How to ACTUALLY Use AI + PYTHON to Analyze Data (Real-Time Example)

Why It Matters

AI‑driven code generation slashes analysis cycles, enabling faster, data‑informed decisions in healthcare operations while still demanding human oversight to ensure accuracy.

Key Takeaways

• AI generates Python code for data loading and merging.
• Google Colab used as free environment for AI‑assisted analysis.
• AI quickly identifies top five busiest days in January 2026.
• Comparative analysis reveals doctors with increased patient visits month‑over‑month.
• Human oversight remains essential to catch AI coding errors.

Summary

The video demonstrates how AI tools like Google Gemini can write Python code in real time to load, merge, and analyze a hospital data set within Google Colab. The presenter walks through three escalating tasks—basic exploratory analysis, answering a specific business question about the busiest days, and a deeper month‑over‑month doctor performance comparison—showcasing AI‑generated scripts for each step. Key insights include using AI to import multi‑sheet Excel files into pandas dataframes, automatically merging patient and doctor tables, filtering data to January 2026, grouping by date to surface the top five busiest days (e.g., Jan 2 with 86 patients), and visualizing results. The AI also produces doctor‑level aggregations, identifies doctors with increased visits between December 2025 and January 2026, and even suggests code refinements when warnings appear. Notable examples feature the presenter’s quote, “I no longer write code from scratch,” and concrete outputs such as a bar chart of the top five days and a list showing Phil Clinton handling 114 patients in January versus 78 in December. The AI’s ability to generate, adjust, and explain code on demand underscores its utility as a coding assistant. The broader implication is that AI dramatically shortens the time required for data preparation and insight generation, allowing analysts to focus on interpretation rather than boilerplate coding. However, the presenter stresses that domain expertise remains crucial to validate AI‑produced code and catch occasional errors, highlighting a collaborative future between humans and intelligent coding assistants.

Original Description

Learn how to analyze hospital data with Python (pandas) with the help of AI tools. We are going to perform 3 levels of data tasks.

1) Data loading and exploratory analysis

2) Answering a specific business question

3) Performing practical analysis of data

Watch this if you currently code in Python. You will become more productive and better at your work with the ideas presented in this.

🕐 Video Topics and Timestamps:

0:00 - Spilling my secrets

0:52 - Chapter 1 - Data Exploration with Python (using AI)

7:03 - Chapter 2 - Answering Business Questions with the help of AI in Python

11:15 - Chapter 3 - Data Analytics with Python (using AI to guide me)

20:18 - Closing Remarks and how to get more out of AI

📂 Sample Dataset: Grab the hospital data file here:

https://github.com/chandoo-org/python/blob/main/awesome-clinic-visits-2026.xlsx

📺 Learn More Python

Python Basics to PRO Masterclass (1 hour, Watch this to learn everything in one place) - https://youtu.be/gaPFwH0r4uo

Pandas for Beginners in 15 Minutes - https://youtu.be/mkYBJwX_dMs

For Loops in Python - Finally Explained - https://youtu.be/vSHUjaxBf14

📺Learn more AI

How I really use AI as a data analyst - https://youtu.be/zQjAmU7RXYw

Don't tell my boss, but AI is doing my Excel work now - https://youtu.be/VOvIu5lKoBQ

You don't need ChatGPT, Excel can do these things already - https://youtu.be/xGqH__hLVcU

CoPilot Tutorial for Excel users - https://youtu.be/l_PsLA28I4A

Happy Coding.

#python #gemini #coding