As we continue to navigate the digital age, data has become an integral part of our lives, and understanding this data is more important than ever. One of the recent advancements in data analysis is the Code Interpreter Beta feature in ChatGPT Pro, a powerful tool that brings programming and data analysis to your fingertips. To illustrate its power, let’s dive into an example where we analyze a dataset of eBay purchases.
Imagine that you’ve been given a CSV file containing information about eBay transactions. The dataset includes the date of purchase, the title of the listing, the total price of the item, and the name of the seller. At first glance, it may seem like a daunting task to extract meaningful information from this raw data. However, with the Code Interpreter Beta feature, we can easily navigate this data and gain valuable insights.
Data Cleaning
The first step in our analysis involves cleaning our data. We noticed that the ‘Total Price’ column in our dataset was stored as text rather than numerical values, which prevents us from performing numerical computations. The power of the Code Interpreter Beta feature shines here as it enables us to quickly convert the ‘Total Price’ column into a numerical format using a few lines of Python code.
Descriptive Statistics
Once our data is cleaned, we can start delving into the interesting stuff: gaining insights from our data. We can use the Code Interpreter Beta feature to easily compute descriptive statistics for the ‘Total Price’ column. With a few lines of code, we can determine the average purchase price, the variability in prices, and the range of prices.
- Count: There are 2988 transactions in the dataset with a valid ‘Total Price’.
- Mean: The average price of a purchase is approximately $42.29.
- Standard Deviation: The standard deviation, a measure of price variability, is approximately $339.88. This high value suggests there’s a large variation in purchase prices.
- Minimum: The least expensive purchase in the dataset cost $0.01.
- 25% (1st Quartile): 25% of the purchases were priced at $5.00 or less.
- Median (50% / 2nd Quartile): The median price, which separates the higher half and the lower half of the purchase prices, is $11.96. This means that 50% of the purchases were less than $11.96, and 50% were more.
- 75% (3rd Quartile): 75% of the purchases were priced at $27.00 or less.
- Maximum: The most expensive purchase in the dataset cost $15,200.00.
Purchase Trends Over Time
Next, we wanted to investigate the trends in eBay purchases over time. Utilizing the date and time functionalities offered by the Code Interpreter Beta feature, we were able to group our purchases by month and year. This allowed us to visualize the number of purchases over time, revealing an increasing trend in purchases from November 2001 to January 2022.
Price Distribution
Finally, we looked into the distribution of purchase prices. Through the visualization tools available in the Code Interpreter Beta feature, we could easily generate histograms to visualize this data. We found that the majority of the purchases were in the lower price range, with a few purchases significantly more expensive. To focus on the majority of transactions, we created a histogram for purchases priced at $200 or less, revealing that most purchases were in the $0-$50 range.
Conclusion
In conclusion, the Code Interpreter Beta feature is a powerful tool that opens up a world of possibilities for data analysis. With its help, we were able to transform a raw eBay transactions dataset into meaningful insights, uncovering trends in purchase prices and their distribution. Its seamless integration of data cleaning, statistical computation, and visualization capabilities makes it a potent tool for any data enthusiast. Whether you’re a seasoned data scientist or a curious beginner, the Code Interpreter Beta feature brings data analysis to your fingertips.
This blogpost was created with help from ChatGPT Pro
Christopher Finlan 