Python QuickStart for People Learning AI

A beginner-friendly guide

Python has become the de facto programming language for AI and data science. Although no-code solutions exist, learning how to code is still essential to build fully custom AI projects or products. In this article, I share a beginner QuickStart guide to AI development with Python. I’ll cover the basics and then share a concrete example with code.

Python is a programming language, i.e., a way to give computers precise instructions to do things we can’t or don’t want to do [1].

This is handy when automating a unique task without an off-the-shelf solution. For example, if I wanted to automate writing and sending personalized meeting follow-ups, I could write a Python script to do this.

With tools like ChatGPT, it’s easy to imagine a future where one could describe any bespoke task in plain English, and the computer would just do it. However, such a consumer product does not exist right now. Until such products become available, there is tremendous value in knowing (at least a little) Python.

Coding is Easier Than Ever

While current AI products (e.g. ChatGPT, Claude, Gemini) haven’t made programming obsolete (yet), they have made it easier than ever to learn how to code. We all now have a competent and patient coding assistant who is always available to help us learn.

Combined with the “traditional” approach of Googling all your problems, programmers can now move faster. For instance, I generously use ChatGPT to write example code and explain error messages. This accelerates my progress and gives me more confidence when navigating new technology stacks.

Who This is For

I’m writing this with a particular type of reader in mind: those trying to get into AI and have done a little coding (e.g., JS, HTML/CSS, PHP, Java, SQL, Bash/Powershell, VBA) but are new to Python.

I’ll start with Python fundamentals, then share example code for a simple AI project. This is not meant to be a comprehensive introduction to Python. Rather, it’s meant to give you just enough to code your first AI project with Python fast.

About me — I’m a data scientist and self-taught Python programmer (5 years). While there’s still much for me to learn about software development, here I cover what I think are the bare essentials of Python for AI/data science projects based on my personal experience.

Installing Python

Many computers come with Python pre-installed. To see if your machine has it, go to your Terminal (Mac/Linux) or Command Prompt (Windows), and simply enter “python”.

If you don’t see a screen like this, you can download Python manually (Windows/ Mac). Alternatively, one can install Anaconda, a popular Python package system for AI and data science. If you run into installation issues, ask your favorite AI assistant for help!

With Python running, we can now start writing some code. I recommend running the examples on your computer as we go along. You can also download all the example code from the GitHub repo.

1) Data Types

Strings & Numbers

A data type (or just “type”) is a way to classify data so that it can be processed appropriately and efficiently in a computer.

Types are defined by a possible set of values and operations. For example, strings are arbitrary character sequences (i.e. text) that can be manipulated in specific ways. Try the following strings in your command line Python instance.

"this is a string"
>> 'this is a string'

'so is this:-1*!@&04"(*&^}":>?'
>> 'so is this:-1*!@&04"(*&^}":>?'

"""and
    this is
        too!!11!"""
>> 'andn    this isn        too!!11!'

"we can even " + "add strings together"
>> 'we can even add strings together'

Although strings can be added together (i.e. concatenated), they can’t be added to numerical data types like int (i.e. integers) or float (i.e. numbers with decimals). If we try that in Python, we will get an error message because operations are only defined for compatible types.

# we can't add strings to other data types (BTW this is how you write comments in Python)
"I am " + 29
>> TypeError: can only concatenate str (not "int") to str

# so we have to write 29 as a string
"I am " + "29"
>> 'I am 29'

Lists & Dictionaries

Beyond the basic types of strings, ints, and floats, Python has types for structuring larger collections of data.

One such type is a list, an ordered collection of values. We can have lists of strings, numbers, strings + numbers, or even lists of lists.

# a list of strings
["a", "b", "c"]

# a list of ints
[1, 2, 3]

# list with a string, int, and float
["a", 2, 3.14]

# a list of lists
[["a", "b"], [1, 2], [1.0, 2.0]]

Another core data type is a dictionary, which consists of key-value pair sequences where keys are strings and values can be any data type. This is a great way to represent data with multiple attributes.

# a dictionary
{"Name":"Shaw"}

# a dictionary with multiple key-value pairs
{"Name":"Shaw", "Age":29, "Interests":["AI", "Music", "Bread"]}

# a list of dictionaries
[{"Name":"Shaw", "Age":29, "Interests":["AI", "Music", "Bread"]}, 
 {"Name":"Ify", "Age":27, "Interests":["Marketing", "YouTube", "Shopping"]}]

# a nested dictionary
{"User":{"Name":"Shaw", "Age":29, "Interests":["AI", "Music", "Bread"]}, 
 "Last_login":"2024-09-06", 
 "Membership_Tier":"Free"}

2) Variables

So far, we’ve seen some basic Python data types and operations. However, we are still missing an essential feature: variables.

Variables provide an abstract representation of an underlying data type instance. For example, I might create a variable called user_name, which represents a string containing my name, “Shaw.” This enables us to write flexible programs not limited to specific values.

# creating a variable and printing it
user_name = "Shaw"
print(user_name)

#>> Shaw

We can do the same thing with other data types e.g. ints and lists.

# defining more variables and printing them as a formatted string. 
user_age = 29
user_interests = ["AI", "Music", "Bread"]

print(f"{user_name} is {user_age} years old. His interests include {user_interests}.")

#>> Shaw is 29 years old. His interests include ['AI', 'Music', 'Bread'].

3) Creating Scripts

Now that our example code snippets are getting longer, let’s see how to create our first script. This is how we write and execute more sophisticated programs from the command line.

To do that, create a new folder on your computer. I’ll call mine python-quickstart. If you have a favorite IDE (e.g., the Integrated Development Environment), use that to open this new folder and create a new Python file, e.g., my-script.py. There, we can write the ceremonial “Hello, world” program.

# ceremonial first program
print("Hello, world!")

If you don’t have an IDE (not recommended), you can use a basic text editor (e.g. Apple’s Text Edit, Window’s Notepad). In those cases, you can open the text editor and save a new text file using the .py extension instead of .txt. Note: If you use TextEditor on Mac, you may need to put the application in plain text mode via Format > Make Plain Text.

We can then run this script using the Terminal (Mac/Linux) or Command Prompt (Windows) by navigating to the folder with our new Python file and running the following command.

python my-script.py

Congrats! You ran your first Python script. Feel free to expand this program by copy-pasting the upcoming code examples and rerunning the script to see their outputs.

4) Loops and Conditions

Two fundamental functionalities of Python (or any other programming language) are loops and conditions.

Loops allow us to run a particular chunk of code multiple times. The most popular is the for loop, which runs the same code while iterating over a variable.

# a simple for loop iterating over a sequence of numbers
for i in range(5):
    print(i) # print ith element


# for loop iterating over a list
user_interests = ["AI", "Music", "Bread"]

for interest in user_interests:
    print(interest) # print each item in list


# for loop iterating over items in a dictionary
user_dict = {"Name":"Shaw", "Age":29, "Interests":["AI", "Music", "Bread"]}

for key in user_dict.keys():
    print(key, "=", user_dict[key]) # print each key and corresponding value

The other core function is conditions, such as if-else statements, which enable us to program logic. For example, we may want to check if the user is an adult or evaluate their wisdom.

# check if user is 18 or older
if user_dict["Age"] >= 18:
    print("User is an adult")

# check if user is 1000 or older, if not print they have much to learn
if user_dict["Age"] >= 1000:
    print("User is wise")
else:
    print("User has much to learn")

It’s common to use conditionals within for loops to apply different operations based on specific conditions, such as counting the number of users interested in bread.

# count the number of users interested in bread
user_list = [{"Name":"Shaw", "Age":29, "Interests":["AI", "Music", "Bread"]}, 
             {"Name":"Ify", "Age":27, "Interests":["Marketing", "YouTube", "Shopping"]}]
count = 0 # intialize count

for user in user_list:
    if "Bread" in user["Interests"]:
        count = count + 1 # update count

print(count, "user(s) interested in Bread")

5) Functions

Functions are operations we can perform on specific data types.

We’ve already seen a basic function print(), which is defined for any datatype. However, there are a few other handy ones worth knowing.

# print(), a function we've used several times already
for key in user_dict.keys():
    print(key, ":", user_dict[key])


# type(), getting the data type of a variable
for key in user_dict.keys():
    print(key, ":", type(user_dict[key]))

# len(), getting the length of a variable
for key in user_dict.keys():
    print(key, ":", len(user_dict[key]))
# TypeError: object of type 'int' has no len()

We see that, unlike print() and type(), len() is not defined for all data types, so it throws an error when applied to an int. There are several other type-specific functions like this.

# string methods
# --------------
# make string all lowercase
print(user_dict["Name"].lower())

# make string all uppercase
print(user_dict["Name"].upper())

# split string into list based on a specific character sequence
print(user_dict["Name"].split("ha"))

# replace a character sequence with another
print(user_dict["Name"].replace("w", "whin"))

# list methods
# ------------
# add an element to the end of a list
user_dict["Interests"].append("Entrepreneurship")
print(user_dict["Interests"])

# remove a specific element from a list
user_dict["Interests"].pop(0)
print(user_dict["Interests"])

# insert an element into a specific place in a list
user_dict["Interests"].insert(1, "AI")
print(user_dict["Interests"])

# dict methods
# ------------
# accessing dict keys
print(user_dict.keys())

# accessing dict values
print(user_dict.values())

# accessing dict items
print(user_dict.items())

# removing a key
user_dict.pop("Name")
print(user_dict.items())

# adding a key
user_dict["Name"] = "Shaw"
print(user_dict.items())

While the core Python functions are helpful, the real power comes from creating user-defined functions to perform custom operations. Additionally, custom functions allow us to write much cleaner code. For example, here are some of the previous code snippets repackaged as user-defined functions.

# define a custom function
def user_description(user_dict):
    """
        Function to return a sentence (string) describing input user
    """
    return f'{user_dict["Name"]} is {user_dict["Age"]} years old and is interested in {user_dict["Interests"][0]}.'

# print user description
description = user_description(user_dict)
print(description)

# print description for a new user!
new_user_dict = {"Name":"Ify", "Age":27, "Interests":["Marketing", "YouTube", "Shopping"]}
print(user_description(new_user_dict))

# define another custom function
def interested_user_count(user_list, topic):
    """
        Function to count number of users interested in an arbitrary topic
    """
    count = 0

    for user in user_list:
        if topic in user["Interests"]:
            count = count + 1
    
    return count

# define user list and topic
user_list = [user_dict, new_user_dict]
topic = "Shopping"

# compute interested user count and print it
count = interested_user_count(user_list, topic)
print(f"{count} user(s) interested in {topic}")

6) Libraries, pip, & venv

Although we could implement an arbitrary program using core Python, this can be incredibly time-consuming for some use cases. One of Python’s key benefits is its vibrant developer community and a robust ecosystem of software packages. Almost anything you might want to implement with core Python (probably) already exists as an open-source library.

We can install such packages using Python’s native package manager, pip. To install new packages, we run pip commands from the command line. Here is how we can install numpy, an essential data science library that implements basic mathematical objects and operations.

pip install numpy

After we’ve installed numpy, we can import it into a new Python script and use some of its data types and functions.

import numpy as np

# create a "vector"
v = np.array([1, 3, 6])
print(v)

# multiply a "vector"
print(2*v)

# create a matrix
X = np.array([v, 2*v, v/2])
print(X)

# matrix multiplication
print(X*v)

The previous pip command added numpy to our base Python environment. Alternatively, it’s a best practice to create so-called virtual environments. These are collections of Python libraries that can be readily interchanged for different projects.

Here’s how to create a new virtual environment called my-env.

python -m venv my-env

Then, we can activate it.

# mac/linux
source my-env/bin/activate

# windows
.my-envScriptsactivate.bat

Finally, we can install new libraries, such as numpy, using pip.

pip install pip

Note: If you’re using Anaconda, check out this handy cheatsheet for creating a new conda environment.

Several other libraries are commonly used in AI and data science. Here is a non-comprehensive overview of some helpful ones for building AI projects.

Example Code: Extracting summary and keywords from research papers

Now that we have been exposed to the basics of Python, let’s see how we can use it to implement a simple AI project. Here, I will use the OpenAI API to create a research paper summarizer and keyword extractor.

Like all the other snippets in this guide, the example code is available at the GitHub repository.

Install Dependencies

We start by installing a few helpful libraries. You can use the same my-env environment we created earlier or make a new one. Then, you can install all the required packages using the requirements.txt file from the {GitHub repo}.

pip install -r requirements.txt

This line of code scans each library listed in requirements.txt and installs each.

Imports

Next, we can create a new Python script and import the needed libraries.

import fitz  # PyMuPDF
import openai
import sys

Next, to use OpenAI’s Python API, we will need to import an AI key. Here’s one way to do that.

from sk import my_sk

# Set up your OpenAI API key
openai.api_key = my_sk

Note that sk is not a Python library. Rather, it is a separate Python script that defines a single variable, my_sk, which is a string consisting of my OpenAI API key i.e. a unique (and secret) token allowing one to use OpenAI’s API.

I shared a beginner-friendly introduction to APIs, OpenAI’s API, and setting up an API key in a previous article.

Cracking Open the OpenAI (Python) API

Read PDF

Next, we will create a function that, given the path to a research paper saved as a .pdf file, will extract the abstract from the paper.

# Function to read the first page of a PDF and extract the abstract
def extract_abstract(pdf_path):

    # Open the PDF file and grab text from the 1st page
    with fitz.open(pdf_path) as pdf:
        first_page = pdf[0]
        text = first_page.get_text("text")
    
    # Extract the abstract (assuming the abstract starts with 'Abstract')
    
    # find where abstract starts
    start_idx = text.lower().find('abstract') 
    
    # end abstract at introduction if it exists on 1st page
    if 'introduction' in text.lower():
        end_idx = text.lower().find('introduction')  
    else:
        end_idx = None
    
    # extract abstract text
    abstract = text[start_idx:end_idx].strip()

    # if abstract appears on 1st page return it, if not resturn None
    if start_idx != -1:
        abstract = text[start_idx:end_idx].strip()
        return abstract
    else:
        return None

Summarize with LLM

Now that we have our abstract text, we can use an LLM to summarize it and generate keywords. Here, I define a function to pass an abstract to OpenAI’s GPT-4o-mini model to do this.

# Function to summarize the abstract and generate keywords using OpenAI API
def summarize_and_generate_keywords(abstract):

    # Use OpenAI Chat Completions API to summarize and generate keywords
    prompt = f"Summarize the following paper abstract and generate (no more than 5) keywords:nn{abstract}"
    
    # make api call
    response = openai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ], 
        temperature = 0.25
    )
    
    # extract response
    summary = response.choices[0].message.content
    return summary

Bring it all together

Finally, we can use our user-defined functions to generate summaries and keywords for any research paper passed to the program from the command line.

# Get the PDF path from the command-line arguments
pdf_path = sys.argv[1]

# Extract abstract from the PDF
abstract = extract_abstract(pdf_path)

# if abstract exists on first page, print summary.
if abstract:
    # Summarize and generate keywords
    summary = summarize_and_generate_keywords(abstract)
    
    print(summary)
else:
    print("Abstract not found on the first page.")

We can then execute our program from the command line.

python summarize-paper.py "files/attention-is-all-you-need.pdf"

Output:
The paper introduces the Transformer, a novel network architecture for sequence
transduction tasks that relies solely on attention mechanisms, eliminating the 
need for recurrent and convolutional structures. The Transformer demonstrates 
superior performance in machine translation tasks, achieving a BLEU score of 
28.4 on the WMT 2014 English-to-German translation and a state-of-the-art 
score of 41.8 on the English-to-French translation task, while also being more 
efficient in training time. Additionally, the Transformer shows versatility by 
successfully applying to English constituency parsing with varying amounts of 
training data.

**Keywords:** Transformer, attention mechanisms, machine translation, 
BLEU score, neural networks.

YouTube-Blog/python-quickstart at main · ShawhinT/YouTube-Blog

What’s Next?

Here, we covered some Python fundamentals and implemented our first AI project! Although we covered a lot, there is still much more to learn.

The next step here is to implement your own AI project. This is the best way to keep learning. However, before we go, here are some tips.

• Use Google and ChatGPT generously whenever you get stuck
• “Figuring it out” is a key skill you must develop as a programmer
• Check out the data scientist’s favorite tool: Jupyter Notebooks
• Hack the example from this guide to get started!

My website: https://www.shawhintalebi.com/

[1] AI Python for Beginners

Python QuickStart for People Learning AI was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.