Python and Jupyter Notebooks

Syllabus

Toggle Themes Toggle sub-themes

Introduction

Course intro and overview

  1. About the course.
  2. Course resources

Set up the environment

  1. Python installation notes
  2. Installing Python Packages and virtual environments (pipenv).
  3. Install Pandas, Matplotlib, Jupyter.

Jupyter Notebook Overview

  1. What is Jupyter Notebook?
  2. Start/stop the server
  3. Create your first Notebook.
  4. Write and execute code in Jupyter Notebook.
  5. Upload your Jupyter Notebook to GitHub (optional)
  6. Interact and share your Notebooks through Binder(optional)
  7. Live demos and hands on.

Python Basics

Getting started with Python

  1. Python Overview.
  2. What's in a Python program?
  3. Python design philosophy and syntax
  4. Write your first Python program in Jupyter.
  5. Live demos and hands on.

Python Built-in Data Types and Basic Operations

  1. Numeric Types and Python arithmetic.
  2. Strings and common operations.
  3. Variables and naming conventions.
  4. Logical Expressions and Conditional Statements.
  5. Loops in Python.
  6. Sequence Data Types (Lists, Tuples, Range).
  7. Strings as sequence.

Functions

  1. What are Functions?
  2. Function Definition.
  3. Function Call.
  4. Function Return Values.
  5. Function Parameters (positional, keywords).
  6. Arguments unpacking - the ** operator.
  7. Variables Scope.

Strings and List manipulations

  1. Basic IO.
  2. Formatted strings.
  3. Membership Testing
  4. Indexing and slicing.
  5. Iterate on Iterate on sequences (strings, lists and ranges).
  6. Built-in String methods (optional).
  7. Sorting lists (optional).

Files manipulations

  1. Directory listing.
  2. Reading from file.
  3. Writing in file.
  4. Remove a file

Jupyter Notebook for Data Analysis

Jupyter Notebook basics

  1. How it works - gentile introduction.
  2. The Notebook Interface.
  3. What's in a Cell?
  4. Basics of Markdown syntax.
  5. Most useful "magic" commands
  6. Useful Keyboard Shortcuts.
  7. Useful Jupyter Notebook Extensions.
  8. Sharing the Notebooks.
  9. Jupyter Lab - Jupyter’s Next-Generation Notebook Interface

Datasets manipulations with Pandas in Jupyter

  1. Pandas and Numpy Overview.
  2. Basic numpy arrays manipulations.
  3. Pandas Series and DataFrame basic operations.
  4. Create DataFrames from various data file formats (JSON, CSV/XLSX, SQL).
  5. Visualize your data with Pandas and Matplotlib/Seaborn.

Real world examples in Jupyter

  1. Prepare the Titanic Data Set with Pandas.
  2. Preprocess and analyze large text data (using scikit-learn and NLTK).