Accessing Data in Jupyter with Python

The text discusses how to access popular data sources using Python in Jupyter Notebooks. It covers various file formats such as CSV, Excel, JSON, TXTs, and Pickle, databases like MySQL, PostgreSQL, MongoDB, Redshift, and Snowflake, APIs from multiple endpoints, and dataset repositories like Kaggle, Data.world, or Hugging Face. The tutorial uses Python 3.10 language and Jupyter Notebook for writing the code. It also explains how to install Python, Jupyter Notebook, and various libraries such as Pandas, requests, sqlite3, mysql-connector, and kaggle. Additionally, it demonstrates loading data from different file formats using pandas library functions like read_excel(), read_csv(), and read_json(). Finally, the text discusses accessing data from databases using SQL queries and APIs using requests module.