We kick off the class by exploring the functionality of the IPython Shell, an enhanced interactive science-centric console. Next we review the Jupyter Notebook, a cell-based environment that renders scripts, plots, and rich media in a web-like interface, making it ideal for sharing and publishing analysis with peers. You’ll leave with a mastery of these tools that will accelerate your productivity and facilitate collaboration.

1. Building a Solid Infrastructure to Go From Exploratory Analysis to Reproducible Workflows

A. Introduction and Setting Up Your Integrated Analysis Environment

• Canopy: Integrated Analysis Environment
• IPython Shell
• Custom environment settings
• Jupyter (IPython) Notebooks
• Script editor
• Packages: NumPy, Pandas, matplotlib, etc.

Next we move into an introduction to Python’s core language features that form part of your universal toolkit for tasks ranging from initial data exploration to extensible application development. We’ll introduce Python’s built-in data structures, including how and where each might be used and what trade-offs are present, and we’ll cover Python’s looping and control flow constructs. Along the way we’ll provide insight into Python’s design choices that will help you understand why Python works the way it does.

1. Using Python to Control and Document Your Workflow

Python Essentials

• Data types and objects
• Loading packages, namespaces
• Reading and writing data
• Simple plotting
• Control flow
• Debugging
• Code profiling

There are a number of “must-have” packages for scientific computing and data analysis with Python. We’ll review three of these in this class that will give you the underpinnings you need to be able to expand your knowledge into additional packages that fit your area of specialization. If you are coming from a background in MATLAB®* or R, you’ll find these libraries essential.

Chief among these packages is NumPy, a tool for rapidly manipulating and processing large data sets. Whether you are a scientist writing short scripts to analyze and plot your analytical results or an analyst writing large-scale quantitative finance applications for Wall Street, NumPy should be part of your toolbox. We give you a jump start with the basics in the classroom, then provide you additional curated lectures to extend your understanding.

Once you’ve crunched your data, you’ll want to visualize it, which is where matplotlib comes in. Matplotlib is a versatile 2D plotting library that allows you to generate plots, histograms, power spectra, bar charts, error charts, scatter plots, and more with just a few lines of code.

Finally, we do a deep dive into the Python Data Analysis Library (Pandas), a powerful package for working with multi-dimensional datasets. Pandas’ powerful data aggregation and reorganization capabilities, including support for labeling data along each dimension, missing values, and time series manipulations, have made Python an indispensable tool for data exploration and analysis.

1. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays and Matplotlib

NumPy Essentials

• The NumPy array
• Indexing and slicing arrays
• Array operations and manipulations
• 2D plotting with matplotlib

2. Data Wrangling, Exploration, and Analysis with Pandas

A. Pandas

• 1D and 2D data structures: Series and DataFrame
• Pandas I/O
• Data visualization
• Data manipulation (alignment, aggregation, and summarization)
• Statistical analysis with Pandas
• Date and time series analysis with Pandas