Pandas with Python
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Perform a multitude of data operations in Python’s popular “pandas” library including grouping, pivoting, joining and more! -
Possess a strong understanding of manipulating 1D, 2D, and 3D data sets -
Learn hundreds of methods and attributes across numerous pandas objects -
Resolve common issues in broken or incomplete data sets
- Basic / intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables etc)
- Basic experience with the Python programming language
- Strong knowledge of data types (strings, integers, floating points, booleans) etc
Why learn pandas?
If you’ve spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!
Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.
Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets — analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!
I call it “Excel on steroids”!
Over the course of more than 19 hours, I’ll take you step-by-step through Pandas, from installation to visualization! We’ll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We’ll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.
Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!
Whether you’re a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!
1. Introduction Series & DataFrame
2. Date Range & Inspecting Data
3. Indexing & Slicing on DataFrame – 1
4. loc & iloc
5. Indexing & Slicing on DataFrame – 2
6. Concatination & Descriptive Statistics
7. Merging DataFrames
8. Working with Text Data
9. Function Application & Loading data in Python
10. Loading Data from CSV, Excel & URL
11. Data Visualization using Pandas
12. What is Data Science
13. What is Machine Learning
- Beginner Python developers curious about Data Science
