I recently finished a series of four Python projects focused on working with CSV data. I started with a simple CSV reader and gradually worked toward a sales report that combined several concepts I had been practicing.
My goal wasn't to build something complicated. I wanted to understand how Python handles structured data, how to break a problem into smaller steps, and how to deal with input that isn't always in the expected format.
Looking back, I can see how much my projects have grown in complexity. In this article, I want to review what I built, what I learned, and the challenges I encountered along the way.
Project 1 — CSV Reader 📗
My first project, CSV Reader, reads data from a CSV file and prints the information to the terminal. It uses Python's csv.DictReader() to read each row as a dictionary. It also checks whether each product's price can be converted to a float value. If the conversion fails, the price is set to 0.
There were two important things I learned from this project:
Working with CSV files: I learned why opening CSV files with newline="" is recommended, allowing Python's CSV module to handle newlines correctly.
Reading structured data: I learned how
DictReaderworks and how it differs from the regularreader, which returns rows as lists rather than dictionaries.
This project was my introduction to the CSV format and how Python can read and work with structured data.
Project 2 — CSV Analyzer 📒
In my second project, CSV Analyzer, I focused more on processing data and turning raw information into useful results.
The program filters product records, keeps the valid data, and uses different functions to calculate information such as the total number of products, the most expensive product, and the average price.
This project taught me how to separate tasks into functions instead of putting all the logic in one place. I also learned how to work with rejected data and how to calculate useful statistics from the records that passed validation.
Project 3 — CSV Cleaner 📙
With CSV Cleaner, I improved the data-validation process from my previous project. Instead of simply filtering out invalid records, the program now provides a specific error message for each rejected row and saves those rows, along with their errors, into a separate CSV file.
This project introduced me to Python's csv.DictWriter, which allows me to write dictionaries into a CSV file. I learned how to create an output file and store information in it instead of displaying every error only in the terminal.
The biggest challenge: keeping the data available
One of my biggest challenges was understanding how to read the input first and write the rejected records afterward.
Initially, I struggled with how to keep the data available after reading the file. The with statement closes the file when its block finishes, so I needed a way to retain the records independently of the open file.
My solution was to create separate lists for valid products and rejected rows. As the program processed each record, it added the suitable information to one of those lists. I could then use the collected data later to create the output file.
This helped me understand an important concept: data doesn't have to remain inside a file to be used later. I can load it into Python data structures, process it, and then write the results to a new file.
Project 4 — CSV Sales Report 📕
The CSV Sales Report was the biggest project I had written so far. It brought together the concepts from my previous projects while introducing a more complex workflow.
The input is a CSV file containing dates, product names, quantities, and prices. The program reads the file, validates the records, saves rejected rows and their error messages to a separate CSV file, and processes the valid records to produce a sales report.
Unlike my earlier projects, which mainly displayed their results in the terminal, this one writes its results to a text file. The goal is to turn raw product data into a report that is easier to read and potentially useful in a real business setting.
1. Validating and organizing the data
One important lesson was that the order of validation checks matters.
Before converting values into the appropriate types, the program checks whether required fields are empty. It then validates the values, including the date format and the numeric fields, and rejects records that fail the checks.
The program also records error messages so that rejected rows can be saved with an explanation of why they were rejected. This makes the output more useful than simply ignoring invalid records.
Keeping valid products and rejected rows in separate lists also allowed me to use the data at different stages of the workflow.
2. Turning raw data into a report
Once the valid records are ready, the program creates a text file and uses different functions to write the results.
First, it calculates basic information, such as the total number of records, the number of units sold, and total revenue.
Next, it groups records by product to calculate each product's total quantity sold and revenue. I used Python's defaultdict to make this easier. It allows me to group information under dictionary keys without manually creating an initial entry every time I encounter a new product.
For example, product names can serve as keys, while their total quantities and revenues are stored as values. The grouped results can then be passed to other functions for further calculations.
3. Finding the top-performing products
After grouping the data, the program identifies the products with the highest sales quantities and the highest revenue. If multiple products share the top result, their names are included rather than reporting only one product.
Finally, the program groups sales by date and calculates the total revenue for each day. This information is also written to the text report.
The result is a structured report that makes it easier to review overall performance, compare products, and understand how revenue changes from day to day.
Why this project was a milestone 🚀
This project felt like a major step forward because I was no longer practicing just one concept at a time. I was combining file handling, data validation, lists, dictionaries, functions, calculations, and output generation into one workflow.
It also helped me see how Python can be used for more than small exercises. With further improvements, a tool like this could be adapted to help a business summarize sales data and reduce some manual reporting work.
Of course, this is still a learning project, and it would need further testing and improvements before being relied on in a real business. But it gave me a better understanding of how a practical data-processing tool can be built step by step.
What These Four Projects Taught Me 💡
Looking back at the entire series, here are the main things I learned:
- File handling: How to read existing files, create new ones, and write or overwrite their contents.
- Data validation: How to check input, filter invalid records, and provide understandable error messages.
- Functions and problem-solving: How to break a larger task into smaller functions, each responsible for a specific job.
- Data processing: How to use Python data structures and functions to organize raw records and calculate meaningful results.
- Reporting: How to present processed information in a readable format, both in the terminal and in an output file.
More importantly, I learned how to combine these skills. My first project simply read and displayed data, while my fourth project followed a series of steps to validate, process, analyze, and report on that data.
That progression is something I can clearly see when I compare my first project with my last one.
What's Next? 👀
Finishing these four projects has given me a stronger foundation in Python and working with structured data. My next step is to move on to JSON projects, continue improving my code, and gradually explore more advanced concepts.
I still have plenty to learn, but I now have a clearer picture of what I can build with the knowledge I've gained so far.
📚 Learning Resource
Python's official CSV documentation
💻 Project Repository
Top comments (1)
The step by step growth across the four projects is clear, and the separate lists for valid and rejected rows are a strong idea. One tip that helps beginners test that part is to build a tiny sample file first with one good row, one missing field, and one bad price, then run the cleaner on that file before using a real file. That way, you can check that each rejected row lands in the right list with a clear reason, instead of guessing from a large file.