CS190/295 Programming in Python for Life Sciences: Lecture 6 Instructor: Xiaohui Xie University of California, Irvine

Announcement Homework #4 will be out this week (will send out s). Due on Feb 16 (next Thur) Lab session next Tuesday.

Control Structures Part I: Decision structures

Multi-way decisions: if-elif-else Python will evaluate each condition in turn looking for the first one that is true. If a true condition is found, the statements indented under that condition are executed, and control passes to the next statement after the entire if-elif-else. If none of the conditions are true, the statements under the else are performed. The else clause is optional; if omitted, it is possible that no indented statement block will be executed.

Control Structures Part 2: Loop Structures

For Loops It allows us to iterate through a sequence of values The loop index variable var takes on each successive value in the sequence, and the statements in the body of the loop are executed once for each value.

Indefinite Loops: while-statement An indefinite loop keeps iterating until certain conditions are met. Here condition is a Boolean expression, just like in if statements. The body is, as usual, a sequence of one or more statements. The body of the loop executes repeatedly as long as the condition remains true. When the condition is false, the loop terminates

Nested Loops Again in the number averaging example, suppose instead of one-per-line we allow any number of values on a line, separated by comma.

Post-Test Loop Suppose you are writing an input algorithm that is supposed to get a nonnegative number from the user. If the user types an incorrect input, the program asks for another value. It continues to reprompt until the user enters a valid value. This process is called input validation. Here is a simple algorithm: repeat get a number from the user until number is >= 0

Post-Test Loop Python does not have a statement that directly implements a post-test loop. However you can implemented with a while statement using the trick of “seeding”: Or use the break statement:

Data Collections

Most real-world programs deal with large collections of data A few examples: Words in a document. Students in a course. Data from an experiment. Customers of a business. Graphics objects drawn on the screen. Cards in a deck.

Example problem: simple statistics Extend this program so that it computes not only the mean, but also the median and standard deviation of the data

Basic summary statistics

Lists Lists are ordered sequences of items, a collection of values denoted by the enclosing square brackets Lists can be created by listing items inside square brackets

Lists vs. Strings In Python strings and lists are both sequences that can be indexed. In fact, all of the built-in string operations that we discussed previously are sequence operations and can also be applied to lists:

Lists vs. Strings: differences The items in a list can be any data type, including instances of programmer- defined classes. Strings, obviously, are always sequences of characters. Second, lists are mutable. That means that the contents of a list can be modified. Strings cannot be changed “in place.”

Tuples A tuple is a sequence of immutable Python objects. Just like lists. The only difference is that types cannot be changed. Tuples are defined using parentheses while lists use square brackets. Examples: Tup1 = (1,2,3,4) Tup2 = (‘UCI’, ‘UCLA,’UCSD’)

List operations Python lists are dynamic. They can grow and shrink on demand. They are also heterogeneous. You can mix arbitrary data types in a single list. In a nutshell, Python lists are mutable sequences of arbitrary objects. This is very different from arrays in other programming languages. A list of identical items can be created using the repetition operator. Typically, lists are built up one piece at a time using the append method.

List operations: remove elements

List operations

Using lists is easy if you keep these basic principles in mind A list is a sequence of items stored as a single object. Items in a list can be accessed by indexing, and sublists can be accessed by slicing. Lists are mutable; individual items or entire slices can be replaced through assignment statements. Lists will grow and shrink as needed.

Statistics with Lists # get a list of numbers and store in a list

Statistics with Lists: Mean

Statistics with Lists: Standard Deviation

Statistics with Lists: Median

Simple Statistics: put together

Non-sequential collections Dictionary is a build-in data type for non-sequential collections in Python. Python dictionaries are mappings: Keys -> values A dictionary can be created by listing key-value pairs inside a curly braces. We can access the value associated with a particular key using: Dictionaries are mutable:

Dictionary Operations You can also extend a dictionary by adding new entries In fact, a common method for building dictionaries is to start with an empty collection and add the key-value pairs one at a time

Dictionary Operations

Dictionary methods: examples

References and Objects Consider the following example: x = [1,2,3,4] y = x y[1] = ‘hello’ >>> y [1,’hello’,3,4] >>> x [1,’hello’,3,4] Note that items in x are also changed after y assignment.

Call by reference in functions Consider the following example: >>> def my_func(x): x.append(’hello’) >>> x = [1,2,3,4] >>> my_func(x) >>> x [1,2,3,4,’hello’]