Published 10/2024
Created by Kichere Magubu
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 8 Lectures ( 1h 39m ) | Size: 485 MB
Master Data Analysis with Pivot Tables in Python Using Pandas.
What you'll learn
pivot() method in pandas
pivot_table() method in pandas
pivot_table() vs pivot() methods.
Data analysis with pivot tables
Requirements
Desire to Learn: A strong willingness to engage with the course material and practice new skills in data analysis.
Preferred Tools: A working installation of Python, preferably using Jupyter Lab or jupyter Notebook, for an interactive
Description
Learn how to use Pivot Tables in Python with Pandas to analyze and summarize data easily. This course is for anyone working with large datasets who wants to improve their data analysis skills. Whether you're a student, a data enthusiast, or a professional in the field, this course will provide you with the essential tools and techniques needed to unlock insights from your data.In this course, you will:Understand what pivot tables are and how they simplify data.Use Pandas' pivot() method for simple data reshaping.Use Pandas' pivot_table() to quickly summarize data.Group, filter, and organize data for better insights.Work with multi-level pivoting and large datasets.Practice with real-world examples to reinforce your learning.Additionally, you will learn best practices for data manipulation and visualization, ensuring you can present your findings effectively. The hands-on projects will enable you to apply the concepts learned throughout the course in practical scenarios, enhancing your understanding of data analytics. By the end, you'll know how to use pivot tables to make complex data easier to analyze, whether you're a beginner or an experienced analyst looking to refine your skills. Join us and take your data analysis expertise to the next level!
Who this course is for
Individuals new to data analysis and those with experience in Python.
Aspiring data analysts and professionals seeking to enhance their data skills.
Students and researchers looking to analyze datasets.
Python enthusiasts interested in data manipulation and analysis.