Many computers come with Python pre-installed. To see if your machine has it, go to the 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/ Waterproof). Alternatively, you can install Anacondaa popular Python packaging system for ai and data science. If you have installation issues, Ask your favorite ai assistant for help!
With Python running, we can now start writing code. I recommend running the examples on your computer as we go along.You can also download all the sample code from GitHub repository.
Chains and numbers
TO data type (or simply “type”) is a way of classifying data so that it can be processed appropriately and efficiently on 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!"""
>> 'and\n this is\n too!!11!'
"we can even " + "add strings together"
>> 'we can even add strings together'
Although strings can be added together (i.e. concatenated), they cannot be added together. numeric data types as int (i.e. integers) either float (i.e. numbers with decimals)If we try that in Python, we will get an error message because the 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 and dictionaries
Beyond the basic types of strings, integers, and floats, Python has types for structuring larger collections of data.
A guy like this is a lista ordered collection of valuesWe 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 fundamental type of data is a dictionarywhich consists of key-value pair sequences where Keys are strings and Values can be of 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"}
So far we have seen some basic Python data types and operations. However, we are still missing one essential feature: variables.
Variables provide an abstract representation of an underlying data type instanceFor example, I could create a variable called username, which represents a string containing my name, “Shaw.” This allows us to write flexible programs that aren’t limited to specific values.
# creating a variable and printing it
user_name = "Shaw"
print(user_name)#>> Shaw
We can do the same with other data types, for example, integers 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').
Now that our example code snippets are getting longer, let's look at how to create our first script. Here's how we do it write and run more sophisticated programs from the command line.
To do this, create a new folder on your computer. I'll call it mine. Python Quick Start Guide. If you have a favorite IDE (for example, integrated development environment)Use it to open this new folder and create a new Python file, for example, 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 (for example, Apple's Text Edit or Windows 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 a Mac, you may need to put the application into plain text mode via Format > Convert to 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
Congratulations! You've run your first Python script. Feel free to Expand this program by copying and pasting the following code examples and rerunning the script. to see your results.
Two fundamental features of Python (or any other programming language) are loops and conditionalities.
Loops Allow us execute a particular piece of code multiple timesThe most popular is the for loopwhich executes 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 main function is conditionslike if-else statements, which Let us program the logicFor example, we might want to check whether the user is an adult or assess 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 is common Use conditionals inside for loops 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 countfor user in user_list:
if "Bread" in user("Interests"):
count = count + 1 # update count
print(count, "user(s) interested in Bread")
Functions are Operations we can perform on specific data types.
We have already seen a basic function print()which is defined for any type of data. However, there are other useful functions that are 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 guy(), long() 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 Python's core functions are useful, the real power comes from creating user defined functions to perform custom operationsAdditionally, custom functions allow us to write much cleaner code. For example, here are some of the code snippets above 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 = 0for 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}")
While we could implement an arbitrary program using basic Python, this can be extremely time-consuming for some use cases. One of the key benefits of Python is its A vibrant developer community and a robust software package ecosystemAlmost everything you 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, pipTo install new packages, we run pip commands from the command line. Here is how we can install Numerous, An essential data science library that implements basic mathematical operations and objects.
pip install numpy
After installing 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 pip command above added numpy to our base Python environment. Alternatively, it is good practice to create so-called virtual environmentsThese are collections of Python libraries that can be easily 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-env\Scripts\activate.bat
Finally, we can install new libraries, such as numpy, using pip.
pip install pip
Note: If you are using Anaconda, please refer to this Practical Cheat Sheet to create a new conda environment.
Other libraries are commonly used in ai and data science. Here is one Non-exhaustive overview of some useful tools for creating ai projects.
Now that we know 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 other snippets in this guide, the sample code is available at GitHub repository.
