[Complete before class on August 31 ] Assigned material 1

Outline

Part 1. Installation of python and anaconda.
Part 2. The Python programming language.
Part 3. Python modules and packages
Part 4. Data structures in python
Part 5. Some basic programming techniques.

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Part 1. Installation

The easiest way to install Python and most of the useful packages for scientific computing is to install Python via the Anaconda distribution from: https://www.anaconda.com/products/individual#download-section. This is a simple one-click installation process which works for Linux/Windows/Mac. Simply follow the instruction, it installs everything!

When you install anaconda and python, select the latest version Python for your system
For most of the computers, you wanted the 64 bit version, not the 32 bit version.

In order to write python script and let your computer run your script, you also need an Integrated development environment (IDE). With the Anaconda you just installed, open Anaconda-navigator by click its icon, and then install Spyder as your IDE. Starting from here, whenever you need to code, just open Anaconda-navigator, and then launch Spyder.

There are many different Integrated development environment (IDE) for python, such as spider, VScode, Pycharm, etc. But unless you are VERY CONFIDENT about setting up and managing a virtual environment, please follow these simple steps and use Spyder as your IDE.

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Part 2. The Python Programming Language

Python is an example of a high-level language. As you might infer from the name high-level language, there are also low-level languages, sometimes referred to as machine languages or assembly languages. Machine language is the encoding of instructions in binary so that they can be directly executed by the computer. Assembly language uses a slightly easier format to refer to the low level instructions. Loosely speaking, computers can only execute programs written in low-level languages. To be exact, computers can actually only execute programs written in machine language.

Thus, programs written in a high-level language (and even those in assembly language) have to be processed before they can run. This extra processing takes some time, which is a small disadvantage of high-level languages. However, the advantages to high-level languages are enormous.

First, it is much easier to program in a high-level language. Programs written in a high-level language take less time to write, they are shorter and easier to read, and they are more likely to be correct. Second, high-level languages are portable, meaning that they can run on different kinds of computers with few or no modifications. Low-level programs can run on only one kind of computer and have to be rewritten to run on another. Today, almost all programs are written in high-level languages. Low-level languages are used only for a few specialized applications.

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Part 3. Python Packages and Modules

Very simply, A module is a file containing Python definitions and statements intended for use in other Python programs. You can think of python packages as directory of python scripts. Each script is a module that contain certain functions, methods, and new types aim to solving particular problems. When a module from an external package is required in a program, that package can be imported and its modules can be put to use.

Think about it this way: you are a wizard who can use spells and charms do many different things. You can think of the “spells” as scripts in python. Oftentimes, scripts related with each other are put together as a ‘Package”.

think of a package as a magic book

Whenever you need to use them, you can simply import them without knowing how the scripts are written. This is awesome, because it means we can use other people’s magic with simple incantations. Some of the packages are available when you first install python, but not all packages are available by default. In order to use those packages, you need o first install them on your own system. After you installed the package on your own system, you can then import and use it.

Here is one example: pandas is a package designed for data manipulation and analysis. We will use it a lot through this semester.

the pandas package in python

When you need to use functions in this package, simply tell python that I need this package. In your python script, type:

In [1]:

import pandas as pd

Now the pandas package has been imported into your python and ready to use! Let's try to get our class roster into python.

We can use the .read_csv module in pandas to do this:

In [1]:

import pandas as pd

classmates = pd.read_csv('class_roster.csv')

Now we have the read our class roster into python. We saved it in a variable called "classmates".

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Part 4. Data Structures in Python

Data structures are a way of organizing and storing data so that they can be accessed and worked with efficiently. They define the relationship between the data, and the operations that can be performed on the data. There are many various kinds of data structures defined that make it easier for the data scientists and the computer engineers, alike to concentrate on the main picture of solving larger problems rather than getting lost in the details of data description and access.

Generally, data structures can be divided into two categories in computer science: primitive and collective data structures. The former are the simplest forms of representing data, whereas the latter are more advanced: they contain the primitive data structures within more complex data structures for special purposes. There are four types of primitive data in Python: Integer, float, Boolean, and String. Commonly used collective data structures include List, Tuple, Dictionary, and Set. Let's look at each of them below.

data structure

4.1. Primitive Data Type

Integer represents numeric data, and more specifically, whole numbers from negative infinity to infinity, like 4, 5, or -1.

In [1]:

x = 1
y = 2

Float stands for 'floating point number'. You can use it for rational numbers, usually ending with a decimal figure, such as 1.11 or 3.14.

In [1]:

x = 1.01
y = 2.04

Boolean is used to sort True and False values in python, and therefore, True and False are the only two boolean values.Remember, Python is case sensitive, so make sure capitalize the first letter when using Boolean values.

In [1]:

print(1 > 2)

Out [1]:

False

String is simply some characters inside quotes. Often times we use strings to represent words or phrases that need to be print out. In data analysis, we need to deal with string a lot. Python has a set of built-in methods that help us play around with strings. Let's see some examples.

Suppose I have string "econ380: computational economics", and I want to convert this string into upper cases. I can use the method .upper() to do this.

In [1]:

my_str = "computational economics"
print(my_str)
print(my_str.upper())

Out [1]:

computational economics
COMPUTATIONAL ECONOMICS

If I want to count how many times the letter "c" occurs in the string "computational economics", I can use the method .count()

In [1]:

my_str = "computational economics"

my_str.count("c")

Out [1]:

There are many other useful string methods, you can check here for a comprehensive list. But most likely, you will be learning more string methods and trick when you need to use them.

4.2. Collective Data Type

List, Tuple, and Set are all used to store multiple items in a single variable. But there are some important distinctions among them.

	Notation	Ordered	Changeable	Allow Duplicates	Example
List	[ ]	Yes	Yes	Yes	[human, bat, orc, human, elf, undead, orc]
Tuple	( )	Yes	No	Yes	(human, bat, orc, human, elf, undead, orc)
Set	{ }	No	No	No	{elf, human, bat, orc, undead}

* Ordered: if the items in it have a defined order;

* Changeable: if we can change the items in it after it was created;

* Allow duplicates: if it can hold two items with the same value.

Dictionary is used to store data values in {key:value} pairs within curly brackets. A dictionary is a collection which is ordered, changeable and does not allow duplicates in keys. That is to say, ou can have as many pairs as you want, but keys in dictionary are unique.A dictionary in python looks like this:

{"human": "cavalier", "bat": "vampire", "orc": "warrior", "elf": "archer"}. Visually, you can imagine the dictionary in table format:

Key	Value
"human"	"cavalier"
"bat"	"vampire"
"orc"	"warrior"
"elf"	"archer"

All the above data structures are very useful in practice, but we will use list and dictionary more often. Let's take a deeper look at these two data structures.

4.2.1. python List

A Python list is an ordered and changeable collection of elements. It is very convenient and useful way to store information, especially when we are trying to handle many data points at once.

Suppose I wanted to record students’ midterm grade, I can create a variable for each record, such as:

In [1]:

grade1 = 87
grade2 = 93
grade3 = 75
grade4 = 66

As you can tell, creating a new python variable for each grade can be very inconvenient and inefficient. Instead, we can create a list and store all the values together. Let’s build a list with squared brackets:

In [1]:

grades = [87, 93, 75, 66]

Elements in list can be any type. In this example, we have integers in the list, but we can also have other types of element in the list, such as string, float, or Boolean. In more advance practices, it is also perfectly ok to have list contain sub-lists. Let's go over some examples together.

Create lists
Accessing elements in lists
Operations on list
- change elements
- add elements
- delete elements
Methods with list

4.2.2. Python Dictionary

Python dictionary is another type data structure. It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: { }. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output. It is a very useful tool for storing information. It stores the data values like a map. You can always look up information in a dictionary based on its key.

In our previous example, we saw how to use list to store student grade information. But one problem with the previous example is that I do not have student’s name associate with the grade. The problem is I won’t be able to know who gets which grade. In situations like this, we can use python dictionary to solve the problem.

Let’s create a dictionary with brackets, and then fill the dictionary with students' names and their midterm and final grades.

In [1]:

gradebook = { } # create an empty dict named "grades"

# then fill the dictionary with students' names 
# and their midterm grades + final grades + majors.

gradebook = {
'Mary' : [87,91,'economics'],
'Lisa' : [93,95,'computer science'],
'Hope' : [75,90,'psychology'],
'Mike' : [66,89,'biology']
}

In the above example, we created a python dictionary named "grades". In this dictionary, the keys are students name, and the values are lists of [midterm, final, major]. To access elements in the dictionary, we can use the .get function associated with dictionary. This method will returns the value of the item with the specified key. For example, if I wanted to know Mike's information, I can do the following:

In [1]:

grades.get('Mike')

When I run the above line in the console, I can get the value associate with the key:

Out [1]:

[66, 89, 'biology']

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Part 5. Some Basic Programming Techniques

5.1. Conditional Statements

In many situations, we many want to the script behave differently based on different conditions, we can do this with CONDITIONAL STATEMENTS in python. Oftentimes, we need to use comparison operators to evaluate if a condition is true or false. Like most of programming languages, python supports the usual logical conditions from mathematics. Below is a list of commonly used comparison operators in python. Please make sure you understand the meaning for each of them.

Operator	Description
==	If the values of two operands are equal, then the condition becomes true.
!=	If values of two operands are not equal, then condition becomes true.
<>	If values of two operands are not equal, then condition becomes true.
>	If the value of left operand is greater than the value of right operand, then condition becomes true.
<	If the value of left operand is less than the value of right operand, then condition becomes true.
>=	If the value of left operand is greater than or equal to the value of right operand, then condition becomes true.
<=	If the value of left operand is less than or equal to the value of right operand, then condition becomes true.

Below are some examples:

In [1]:

a = 5
b = 10
a == b

Out [1]:

False

In [2]:

a = 5
b = 10
a != b

Out [2]:

True

5.2. Use conditions and if statement to branch code

The if command checks whether a certain condition is true. If it is true, we then can assign certain chain of commands under this condition. If the condition is not true, we can branch off the program into another direction and assign different commands. Based on this simple logic, we can branch the program into many separate possible directions. The keyword elif is similar, it is the python way to say, “if the previous conditions were not true, then try this condition”. Lastly, the keyword else catches anything which isn’t caught by the preceding conditions.

The general syntax of if statements are as follow:

if condition1:
    branch1

elif condition2:
    branch2

else:
    branch3

Now Let's look at some specific examples:

In [1]:

curent_price = 40
equilibrium_price = 50

if current_price < equilibrium_price:
    print('There will be a shortage on the market.')
elif current_price > equilibrium_price:
    print('There will be a surplus on the market.')
else:
    print('The market is clear.')

In the above example, I wanted to branch my script condition on the current market situation (i.e., the comparison of current price and equilibrium price). The if ... elif ... else ... statements captured all possible situations. When I run the script, I get:

Out [1]:

There will be a shortage on the market

5.2. Indentation in Python

One thing we need to pay special attention on is that PTYHON IS INDENT SENSITIVE! This is different from most of other programming languages. In python, indentation is a way telling python that the group of statement below belongs to a particular block of code. If you didn’t use indentation properly, you will encounter errors when running the script.

In the above if-statements example, the branches are indented four spaces, which is a typical amount of indentation for Python. If I forgot the indentation rule, the code becomes:

In [2]:

curent_price = 40
equilibrium_price = 50

if current_price < equilibrium_price:
print('There will be a shortage on the market.')
elif current_price > equilibrium_price:
print('There will be a surplus on the market.')
else:
print('The market is clear.')

In such case, python won't be able to complete the task for you. It will generates an error message to help you correct the code:

Out [2]:

IndentationError: expected an indented block

I personally like the indent sensitive feature a lot, as it is an effective way to keep tracking the logic of my code. It also makes my code looks pretty and friendly to read.

5.3. Loops

There are several techniques you can use to repeatedly execute Python code. A loop is a structure that repeats a sequence of instructions until a specific condition is met. The loop structure is very useful when we wanted to repeat something over and over again. Two of the most common types of loops are while-loops and for-loops.

While-loops are like repeated if statements,
the for-loop iterates over all kinds of data structures.

5.3.1 While-Loop

The while loop executes a set of statements as long as a condition is true. Let’s look at some examples:

In [1]:

output = 10
equilibrium_quantity = 50

while output < equilibrium_quantity:
    output = output + 10
    print('current output level is %s.' %(output))

In this example, as long as current output is smaller than the equilibrium_quantity, we increase the output by 10 units and print out the current output level. Then we go back to the top of the while loop and check the condition again, until output level is not smaller than equilibrium_quantity. When run the above code, we get:

Out [1]:

current output level is 20.
current output level is 30.
current output level is 40.
current output level is 50.

5.3.2 For-Loop

A for loop is used for iterating over a sequence. The sequence can be either a list, a tuple, a dictionary, a set, or a string. Here is one example:Suppose we have a list of student name, we can print out the students in this list using for loop.

In [1]:

students = ['Mary','Lisa','Hope','Mike']
for x in students:
    print(x)

Out [1]:

Mary
Lisa
Hope
Mike

When combining the loops and conditional statements together, we can let python to deal with more complex tasks. For example, with the grade book we created earlier, we can let python find the students whose midterm grade is better than 90.

In [2]:

for student in gradebook:
    if gradebook[student][0]>=90:
        print(student)

Out [2]:

Lisa

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