Introduction to Python

This is a collection of Jupyter notebooks that is intended to provide an introduction to the Python programming language. Although this collection is aimed to the beginner data science student, I found it very useful for any beginner in python programming. All notebooks were developed and released by IBM Cognitive Class, with some minors changes, organization and customizations provided by me.

Notebooks

The notebooks are divided by the following topics. I also provided the estimated time required to complete each lesson, a link to the source code, and the Google Colab link where anyone can use to follow the lessons and run the examples.

Python Basics

This section covers the python basics: print, import, types, expressions and strings.

| Lesson | Estimated time needed | Source Code | Colab | | ------------- |:---------------------:| :-----------:| -----:| | Your first program | 10 min | Open | Open | | Types | 10 min | Open | Open | | Expressions and Variables | 10 min | Open | Open | | String Operations | 15 min | Open | Open | | Total | 45 min | | |

Python Data Structures

This section covers the main Python data structures.

| Lesson | Estimated time needed | Source Code | Colab | | ------------- |:---------------------:| :-----------:| -----:| | Tuples | 15 min | Open | Open | | Lists | 15 min | Open | Open | | Dictionaries | 20 min | Open | Open | | Sets | 20 min | Open | Open | | Total | 75 min | | |

Python Programming Fundamentals

This section covers the fundamentals of Python language, logic and control structures, functions, and object-oriented programming in Python.

| Lesson | Estimated time needed | Source Code | Colab | | ------------- |:---------------------:| :-----------:| -----:| | Conditions and Branching | 20 min | Open | Open | | Loops | 20 min | Open | Open | | Functions | 40 min | Open | Open | | Classes and Objects | 40 min | Open | Open | | Total | 120 min | | |

Files

This section covers the basics of File handling in Python.

| Lesson | Estimated time needed | Source Code | Colab | | ------------- |:---------------------:| :-----------:| -----:| | Reading files with open | 40 min | Open | Open | | Writing files with open | 15 min | Open | Open | | Total | 55 min | | |

Python Data Analysis Library (Pandas)

This section covers an introduction to pandas, an open source library that provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

| Lesson | Estimated time needed | Source Code | Colab | | ------------- |:---------------------:| :-----------:| -----:| | Reading files with open | 15 min | Open | Open |

NumPy

This section covers an introduction to NumPy, the fundamental package for scientific computing with Python.

NumPy makes it easier to do many operations that are commonly performed in data science. The same operations are usually computationally faster and require less memory in NumPy compared to regular Python.

| Lesson | Estimated time needed | Source Code | Colab | | ------------- |:---------------------:| :-----------:| -----:| | 1D NumPy in Python | 30 min | Open | Open | | 2D NumPy in Python | 20 min | Open | Open | | Total | 50 min | | |

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Cognitive Class
• IBM Cloud
• Joseph Santarcangelo
• Mavis Zhou
• Python for Data Science and AI (Coursera)
• Jupyter Notebook
• Google Colab
• Raph Trajano

And a special thanks to Raph Trajano for reviewing and fixing the materials.