Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge. The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study. Data collection is a crucial step in the process of obtaining valuable insights and making informed decisions. In today’s interconnected world, data can be found in a multitude of sources, ranging from traditional files such as .csv, .html, .txt, .xlsx, .html, and .json, to databases powered by SQL, websites hosting relevant information, and APIs (Application Programming Interfaces) offered by companies. To efficiently gather data from these diverse sources, various tools can be employed. Python offers a rich ecosystem of packages for data collection. Some commonly used Python packages for data collection include: including: Pandas, BeautifulSoup, Requests, mysql-connector-python, psycopg2, and sqlite3. This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.