Pandas with Python
- 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
Who this course is for:
- Beginner Python developers curious about Data Science
- 02:02Session 1. Introduction Series & DataFrame
- 41:00Introduction Series & DataFrame
- 00:50Session 2. Date Range & Inspecting Data
- 29:36Date Range & Inspecting Data
- 00:44Session 3. Indexing & Slicing on DataFrame - 1
- 30:13Indexing & Slicing on DataFrame - 1
- 00:45Session 4. loc & iloc
- 31:18loc & iloc
- 00:27Session 5. Indexing & Slicing on DataFrame - 2
- 18:24Indexing & Slicing on DataFrame - 2
- 00:55Session 6. Concatenation & Descriptive Statistics
- 31:54Concatenation & Descriptive Statistics
- 00:54Session 7. Merging DataFrames
- 29:28Merging DataFrames
- 00:43Session 8. Working with Text Data
- 18:40Working with Text Data
- 00:59Session 9. Function Application & Loading data in Python
- 40:33Function Application & Loading data in Python
- 00:56Session 10. Loading Data from CSV, Excel & URL
- 21:24Loading Data from CSV, Excel & URL
- 00:45Session 11. Data Visualization using Pandas
- 19:33Data Visualization using Pandas
- 01:43Data Science
- 30:4212. What is Data Science
- 01:27Machine Learning
- 25:0613. What is Machine Learning
Having 10+ Years of Experience in Software Industry which includes Development, Support & Training.
My Experience Includes Managing, Processing, Predicting and Analyzing of Large volume of Business Data.
Expertise in Data Management, BI Technologies & Data Science with Data Analytics, Machine Learning, Deep Learning & Artificial Intelligence using R Programming, Python Programming, WEKA and EXCEL.
Having publications and patents in various fields such as machine learning, data security, and data science technologies.
I received my Masters of Technology in Computer Science & Engineering from JNTU.
Professionally, I am a Data Science management consultant with over 8 years of experience in finance, retail, transport and other industries.