Скачать или смотреть Python Lists for Data Science | Create, Access, Modify, Iterate, Best Practices & Real Examples

Learn everything about Python lists in this comprehensive tutorial designed for Data Science and Data Analytics learners who want to master one of Python’s most versatile and powerful data structures. Lists are ordered, mutable collections that allow you to store heterogeneous data types and play a crucial role in data science workflows, from preprocessing raw datasets to building efficient machine learning pipelines. In this in-depth video, we cover the fundamentals of creating lists, initializing with literals or constructors, and understanding the importance of indexing and slicing to access individual elements or sublists. You’ll learn how to update list items, add new elements with append() and extend(), insert values at specific positions, and remove items using pop(), remove(), and del statements. Iterating over lists with for-loops, list comprehensions, and functional constructs like map() and filter() is demonstrated with practical examples, showing how lists can be used to process numerical data, text data, or even nested structures in exploratory data analysis (EDA). We also explore advanced features like nested lists, multidimensional lists, and list unpacking, which are frequently used when handling structured datasets before passing them to libraries such as NumPy or Pandas. The video provides insights into how lists handle mutability, why shallow copies and deep copies behave differently, and how to avoid common pitfalls like unintentional aliasing or inefficient concatenation in large-scale operations. Performance considerations are highlighted, including the differences between lists and arrays, when to choose lists over NumPy arrays, and how to optimize memory and speed in data-heavy projects. Real-world applications include storing cleaned records, holding intermediate results in feature engineering, handling categorical labels, and organizing data for custom machine learning workflows. Best practices for list usage are emphasized, such as choosing descriptive variable names, avoiding excessive nesting for readability, and leveraging list comprehensions for concise, Pythonic code. We also demonstrate error handling techniques when working with lists, such as managing index errors, type mismatches, and unexpected empty lists in data pipelines. By the end of this tutorial, you will be confident in using Python lists effectively to manage, manipulate, and process data, enabling you to create efficient, scalable, and maintainable data science scripts. Whether you are just starting your Python journey or already building machine learning pipelines, mastering lists is a fundamental skill every data professional must acquire. Subscribe and turn on notifications 🔔 for weekly videos covering Python programming, Data Science, Data Analytics, Machine Learning, Pandas, NumPy, Matplotlib, data preprocessing, and industry best practices for automation and analytics.