Introduction to Python for Data Science: Lesson Design

Help Wanted

We are filling in the exercises below in order to make the lesson plan more concrete. Contributions (both in the form of pull requests with filled-in exercises, and comments on specific exercises, ordering, and timings) are greatly appreciated.

Process Used

Michael Pollan’s advice if he taught R or Python programming:

1. Write code.
2. Not too much.
3. Mostly plots.

— Michael Koontz

This lesson was developed using a slimmed-down variant of the “Understanding by Design” process. The main sections are:

1. Assumptions about audience, time, etc. (The current draft also includes some conclusions and decisions in this section - that should be refactored.)

2. Desired results: overall goals, summative assessments at half-day granularity, what learners will be able to do, what learners will know.

3. Learning plan: each episode has a heading that summarizes what will be covered, then estimates time that will be spent on teaching and on exercises, while the exercises are given as bullet points.

Stage 1: Assumptions

• Audience
  • Graduate students in numerate disciplines from cosmology to archaeology
  • Who have manipulated data in spreadsheets and with interactive tools like SAS
  • But have not programmed beyond CPD (copy-paste-despair)
• Constraints
  • One full day 09:00-16:30
    • 06:15 class time
    • 0:45 lunch
    • 0:30 total for two coffee breaks
  • Learners use native installs on their own machines
    • May use VMs or cloud resources at instructor’s discretion
    • But must keep native local install as an option
  • No dependence on other Carpentry modules
    • In particular, does not require knowledge of shell or version control
  • Use the Jupyter Notebook
    • Authentic tool used by many instructors
    • There isn’t really an alternative
    • And means that even people who have seen a bit of Python before will probably learn something
• Motivating Example
  • Creating 2D plots suitable for inclusion in papers
  • Appeals to almost everyone
  • Makes lesson usable by both Carpentries
    • And means that even people who have seen a bit of Python before will probably learn something
• Data
  • Use the gapminder data throughout
  • But break into multiple files by continent
    • To make display of output from examples tidier (e.g., use Australia/New Zealand, which is only two lines)
    • And allow examples showing use of multiple data sets
• Focus on Pandas instead of NumPy
  • Makes lesson usable by both Data Carpentry and Software Carpentry
  • Genuine novices are likely to want data analysis
  • And people with some prior experience:
    • will accept data analysis as an authentic task,
    • and are unlikely to have encountered Pandas, so they’ll still get something useful out of the lesson
• Challenges will mostly not be “write this code from scratch”
  • Want lots of short exercises that can reliably be finished in allotted time
  • So use MCQs, fill-in-the-blanks, Parsons Problems, “tweak this code”, etc.

Stage 2: Desired Results

Questions

How do I…

• …read tabular data?
• …plot a single vector of values?
• …create a time series plot?
• …create one plot for each of several data sets?
• …get extra data from a single data set for plotting?
• …write programs I can read and re-use in future?

Skills

I can…

• …write short scripts using loops and conditionals.
• …write functions with a fixed number of parameters that return a single result.
• …import libraries using aliases and refer to those libraries’ contents.
• …do simple data extraction and formatting using Pandas.

Concepts

I know…

• …that a program is a piece of lab equipment that implements an analysis
  • Needs to be validated/calibrated before/during use
  • Makes analysis reproducible, reviewable, shareable
• …that programs are written for people, not for computers
  • Meaningful variable names
  • Modularity for readability as well as re-use
  • No duplication
  • Document purpose and use
• …that there is no magic: the programs they use are no different in principle from those they build
• …how to assign values to variables
• …what integers, floats, strings, NumPy arrays, and Pandas dataframes are
• …how to trace the execution of a for loop
• …how to trace the execution of if/else statements
• …how to create and index lists
• …how to create and index NumPy arrays
• …how to create and index Pandas dataframes
• …how to create time series plots
• …the difference between defining and calling a function
• …where to find documentation on standard libraries
• …how to find out what else scientific Python offers

Stage 3: Learning Plan

Summative Assessment

• Midpoint: create time-series plot for each file in a directory.
• Final: extract data from Pandas dataframe and create comparative multi-line time series plot.

Running and Quitting Interactively (9:00)

• Teaching: 15 min (because setup issues)
  • Launch the Jupyter Notebook, create new notebooks, and exit the Notebook.
  • Create Markdown cells in a notebook.
  • Create and run Python cells in a notebook.
• Challenges: 0 min (accounted for in teaching time - no separate exercise)
  • Creating lists in Markdown
  • What is displayed when several expressions are put in a single cell?
  • Change an existing cell from code to Markdown
  • Rendering LaTeX-style equations

Variables and Assignment (9:15)

• Teaching: 10 min
  • Write programs that assign scalar values to variables and perform calculations with those values.
  • Correctly trace value changes in programs that use scalar assignment.
• Challenges: 10 min
  • Trace execution of code swapping two values using an intermediate variable.
  • Predict final values of variables after several assignments.
  • What happens if you try to index a number?
  • Which is a better variable name, m, min, or minutes?
  • What do the following slice expressions produce?

Data Types and Type Conversion (09:35)

• Teaching: 10 min
  • Explain key differences between integers and floating point numbers.
  • Explain key differences between numbers and character strings.
  • Use built-in functions to convert between integers, floating point numbers, and strings.
• Challenges: 10 min
  • What type of value is 3.4?
  • What type of value is 3.25 + 4?
  • What type of value would you use to represent:
    • Number of days since the start of the year.
    • Time elapsed since the start of the year.
    • Etc.
  • How can you use // (integer division) and % (modulo)?
  • What does int("3.4") do?
  • Given these float, int, and string values, which expressions will print a particular result?
  • What do you expect 1+2j + 3 to produce?

Built-in Functions and Help (09:55)

• Teaching: 15 min
  • Explain the purpose of functions.
  • Correctly call built-in Python functions.
  • Correctly nest calls to built-in functions.
  • Use help to display documentation for built-in functions.
  • Correctly describe situations in which SyntaxError and NameError occur.
• Challenges: 10 min
  • Explain the order of operations in the following complex expression.
  • What will each nested combination of min and max calls produce?
  • Why don’t max and min return None when given no arguments?
  • Given what we have seen so far, what index expression will get the last character in a string?

Coffee: 15 min (10:20)

Libraries (10:35)

• Teaching: 10 min
  • Explain what software libraries are and why programmers create and use them.
  • Write programs that import and use libraries from Python’s standard library.
  • Find and read documentation for standard libraries interactively (in the interpreter) and online.
• Challenges: 10 min
  • Which function from the standard math library could you use to calculate a square root?
  • What library would you use to select a random value from data?
  • If help(math) produces an error, what have you forgotten to do?
  • Fill in the blanks in code below so that the import statement and program run.

Reading Tabular Data (10:55)

• Teaching: 10 min
  • Import the Pandas library.
  • Use Pandas to load a simple CSV data set.
  • Get some basic information about a Pandas DataFrame.
• Challenges: 10 min
  • Read the data for the Americas and display its summary statistics.
  • What do .head and .tail do?
  • What string(s) should you pass to read_csv to read files from other directories?
  • How can you write CSV data?

DataFrames (11:15)

• Teaching: 15 min
  • Select individual values from a Pandas dataframe.
  • Select entire rows or entire columns from a dataframe.
  • Select a subset of both rows and columns from a dataframe in a single operation.
  • Select a subset of a dataframe by a single Boolean criterion.
• Challenges: 15 min
  • Write an expression to find the Per Capita GDP of Serbia in 2007.
  • What rule governs what is (or isn’t) included in numerical and named slices in Pandas?
  • What does each line in the following short program do?
  • What do idxmin and idxmax do?
  • Write expressions to get the GDP per capita for all countries in 1982, for all countries after 1985, etc.
  • Given the way its borders have changed since 1900, what would you do if asked to create a table of GDP per capita for Poland for the Twentieth Century?

Plotting (11:45)

• Teaching: 15 min
  • Create a time series plot showing a single data set.
  • Create a scatter plot showing relationship between two data sets.
• Exercise: 15 min
  • Fill in the blanks to plot the minimum GDP per capita over time for European countries.
  • Modify the example to create a scatter plot of GDP per capita in Asian countries.
  • Explain what each argument to plot does in the following example.

Lunch (12:15): 45 min

Lists (13:00)

• Teaching: 10 min
  • Explain why programs need collections of values.
  • Write programs that create flat lists, index them, slice them, and modify them through assignment and method calls.
• Challenges: 10 min
  • Fill in the blanks so that the program produces the output shown.
  • How large are the following slices?
  • What do negative index expressions print?
  • What does a “stride” in a slice do?
  • How do slices treat out-of-range bounds?
  • What are the differences between sorting these two ways?
  • What is the difference between new = old and new = old[:]?

Loops (13:20)

• Teaching: 10 min
  • Explain what for loops are normally used for.
  • Trace the execution of a simple (unnested) loop and correctly state the values of variables in each iteration.
  • Write for loops that use the Accumulator pattern to aggregate values.
• Challenges: 15 min
  • Is an indentation error a syntax error or a runtime error?
  • Trace which lines of this program are executed in what order.
  • Fill in the blanks in this program so that it reverses a string.
  • Fill in the blanks in this series of examples to get practice accumulating values.
  • Reorder and indent these lines to calculate the cumulative sum of the list values.

Looping Over Data Sets (13:45)

• Teaching: 5 min
  • Be able to read and write globbing expressions that match sets of files.
  • Use glob to create lists of files.
  • Write for loops to perform operations on files given their names in a list.
• Challenges: 10 min
  • Which filenames are not matched by this glob expression?
  • Modify this program so that it prints the number of records in the shortest file.
  • Write a program that reads and plots all of the regional data sets.

Writing Functions (14:00)

• Teaching: 10 min
  • Explain and identify the difference between function definition and function call.
  • Write a function that takes a small, fixed number of arguments and produces a single result.
• Challenges: 15 min
  • This code defines and calls a function - what does it print when run?
  • Explain why this short program prints things in the order it does.
  • Fill in the blanks to create a function that finds the minimum value in a data file.
  • Fill in the blanks to create a function that finds the first negative value in a list. What does your function do if the list is empty?
  • Why is it sometimes useful to pass arguments by naming the corresponding parameters?
  • Fill in the blanks and turn this short piece of code into a function.

Variable Scope (14:25)

• Teaching: 10 min
  • Identify local and global variables.
  • Identify parameters as local variables.
  • Read a traceback and determine the file, function, and line number on which the error occurred.
• Challenges: 10 min
  • Trace the changes to the values in this program, being careful to distinguish local from global values.

Coffee (14:45): 15 min

Conditionals (15:00)

• Teaching: 10 min
  • Correctly write programs that use if and else statements and simple Boolean expressions (without logical operators).
  • Trace the execution of unnested conditionals and conditionals inside loops.
• Challenges: 15 min
  • Trace the execution of this conditional statement.
  • Fill in the blanks so that this function replaces negative values with zeroes.
  • Modify this program so that it only processes files with fewer than 50 records.
  • Modify this program so that it always finds the largest and smallest values in a list no matter what the list’s values are.

Programming Style (15:25)

• Teaching: 15 min
  • How can I make my programs more readable?
  • How do most programmers format their code?
  • How can programs check their own operation?
• Challenges: 15 min
  • Which lines in this code will be available as online help?
  • Turn the comments in this program into docstrings.
  • Rewrite this short program to be more readable.

Wrap-Up (15:55)

• Teaching: 20 min
  • Name and locate scientific Python community sites for software, workshops, and help.
• Challenges: 0 min
  • None.

Feedback (16:15)

• Teaching: 0 min
• Challenges: 15 min
  • Collect feedback

Finish (16:30)