Data visualization is an essential skill for working with data, but it can be time-consuming and require specialized tools. That’s where ChatGPT comes in. With its latest updates, ChatGPT makes data visualization faster and easier than ever before. Instead of switching between different options like GPT-4, GPT4 with advanced analysis, or DALLE-3, you can simply type in a prompt, and ChatGPT will interpret your request and generate the desired results automatically.
In this blog post, we will explore how to generate various data visualizations using plain English prompts. Thanks to ChatGPT’s advanced data analysis, you don’t need to process the data or run Python code. We’ll cover simple pie and bar charts, as well as more complex visualizations using real-world datasets.
Pie Chart
To generate a pie chart visualization based on nutrient data, make sure you are using the GPT-4 model. Use the following prompt:
Prompt: Generate a pie chart of values {"Vitamin A":5, "Vitamin B": 1, "Vitamin C": 4, "Water": 90} to keep the color combination light.
By requesting a lighter color combination, you can customize the appearance of the chart.
Bar Chart
For a bar plot of CO2 emissions data for different cars, use the following prompt:
Prompt: Generate a bar plot of CO2 emissions values {"Car A":30, "Car B": 25, "Car C": 20}.
The generated bar chart will include a title, x and y labels, and ensure a descending order.
Instead of tightly controlling ChatGPT’s output, you can let it create results independently, similar to Python AutoViz libraries. Provide the dataset and request a complete exploratory data analysis to generate the necessary plots. For example:
Prompt: Perform exploratory data analysis on the customer shopping trends dataset and display only plots.
ChatGPT will process and analyze the consumer trends quickly, generating the required visualizations.
If you want to see more complex visualizations, you can specifically ask ChatGPT for them. For example:
Prompt: Use the dataset to plot various complex visualizations.
Data visualization is crucial for evaluating models. You can train and evaluate multiple machine learning models using the Diabetes Dataset from Kaggle with the following prompt:
Prompt: Multiple machine learning models should be trained using the target column "Outcome", and the resulting model evaluation visualization should include a confusion matrix, precision-recall, and model comparison chart.
ChatGPT will train the models and generate the evaluation metrics and comparison plots.
ChatGPT has revolutionized the ease of creating data visualizations. With its advanced data analysis capabilities, you can generate stunning and informative visualizations in seconds using simple English prompts. Whether you need a simple bar chart, advanced model analysis, or quick insights into datasets, ChatGPT delivers exceptional results with minimal effort. It’s an exciting time to level up your data visualization skills with this AI assistant.
About the author: Abid Ali Awan is a certified data scientist professional with a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. He focuses on content creation and writing technical blogs on machine learning and data science technologies.
