Working with JSON/SQL | Day 16 | 100 Days of Machine Learning

Brief Summary

This video explains how to work with JSON and SQL data using Python's Pandas library. It covers reading JSON data from files and URLs, and importing SQL data from a MySQL database into Pandas DataFrames. The video also touches on the importance and usage of JSON as a universal data format and SQL for direct database interaction.

• Working with JSON data using pd.read_json() from local files and URLs.
• Connecting to a MySQL database using the mysql.connector library.
• Importing SQL data into Pandas DataFrames using pd.read_sql() and manipulating data with SQL queries.

Introduction to JSON and SQL

The video introduces JSON (JavaScript Object Notation) as a universal data format, widely supported across programming languages and commonly used in APIs. It also introduces SQL (Structured Query Language) for direct data retrieval from databases. The presenter outlines the plan to demonstrate how to work with both JSON and SQL data using Python.

Working with JSON Data

This section demonstrates how to read JSON data into a Pandas DataFrame using the pd.read_json() function. The presenter uses a dataset of recipes in JSON format, showing how to load the data from a local file. The resulting DataFrame contains columns for cuisine names and ingredients. The video also highlights the various options available in pd.read_json(), such as specifying data types, encoding, and chunk sizes for large files. Additionally, it demonstrates how to load JSON data directly from a URL, using an exchange rate API as an example.

Working with SQL Data

This section explains how to import data from a SQL database into a Pandas DataFrame. The presenter uses a MySQL database containing world cities data. First, the presenter explains how to set up a local MySQL server using XAMPP and import a SQL file containing the database. Then, the video demonstrates how to connect to the MySQL database using the mysql.connector library and how to use the pd.read_sql() function to execute SQL queries and load the results into a Pandas DataFrame. The presenter shows examples of querying the database, filtering data using SQL WHERE clauses, and manipulating the data within Pandas.