dreadlord1984 / python-for-data-analysis

An introduction to data science using Python and Pandas with Jupyter notebooks.

Python for Data Analysis

Course in data science. Learn to analyze data of all types using the Python programming language. No programming experience is necessary.

Quick links: 📁 lessons ⏬ Lesson Schedule

Software covered:

• IPython environment and Jupyter notebooks
• Conda for package management and virtual environments
• Python 3

Course topics include:

• Introduction to/review of the command line
• Fundamentals of Python and its data types
• Data analysis packages Numpy and Pandas
• Plotting packages Matplotlib and Seaborn
• Statistics
• Regular expressions
• Interactive visualization
• Modules and classes
• Git and GitHub

Instructor

• Luke Thompson, Ph.D.
• Lecturer at Scripps Institution of Oceanography, Research Associate at NOAA
• Email: [email protected], [email protected]
• Pages: GitHub, Google Scholar, NOAA profile + CV

Online Content

• GitHub repository: https://github.com/cuttlefishh/python-for-data-analysis
• YouTube channel: https://www.youtube.com/channel/UCVZrIrWtcvTzYlrNx7RcDyg

Textbooks

• Learn Python 3 the Hard Way by Zed Shaw (Addison-Wesley) -- Step-by-step introduction to Python with no prior knowledge assumed; includes appendix Command Line Crash Course.
• Learning Python 3rd Edition by Mark Lutz (O'Reilly) -- Optional; more traditional introduction to Python as a computer language.
• Python for Data Analysis 2nd Edition by Wes McKinney (O'Reilly) -- Manual focused on Pandas, the popular Python package for data analysis, by its creator. GitHub page: https://github.com/wesm/pydata-book.

Note: O'Reilly Media titles are free to UCSD affiliates with Safari Books Online.

Additional Materials

Command Line Resources

• Git for Windows -- BASH emulator and Git software for Windows
• Learning the Shell -- Great intro to the Unix shell
• Unix Tutorial by Julian Catchen -- From Evomics 2015 workshop in Czech Republic
• Learn the Command Line -- Code Academy
• Learn Unix The Hard Way by Zed Shaw -- More detail than you will get from Appendix A of Shaw's LPTHW

Python Resources

• MIT OpenCourseWare
• SciPy Lectures
• MatPlotLib Cheatsheet

IPython Resources from Cyrille Rossant

• IPython Interactive Computing and Visualization Cookbook
• Learning IPython for Interactive Computing and Data Visualization -- GitHub repo

Data Analysis Resources

• 10-minute Tour of Pandas by Wes McKinney -- Basic video tour
• R vs. Python for Data Analysis -- Fun cartoon to abate or fuel your biases
• Python Scripting for Computational Science by H. P. Langtangen -- Deeper and more mathematical treatment
• An Introduction To Applied Bioinformatics by Greg Caporaso
• A Dramatic Tour through Python's Data Visualization Landscape

Course Philosophy

1. Just like anything else, you learn Python by doing. With a few exceptions, you're not going to break your computer by trying new commands. So just try it and see what happens. Print output of commands. Print values of variables. Kick the thing until it works.
2. When you don't know how to do something, google it. You'll be amazed by the solutions you'll find to do thing x if you google "python thing x".
3. Learn keyboard shortcuts, as many as you can. Tab-complete in the shell and IPython/Jupyter!
4. Remember Zed's sage wisdom:
   • Practice every day.
   • Don't over-do it. Slow and steady wins the race.
   • It's alright to be totally lost at first.
   • When you get stuck, get more information.
   • Try to solve it yourself first.

Assignments

Weekly assignments

Weekly take-home assignments will follow the course schedule, reinforcing skills with exercises to analyze and visualize scientific data. Assignments will given out on Thursdays and will be due the following Thursday, using TritonEd.

Final Project

You will choose a data set of your own or provided in one of the texts and write a Python program (or set of Python programs or mixture of .ipynb and .py/.sh scripts) to carry out a revealing data analysis. Have a look at Shaw Ex43-52 and McKinney Ch10-12 for more ideas.

Requirements:

• Submit your project as either: a Jupyter notebook (or collection of notebooks), a Python script (or collection of scripts), or a combination of the two.
• Use pandas and at least three (3) additional libraries/packages, such as:
  • Plotting: matplotlib, seaborn
  • Statistics and modeling: statsmodels, scikit-learn
  • Bioinformatics: scikit-bio, biopython
  • Climate science: cdms, iris
  • Other domain-specific libraries/packages
• Use at least three (3) user-defined functions.
• Optional: Create user-defined modules and classes for use in your code.
• Optional: Share your code on GitHub.

Note: There are no midterm or final exams.

Schedule Overview

Schedule is subject to change.

The course consists of 20 lessons. It was originally taught as 2 lessons per week for 10 weeks, but the material can be covered at any pace.

Lessons 1-3 will be an introduction to the command line. By the end of this tutorial, everyone will be familiar with basic Unix commands.

Lessons 4-9 will be an introduction to programming using Python. The main text will be Shaw's Learn Python 3 the Hard Way. For those with experience in a programming language other than Python, Lutz's Learning Python will provide a more thorough introduction to programming Python. We will learn to use IPython and IPython Notebooks (also called Jupyter), a much richer Python experience than the Unix command line or Python interpreter.

Lessons 10-18 will focus on Python packages for data analysis. We will work through McKinney's Python for Data Analysis, which is all about analyzing data, doing statistics, and making pretty plots. You may find that Python can emulate or exceed much of the functionality of R and MATLAB.

Lessons 19-20 conclude the course with two skills useful in developing code: writing your own classes and modules, and sharing your code on GitHub.

Lesson Schedule

• Course material is available as .md or .ipynb files by clicking on the lesson number below.
• In addition to doing the readings, please follow along writing code (this is integral to the Shaw readings), and do any Study Drills (Shaw) and Chapter Quizzes (Lutz).

Lesson	Title	Readings	Topics	Assignment
1	Overview	--	Introductions and overview of course	Pre-course survey; Acquire texts
2	Command Line Part I	Shaw: Introduction, Ex0, Appendix A	Command line crash course; Text editors	Assignment 1: Basic Shell Commands
3	Command Line Part II	Yale: The 10 Most Important Linux Commands	Advanced commands in the bash shell	--
4	Conda, IPython, and Jupyter Notebooks	Geohackweek: Introduction to Conda	Conda tutorial including Conda environments, Python packages, and PIP, Python and IPython in the command line, Jupyter notebook tutorial and Python crash course	Assignment 2: Bash, Conda, IPython, and Jupyter
5	Python Basics, Strings, Printing	Shaw: Ex1-10; Lutz: Ch1-7	Python scripts, error messages, printing strings and variables, strings and string operations, numbers and mathematical expressions, getting help with commands and Ipython	--
6	Taking Input, Reading and Writing Files, Functions	Shaw: Ex11-26; Lutz: Ch9,14-17	Taking input, reading files, writing files, functions	Assignment 3: Python Fundamentals I
7	Logic, Loops, Lists, Dictionaries, and Tuples	Shaw: Ex27-39; Lutz: Ch8-13	Logic and loops, lists and list comprehension, tuples, dictionaries, other types	--
8	Python and IPython Review	McKinney: Ch1, Ch2, Ch3	Review of Python commands, IPython review -- enhanced interactive Python shells with support for data visualization, distributed and parallel computation and a browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media	Assignment 4: Python Fundamentals II
9	Regular Expressions	Kuchling: Regular Expression HOWTO	Regular expression syntax, Command-line tools: grep, sed, awk, perl -e, Python examples: built-in and re module	--
10	Numpy, Pandas and Matplotlib Crashcourse	Pratik: Introduction to Numpy and Pandas	Numpy, Pandas, and Matplotlib overview	Assignment 5: Regular Expressions
11	Pandas Part I	McKinney: Ch4, Ch5	Introduction to NumPy and Pandas: ndarray, Series, DataFrame, index, columns, dtypes, info, describe, read_csv, head, tail, loc, iloc, ix, to_datetime	--
12	Pandas Part II	McKinney: Ch6, Ch7, Ch8	Data Analysis with Pandas: concat, append, merge, join, set_option, stack, unstack, transpose, dot-notation, values, apply, lambda, sort_index, sort_values, to_csv, read_csv, isnull	Assignment 6: Pandas Fundamentals
13	Plotting with Matplotlib	McKinney: Ch9; Johansson: Matplotlib 2D and 3D plotting in Python	Matplotlib tutorial from J.R. Johansson	--
14	Plotting with Seaborn	Seaborn Tutorial	Seaborn tutorial from Michael Waskom	Assignment 7: Plotting
15	Pandas Time Series	McKinney: Ch11	Time series data in Pandas	--
16	Pandas Group Operations	McKinney: Ch10	groupby, melt, pivot, inplace=True, reindex	Assignment 8: Time Series and Group Operations
17	Statistics Packages	Handbook of Biological Statistics	Statitics capabilities of Pandas, Numpy, Scipy, and Scikit-bio	--
18	Interactive Visualization with Bokeh	Bokeh User Guide	Quickstart guide to making interactive HTML and notebook plots with Bokeh	Assignment 9: Statistics and Interactive Visualization
19	Modules and Classes	Shaw: Ex40-52	Packaging your code so you and others can use it again	--
20	Git and GitHub	GitHub Guides	Sharing your code in a public GitHub repository	Final Project