Week 1

Python for Spatial Analysis - PY4SA

Dr Fernando Benitez-Paez

School of Geography and Sustainable Development

Housekeeping

Lecture with basic concepts and some demonstrations
Labs with exercises, Assessment No 2
Course Resources:
- Labs, assessment, Slides
If something is not clear, stop me and ask.
During exercises (you can ask for assistance from the TAs).

Module in a glance

What is Python, and why will you use it
Building blocks – What components will we use
Miniconda
Jupyter Notebook
Git – GitHub Account
Python Basics
- Two notebooks for guidance
- One notebook for practice

Let me ask you something

Do you have any programming experience?
- Why should we learn to program?
Why should we learn Python if we can use QGIS or another GIS tool?

GIS Tools Trends

Python and R as the tools as the programming languages to master as spatial data researcher

Here make mistakes is worthy

You will fail, but that is ok, it is part of the process.
Don't be afraid of it.
Error messages are scary, I know, but they are useful.

Why Python

A free and open-sourced scripting language that was commonly used to automate tasks (GIS world).
Nowadays can be used to read, process and map spatial data. It uses a code structure, referred to as syntax, that is easy to read and understand.
Syntax helps beginners to learn the language and helps professionals to minimise development and maintenance costs.
Python has thousands of packages so that you can integrate a vast number of tools and programs.

What makes a Python script?

A Python script is a sequence of instructions. The structure and arrangement of the instructions are referred to as syntax. Python instructions use various language elements, including data types, statements, and functions.

Why is it so powerful?

Modules are collections of related code that contain variables, definitions, and instructions.
These modules are organised into packages, which can be further grouped into libraries.
These libraries are typically specialised and enable you to perform higher-level tasks related to those specialities.
For example, Pandas enables you to work with tabular data, NumPy grant the ability to make advanced mathematical and scientific computations, and Matplotlib allows you to visualise data graphically.

Python Modes

There are many ways to write and run a python script:

Your terminal (Mac, Win, Linux)
Python scripts (.py) (Programs)
Ipython - Jupyter Notebook (.ipynb): Interactive, widely use in research
Google Colab (Cloud service, require a google account)
More…

Demo No 1:

Running a python Script

a = 5
b = 4
c = a+b

Demo No 1:

Running a python Script

a = 5
b = 4
c = a+b

print (c)
print ("Hello world")

9
Hello world

`Ingredients` for this course

Python (ideally v3.10)
Conda, more specifically MiniConda
Jupyter Notebook Lab
Git – GitHub Account

Lot of attractive packages (it’s like the seasoning) Like in any recipe, the right ingredients define the dish’s success; In here; installing your components appropriately will help run the python scripts correctly.

Conda - Miniconda

Conda is an open-source package and environment management system that runs on Windows, macOS, and Linux.
Conda easily creates, saves, loads and switches between environments on your local computer. It was created for Python programs but can package and distribute software for any language.
Miniconda is a minimal installer for conda. It is a small version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others.

Jupyter Notebook (Lab)

This is the python environment we will use to learn python and for our labs
Easy-to-use environment
Web-based
Combines both text and code into one
Come with a great number of useful packages

Jupyter Notebook Structure

Notebook Structure

Markdown

Markdown is a lightweight markup language that you can use to add formatting elements to plaintext text documents. Created in 2004, Markdown is now one of the world’s most popular markup languages.

Markdown Structure

Markdown can be used for everything
Markdown is portable
Markdown is platform-independent

Demo No 2:

Jupyter Lab

Version control - GIT

Git is open-source software for version control, which means that it tracks changes to your files as you work on them over time.

Similar to track changes feature in a word document, except you must choose which versions to include in the tracking—it is not automatic.

It can be tricky, and we may or may not have issues; it happens to everyone. Widely used in academia and industry.

Github?

GitHub is a web-based version control system which uses Git, the open-source version control software. It provides a place for developers to host and share their projects — a space to save earlier iterations and collaborate with other developers, colleagues, or co-workers.

It can be used even to host websites, research projects, portfolios, data, and more.

It is widely popular and can be a way to get a job nowadays in the GIScience field.

How does it work?

Version control terminology

Repository: Is a project (a folder) containing files and sub-folders. A repository tracks versions of files and folders.
Branch: A branch is a parallel version of your repository. By default, your repository has one branch named main and is considered the definitive branch. You can use branches to have different project versions at once.
Clone: Local copy of a repository stored on the cloud. Clones can be synced up with their originals in the GitHub repository.
Commit: A commit is a set of changes to the files and folders in your project. A commit exists in a branch.
Fork: “Forking” is when you copy another user’s repository to your account. You can make any changes to your forked copy, leaving the original unaffected.
Pull/push: Merging changes made to the repository files into the local copy you’re working on is called “pulling.” A “push” is the reverse — updating the repository files with changes you’ve made to your local copy.
Pull request: Collaboration happens on a pull request. The pull request shows the changes in your branch to other people. This pull request will keep the changes you just made on your branch and propose applying them to the main branch.
Merge: A merge adds the changes in your pull request and branch into the main branch

Demo No 3:

Git and GitHub

Python Basics

Variables
Types of variables
Arithmetic operators
Boolean logic
Strings (Text)
Printing
Comments

As simple as a calculator

Let us calculate the distance between U St Andrews and Edinburgh, but in kms

53.2 * 1.609344 # the distance in miles multiply by the kms in one mile

85.61710080000002

Variables

Great calculator, but how can we make it store values?
Do this by defining variables
Can later be called by the variable name
Variable names are case-sensitive and unique

DistanceStAndrewsToEDI = 53.2
MilesToKm = 1.609344
DistanceFromStAndrewsToEDI = DistanceStAndrewsToEDI * MilesToKm
DistanceFromStAndrewsToEDI

85.61710080000002

Variables

We can now reuse the variable MileToKms in the next cell without having to define it again!

MarathonDistanceInMiles = 26.219
MarathonDistanceInKms = MilesToKm * MarathonDistanceInMiles
MarathonDistanceInKms

42.195390336

Types of variable

Variables actually have a type, which defines the way it is stored. The basic types are:

Why this is important?

To remember! We can’t do arithmetic operations on variables of different types. Therefore, make sure that you are always aware of your variables types! You can find the type of a variable by adding type(). For example: type(x).

x = 10
type(x)

int

Casting types

Luckily, Python offers us a way of converting variables to different types!

Casting – the operation of converting a variable to a different type

y = '20'
a = x + int(y)
print(a)

Similar methods exist for other data types: int(), float(), str()

Quiz

What would be the result?

x = '10'
y = '20'
x+y

'1020'

Operations

(Arithmetic, Logical, Comparison)

List

Allow to store multiple numbers, strings, or booleans in a single variable.
Square brackets are used to denote lists. Items in a list are ordered, indexed, and allow for duplicate members.
Indexing starts at 0.

lst1 = [6, 7, 8, 9, 11, 2, 0]
lst2 = ["A", "B", "C"]
lst3 = [True, False, True]
print(lst1[0])
print(lst1[0:3])
print(lst2[-4:-1])

6
[6, 7, 8]
['A', 'B']

Dictionaries

Are unordered, changeable, indexed, and do not allow duplicate elements. In contrast to lists each value is also assigned with key.

Values can be selected using the associated key. You can also use the key to define a value to change.

cls = {"code": "GG3209", "Name":"Spatial Analysis with Python"}
print(cls)
print(cls["Name"])
cls["code"] = 461
print(cls)

Dictionaries

Are unordered, changeable, indexed, and do not allow duplicate elements. In contrast to lists each value is also assigned with key.

Values can be selected using the associated key. You can also use the key to define a value to change.

cls = {"code": "GG3209", "Name":"Spatial Analysis with Python"}
print(cls)
print(cls["Name"])
cls["code"] = 461
print(cls)

{'code': 'GG3209', 'Name': 'Spatial Analysis with Python'}
Spatial Analysis with Python
{'code': 461, 'Name': 'Spatial Analysis with Python'}

Arrays

Similar to lists; however, they must be declared. In this course, we will primarily work with NumPy arrays, which will be discussed in more detail in a later module.

my_array = [1, 2, 3, 4]
print(my_array)

print(my_array[0])   # Output: 1
print(my_array[2])   # Output: 3
my_array[1] = 7

print(my_array)

[1, 2, 3, 4]
1
3
[1, 7, 3, 4]

Commenting

Useful when you want to remove the code from execution but not permanently
Useful when your code needs further explanation. Whether for you or anybody else.
Comments in Python are done with #

### You can comment before the lines
a=3 # You can comment next to the line
b=5
# After the lines
'''
This is a multiline
comment.
'''

Lab No 1

Install your python environment and run the initial scripts.
Ask us anything. Ask among yourselves as well
Do not freak out; failure is progress!

References

Python for Data Analysis

(Python for Data Analysis)[https://wesmckinney.com/book/]

Week 1

Housekeeping

Module in a glance

Let me ask you something

GIS Tools Trends

Here make mistakes is worthy

Why Python

What makes a Python script?

Why is it so powerful?

Python Modes

Demo No 1:

Demo No 1:

Ingredients for this course

Conda - Miniconda

Jupyter Notebook (Lab)

Jupyter Notebook Structure

Notebook Structure

Markdown

Markdown Structure

Demo No 2:

Version control - GIT

Github?

How does it work?

Version control terminology

Demo No 3:

Python Basics

As simple as a calculator

Variables

Variables

Types of variable

Why this is important?

Casting types

Quiz

Operations

List

Dictionaries

Dictionaries

Arrays

Commenting

Lab No 1

References

`Ingredients` for this course