Merge branch 'master' of github.com:thomasabishop/computer-science

This commit is contained in:
thomasabishop 2023-02-15 17:44:00 +00:00
commit 365e7c3e43
27 changed files with 1337 additions and 67 deletions

93
.vscode/markdown-styles.css vendored Normal file
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body {
background: #000e07;
}
h1, h2, h3, h4, h5, h6 {
font-weight: 600;
}
pre {
background: #0f1610 !important;
}
blockquote {
border-left-color: #2f7e25;
border-left-width: 3px;
background: #637d7510;
}
table {
table-layout: fixed;
width: 100%;
}
th,
td {
background: #637d7510;
padding: 0.5rem;
}
tbody tr:nth-child(odd) {
background: #637d7510;
}
tbody tr:nth-child(even) {
background: #000e0740;
}
thead {
border-bottom-width: 0 !important;
}
th {
border-bottom: 0px !important;
}
span.hljs-comment {
color: #5a8055ad;
}
span.hljs-string, span.hljs-params {
color: #637d75;
}
span.hljs-built_in, span.hljs-title.class_, span.hljs-name {
color: #717f24;
}
span.hljs-keyword {
color: #2f7e25;
}
span.hljs-number {
color: #00e0c4;
}
span.hljs-attr, span.hljs-subst, span.hljs-variable {
color: #327f77;
}
span.hljs-variable.language_ {
color: #7f2b27;
}
.code-line code, .code-line {
color: #637d75 !important;
}
code {
font-size: 15px;
}
span.hljs-property {
color: #2f6a7f;
}
span.hljs-title.function_, span.hljs-function {
color: #717f24 !important;
}
span.hljs-literal {
color: #18e000;
}

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- react-hooks
---
# Memoization with `useCallback` and `useMemo`
# Memoization with useCallback and useMemo
## Rationale
@ -23,7 +23,7 @@ The `useCallback` hook is used to wrap functions. It tells React to not re-creat
`useCallback` returns a memoized version of the callback function it is passed. This means that the function object returned from useCallback will be the same between re-renders.
Remember that in JavaScript, functions are objects and components are functions. As a result, every time a component containing a function re-renders, it creates a new instance of the function in memory.
Remember that in JavaScript, functions are objects and components are functions. As a result, every time a component containing a function re-renders, it create a new instance of the function in memory.
> Given the same dependency value, the `useCallback` hook returns the same function instance between renderings (aka memoization).

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---
categories:
- Programming Languages
tags: [backend, node-js, REST, APIs]
---
# Creating a RESTful API: `DELETE` requests
```js
router.delete("/:id", (req, res) => {
const course = courses.find((c) => c.id === parseInt(req.params.id));
if (!course)
return res.status(404).send("A course with the given ID was not found");
courses.indexOf(course);
courses.splice(index, 1);
res.send(course);
});
```

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# BBC Python Course notes
## Day 2
With lists you have to use copy if you wish to make a new version. You cannot just reassign to a new version. This will still update the original. Since it copies the pointer.
Distinguish functions that will create new list and methods which will modify existing list
Functions: named parameter passing, use for default parameter values
Python does not have constants but has a convention of upper case to mimic constants
More on addresses and pointers in Python
With classes we don't need to use `new` when instantiating an instance of a class.
You do not need to define properties in classes if they exist in the constructor

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---
title: Python data-types
categories:
- Programming Languages
tags: [python, data-types]
---
# Python datatypes
The core data-types are as follows:
- str
- bool
- float
- double
- ...
## Converting data-types
For every data-type there is a corresponding converter method, e.g:
```python
a_string_int = "32"
as_int = int(a_string_int)
# 32
a_float_int = "32.2"
as_float = float(a_float_int)
# 32.2
a_bool = "true"
as_bool = bool(a_bool)
# True
```

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---
title: Python data-types
categories:
- Programming Languages
tags: [python, data-types]
---
# Python data-types
- Python is dynamically typed rather than untyped. It updates the types on the fly as you are writing your code.
- Type-hints in the editor like `-> str` mean "at the moment it is a string". It doesn't mean you can't redefine the value as something else.
- Each data type in Python inherits off of a built-in class, similar to prototypes in JS
The core data-types are as follows:
- str
- bool
- float
- double
We can identify types using the built-in `type()` function:
```python
# Integer number
my_variable = 422
print(my_variable)
print(type(my_variable))
# <class 'int'>
# String type
my_variable = 'Natalia'
print(my_variable)
print(type(my_variable))
# <class 'str'>
# Boolean type
my_variable = True
print(my_variable)
print(type(my_variable))
# <class 'bool'>
```
## Converting data-types
For every data-type there is a corresponding converter method, e.g:
```python
a_string = '32'
print(f'a_string {a_string} is {type(a_string)}')
an_int = int(a_string)
print(f'an_int {a_string} is {type(an_int)}')
a_float = float(a_string)
print(f'a_float {a_string} is {type(a_float)}')
another_string = str(42)
print(f'another_string {a_string} is {type(another_string)}')
```

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---
categories:
- Programming Languages
tags: [python, data-types]
---
# Python execution
For immediately executable scripts, we have to have a Python shebang at the top:
```
#! /usr/local/bin/python3
```
With programs we can just run the `main` file with `python main.py`.

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---
categories:
- Programming Languages
tags: [python, data-types]
---
# Package management
- It is better to use `conda` (the package manager that comes with `anaconda`), since this makes it easier to work with conflicting package libraries (a bit like a package lock).
- The alternative is the native `pip` but you have to create virtual environments (`venv`) to manage packages at different versions.
It works a bit like this:
![](/_img/Screenshot%202023-02-13%20at%2010.43.17.png)
To make use of virtual environments in `pip` you have to create the virtual environment before installing anything:
```
python3 -m venv venv3
source venv3/bin/activate
pip [library_name]
```
- pypi.org > is package registry like NPM

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---
categories:
- Programming Languages
tags: [python]
---
# Conditional statements in Python
## Basic example
```python
input_string = input('Please input a number: ')
if input_string.isnumeric():
print('The number is accepted')
else:
print('The input is invalid')
# 5
# The number is accepted
# Using an and in the condition
print('-' * 25)
age = 15
status = None
if age > 12 and age < 20:
status = 'teenager'
else:
status = 'not teenager'
print(status)
```
## Else if
```python
savings = float(input("Enter how much you have in savings: "))
if savings == 0:
print("Sorry no savings")
elif savings < 500:
print('Well done')
elif savings < 1000:
print('That is a tidy sum')
elif savings < 10000:
print('Welcome Sir!')
else:
print('Thank you')
```
## Nested conditions
```python
snowing = True
temp = -1
if temp < 0:
print('It is freezing')
if snowing:
print('Put on boots')
print('Time for Hot Chocolate')
print('Bye')
```
## Ternaries/ shorthand conditionals
```python
status = 'teenager' if age > 12 and age < 20 else 'not teenager'
print(status)
num = int(input('Enter a simple number: '))
result = -1 if num < 0 else 1
print('Result is ', result)
```

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---
categories:
- Programming Languages
tags: [python, data-structures]
---
# Dictionaries in Python
Dictionaries are basically the Python equivalent of objects in JS.
Dictionaries:
- Are ordered (in contrast to JS)
- Are mutable
- Are indexed by a key which references a value
- Can be increased/decreased in length by adding/removing new members.
## Basic usage
Dictionaries are declared with `{...}`:
```python
cities = {
'Wales': 'Cardiff',
'England': 'London',
'Scotland': 'Edinburgh',
'Northern Ireland': 'Belfast',
'Ireland': 'Dublin'
}
print(type(citites))
# <class 'dict'>
```
## Accessing entries
```python
print(cities['Wales'])
# Cardiff
print(cities.get('Ireland'))
# Dublin
print(cities.values())
# ['Cardiff', 'London', 'Edinburgh', 'Belfast', 'Dublin']
print(cities.keys())
# ['Wales', 'England', 'Scotland', 'Northern Ireland', 'Ireland']
print(cities.items())
# [('Wales', 'Cardiff'), ('England', 'London'), ('Scotland', 'Edinburgh'), ('Northern Ireland', 'Belfast'), ('Ireland', 'Dublin')]
```
## Predicates
```py
print('Wales' in cities)
# True
print('France' not in cities)
# True
```
## Looping
```py
for country in cities:
print(country, end=', ')
print(cities[country])
"""
Wales, Cardiff
England, London
Scotland, Edinburgh
Northern Ireland, Belfast
Ireland, Dublin
"""
for e in country.values():
print(e)
```
## Updating values
```py
cities['Wales'] = 'Swansea'
print(cities)
```
## Removing values
```py
# Remove last item
cities.popitem()
print(cities)
# {'Wales': 'Cardiff', 'England': 'London', 'Scotland': 'Edinburgh', 'Northern Ireland': 'Belfast'}
# Remove specific entry by key
cities.pop('Northern Ireland')
print(cities)
# {'Wales': 'Cardiff', 'England': 'London', 'Scotland': 'Edinburgh'}
del cities['Scotland']
print(cities)
{'Wales': 'Cardiff', 'England': 'London'}
```
## Containers as values
```py
seasons = {
'Spring': ('Mar', 'Apr', 'May'),
'Summer': ('June', 'July', 'August'),
'Autumn': ('September', 'October', 'November'),
'Winter': ('December', 'January', 'February')}
print(seasons['Spring'])
print(seasons['Spring'][1])
"""
('Mar', 'Apr', 'May')
Apr
"""
```

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---
categories:
- Programming Languages
tags: [python]
---
# Functions
- Convention is to leave a double line-break after a function definition (but not with nested functions - here, a single linebreak is sufficient)
- Scope within functions is demarcated by indents, as everything in Python
- We use a docstring _within_ the function body, to document our function. This text will then show up in Intellisense etc.
## Basic examples
```py
# No params, no return
def print_msg():
""" A function that prints hello world """
print('Hello World!')
print_msg()
# Hello World!
print(type(print_msg))
# <class 'function'>
# Params, no return
def print_my_msg(msg):
""" A simple function to print a message """
print(msg)
print_my_msg('Good day')
# Good day
# Params and return
def square(n):
return n * n
print(square(2))
# 4
result = square(4)
print(result)
# 16
```
## Default parameters
```py
def greeter(name, message='Live Long and Prosper'):
print('Welcome', name, '-', message)
greeter('Eloise')
# Welcome Eloise - Live Long and Prosper
```
## Function with arbitrary parameter list
```python
def greeter(*args):
for name in args:
print('Welcome', name)
greeter('John', 'Denise', 'Phoebe', 'Adam', 'Gryff', 'Natalia')
"""
Welcome John
Welcome Denise
Welcome Phoebe
Welcome Adam
Welcome Gryff
Welcome Natalia
"""
```
## Scoping
Function variables are locally scoped by default.
They can access variables that are outer to them and can redefine them within their own scope _and_ within the global scope using the keywords `global` and `nonlocal`.
Below a global variable is accessed and changed but only internally within a function scope
```py
max = 100
print('initial value of max:', max)
def print_max():
global max
max = max + 1
print('inside function:', max)
print_max()
print('outside function:', max)
"""
initial value of max: 100
inside function: 101
outside function: 101
"""
```
Below a higher-scoped variable is redefined from within the lower scope:
```py
def myfunc1():
x = "John"
def myfunc2():
nonlocal x
x = "hello"
myfunc2()
return x
print(myfunc1())
# hello
```
We cannot however redefine a global variable from a function scope permanently. It will remain whatever it is in global scope, after the function has run.

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---
categories:
- Programming Languages
tags: [python]
---
# Lambdas in Python
In Python, anonymous functions like arrow-functions in JavaScript (`() => {}`) are immediately invoked and unnamed. They are called lambdas.
Whilst they are unnamed, just like JS, the value they return can be stored in a variable. They do not require the `return` keyword.
They are most often used unnamed with the functional methods [map, filter and reduce](/Programming_Languages/Python/Syntax/Map_filter_reduce_in_Python.md)
Here is the two syntaxes side by side:
```js
const double = (x) => x * x;
```
```py
double = lambda x: x * x
```
Here is a lambda with multiple parameters:
```py
func = lambda x, y, z: x + y + z
print(func(2, 3, 4))
# 9
```
> Lambdas obviously enshrine functional programming paradigms. Therefore they should be pure functions, not mutating values or issueing side effects. For example, it would be improper (though syntactically well-formed) to use a lambda to `print` something

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---
categories:
- Programming Languages
tags: [python, data-structures]
---
# Lists in Python
Lists are the equivalent of a simple array in JavaScript.
Lists have the following properties:
- They are **ordered**
- They are **mutable** and can be modified
- They **allow duplicate** members
- They are **indexed**
- You can increase/decrease their length by adding/removing new members
> Lists are denoted with `[...]`
## Basic usage
```python
# Defining a list
list1 = ['John', 'Paul', 'George', 'Ringo']
list2 = [4]
# Empty list
list3 = [] # empty list
list3 = list() # Also empty list
# Nested list
list5 = [[2, 3], [6, 8]]
```
## Slicing
```python
list1 = ['John', 'Paul', 'George', 'Ringo']
print(list1[1])
print(list1[-1])
print(list1[1:3])
print(list1[:3])
print(list1[1:])
"""
Ringo
['Paul', 'George']
['John', 'Paul', 'George']
['Paul', 'George', 'Ringo']
"""
```
## Adding additional values to existing list
```python
list1 = ['John', 'Paul', 'George', 'Ringo']
# Add single element to the end of a list
list1.append('Pete')
# ['John', 'Paul', 'George', 'Ringo', 'Pete']
# Add multiple elements to end of a list
list1.extend(['Albert', 'Bob'])
list1 += ['Ginger', 'Sporty']
# ['John', 'Paul', 'George', 'Ringo', 'Pete', 'Albert', 'Bob', 'Ginger', 'Sporty']
## Insert at specific index
list1.insert(2, 7)
['John', 'Paul', 7, 'George', 'Ringo', 'Pete', 'Albert', 'Bob', 'Ginger', 'Sporty']
a_list = ['Adele', 'Madonna', 'Cher']
print(a_list)
a_list.insert(1, 'Paloma')
print(a_list)
# ['Adele', 'Paloma', 'Madonna', 'Cher']
```
## Removing elements
We distinguish `del` from `remove` when removing elements from lists:
- `del` requires an index value
- `remove` requires a value reference (i.e. the mame of the element rather than its index)
`del` is simple deletion whereas `remove` searches the list. Therefore `del` is more efficient.
```python
# Remove and return element removed
list6 = ['Once', 'Upon', 'a', 'Time']
print(list6.pop(2))
# a
# Remove and return last element
list6 = ['Once', 'Upon', 'a', 'Time']
print(list6.pop())
list6.pop()
print(list6)
# Time
list6.remove('Upon')
print(list6)
# ['Once', 'a']
my_list = ['A', 'B', 'C', 'D', 'E']
print(my_list)
# ['A', 'B', 'C', 'D', 'E']
del my_list[2]
print(my_list)
# ['A', 'B', 'D', 'E']
print(my_list)
# ['A', 'B', 'C', 'D', 'E']
del my_list[1:3]
print(my_list)
# ['A', 'D', 'E']
```
## Retrieve elements by index
```python
list7 = [2, 3, 6, 8]
print(list7.index(8))
# 3
list6 = ['Once', 'Upon', 'a', 'Time']
print(list6.index('a'))
# 2
```
## Nesting lists
```python
l1 = [1, 43.5, 'Phoebe', True]
l2 = ['apple', 'orange', 31]
root_list = ['John', l1, l2, 'Denise']
print(root_list)
# ['John', [1, 43.5, 'Phoebe', True], ['apple', 'orange', 31], 'Denise']
```
## List comprehension
> List comprehension is an older feature of Python. Now the same functionality can be achieved with greater concision using functional methods like `map` and `filter`. But you may see it used in older code.
```python
values = [1, 2, 4, 6, 8, 9]
new_values = [i + 1 for i in values]
print('new_values', new_values)
# new_values [2, 3, 5, 7, 9, 10]
new_list = [item + 1 for item in values if item % 2 == 0]
print('new_list:', new_list)
# new_list: [3, 5, 7, 9]
```

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---
categories:
- Programming Languages
tags: [python]
---
# Loops in Python
## While
```python
count = 0
print('Starting')
while count < 10:
print(count, '', end='')
count += 1
print() # not part of the while loop
print('Done')
"""
Starting
0 1 2 3 4 5 6 7 8 9
Done
"""
```
> There are no `do while` loops in Python
## For
```python
# Loop over a set of values in a range
print('Print out values in a range')
for i in range(0, 10):
print(i, ' ', end='')
print()
print('Done')
"""
Print out values in a range
0 1 2 3 4 5 6 7 8 9
Done
"""
# Now use values in a range but increment by 2
print('Print out values in a range with an increment of 2')
for i in range(0, 10, 2):
print(i, ' ', end='')
print()
print('Done')
"""
Print out values in a range with an increment of 2
0 2 4 6 8
Done
"""
# Now use an 'anonymous' loop variable
for _ in range(0, 10):
print('.', end='')
print()
print('-' * 25)
# Illustrates use of break statement
print('Only print code if all iterations completed')
num = int(input('Enter a number to check for: '))
for i in range(0, 6):
if i == num:
break
print(i, ' ', end='')
print('Done')
"""
Only print code if all iterations completed
Enter a number to check for: 7
0 1 2 3 4 5 Done
"""
# Illustrates use of continue statement
for i in range(0, 10):
print(i, ' ', end='')
if i % 2 == 1:
continue
print('hey its an even number')
print('we love even numbers')
print('Done')
"""
0 hey its an even number
we love even numbers
1 2 hey its an even number
we love even numbers
3 4 hey its an even number
we love even numbers
5 6 hey its an even number
we love even numbers
7 8 hey its an even number
we love even numbers
9 Done
"""
# Illustrates use of else statement with a for loop
print('Only print code if all iterations completed')
num = int(input('Enter a number to check for: '))
for i in range(0, 6):
if i == num:
break
print(i, ' ', end='')
else:
print()
print('All iterations successful')
print('Done')
"""
Only print code if all iterations completed
Enter a number to check for: 6
0 1 2 3 4 5
All iterations successful
Done
"""
```

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---
categories:
- Programming Languages
tags: [python]
---
# Map and filter in Python
## Map
```py
data = [1, 3, 5, 2, 7, 4, 10]
new_data = map(lambda i: i + 10, data)
print(new_data)
```
We can also pass-in a function rather than use a lambda:
```py
def add_one(i):
return i + 1
x = list(map(addOne, data))
# [2, 4, 6, 3, 8, 5, 11]
# necessary to add `list` to get some output
```
## Filter
```py
data = [1, 3, 5, 2, 7, 4, 10]
d1 = list(filter(lambda i: i % 2 == 0, data))
print(d1)
# [2, 4, 10]
def is_even(i):
return i % 2 == 0
# Filter for even numbers using a named function
d2 = list(filter(is_even, data))
# [2, 4, 10]
```
## Chaining
```py
data = [1, 3, 5, 2, 7, 4, 10]
new_data = list(map(lambda i: i + 10, filter(is_even, data)))
print(new_data)
# new_data: [12, 14, 20]
```

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---
categories:
- Programming Languages
tags: [python]
---
# Match statements in Python
> A `match` statement is the equivalent of a switch or case statement in Python
```python
command = input("What are you doing next? ")
match command:
case "quit":
print("Goodbye!")
case "look":
print("Looking out")
case "up" | "down":
print("up or down")
case _:
print("The default")
"""
What are you doing next? up
up or down
"""
match command.split():
case ["go", "left"]:
print("go left")
case ["go", ("fast" | "slow")]:
print("go fast or slow")
point = (3, 3)
match point:
case (x, y) if x == y:
print(f"The point is located on the diagonal Y=X at {x}.")
case (x, y):
print(f"Point is not on the diagonal.")
"""
The point is located on the diagonal Y=X at 3.
"""
```

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---
categories:
- Programming Languages
tags: [python, data-types]
---
# None in Python
`None` is not `null`, it is closer to `undefined` in JS. If you define a variable as `None`, the variable exists, it is just not yet defined.
Using `None` is a pattern similar to using `let` in JS to name a variable and definine it later on.
```python
temperature = None
```
If we logged `temperature` it would give us `None` rather than a null pointer error.
With None we can use `is None` and `is not None`, special predicates for working with `None` only. This is a akin to using `if (x !== undefined)` in TypeScript
```python
winner = None
print('winner:', winner)
# winner: None
print('winner is None:', winner is None)
# winner is None: True
print('winner is not None:', winner is not None)
# winner is not None: False
print(type(winner))
# <class 'NoneType'>
# Now set winner to be True
print('Set winner to True')
# Set winner to True
winner = True
print('winner:', winner)
# winner: True
print('winner is None:', winner is None)
# winner is None: False
print('winner is not None:', winner is not None)
# winner is not None: True
print(type(winner))
# <class 'bool'>
```

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---
categories:
- Programming Languages
tags: [python, data-types]
---
# Numbers in Python
## Distinguishing `int` and `float`
- In Python we have floats and integers and we can coerce one into the other
- A `//` as an operator means float division. This obviously provides greater precision than int division `/`.
- There is no increment (`++`) or decrement (`--`) operator in Python
```python
# Integers and floats
count = 1
print(count)
# 1
print(type(count))
# <class 'int'>
exchange_rate = 1.83
print(exchange_rate)
# 1.83
print(type(exchange_rate))
# <class 'float'>
print(float(count))
# 1.0
print(int(exchange_rate))
# 1
```

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---
categories:
- Programming Languages
tags: [python, data-structures]
---
# Sets in Python
- They are **unordered**
- You can increase/decrease their length by adding/removing new members
- They **do not allow duplicate members**
- **Can only hold immutable objects**
> Sets are denoted with `{...}`
## Basic usage
```python
basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
print(basket) # show that duplicates have been removed
print(len(basket))
# {'apple', 'pear', 'banana', 'orange'}
# 4
```
## Looping through sets
```python
for item in basket:
print(item)
"""
apple
pear
banana
orange
"""
```
## Check for membership
```python
basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
print('apple' in basket)
# True
```
## Remove items from set
> `remove` will raise an error if the specified item does not exist, `discard` will not
```python
basket.remove('apple')
basket.discard('apricot')
print(basket)
# {'pear', 'banana', 'orange'}
basket.clear()
print(basket)
#set
```
## Add items to a set
```python
basket.add('apricot')
print(basket)
# {'apricot', 'pear', 'banana', 'orange'}
```
## Apply unions and intersections
```python
s1 = {'apple', 'orange', 'banana'}
s2 = {'grapefruit', 'lime', 'banana'}
print('Union:', s1 | s2)
# Union: {'apple', 'orange', 'grapefruit', 'lime', 'banana'}
print('Intersection:', s1 & s2)
# Intersection: {'banana'}
print('Difference:', s1 - s2)
# Difference: {'orange', 'apple'}
print('Symmetric Difference:', s1 ^ s2)
#Symmetric Difference: {'apple', 'orange', 'grapefruit', 'lime'}
```

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@ -0,0 +1,78 @@
---
categories:
- Programming Languages
tags: [python, data-types]
---
# Strings in Python
> Generally, anything that changes a string will be a method on the `str` class, rather than a built-in function like `len()`, as such it will use dot notation
- Strings are **immutable**: string operations produce a new string.
```python
# Working with Strings
my_variable = 'Bob'
print(my_variable)
# Bob
my_variable = "Eloise"
print(my_variable)
# Eloise
# A multi line string
my_variable = """
Hello
World
"""
print(my_variable)
"""
Hello
World
"""
my_string = 'Hello World'
print(len(my_string))
# 11
string_1 = 'Good'
string_2 = " day"
string_3 = string_1 + string_2
print(string_3)
# Good day
msg = 'Hello Lloyd you are ' + str(21)
print(msg)
# Hello Lloyd you are 21
# Range of String operations
msg = 'Hello World'
print(msg.replace("Hello", "Goodbye"))
# Goodbye World
print('Edward Alan Rawlings'.find('Alan'))
# 7
print('Edward John Rawlings'.find('Alan'))
# -1
print('James' == 'James') # prints True
print('James' != 'John') # prints True
print("msg.startswith('H')", msg.startswith('H'))
# msg.startswith('H') True
print("msg.endswith('d')", msg.endswith('d'))
# msg.endswith('d') TRUE
print('some_string.upper()', msg.upper())
# some_string.upper() HELLO WORLD
print('sub string: ', 'Hello-World'[1:5])
# sub string: ello
# String interpolation
user_age = input("Please enter your age: ")
print(f'You are {user_age}')
```

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@ -0,0 +1,106 @@
---
categories:
- Programming Languages
tags: [python, data-structures]
---
# Tuples in Python
Tuples are one of the main data-structures or containers in Python.
Tuples have the following properties:
- They are **ordered**
- They have a **fixed size**
- They are **immutable** and cannot be modified
- **Allow duplicate** members
- They are **indexed**
As with all containers in Python they permit any data type.
> Tuples are denoted with `(...)`
## Basic usage
```python
tup1 = (1, 3, 5, 7)
print(tup1[0])
print(tup1[1])
print(tup1[2])
print(tup1[3])
"""
1
3
5
7
"""
```
## Slicing
```python
tup1 = (1, 3, 5, 7)
print(tup1[1:3])
print(tup1[:3])
print(tup1[1:])
print(tup1[::-1])
"""
(3, 5)
(1, 3, 5)
(3, 5, 7)
(7, 5, 3, 1)
"""
```
## Looping
```python
tup3 = ('apple', 'pear', 'orange', 'plum', 'apple')
for x in tup3:
print(x)
"""
apple
pear
orange
plum
apple
"""
```
## Useful methods and predicates
```python
tup3 = ('apple', 'pear', 'orange', 'plum', 'apple')
# Count instances of a member
print(tup3.count('apple'))
# 2
# Get index of a member
print(tup3.index('pear'))
# 1
# Check for membership
if 'orange' in tup3:
print('orange is in the Tuple')
# orange is in the Tuple
```
## Nest tuples
```python
tuple2 = ('John', 'Denise', 'Phoebe', 'Adam')
tuple3 = (42, tuple1, tuple2, 5.5)
print(tuple3)
# (42, (1, 3, 5, 7), ('John', 'Denise', 'Phoebe', 'Adam'), 5.5)
```
// TODO: How to flatten a tuple?

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@ -33,7 +33,7 @@ echo /tmp/{1..3}/file.txt
/tmp/1/file.txt /tmp/2/file.txt /tmp/3/file.txt
```
```
```bash
echo {1..5}
1 2 3 4 5
@ -46,7 +46,7 @@ a b c
We can also set sequences. If we wanted to count to twenty in intervals of two
```
```bash
echo {1..20..2}
1 3 5 7 9 11 13 15 17 19
```

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@ -15,7 +15,7 @@ So say we have this object:
```js
const age = {
name: 'Thomas',
name: "Thomas",
yearOfBirth: 1988,
currentYear: 2021,
ageNow: function () {
@ -43,7 +43,7 @@ We could now re-write the first `age` object as an object of type `Age` :
let thomas: typeof Age;
thomas = {
name: 'Thomas',
name: "Thomas",
yearOfBirth: 1988,
currentYear: 2021,
ageNow: function () {
@ -67,7 +67,7 @@ We could then create objects based on this:
```tsx
const thomas: Age = {
name: 'Thomas',
name: "Thomas",
yearOfBirth: 1988,
currentYear: 2021,
ageNow: function () {
@ -97,10 +97,10 @@ With custom (object types) this means that the following expression of an object
```tsx
const martha = {
name: 'Martha',
name: "Martha",
yearOfBirth: 1997,
currentYear: 2021,
gender: 'female',
gender: "female",
};
const addition: Age = martha;
@ -110,10 +110,10 @@ But if we tried to add this extra property whilst defining `martha` as an instan
```tsx
const martha: Age = {
name: 'Martha',
name: "Martha",
yearOfBirth: 1997,
currentYear: 2021,
gender: 'female',
gender: "female",
};
```
@ -134,17 +134,17 @@ function logPoint(p: Point) {
}
// logs "12, 26"
const point = {x: 12, y: 26};
const point = { x: 12, y: 26 };
logPoint(point);
```
Shape matching only requires a subset of the object's fields to match:
```tsx
const point3 = {x: 12, y: 26, z: 89};
const point3 = { x: 12, y: 26, z: 89 };
logPoint(point3); // logs "12, 26"
const rect = {x: 33, y: 3, width: 30, height: 80};
const rect = { x: 33, y: 3, width: 30, height: 80 };
logPoint(rect);
```

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@ -96,6 +96,10 @@ A. Sweighart. 2020. **Beyond the Basic Stuff with Python**
A. Sweighart. 2015. **Automate the Boring Stuff with Python**
J. Hunt. 2019. **A Beginner's Guide to Python Programming**
J. Hunt. 2019. **An Advanced Guide to Python Programming**
[Tiny Python Projects (O'Reilly)](https://learning.oreilly.com/library/view/tiny-python-projects/9781617297519/)
[Learning Arduino with Python](https://realpython.com/arduino-python/)

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@ -1,5 +1,25 @@
# Learning Topic Log
## Python
- Research: best practice for separating projects into `conda` environments like npm
- Read-up more on types: what does it mean for Python to be dynamically typed. What is type-hinting really?
- Use provided pdfs and John's books
- Is `dictionary.values()`/ `dictionary.keys()` of type list?
- Is `dictionary.items()` a list of tuples for key, value?
- How to run test suites via VSCode?
BBC Course, remaining topics:
- Classes and object-oriented paradigms in Python
- Modules
- Error handling
- Testing
## Bash
- Best way to run a command in a script - is it to `echo` it?