucket list. While APIs are great if you really need to interact with the system but if you are only looking to extract data from the website, web scraping is a much better option. […]

Web Crawling Software or Web Crawling Service

Some people ask us if we are a “service” or a “software”. We simply tell them – we are a service, with killer software that runs behind the scenes! 🙂 Also, lot of our customers ask us, why go for a Web Crawling Service over a Web Crawling Software? The answer is pretty straight forward. […]

Managed Data Extraction Service

Grepsr is what we like to call, “Managed Data Extraction Service”. Here are some of the reasons why we call it “managed”: We let you focus on your business and use the data — worrying about technical details of extraction is our job, and we will do it for you. We let you describe your […]

Official Launch of Grepsr (Beta)

We are immensely proud to launch Grepsr today. Grepsr is probably one of the first Web 2.0 Software as a Service (SaaS) products for website data extraction. So what does this mean for the customers? Cheaper costs – you pay a flat monthly fee no matter how big or small your extraction needs are. Fully […]

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