483 lines
13 KiB
Plaintext
483 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5c291475-8c7c-461c-9b12-545a887b2432",
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"metadata": {},
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"source": [
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"# Intermediate Level Python\n",
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"\n",
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"## A briefing on more advanced features of Python\n",
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"\n",
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"This section assumes you're up to speed on the foundations - and now we cover some important features of python that we use on the course.\n",
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"\n",
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"1. Comprehensions \n",
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"2. Generators \n",
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"3. Sub-classes, Type Hints, Pydantic \n",
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"4. Decorators\n",
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"5. Docker (not really python, but we use it to run python code!)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5802e2f0-0ea0-4237-bbb7-f375a34260f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# First let's create some things:\n",
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"\n",
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"fruits = [\"Apples\", \"Bananas\", \"Pears\"]\n",
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"\n",
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"book1 = {\"title\": \"Great Expectations\", \"author\": \"Charles Dickens\"}\n",
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"book2 = {\"title\": \"Bleak House\", \"author\": \"Charles Dickens\"}\n",
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"book3 = {\"title\": \"An Book By No Author\"}\n",
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"book4 = {\"title\": \"Moby Dick\", \"author\": \"Herman Melville\"}\n",
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"\n",
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"books = [book1, book2, book3, book4]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9b941e6a-3658-4144-a8d4-72f5e72f3707",
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"metadata": {},
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"source": [
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"# Part 1: List and dict comprehensions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "61992bb8-735d-4dad-8747-8c10b63aec82",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Simple enough to start\n",
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"\n",
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"for fruit in fruits:\n",
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" print(fruit)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c89c3842-9b74-47fa-8424-0fcb08e4177c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's make a new version of fruits\n",
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"\n",
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"fruits_shouted = []\n",
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"for fruit in fruits:\n",
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" fruits_shouted.append(fruit.upper())\n",
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"\n",
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"fruits_shouted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4ec13b3a-9545-44f1-874a-2910a0663560",
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"metadata": {},
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"outputs": [],
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"source": [
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"# You probably already know this\n",
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"# There's a nice Python construct called \"list comprehension\" that does this:\n",
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"\n",
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"fruits_shouted2 = [fruit.upper() for fruit in fruits]\n",
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"fruits_shouted2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ecc08c3c-181d-4b64-a3e1-b0ccffc6c0cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# But you may not know that you can do this to create dictionaries, too:\n",
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"\n",
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"fruit_mapping = {fruit: fruit.upper() for fruit in fruits}\n",
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"fruit_mapping"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "500c2406-00d2-4793-b57b-f49b612760c8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# you can also use the if statement to filter the results\n",
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"\n",
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"fruits_with_longer_names_shouted = [fruit.upper() for fruit in fruits if len(fruit)>5]\n",
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"fruits_with_longer_names_shouted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "38c11c34-d71e-45ba-945b-a3d37dc29793",
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"metadata": {},
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"outputs": [],
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"source": [
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"fruit_mapping_unless_starts_with_a = {fruit: fruit.upper() for fruit in fruits if not fruit.startswith('A')}\n",
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"fruit_mapping_unless_starts_with_a"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c97d8e8-31de-4afa-973e-28d8e5cab749",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Another comprehension\n",
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"\n",
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"[book['title'] for book in books]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50be0edc-a4cd-493f-a680-06080bb497b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# This code will fail with an error because one of our books doesn't have an author\n",
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"\n",
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"[book['author'] for book in books]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "53794083-cc09-4edb-b448-2ffb7e8495c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# But this will work, because get() returns None\n",
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"\n",
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"[book.get('author') for book in books]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b8e4b859-24f8-4016-8d74-c2cef226d049",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And this variation will filter out the None\n",
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"\n",
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"[book.get('author') for book in books if book.get('author')]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c44bb999-52b4-4dee-810b-8a400db8f25f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And this version will convert it into a set, removing duplicates\n",
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"\n",
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"set([book.get('author') for book in books if book.get('author')])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "80a65156-6192-4bb4-b4e6-df3fdc933891",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And finally, this version is even nicer\n",
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"# curly braces creates a set, so this is a set comprehension\n",
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"\n",
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"{book.get('author') for book in books if book.get('author')}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c100e5db-5438-4715-921c-3f7152f83f4a",
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"metadata": {},
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"source": [
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"# Part 2: Generators\n",
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"\n",
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"We use Generators in the course because AI models can stream back results.\n",
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"\n",
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"If you've not used Generators before, please start with this excellent intro from ChatGPT:\n",
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"\n",
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"https://chatgpt.com/share/672faa6e-7dd0-8012-aae5-44fc0d0ec218\n",
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"\n",
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"Try pasting some of its examples into a cell."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1efc26fa-9144-4352-9a17-dfec1d246aad",
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"metadata": {},
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"outputs": [],
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"source": [
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"# First define a generator; it looks like a function, but it has yield instead of return\n",
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"\n",
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"import time\n",
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"\n",
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"def come_up_with_fruit_names():\n",
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" for fruit in fruits:\n",
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" time.sleep(1) # thinking of a fruit\n",
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" yield fruit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eac338bb-285c-45c8-8a3e-dbfc41409ca3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Then use it\n",
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"\n",
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"for fruit in come_up_with_fruit_names():\n",
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" print(fruit)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f6880578-a3de-4502-952a-4572b95eb9ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Here's another one\n",
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"\n",
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"def authors_generator():\n",
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" for book in books:\n",
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" if book.get(\"author\"):\n",
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" yield book.get(\"author\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9e316f02-f87f-441d-a01f-024ade949607",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use it\n",
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"\n",
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"for author in authors_generator():\n",
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" print(author)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7535c9d0-410e-4e56-a86c-ae6c0e16053f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Here's the same thing written with list comprehension\n",
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"\n",
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"def authors_generator():\n",
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" for author in [book.get(\"author\") for book in books if book.get(\"author\")]:\n",
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" yield author"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dad34494-0f6c-4edb-b03f-b8d49ee186f2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use it\n",
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"\n",
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"for author in authors_generator():\n",
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" print(author)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "abeb7e61-d8aa-4af0-b05a-ae17323e678c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Here's a nice shortcut\n",
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"# You can use \"yield from\" to yield each item of an iterable\n",
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"\n",
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"def authors_generator():\n",
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" yield from [book.get(\"author\") for book in books if book.get(\"author\")]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "05b0cb43-aa83-4762-a797-d3beb0f22c44",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use it\n",
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"\n",
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"for author in authors_generator():\n",
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" print(author)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fdfea58e-d809-4dd4-b7b0-c26427f8be55",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And finally - we can replace the list comprehension with a set comprehension\n",
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"\n",
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"def unique_authors_generator():\n",
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" yield from {book.get(\"author\") for book in books if book.get(\"author\")}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3e821d08-97be-4db9-9a5b-ce5dced3eff8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use it\n",
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"\n",
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"for author in unique_authors_generator():\n",
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" print(author)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "905ba603-15d8-4d01-9a79-60ec293d7ca1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And for some fun - press the stop button in the toolbar when bored!\n",
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"# It's like we've made our own Large Language Model... although not particularly large..\n",
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"# See if you understand why it prints a letter at a time, instead of a word at a time. If you're unsure, try removing the keyword \"from\" everywhere in the code.\n",
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"\n",
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"import random\n",
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"import time\n",
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"\n",
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"pronouns = [\"I\", \"You\", \"We\", \"They\"]\n",
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"verbs = [\"eat\", \"detest\", \"bathe in\", \"deny the existence of\", \"resent\", \"pontificate about\", \"juggle\", \"impersonate\", \"worship\", \"misplace\", \"conspire with\", \"philosophize about\", \"tap dance on\", \"dramatically renounce\", \"secretly collect\"]\n",
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"adjectives = [\"turqoise\", \"smelly\", \"arrogant\", \"festering\", \"pleasing\", \"whimsical\", \"disheveled\", \"pretentious\", \"wobbly\", \"melodramatic\", \"pompous\", \"fluorescent\", \"bewildered\", \"suspicious\", \"overripe\"]\n",
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"nouns = [\"turnips\", \"rodents\", \"eels\", \"walruses\", \"kumquats\", \"monocles\", \"spreadsheets\", \"bagpipes\", \"wombats\", \"accordions\", \"mustaches\", \"calculators\", \"jellyfish\", \"thermostats\"]\n",
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"\n",
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"def infinite_random_sentences():\n",
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" while True:\n",
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" yield from random.choice(pronouns)\n",
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" yield \" \"\n",
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" yield from random.choice(verbs)\n",
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" yield \" \"\n",
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" yield from random.choice(adjectives)\n",
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" yield \" \"\n",
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" yield from random.choice(nouns)\n",
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" yield \". \"\n",
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"\n",
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"for letter in infinite_random_sentences():\n",
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" print(letter, end=\"\", flush=True)\n",
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" time.sleep(0.02)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "04832ea2-2447-4473-a449-104f80e24d85",
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"metadata": {},
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"source": [
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"# Exercise\n",
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"\n",
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"Write some python classes for the books example.\n",
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"\n",
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"Write a Book class with a title and author. Include a method has_author()\n",
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"\n",
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"Write a BookShelf class with a list of books. Include a generator method unique_authors()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "35760406-fe6c-41f9-b0c0-3e8cf73aafd0",
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"metadata": {},
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"source": [
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"# Part 3: Sub-classes, Type Hints, Pydantic\n",
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"\n",
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"Here are some intermediate level details of Classes from our AI friend, including use of type hints, inheritance and class methods. This includes a Book example.\n",
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"\n",
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"https://chatgpt.com/share/67348aca-65fc-8012-a4a9-fd1b8f04ba59\n",
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"\n",
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"And here is a comprehensive tutorial on Pydantic classes covering everything you need to know about Pydantic.\n",
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"\n",
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"https://chatgpt.com/share/68064537-6cfc-8012-93e1-f7dd0932f321"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6bbc9c63",
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"metadata": {},
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"source": [
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"## Part 4: Decorators\n",
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"\n",
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"Here is a briefing, with an example from OpenAI Agents SDK:\n",
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"\n",
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"https://chatgpt.com/share/6806474d-3880-8012-b2a2-87b3ee4489da"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0beef7e9",
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"metadata": {},
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"source": [
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"## Part 5: Docker\n",
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"\n",
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"Here is a convenient tutorial to introduce Docker.\n",
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"\n",
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"In the last section, this also covers an answer to a question in Week 6 - what does it mean to run an MCP server in Docker? But you can ignore this question if you're not on week 6 yet.\n",
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"\n",
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"https://chatgpt.com/share/6814bc1d-2f3c-8012-9b18-dddc82ea421b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "73e215b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# You need to install docker to run this example\n",
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"# This will download the Docker image for python 3.12, create a container,\n",
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"# Run some python code and print the result\n",
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"\n",
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"!docker run --rm python:3.12 python -c \"print(2 + 2)\""
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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|
"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython3",
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"version": "3.12.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|