Python training (1 of 4): The fundamentals

This hands-on programming course – directed at beginners – will get you started on using Python 3 and the program Spyder to import, explore, analyse and visualise data.

Setup

The easiest way to use Python 3 and Spyder is to install the Anaconda Distribution, a data science platform for Windows, Linux and macOS.

Open the Anaconda Navigator (you might have to run anaconda-navigator from a terminal on Linux), and launch Spyder. On some operating systems, you might be able to find Spyder directly in your applications.

Introducing Python and Spyder

Python is a programming language that can be used to build programs (i.e. a “general programming language”), but it can also be used to analyse data by importing a number of useful modules.

We are using Spyder to interact with Python more comfortably. If you have used RStudio to interact with R before, you should feel right at home: Spyder is a program designed for doing data science with Python.

We will start by using the console to work interactively. This is our direct line to the computer, and is the simplest way to run code. Don’t worry about any unfamiliar language, fonts or colours - we can ignore most of it for now - all you need to know is that

In [1]: ... is code that we’ve sent to the computer, and
Out[1]: ... is its response.

Maths

To start with, we can use Python like a calculator. Type the following commands in the console, and press Enter to execute them:

After running each command, you should see the result as an output.

Variables

Numbers

Most programming languages are like spoken language in that they have nouns and verbs - you have “things” and they “do things”. In Python, we have variables and functions. We’ll look first at variables, the nouns of Python, which store data.

To create a variable, we choose its name (e.g. favNumber) and assign (=) it a value (e.g. 42):

example_int = 42

You can then retrieve the value by running the variable name on its own:

example_int

Let’s create more variables. We can use the variable names in place of their values, so we can perform maths:

example_float = 5.678
example_int * example_float

238.476

So far, we’ve only looked at numbers. If you click on the “variable explorer” tab, you should see two variables.

Notice that the “Type” of example_int is int, while the other is float. These are different variable types and can operate differently. int means integer, and corresponds to whole numbers, while float stands for floating point number, meaning decimals. You may occasionally encounter errors where you can only use one type.

Booleans

Even simpler than integers is the boolean type. These are either 1 or 0 (True or False), representing a single binary unit (bit). Don’t be fooled by the words, these work like numbers: True + True gives 2.

example_bool = True

In Python, the boolean values True and False must begin with a capital letter.

Sequences

Let’s look at variable types which aren’t (necessarily) numbers. Sequences are variables which store more than one data point. For example, strings store a sequence of characters and are created with quotation marks '<insert string>' or "<insert string>":

example_string = 'Hello world!'

We can also create lists, which will store several variables (not necessarily of the same type). We need to use square brackets for that:

example_list = [38, 3, 54, 17, 7]
diverse_list = [3, 'Hi!', 9.0]

Lists are very flexible as they can contain any number of items, and any type of data. You can even nest lists inside a list, which makes for a very flexible data type.

Operations on sequences are a bit different to numbers. We can still use + and *, but they will concatenate (append) and duplicate, rather than perform arithmetic.

example_string + ' How are you?'
example_list + diverse_list
3 * example_list

[38, 3, 54, 17, 7, 38, 3, 54, 17, 7, 38, 3, 54, 17, 7]

However, depending on the variable, some operations won’t work:

example_string + example_int

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[9], line 1
----> 1 example_string + example_int

TypeError: can only concatenate str (not "int") to str

There are other data types like tuples, dictionaries and sets, but we won’t look at those in this session. Here’s a summary of the ones we’ve covered:

Category	Type	Short name	Example	Generator
Numeric	Integer	`int`	`3`	`int()`
Numeric	Floating Point Number	`float`	`4.2`	`float()`
Numeric	Boolean	`bool`	`True`	`bool()`
Sequence	String	`str`	`'A sentence '`	`" "` or `' '` or `str()`
Sequence	List	`list`	`['apple', 'banana', 'cherry']`	`[ ]` or `list()`

The generator commands are new. We use these to manually change the variable type. For example,

int(True)

yields 1, converting a boolean into an integer. These commands are functions, as opposed to variables - we’ll look at functions a bit later.

Indexing

We can access part of a sequence by indexing. Sequences are ordered, starting at 0, so the first element has index 0, the second index 1, the third 2 and so on. For example, see what these commands return:

example_string[0]
example_string[6]
example_list[4]

If you want more than one element in a sequence, you can slice. Simple slices specify a range to slice, from the first index to the last, but not including the last. For example:

example_list[0:4]

[38, 3, 54, 17]

That command returns elements from position 0 up to - but not including! - position 4.

Scripts and Projects

So far, we’ve been working in the console, our direct line to the computer. However, it is often more convenient to use a script. These are simple text files which store code and run when we choose. They are useful to

write code more comfortably,
store clearly defined steps in chronological order,
share a process with peers easily, and
make your work reproducible

Let’s create a folder system to store our script in by creating a project.

Press Projects > New project... and name your project, perhaps “python_fundamentals”.
Create a new script with ctrl+N, File > New file... or the new file button .

You should now see a script on the left panel in Spyder, looking something like this:

Try typing a line of code in your new script, such as

1 + 1

Press F9 to run each line, or ctrl+enter for the whole script. You should see something like the following appear in the console (depending on how you ran it):

We’ll work out of a script for the rest of the session. Don’t forget to save your script by pressing ctrl+S or the save button save file symbol .

Functions

Functions are little programs that do specific jobs. These are the verbs of Python, because they do things to and with our variables. Here are a few examples of built-in functions:

example_list = [1,2,3,4]
len(example_list)
min(example_list)
max(example_list)
sum(example_list)
round(5.123)

Functions always have parentheses after their name, and they can take one or several arguments, or none at all, depending on what they can do, and how the user wants to use them.

Here, we use two arguments to modify the default behaviour of the round() function:

round(5.123, 2)

5.12

Notice how Spyder gives you hints about the available arguments after typing the function name?

Operators

Operators are a special type of function in Python with which you’re already familiar. The most important is =, which assigns values to variables. Here is a summary of some important operators, although there are many others:

General

Operator	Function	Description	Example command
=	Assignment	Assigns values to variables	`a = 7`
#	Comment	Excludes any following text from being run	`# This text will be ignored by Python`

Mathematical

Operator	Function	Description	Example command	Example output
+	Addition	Adds or concatenates values, depending on variable types	`7 + 3` or `"a" + "b"`	`10` or `'ab'`
-	Subtraction	Subtracts numerical values	`8 - 3`	`5`
*	Multiplication	Multiplies values, depending on variable types	`7 * 2` or `"a" * 3`	`14` or `'aaa'`
/	Division	Divides numerical vlues	`3 / 4`	`0.75`
**	Exponentiation	Raises a numerical value to a power	`7 ** 2`	`49`
%	Remainder	Takes the remainder of numerical values	`13 % 7`	`6`

Comparison

Operator	Function	Description	Example command	Example output
==	Equal to	Checks whether two variables are the same and outputs a boolean	`1 == 1`	`True`
!=	Not equal to	Checks whether two variables are different	`'1' != 1`	`True`
>	Greater than	Checks whether one variable is greater than the other	`1 > 1`	`False`
>=	Greater than or equal to	Checks whether greater than (>) or equal to (==) are true	`1 >= 1`	`True`
<	Less than	Checks whether one variable is less than the other	`0 < 1`	`True`
<=	Less than or equal to	Checks whether less than (<) or equal to (==) are true	`0 <= 1`	`True`

Finding help

To find help about a function, you can use the help() function, or a ? after a function name:

help(max)
print?

Help on built-in function max in module builtins:

max(...)
    max(iterable, *[, default=obj, key=func]) -> value
    max(arg1, arg2, *args, *[, key=func]) -> value

    With a single iterable argument, return its biggest item. The
    default keyword-only argument specifies an object to return if
    the provided iterable is empty.
    With two or more arguments, return the largest argument.

In Spyder, you can use the Ctrl + I keyboard shortcut to open the help in a separate pane.

The help information can often be dense and difficult to read at first, taking some practice. In the next session we look closer at interpreting this documentation, one of the most important Python skills.

For a comprehensive manual, go to the official online documentation. For questions and answers, typing the right question in a search engine will usually lead you to something helpful. If you can’t find an answer, StackOverflow is a great Q&A community.

Activity 1

In this first activity, write a program which takes an age in years and outputs how many minutes they’ve lived for. Note that

\[\text{Age (minutes)} = \text{Age (years)} \times 365 \times 24 \times 60\]

Steps

Store the age in years in a variable
Calculate the age in minutes
Print a message with the output

Note: if you want to print a number (e.g. the age), the easiest way is to send multiple arguments to the print function. For example, print("The first number is", 1)

Solution

We have three lines of code corresponding to the steps above:

### Age in minutes calculator
  
# Input age
age_years = 56

# Calculate age in mins
age_mins = age_years * 365 * 24 * 60

# Print result
print("You have lived for", str(age_mins), "minutes!")

Conditionals

Funnelling code through different blocks based on conditions is a fundamental element of all programming, and achieved in Python with conditionals. The most important is the if statement, which checks a condition and runs the indented code block if it returns True:

if 1 + 1 == 2:
    print("We are inside the if statement!")

We are inside the if statement!

Here’s how it works 1. if starts the conditional. 2. 1 + 1 == 2 is the condition - this comes after if, and must return True (1) or False (0). 3. The colon, :, indicates that the condition is finished and the code block is next 4. print(" ... the indented code is only run if the condition is True (1).

Try altering the condition, and see if the code runs.

Obviously, \(1 + 1 = 2\), so this will always run and we don’t need to use an if statement. Let’s replace the condition with variables:

name = "your_name"

if len(name) > 5:
  print(name + " is longer than 5 letters!")

your_name is longer than 5 letters!

Here, we’re checking if the length of name is greater than 5. Note that name + " is longer than 5!" concatenates (combines) the strings together.

Advanced

Using name + " is longer than 5!" is a bit clunky, there is a better way to include variables in strings, called f-strings.
name = "your_name"

if len(name) > 5:
  print(f"{name} is longer than 5 letters!")
your_name is longer than 5 letters!
By putting f before ' or ", Python will insert any code run between curly brackets { } into the string. Here, running name just returns “apple”.

`elif` and `else`

There are two other commands for conditionals: elif and else.

elif is short for else if, and can be used after an if statement to apply another condition, if the first one fails.

name = "your_name"

if len(name) > 5:
  print(name + " is longer than 5 letters!")

elif len(name) > 3:
  print(name + " is longer than 3 letters, but not more than 5")

your_name is longer than 5 letters!

Here, if the name is longer than 5, it will run in the if block and skip elif. Otherwise, it will check the elif condition and run if it’s True.

Finally, else comes at the end of a conditional and will run if all other conditions failed

name = "your_name"

if len(name) > 5:
    print(name + " is longer than 5 letters!")

elif len(name) > 3:
    print(name + " is longer than 3 letters, but not more than 5")

else:
    print(name + " is 3 letters long or shorter.")

your_name is longer than 5 letters!

You can only have one if and else statement, but as many elifs as you’d like.

Packages

Python is set apart from other languages by the scale of its community packages and the ease with which you import them. While you could code everything you need from scratch, it’s often more effective to import someone else’s predefined functions.

Built-in packages

Python comes with a number of pre-installed packages, so they’re already on your computer. However, your specific Python application doesn’t have access to them until they’re imported:

import math

The module math brings in some mathematics constants and functions. For example, you will get an error if you run pi on its own, but we can access the constant using the module:

math.pi
2*math.pi
math.cos(math.pi)

-1.0

Note that we use a period . in order to access objects inside the module. In general, we use periods in Python to access objects stored inside other objects.

Naming

Some modules have long names and use abbreviated nicknames when imported.

import math as m
m.pi

3.141592653589793

Here the module math is stored as m in Python.

Where this naming is used, it is usually the standard, and sharing code with different (including original/full) module names will not be compatible with other programmers.

External packages

There are hundreds of thousands of external packages available, and you need to install them before importing them as above. There are many ways to install packages, and two most common:

Using `pip`

If you are not using Anaconda, then the most common way to install a package is using the command pip, which installs packages from the Python Package Index (PyPI)

pip install numpy

If this doesn’t work for you, try using !pip install numpy - using an exclamation mark ! sends your command straight to your operating system shell

Using `conda`

If you are using Anaconda, then the recommended installation method is using the conda command, which installs from Anaconda’s package database:

conda install numpy

As above, if this doesn’t work for you, try using !pip install numpy - using an exclamation mark ! sends your command straight to your operating system shell

Common packages

Once installed, you can import these modules as before. Here, we’ve installed numpy, which is a popular numerical package.

import numpy as np

Numpy is useful for its arrays, which allow you to apply mathematics to lists. For example, we can multiply each element in the array by \(4\) with

example_list = [1,2,3,4]
example_array = np.array(example_list)
example_array * 4

array([ 4,  8, 12, 16])

Some popular packages include

Package	Install command	Import command	Description
NumPy	`pip/conda install numpy`	`import numpy as np`	A numerical Python package, providing mathematical functions and constants, vector analysis, linear algebra etc.
Pandas	`pip/conda install pandas`	`import pandas as pd`	Panel Data - data transformation, manipulation and analysis
Matplotlib	`pip/conda install matplotlib`	`import matplotlib.pyplot as plt`	Mathematical ploting library, a popular visualisation tool. Note that there are other ways to import it, but the `.pyplot` submodule as `plt` is most common.
Seaborn	`pip/conda install seaborn`	`import seaborn as sns`	Another visualisation tool, closer to ggplot2 in R, built upon a matplotlib framework.
SciPy	`pip/conda install scipy`	`import scipy` or `import scipy as sp`	A scientific Python package with algorithms and models for analysis and statistics.
Statsmodels	`pip/conda install statsmodels`	`import statsmodels.api as sm` and/or `import statsmodels.tsa.api as tsa`	Statistical modelling. The first import `sm` is for cross-sectional models, while `tsa` is for time-series models.
Requests	`pip/conda install requests`	`import requests`	Make HTTP (internet) requests.
Beautiful Soup	`pip/conda install beautifulsoup4`	`from bs4 import BeautifulSoup`	Collect HTML data from websites.

Activity 2

In this final activity, we’re going to create some sample data and visualise it.

Our goal is to import and visualise random BMI data

We’ll complete this in two parts. Before we begin, we need to set things up by importing the modules we need

import pandas as pd
import seaborn as sns

If importing any of these causes an error, it could be because you haven’t installed it yet. See above for information on how to do so.

Before we begin this activity we should bring in the data. To do this, we use the pd.read_csv() function, specifying the file path as the first argument (this can be a URL), and store it in a variable (typically df). For example,

df = pd.read_csv("insert_filepath_here")

Today’s data is five (random) people’s height and weight. You can download it here.

Download the data
Move the data into your project folder
Read it in with df = pd.read_csv("BMI_data.csv")

Part 1: Modifying the data

For the first part of the challenge, you’ll need to compute each person’s BMI, and store it in a new column. For reference, we access columns by indexing based on their name, e.g. df["Weight"] is the Weight column. To make a new column, we pretend that it already exists and assign into it. For example, to convert from kilograms to pounds,

# Create a new column called Weight (lb) and store the weight in pounds
df["Weight (lb)"] = df["Weight"]*2.205

To compute the BMIs, make another new column and use the following formula to calculate the BMI.

\[ \text{BMI} = \frac{\text{Weight (kg)}}{(\text{Height (cm)})^2} \]

It should look something like

df["BMI"] = ...

Hint: \(x^2\) is x**2

Once you’ve done these steps, you should see the following:

	Names	Height	Weight	Weight (lb)	BMI
0	Alice	1.90	94	207.27	26.038781
1	Bob	1.81	102	224.91	31.134581
2	Charlie	1.87	108	238.14	30.884498
3	Dilsah	1.88	84	185.22	23.766410
4	Eliza	1.68	108	238.14	38.265306

Solution

One solution could be the following:

# Import packages
import pandas as pd
import seaborn as sns

# Import data - don't forget to change the file path as you need
df = pd.read_csv("BMI_data.csv")

# Example - create a new column called Weight (lb) and store the weight in pounds
df["Weight (lb)"] = df["Weight"]*2.205

# Create BMI column
df["BMI"] = df["Weight"] / (df["Height"]**2)

# Look at the data
df

Part 2: Visualisation

To visualise the data, we can use the seaborn module, with the function sns.catplot( ... ). Inside the function, we’ll need to specify the x and y values, and if we specifically want a bar plot, kind as well. Use the help() documentation to see if you can visualise the data we just created. See if you can produce something like the following plot:

You’ll need to start with

sns.catplot(data = df, x = ...)

Hint: You only need to use the data =, x =, y = and kind = parameters, so try figure out what they require!

Solution

The plot above is produced with the code

# Visualise
sns.catplot(data = df, x = "Names", y = "BMI", kind = "bar")

Conclusion and saving your work

Your project can be reopened from the “Projects” menu in Spyder.

By default, your variables are not saved, which is another reason why working with a script is important: you can execute the whole script in one go to get everything back. You can however save your variables as a .spydata file if you want to (for example, if it takes a lot of time to process your data).

Summary

Today we looked at a lot of Python features, so don’t worry if they haven’t all sunk in. Programming is best learned through practice, so keep at it! Here’s a rundown of the concepts we covered

Concept	Desctiption
The console vs scripts	The console is our window into the computer, this is where we send code directly to the computer. Scripts are files which we can write, edit, store and run code, that’s where you’ll write most of your Python.
Variables	Variables are the nouns of programming, this is where we store information, the objects and things of our coding. They come in different types like integers, strings and lists.
Indexing	In order to access elements of a sequence variable, like a list, we need to index, e.g. `myList[2]`. Python counts from 0.
Functions	Functions are the verbs of programming, they perform actions on our variables. Call the function by name and put inputs inside parentheses, e.g. `round(2.5)`
Help	Running `help( ... )` will reveal the help documentation about a function or type.
Conditionals	`if`, `elif` and `else` statements allow us to run code if certain conditions are true, and skip it otherwise.
Loops	`while` loops will repeatedly run code until a condition is no longer true, and `for` loops will iterate through a variable
Packages	We can bring external code into our environment with `import ...`. This is how we use packages, an essential for Python. Don’t forget to install the package first!

Next session

Thanks for completing this introductory session to Python! You’re now ready for our next session, Data Transformation, which looks at using the pandas package in greater depth.

Before you go, don’t forget to check out the Python User Group, a gathering of Python users at UQ.

Finally, if you need any support or have any other questions, shoot us an email at training@library.uq.edu.au.

Setup

Introducing Python and Spyder

Maths

Variables

Numbers

Booleans

Sequences

Indexing

Scripts and Projects

Functions

Operators

General

Mathematical

Comparison

Finding help

Activity 1

Conditionals

Advanced

elif and else

Packages

Built-in packages

Naming

External packages

Using pip

Using conda

Common packages

Activity 2

Part 1: Modifying the data

Part 2: Visualisation

Conclusion and saving your work

Summary

Next session

`elif` and `else`

Using `pip`

Using `conda`