What Is ChatGPT Advanced Data Analysis (formerly Code Interpreter)?
ChatGPT Advanced Data Analysis, previously known as Code Interpreter, is an AI-powered tool, provided as part of OpenAI’s popular generative AI platform, ChatGPT. It is designed to perform a wide range of data-related tasks, allowing users to interact with data using natural language prompts. This makes it accessible to those without coding expertise.
The tool can process, analyze, and visualize data through the automatic generation and execution of Python code. By leveraging a secure code execution environment, it can perform complex operations, such as statistical analysis, data manipulation, and the creation of visualizations, directly within the chat interface.
In this article:
- Supported File Types for ChatGPT’s Data Analysis
- How ChatGPT’s Data Analysis Works
- What Can You Do with ChatGPT Data Analysis? Key Use Cases
- How to Use ChatGPT Advanced Data Analysis
Supported File Types for ChatGPT’s Data Analysis {#supported-file-types-for-chatgpt’s-data-analysis}
ChatGPT’s Advanced Data Analysis tool supports several file types to help users work efficiently with data. Users can upload files in formats such as Excel (.xls and .xlsx), Comma-Separated Values (.csv), PDFs, and JSON.
These formats enable users to conduct various types of analysis, whether it’s statistical calculations, creating visualizations, or processing structured information. It is recommended that users ensure their datasets follow best practices, such as using clear, descriptive column headers and avoiding blank rows or unnecessary sections in spreadsheets.
Additionally, users can upload up to 10 files at once in a conversation, or 20 files when using a GPT equipped with file analysis capabilities. The maximum size for any single file is 512 MB, with limits for CSV files being around 50 MB depending on the dataset’s complexity.
How ChatGPT’s Data Analysis Works {#how-chatgpt’s-data-analysis-works}
ChatGPT’s Data Analysis leverages AI to perform complex data processing tasks. When you upload data, the tool first examines the initial rows to understand the structure and types of values in your dataset. This step is crucial for determining how to process and analyze the data effectively.
Once the data is understood, ChatGPT writes and executes Python code within a secure code execution environment. This environment is equipped with several Python libraries, such as pandas for data manipulation and matplotlib for generating both static and interactive charts. The tool can handle a variety of data-related tasks, from performing statistical analyses to generating visualizations, all based on the natural language prompts provided by the user.
As it processes your queries, ChatGPT automatically generates the required code, runs it, and integrates the results into its responses. This capability allows it to execute complex operations, like filtering data, running calculations, or even creating custom datasets. The generated code can be reviewed and copied by the user, allowing for further customization or use in local environments.
The secure execution environment also ensures that the data remains safe during analysis. Each conversation creates a new instance of this environment, which is isolated and automatically destroyed after a period of inactivity. This design guarantees both the security and the efficiency of the data analysis process.
What Can You Do with ChatGPT Data Analysis? Key Use Cases {#what-can-you-do-with-chatgpt-data-analysis-key-use-cases}
ChatGPT Data Analysis supports many use cases. Here are some of the main ones.
Expense Tracking and Budget Analysis
Users can upload financial data in formats like CSV or Excel to get instant insights into their spending patterns. The tool can categorize expenses, calculate monthly averages, and generate visualizations like pie charts or bar graphs to display how funds are allocated across different categories. This allows users to quickly identify areas of overspending and make adjustments to improve financial management.
Additionally, ChatGPT can assist in creating customized reports, such as tracking budget variances over time or comparing expenses across different periods. By leveraging Python libraries like pandas for data manipulation and matplotlib for visualization, it lets non-technical users get a detailed breakdown of their financial data and generate actionable insights from it.
Customer Data Exploration for Marketing
Marketers can upload datasets containing customer information—such as demographics, purchase history, or engagement metrics—and the tool can quickly segment the data based on user-defined criteria like age, location, or spending habits. This helps in identifying target audiences for campaigns, creating customer profiles, and understanding consumer behavior.
ChatGPT can generate visual representations of customer trends, such as heat maps of geographic distribution or scatter plots of customer spending vs. engagement. The tool can also calculate key performance indicators (KPIs), like customer lifetime value (CLV) or churn rate, helping marketers make data-driven decisions to optimize their marketing strategies.
Cleaning and Analyzing Large Datasets
Users can upload extensive files, and ChatGPT Data Analysis can automatically detect and remove duplicates, fill in missing values, or standardize data formats. This ensures that datasets are well-prepared for further analysis. Users can also prompt the tool to split columns, merge data, or filter rows to make data more manageable.
Once cleaned, these large datasets can be analyzed to extract meaningful insights. ChatGPT can perform statistical operations, identify trends, and create visualizations like histograms or box plots to represent the data distribution. This makes it easier to uncover patterns or outliers that would otherwise be difficult to detect in a raw dataset.
Real-Time Marketing Campaign Performance Analysis
Marketers can upload data from multiple campaigns, whether from Google Ads, email marketing platforms, or social media channels, to evaluate performance metrics like click-through rates (CTR), conversion rates, and return on ad spend (ROAS). By processing this data instantly, ChatGPT can provide up-to-date performance reports, highlight the most successful channels, and suggest optimizations based on past campaign outcomes.
The tool can also compare performance over time or across different demographics, helping marketers understand how their campaigns resonate with different audience segments. By visualizing trends and key metrics in real-time, teams can make data-driven adjustments to campaigns, boosting overall effectiveness and maximizing ROI.
Sales Forecasting for eCommerce
eCommerce businesses can upload historical sales data, and ChatGPT’s Data Analysis can apply time series analysis, regression models, or other forecasting techniques to predict future sales trends. This capability helps users optimize inventory management, prepare for peak seasons, and set more realistic sales targets.
Additionally, ChatGPT can visualize forecast results through line charts or forecast intervals, helping users better understand potential sales changes. It can also break down forecasts by product categories, regions, or customer segments, allowing for more granular insights and tailored business strategies.
How to Use ChatGPT Advanced Data Analysis {#how-to-use-chatgpt-advanced-data-analysis}
Here’s an overview of how to use this tool.
Accessing Advanced Data Analysis
As of the time of this writing, ChatGPT has Advanced Data Analysis (formerly Code Interpreter) is turned on by default. You can turn it on and off by clicking your user icon at the top right of the ChatGPT interface and selecting Customize ChatGPT. At the bottom of this dialog, the Code checkbox indicates whether Advanced Data Analysis should be available within ChatGPT or not.
Generating Code and Using Mathematics
ChatGPT’s Advanced Data Analysis can generate Python code to solve mathematical problems and perform complex calculations. Users can input problems involving algebra, geometry, or statistics, and the tool will write and execute code to calculate the results. For example:
-
Input the problem in ChatGPT: Start by typing something like:
*"I have two cars traveling toward each other from different cities. Car 1 is going 50 km/h and Car 2 is going 60 km/h. The distance between them is 200 km. Can you calculate how long it will take for the two cars to meet?"* -
Generated code: ChatGPT will generate Python code to solve this, including the steps for calculating the time for the two cars to meet, like this:
# Speeds of the cars
speed_car1 = 50 # km/h
speed_car2 = 60 # km/h
# Total distance
total_distance = 200 # km
# Combined speed when moving towards each other
combined_speed = speed_car1 + speed_car2
# Time to meet (distance / combined speed)
time_to_meet = total_distance / combined_speed # hours
Time_to_meet
-
Result: The code will calculate the time it takes for the cars to meet, and ChatGPT will output the result, which in this case would be:
2 hours - Code review: You can click on the View Analysis link to see the Python code that ChatGPT used. This allows you to review the logic, make adjustments if needed, or copy the code to use in your own Python environment.
Analyzing Data
To analyze data, users can upload a variety of file types, such as CSV or Excel files. Once uploaded, the tool generates insights from the dataset, such as performing descriptive statistics, anomaly detection, or suggesting data cleaning steps. You can also ask the tool to execute analyses like regression or time-series analysis.
Input example:
*"I have uploaded a dataset with columns for 'Date', 'Sales', and 'Region'. Can you analyze the total sales per region and provide insights?"*
Output example:
"Based on the uploaded data, here are the total sales for each region:
North: $45,000
South: $30,000
East: $20,000
West: $35,000
The North region has the highest sales, while the East region has the lowest. Would you like to visualize these results?"
Visualizing Data
ChatGPT Advanced Data Analysis can also create visualizations directly from the data you upload. By specifying the type of chart—such as scatter plots, bar charts, or histograms—you can generate visual representations of your data.
The tool uses Python libraries like Matplotlib to generate both static and interactive visualizations. This feature is particularly useful for understanding patterns and trends within large datasets.
Build Applications Based on Advanced Data Analysis with Acorn
To see what you can start building today with GPTScript, visit our docs at https://gptscript-ai.github.io/knowledge/. For a great example of a code interpreter at work using GPTScript check out our tutorial series on GPTReview: An AI Based Code Reviewer.