GitHub Copilot Prompt for Extracting Data Insights

GitHub Copilot, the AI coding assistant integrated into IDEs, is a powerful ally for extracting insights from raw data. Whether you are working with CSV files, SQL databases, or pandas DataFrames, Copilot can quickly generate exploratory analysis, visualization, and statistical summary code. The main challenge lies in crafting the prompt: a well-structured prompt allows Copilot to produce relevant analysis code on the first try, without unnecessary iterations. By clearly specifying the data format, the metrics you're looking for, and the type of visualization desired, you turn Copilot into a true data analyst capable of detecting trends, anomalies, and hidden correlations in your datasets. This page offers an optimized main prompt as well as variants tailored to your skill level, to get the most out of GitHub Copilot in your data analysis workflows.

The prompt

GitHub Copilot

Analyze the DataFrame 'df' which contains the following columns: [LIST_OF_COLUMNS]. For each numerical column, calculate descriptive statistics (mean, median, standard deviation, quartiles, outliers via IQR). Identify significant correlations between variables (threshold > 0.7). Generate a structured report including: 1) An executive summary of main trends detected, 2) Anomalies and outliers identified with their context, 3) Natural segments or clusters in the data, 4) matplotlib/seaborn visualizations for each key insight (correlation heatmap, distribution of important variables, time evolution if applicable). Add explanatory comments in the code for each analysis step. End with a list of actionable recommendations based on the extracted insights.

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Why it works

This prompt is effective because it structures the analysis into clear sequential steps that Copilot can follow methodically, avoiding fragmented suggestions. By specifying exact metrics (IQR, correlation threshold) and expected libraries (matplotlib, seaborn), ambiguity is reduced and directly executable code is obtained. The request for explanatory comments forces Copilot to contextualize each code block, improving the quality and readability of the generated analysis.

Expected result

You obtain a complete and structured Python script that loads your data, performs an in-depth exploratory analysis, and produces ready-to-use visualizations. The code includes a text report synthesizing major trends, detected anomalies, and significant correlations, accompanied by annotated graphs and concrete recommendations for decision-making.

Variants by level

FAQ

Can GitHub Copilot directly analyze my data files?

GitHub Copilot does not directly read your data files. It generates analysis code based on the description you provide (column names, data types, context). For best results, paste a sample of your data in a comment in your Python file, or precisely describe the structure of your DataFrame. Copilot will use this information to produce pandas, numpy, or scikit-learn code tailored to your specific dataset.

How can I get relevant visualizations rather than generic charts?

The key is to specify in your prompt the type of visualization desired (heatmap, boxplot, scatter plot), the preferred library (matplotlib, seaborn, plotly), and most importantly the business context of your data. For example, specify "monthly evolution of revenue by customer segment" rather than "make a chart." The more specific you are about what you want to show, the more Copilot will produce targeted and informative visualizations with the right axis parameters, legends, and annotations.

What is the difference between using Copilot and a classic Jupyter notebook for data analysis?

Copilot significantly speeds up the analysis code writing phase by suggesting complete blocks of processing, statistics, and visualization. However, it does not replace human interpretation of the results. The optimal approach is to use Copilot in a Jupyter notebook: you write your prompts in comments or markdown cells, Copilot generates the corresponding code, and you validate the results cell by cell. This combination offers the speed of Copilot generation with the iterative control of the notebook.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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