How to Analyze API Data in Python: Requests, Pandas & Matplotlib Tutorial

In this video, we build a complete end-to-end data pipeline using Python. You will learn how to fetch real data from an API, handle messy "nested" JSON structures using Pandas, and visualize your insights using Matplotlib.

This is the exact workflow used in real-world data science projects. We start from a blank file and finish with a visual report.

🔗 Data Source Used: https://jsonplaceholder.typicode.com/...

📝 What You Will Learn:

Fetching Data: How to use the Python requests library to pull live data from the web.

Fixing Nested Data: Why standard DataFrames fail with API data and how to fix it instantly using pd.json_normalize.

Library Management: Three ways to install Python libraries (PyCharm Lightbulb, Terminal, and Jupyter Cells).

Data Analysis: Filtering, Sorting, Grouping, and Feature Engineering with Pandas.

Visualization: Creating and customizing Bar Plots and Line Plots with Matplotlib.

🚀 Tools Used:

PyCharm (for scripting)

Jupyter Notebook (for analysis)

Firefox (for JSON viewing)

Timestamps:
0:00 - Introduction & Data Source
1:45 - Fetching API Data (Requests Library)
2:56 - The Problem: Nested JSON Data
3:34 - The Solution: pd.json_normalize
4:16 - Setting up Jupyter Notebook
4:53 - Data Analysis (Filtering, Sorting, Stats)
7:18 - Visualizing Data with Matplotlib
8:48 - Final Result & Summary

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