Introduction to Coding and Data Analysis for Scientists

Week 5: Consolidation

Today’s Lecture

Lecture 5: Consolidation
- Recap: The Course So Far
- Tackling Difficult Problems
- A Worked Example
- Practical

Recap: The Course So Far

Over the last 4 weeks, we’ve covered the basics of Python
We’ve looked at:
- Datatypes
- Conditional Logic
- Loops
- Functions

Recap: The Course So Far

Before moving on to the next parts of the course…
- NumPy, Pandas and Matplotlib
… this week we are going to do some “consolidation challenges”
- Using what we have learnt to tackle the kinds of tasks you might encounter in the real world

Aside: Dictionaries

Before we dive into today’s work, there is one concept we’d like to revisit in more detail
A dictionary, or dict, is a data type in Python
- It allows us to store values under keys
Think of a real-life dictionary
- You look up a word (the key)
- You read its definition (the value)
In the same way, a Python dict lets you
- You look up a key
- Get back the value associated with it

Welcome page for the unit on Blackboard

Aside: Dictionaries

New values can be added to the dictionary using the syntax
- my_dict[key] = value

Aside: Dictionaries

And keys and values can be any datatype you like
- They don’t have to be strings!

Aside: Dictionaries

Dictionaries will be crucial to some of today’s challenges as well as throughout the rest of this course
If you are unsure about these please take the time to read over:
- Week 1's intermediate notebook Section: Sets and Dictionaries
You can do this either in today’s class or over the week 6 break

Tackling Difficult Problems

Today, we’re going to be looking at some challenge problems
These problems may feel a little more open-ended than you are used to
- This can be a little daunting at first
- But do not worry…
- You already have all the skills necessary to complete the tasks!
When faced with a difficult coding problem, here’s some general tips to help you get started out

Tackling Difficult Problems

Plan before you code
- Before you start coding, grab a pen and paper and try to solve the problem by hand!
Clarify inputs and outputs
- When getting started think about what you have and what you need – these are probably your inputs and outputs
Look for structure and repetition
- When you convert your thoughts to code, ask yourself which parts of the code are repetitive or self-contained

Tackling Difficult Problems

Divide and Conquer
- Try to break the task down into smaller chunks where possible!
Talk things through
- If you are struggling to get started, talk through the problem with a friend or peer
Use Resources
- Googling and looking up documentation are key coding skills
Trust your own process
- Try to avoid using AI to make structural decisions about your code

Tackling Difficult Problems

Above all, remember…
- Coding can be hard!
It can feel like you are running into the same problems over and over again sometimes
- This is a normal part of the process
If you keep trying you will get there

A Worked Example

Let’s go through an example together to see these principles in action:

Question: Write some code which, for a given positive integer $n$, finds the largest prime number that is less than or equal to $n$.

Principles:
- Plan before you code
- Clarify inputs and outputs
- Look for structure and repetition
- Divide and conquer
- Talk things through
- Trust your own process

Practical

We now move over to Python
Please open week_05_home.ipynb
For the rest of today, you must work through a Python notebook
This week is a little different…
Please choose the challenge according to your course

--- title: Introduction to Coding and Data Analysis for Scientists jupyter: python3 --- ## Week 5: Consolidation ## Today's Lecture - Lecture 5: Consolidation - Recap: The Course So Far - Tackling Difficult Problems - A Worked Example - Practical ## Recap: The Course So Far - Over the last 4 weeks, we've covered the basics of Python - We've looked at: - Datatypes - Conditional Logic - Loops - Functions ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # ----------------------------------- # Data Types # ----------------------------------- x= 7 # an integer y= "hello" # a string of text z= True # a boolean (true/false) # ----------------------------------- # ----------------------------------- # Conditional Statements # ----------------------------------- number = 5 my_boolean = number > 0 if my_boolean: print("Positive number") # ----------------------------------- # ----------------------------------- # While Loops # ----------------------------------- number = 5 my_boolean = number > 0 while my_boolean: number = number - 1 my_boolean = number > 0 print("number is " + str(number)) # ----------------------------------- # ----------------------------------- # For Loops # ----------------------------------- numbers = [1, 2, 3, 4, 5] for n in numbers: print(n) # ----------------------------------- # ----------------------------------- # Functions # ----------------------------------- # This is a function: def print_double_and_add_one(x): # Double x and add 1 y = x * 2 + 1 print(y) # ----------------------------------- ``` ## Recap: The Course So Far - Before moving on to the next parts of the course... - `NumPy`, `Pandas` and `Matplotlib` - ... this week we are going to do some "consolidation challenges" - Using what we have learnt to tackle the kinds of tasks you might encounter in the real world ## Aside: Dictionaries - Before we dive into today's work, there is one concept we'd like to revisit in more detail - A dictionary, or `dict`, is a data type in Python - It allows us to store `value`s under `key`s - Think of a real-life dictionary - You look up a word (the `key`) - You read its definition (the `value`) - In the same way, a Python `dict` lets you - You look up a `key` - Get back the `value` associated with it <img src="./images/dict.png" alt="Welcome page for the unit on Blackboard" style="display:block;margin-left:auto;margin-right:auto;width:60%"/> ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # Define a dictionary with two key-value pairs my_dict = { "apple": "a sweet red or green fruit", "book": "a set of written or printed pages bound together" } # Look up the value for the key 'apple' print(my_dict["apple"]) ``` ## Aside: Dictionaries - New values can be added to the dictionary using the syntax - `my_dict[key] = value` ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # Existing dictionary my_dict = { "apple": "a sweet red or green fruit", "book": "a set of written or printed pages bound together" } # Add a new key-value pair my_dict["chair"] = "a piece of furniture for sitting" # Print the value for the key 'chair' print(my_dict["chair"]) ``` ## Aside: Dictionaries - And keys and values can be any datatype you like - They don't have to be strings! ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # Dictionary with different key and value types my_dict = { 1: "one", "fruit": ["apple", "banana", "cherry"], 3.14: "pi" } # Print the value for the key "fruit" print(my_dict["fruit"]) # Print the value for the key 3.14 print(my_dict[3.14]) ``` ## Aside: Dictionaries - Dictionaries will be crucial to some of today's challenges as well as throughout the rest of this course - If you are unsure about these please take the time to read over: - `Week 1's intermediate notebook` `Section: Sets and Dictionaries` - You can do this either in today's class or over the week 6 break ## Tackling Difficult Problems - Today, we're going to be looking at some challenge problems - These problems may feel a little more open-ended than you are used to - This can be a little daunting at first - But do not worry... - You already have all the skills necessary to complete the tasks! - When faced with a difficult coding problem, here's some general tips to help you get started out ## Tackling Difficult Problems - **Plan before you code** - Before you start coding, grab a pen and paper and try to solve the problem by hand! - **Clarify inputs and outputs** - When getting started think about what you have and what you need – these are probably your inputs and outputs - **Look for structure and repetition** - When you convert your thoughts to code, ask yourself which parts of the code are repetitive or self-contained ## Tackling Difficult Problems - **Divide and Conquer** - Try to break the task down into smaller chunks where possible! - **Talk things through** - If you are struggling to get started, talk through the problem with a friend or peer - **Use Resources** - Googling and looking up documentation are key coding skills - **Trust your own process** - Try to avoid using AI to make structural decisions about your code ## Tackling Difficult Problems - Above all, remember… - Coding can be hard! - It can feel like you are running into the same problems over and over again sometimes - This is a normal part of the process - If you keep trying *you will get there* ## A Worked Example - Let's go through an example together to see these principles in action: > **Question:** Write some code which, for a given positive integer $n$, finds the largest prime number that is less than or equal to $n$. - Principles: - Plan before you code - Clarify inputs and outputs - Look for structure and repetition - Divide and conquer - Talk things through - Trust your own process ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # Write your code here... ``` ## Practical - We now move over to Python - Please open [week_05_home.ipynb](./week_05_home.qmd) - For the rest of today, you must work through a Python notebook - This week is a little different... - Please choose the challenge according to your course - [Chemistry Challenge](week_05_chemistry.qmd) - [Data Science Challenge](week_05_data_science.qmd) - [Physics Challenge](week_05_physics.qmd)