Mastering Data Analysis with Polars in Python - Crash Course

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Mastering Data Analysis with Polars in Python - Crash Course (Size: 956.3 MB)
  1. Analyzing Financial Data with Polars.mp4 97.4 MB
  1. Conclusion And Recap.mp4 25.1 MB
  1. Functions in Python Definition and Usage.mp4 24.7 MB
  1. Getting Started with Series Polars Basic Operations.mp4 38.1 MB
  1. Installing Python and Setting Up Your Environment.mp4 35.4 MB
  1. Introduction to Aggregations and Grouping in Polars.mp4 41.3 MB
  1. Introduction to Filtering and Selecting Data in Polars.mp4 29.6 MB
  1. Joins and Concatenations Lecture.mp4 85.8 MB
  1. Memory Management Handling Large Datasets with Polars.mp4 52 MB
  1.1 code.ipynb 7.9 KB
  2. Aggregating Data with Polars min, max, mean, median, sum.mp4 22.4 MB
  2. Filtering Data with Polars.mp4 22.9 MB
  2. Functions in Python Definition and Usage - QUIZ.html 204.8 B
  2. Getting Started with DataFrame Polars Basic Operations.mp4 28.9 MB
  2. How To create VENV.mp4 23.3 MB
  2. Parallel Processing Speeding Up Data Analysis with Polars.mp4 42 MB
  2. Real-world Applications and Case Studies QUIZ.html 204.8 B
  2. Understanding Concatenation in Polars.html 3.2 KB
  2.1 code.ipynb 5.1 KB
  3. Functions in Python Definition and Usage - Code Exercise.html 204.8 B
  3. How to Install Python 3 and Use Virtual Environments (venv) on Windows- Article.html 3.8 KB
  3. Optimizing Performance with Polars QUIZ.html 204.8 B
  3. Ranking Data with Polars.mp4 40.9 MB
  3. Reading and Writing CSV Files with Polars.mp4 35.6 MB
  3. Selecting Columns and Rows with Polars.mp4 29.2 MB
  3. Understanding Join Types in Polars.html 4.8 KB
  3.1 code.ipynb 7.5 KB
  3.2 output.csv 512 B
  3.3 tips.csv 18.1 KB
  4. Grouping Data with Polars.mp4 41 MB
  4. How to Install Python 3 and Use Virtual Environments (venv) on linux- Article.html 3.7 KB
  4. Merging DataFrames with Polars QUIZ.html 204.8 B
  4. Modules and Packages Organizing Code.mp4 22 MB
  4. Reading and Writing Excel Files with Polars.mp4 12.8 MB
  4. Slicing and Sampling Data with Polars.mp4 28.3 MB
  4.1 code.ipynb 7.6 KB
  4.2 example.xlsx 8.4 KB
  5. Converting Pandas DataFrames to Polars.mp4 18.6 MB
  5. How to Install Python 3 and Use Virtual Environments (venv) on Mac- Article.html 4.2 KB
  5. Modules and Packages Organizing Code - QUIZ.html 204.8 B
  5. Pivot Tables and Cross-Tabulations with Polars.mp4 53 MB
  5. Sorting Data with Polars.mp4 32.4 MB
  5.1 code.ipynb 6.5 KB
  6. Aggregations and Grouping in Polars QUIZ.html 204.8 B
  6. Basic Data Processing with Polars QUIZ.html 204.8 B
  6. How to Install Jupyter Lab - Practicle.mp4 30.6 MB
  6. Modules and Packages Organizing Code - Coding Exercise.html 204.8 B
  6. Test your understanding of basic Polars concepts..html 204.8 B
  7. How to Install Jupyter Lab - Article.html 7.4 KB
  7. Understanding Python Classes and Objects.mp4 42.7 MB
  8. Understanding Python Classes and Objects - QUIZ.html 204.8 B
  9. Understanding Python Classes and Objects - Coding Exercise.html 204.8 B
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 67 total files

Description


Mastering Data Analysis with Polars in Python: Crash Course

https://FreeCourseWeb.com

Published 4/2024
Created by ‪Idan Chen‬‏
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 32 Lectures ( 2h 48m ) | Size: 956 MB

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).

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