Mastering Data Analysis With Polars In Python: Crash Course
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English
| Size: 863.32 MB
| Duration: 2h 49m
Unlock the Power of Polars for Fast and Efficient Data Analysis in Python - Dive into Data Science Today!
What you'll learn
Understand the fundamentals of Polars, a high-performance data manipulation library in Python.
Learn essential data processing techniques including filtering, aggregating, and transforming data.
Master advanced data manipulation tasks such as joins, groupings, and window functions.
Gain insights into optimizing performance and improving efficiency when working with large datasets.
Develop the skills to tackle complex data analysis challenges and derive meaningful insights.
Explore practical examples and real-world datasets to solidify understanding.
Become proficient in leveraging Polars for fast and efficient data analysis in Python.
Understand techniques for working with large CSV files efficiently using Polars.
Learn strategies to optimize memory usage and processing speed when dealing with massive datasets.
Gain practical experience in applying Polars to analyze and manipulate extensive CSV datasets with ease.
Requirements
Basic understanding of Python programming.
Familiarity with data structures like lists, dictionaries, and tuples.
Prior knowledge of data analysis concepts is beneficial but not required.
Access to a computer with Python and Polars library installed (installation instructions will be provided).
Description
Welcome to "Mastering Data Analysis with Polars in Python: Crash Course"! Are you ready to take your data analysis skills to the next level? In this course, we'll explore the powerful capabilities of Polars, a high-performance data manipulation library, and discover how it can revolutionize your approach to data analysis. Get ready to dive into a hands-on learning experience that will propel you toward becoming a proficient data analyst in Python!What You Will Learn:Understand the fundamentals of Polars and its advantages over other data manipulation libraries.Learn essential data processing techniques, including filtering, aggregating, and transforming data using Polars.Master advanced data manipulation tasks such as joins, groupings, and window functions with ease.Explore practical examples and real-world datasets to solidify your understanding of Polars in action.Gain insights into optimizing performance and improving efficiency when working with large datasets.Develop the skills to tackle complex data analysis challenges and derive meaningful insights from your data.Who Is This Course For:This course is designed for Python enthusiasts, data analysts, data scientists, and anyone interested in unlocking the power of Polars for efficient data analysis. Whether you're a beginner looking to dive into data analysis or an experienced professional seeking to enhance your skills, this crash course will provide you with the knowledge and tools you need to succeed.Join us on this exciting journey as we delve into the world of data analysis with Polars in Python. By the end of this course, you'll be equipped with the expertise to tackle a wide range of data analysis tasks efficiently and effectively. Don't miss out on this opportunity to elevate your data analysis skills and become a master of Polars. Enroll now and let's embark on this transformative learning experience together!
Overview
Section 1: Introduction and Setting Up Your Environment
Lecture 1 Installing Python and Setting Up Your Environment
Lecture 2 How To create VENV
Lecture 3 How to Install Python 3 and Use Virtual Environments (venv) on Windows- Article
Lecture 4 How to Install Python 3 and Use Virtual Environments (venv) on linux- Article
Lecture 5 How to Install Python 3 and Use Virtual Environments (venv) on Mac- Article
Lecture 6 How to Install Jupyter Lab - Practicle
Lecture 7 How to Install Jupyter Lab - Article
Section 2: Python Programming Foundations
Lecture 8 Functions in Python: Definition and Usage
Lecture 9 Modules and Packages: Organizing Code
Lecture 10 Understanding Python Classes and Objects
Section 3: Introduction to Polars and Data Analysis
Lecture 11 Getting Started with Series Polars: Basic Operations
Lecture 12 Getting Started with DataFrame Polars: Basic Operations
Lecture 13 Reading and Writing CSV Files with Polars
Lecture 14 Reading and Writing Excel Files with Polars
Lecture 15 Converting Pandas DataFrames to Polars
Section 4: Basic Data Processing with Polars
Lecture 16 Introduction to Filtering and Selecting Data in Polars
Lecture 17 Filtering Data with Polars
Lecture 18 Selecting Columns and Rows with Polars
Lecture 19 Slicing and Sampling Data with Polars
Lecture 20 Sorting Data with Polars
Section 5: Aggregations and Grouping in Polars
Lecture 21 Introduction to Aggregations and Grouping in Polars
Lecture 22 Aggregating Data with Polars: min, max, mean, median, sum
Lecture 23 Ranking Data with Polars
Lecture 24 Grouping Data with Polars
Lecture 25 Pivot Tables and Cross-Tabulations with Polars
Section 6: Merging DataFrames with Polars
Lecture 26 Joins and Concatenations Lecture
Lecture 27 Understanding Concatenation in Polars
Lecture 28 Understanding Join Types in Polars
Section 7: Optimizing Performance with Polars
Lecture 29 Memory Management: Handling Large Datasets with Polars
Lecture 30 Parallel Processing: Speeding Up Data Analysis with Polars
Section 8: Real-world Applications and Case Studies
Lecture 31 Analyzing Financial Data with Polars
Section 9: Conclusion
Lecture 32 Conclusion And Recap
Python enthusiasts eager to enhance their data analysis skills.,Data analysts seeking to expand their toolkit with Polars.,Data scientists interested in leveraging efficient data manipulation techniques.,Beginners looking to enter the field of data analysis with Python.,Professionals aiming to optimize their data processing workflows.,Individuals familiar with Pandas who want to explore alternative data manipulation libraries like Polars.,Python developers looking to transition from Pandas to Polars for faster and more efficient data analysis.
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