Welcome to the AI and Statistics series! Let’s dive into how AI can transform tabular data into various types of charts.
Today, we will be transforming a 2D data table into a column format and then generating two different types of bar charts.
Don’t worry about using the AI Agent-driven Bayeslab, all you need is natural language to get the data analysis result.
All content will be explained in the most comprehensible natural language descriptions to help you get started with data analysis from scratch.
We’ll start with a data table featuring California 2024–01–01 to 2025–01–31 weather data.
This chart will illustrate the temperature variations, including maximum, minimum, and mean values over this period.
We’ll delve into how these prompts influence the final charts and uncover techniques for effective data visualization.
In just 2 minutes, you’ll learn to transform weather data into insightful bar charts with easy visual comparisons! Let’s start it right now.
Using different prompt inputs, we’ll demonstrate how AI generates two types of bar chart results, our steps will include:
· Step 1 — Table Structure Processing:
· Step 2 — Stacked Bar Chart:
· Step 3 — Separated Bar Chart:
Step 1 — Table Structure Processing
Transform the 2D data table into a column list to highlight key temperature statistics for each month.
Prompt：
Read California 2024–01–01 to 2025–01–31.csv create a new table in the columnar format.
Columns: The column headers are the months from Jan to Dec.
Rows: The column header for rows is empty, and the titles for the rows are Lower, Mean, and Upper.
This table should present the maximum, minimum, and mean values for each month.
Save the resulting table locally with the name “Temperature”. Ensure that all numeric data is saved with 3 decimal places.
Additionally, “columnar format” means that the columns have grouping variables, while the rows do not have grouping variables but have titles (the column header for the row titles is empty).
Step 2 — Stacked Bar Chart
Create a stacked bar chart to depict the maximum, minimum, and mean temperatures, with the mean highlighted.
Prompt:
Read California 2024–01–01 to 2025–01–31.csv Group the data by month to process it.
I need the maximum, minimum, and mean values.
Then, based on the processed data, create Stacked bar segments with the Mean value represented by a line segment within each bar at a monthly granularity.
Step 3 — Separated Bar Chart
Construct a separated bar chart to provide a clear comparison of monthly temperature statistics.
Prompt:
Read California 2024–01–01 to 2025–01–31.csv Group the data by month to process it.
I need the maximum, minimum, and mean values
Then, based on the processed data, create Separated bar segments with the Mean value represented by a line segment within each bar at a monthly granularity.
Note it is a Separated bar chart not Stacked style one , no need to show the processed data
Thank you for reading this installment of the AI and Statistics series!
We showed how to transform a 2D data table into a column format and generate meaningful bar charts, illustrating both stacked and separated views for insightful comparisons.
Stay tuned for our upcoming demonstrations to explore more fascinating data visualization.
Using AI Agent and Bayeslab, anyone can organize, analyze, plot data charts, and make business data predictions like a professional data analyst based on previous data.