Overview
This is a rapid introduction to NumPy, pandas and matplotlib for experienced Python programmers who
are new to those libraries. Students will learn to: use NumPy to work with arrays and matrices of
numbers; work with pandas to analyze data; and work with matplotlib from within pandas.

Audience
Students already familiar with Python programming.

Prerequisites
Basic Python programming experience. In particular, you should be very comfortable
with: working with strings, lists, tuples and dictionaries; loops and conditionals; and writing your own
functions.

Course Outline

Module 1. Jupyter Notebook
Getting Started with Jupyter Notebook
Creating Your First Jupyter Notebook
Jupyter Notebook Modes
Useful Shortcut Keys
Markdown
Magic Commands
Getting Help

Module 2. NumPy
One-dimensional Arrays
Multi-dimensional Arrays
Getting Basic Information about an Array
NumPy Arrays Compared to Python Lists
Universal Functions
Modifying Parts of an Array
Adding a Row Vector to All Rows
Random Sampling

Module 3. pandas
Series and DataFrames
Accessing Elements from a Series
Series Alignment
Comparing One Series with Another
Element-wise Operations
Creating a DataFrame from NumPy Array
Creating a DataFrame from Series
Creating a DataFrame from a CSVl
Getting Columns and Rows
Cleaning Data
Combining Row and Column Selection
Scalar Data: at[] and iat[]
Boolean Selection
Plotting with matplotlib