I'm sharing my Kaggle Pandas Course - Exercise Notebook which I have solved while undertaking this course in my journey of Data Science.
For more detials, refer: Data Analyst Roadmap ⌛, Python Lessons 📑 & Python Libraries for Data Science 🗂️
The Pandas Library is arguably the most important library in the Python Data Science stack. It is the library of choice for working with small to medium sized data (anything that isn't quote-unqutoe "big data"). Much to most of the data manipulation that's done in the Python world is done using Pandas memory structures and tools, making it one of the most important libraries in the ecosystem.
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Data Analysis with Python - by IBM
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Data Visualization with Python - by IBM
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Numpy & Matplotlib - by Great Learning
Pandas 
- Python Library for Data Science by Kaggle
Prerequisite: Python Lessons 📑
| Sr.No. 🔢 | Lessons 📕 | Reference Links 🔗 | Exercises 👨💻 |
|---|---|---|---|
| 1 | Basics, Data Structures - Series, DataFrame, Panel | Pandas Course - by Kaggle | Exercise 1 |
| 2 | Summary Functions and Maps, Operations - Slicing, Merging | Kaggle Notebooks on Pandas | Exercise 2 |
| 3 | Operations - Joining, Concatenation | GitHub Repo on Pandas | Exercise 3 |
| 4 | Changing Index & Column Header, Data Munging | JavaTpoint | Exercise 4 |
| 5 | Grouping & Sorting, Data Types & Missing Values | YouTube | Exercise 5 |
| 6 | Renaming and Combining | TutorialsPoint | Exercise 6 |
| 7 | Pandas-Matplotlib | ✅ |
| Sr.No. 🔢 | Projects 👨💻 | Reference Links 🔗 |
|---|---|---|
| Python Project 1 | Spotify Data Analysis using Python | GitHub Project & Kaggle Notebook |
| Python Project 2 | Boston Housing Data Analysis using Python | Project |
Spotify Data Analysis using Python 📊
Sales Insights - Data Analysis using Tableau & SQL 📊
Statistics for Data Science using Python 📊
Python Libraries for Data Science 🗂️
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