Building a CSV Data API with FastAPI and Pandas

In this video, we explore how to create an API using FastAPI and Pandas to serve a CSV dataset. This approach is useful for sharing CSV data stored on a local computer with a team or the public via an API. We start by downloading a dataset of Europe bike store sales from Kaggle and proceed with exploratory data analysis (EDA). Next, we set up a FastAPI framework in Visual Studio Code, create API endpoints, and perform advanced data processing techniques. The video covers basic EDA, setting up asynchronous context managers, implementing data summary endpoints, and handling query parameters for filtered data queries. The tutorial also demonstrates how to organize the code into modules for better maintainability. By the end, we have a fully functional API capable of serving summarized CSV data, which can be easily consumed by frontend applications. Future episodes will cover deployment strategies and integration with machine learning models.

dataset
https://www.kaggle.com/datasets/prepi...


00:00 Introduction to FastAPI and Pandas
01:11 Setting Up the Environment
01:45 Exploratory Data Analysis (EDA)
09:12 Building the FastAPI Application
12:19 Creating API Endpoints
27:12 Advanced API Features and KPIs
32:56 Using Routers for Cleaner Code
34:44 Conclusion and Future Directions