Community Data Science Workshops (Core)/Day 1 Lecture

Welcome to the Saturday lecture section of the Community Data Science Workshop! For about 2 hours, we'll work through an introduction to the Python programming language via both a lecture and hand-on exercises.

At the beginning of the lecture, we'll give a short pre-lecture talk to motivate the sessions.

Resources

 * Python data types cheat sheet
 * Python loops cheat sheet
 * state_capitals.py -- the state capitals example.
 * Screencast/recording of the lecture (433MB) — The file should be viewable in Firefox and many other browsers. If you have trouble playing it, you can download the VLC media player which will be a able to play it on Windows, OSX, or GNU/Linux.

Review Friday material

 * math: using python as a calculator
 * addition, subtraction, multiplication, division
 * division shows something different:  versus
 * type
 * there are different types of things in python (called objects)
 * variables that "know about the decimal place" (int) and variables that don't (floats)
 * variables
 * assignment of variaibles
 * e.g., math with variables: scale up a recipe, into an assignment
 * you can assign to a variable and it will replace the old value
 * strings
 * things within quotation marks
 * adding strings with "concatination" (smushing things together)
 * e.g.,
 * concatenating strings and integers don't work (e.g., )
 * 1 is different than "1"; name is different than "name"
 * single quotes versus double quotes (python doesn't care)
 * you can also multiply strings! (although it's not clear why you want to weird)
 * booleans
 * comparisons (e.g.,  or  )
 * you can compare strings (case sensative!)
 * also >, <, and !=
 * type shows that the output of True or False is
 * e.g.,
 * e.g.,
 * if/elif/else (move to external file)
 * if, something that evaluates to a boolean, and then colon
 * e.g.,
 * e.g., adding else example:
 * e.g., temperature range (e.g., if temp<65 is cold; temp>80 is hot; otherwise, just right)
 * e.g., adding elif: fix the bug in the previous program if they were the same age
 * indent with spaces (we use 4 spaces!)
 * functions
 * has a parentheses
 * we've already learnd examples of this: exit, help, type

Lists

 * purpose
 * Stores things in order
 * initialization
 * making a list called my list:
 * comma separated elements. in python they can be a mix of any kind of types
 * len review
 * accessing elements
 * indexing like my_list[0]
 * indexing starts from the front and we start counting at 0 (now you understand all the zeros we've been using
 * we go from the end with negative numbers
 * what happens if we try to move outside of the range? ('error!)
 * adding elements
 * using the the  function
 * the  function is a special kind of function that lists know about
 * changing elements
 * replacing elements like
 * finding elements in list
 * e.g.,
 * slicing lists
 * the colon inside the [] is the slicing syntax
 * e.g.,  is 0th up to, but not including, the 2nd
 * e.g.,
 * e.g.,
 * e.g.,
 * strings are like lists
 * we can slice lists
 * len
 * length of the empty string
 * many other interesting functions for lists
 * e.g.,  and
 * e.g., create a list of names and sort it
 * e.g., create a list of names and sort it

loops and more flow control

 * for loops
 * e.g.,
 * e.g.,
 * Super powerful because it can do something many many times. Data science is about doing tedious things very quickly. For is the workhorse that makes this possible.
 * Look and see name is after we're done looping.
 * Move to text editor
 * if statements inside for loops
 * e.g.,  then print "starts with a vowel"
 * show we can test things outside the loop to show how the comparisons are working
 * add an else statement to capture words that start with a consonant
 * append to a list within a for loop
 * create a counter within a for loop (keep track)
 * build up a sentence
 * nested for</tt> loops

dictionaries

 * purpose
 * initialization
 * accessing elements
 * adding elements
 * changing elements
 * keys</tt> and values</tt>

modules

 * purpose
 * importing with import</tt>
 * import random</tt>
 * random.randint</tt>
 * random.sample</tt>

extra functions

 * input</tt>

walk through state_capitals.py
Where state_capitals.py from http://mako.cc/teaching/2015/cdsw-spring/state_capitals.py is the grand finale and synthesis of lecture material.