Python Modules

Learn Programming for Data Science

Keywords

python modules, math module, json module, date time module, random module, what is a python module, why use modules

Photo by DATAIDEA

Modules

A module in Python is a python file that can contain variables, functions and classes

Why use Modules?

Modules allow us to split our code into multiple files

Instead of writing all our codes inside a sigle Python file, we can use modules

Note!

That way, our code will be easier to read, understand and maintain

Creating a Module

There is nothing so special with creating a module, simply write you Python code and save it with the .py extension.

In this example, we have a module saved as my_module.py and it contains the following code

# this is my_module.py

first_name = 'Viola'
last_name = 'Akullu'

def add(number1, number2):
    return number1 + number2

def multiply(number1, number2):
    return number1 * number2

After that, to use my_module.py, we need to import it.

To import, use the import statement and the module name.

Then we can use the variables and functions in the module.

In this example, the code below is saved as main_code.py and it imports the module.py.

# this is main_code.py

import my_module

full_name = my_module.first_name + my_module.last_name
print('Full name:', full_name)

summation = my_module.add(3, 7)
print('Summation:', summation)

Full name: ViolaAkullu
Summation: 10

Don’t Miss Any Updates!

Before we continue, we have a humble request, to be among the first to hear about future updates of the course materials, simply enter your email below, follow us on (formally Twitter), or subscribe to our YouTube channel.

Using Aliases

We can use an alias to refer to the module

To use an alias, use the as keyword

# this is main_code.py

import my_module as mm

full_name = mm.first_name + mm.last_name
print('Full name:', full_name)

summation = mm.add(3, 7)
print('Summation:', summation)

Full name: ViolaAkullu
Summation: 10

Importing Parts of a Module

We can choose to import only some specific parts of a module

Note! When we import a part of a module, we will be able to use its variables and functions directly

Use the from keyword to import a part of a module.

In this example, we will import the first_name variable and access it directly

from my_module import first_name

# now we can use it directly as 
print(first_name)

Viola

The dir() Function

The dir() function returns a list of all the variables, functions and classes available in a module

import my_module

dir_ = dir(my_module)

print(dir_)

['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'add', 'first_name', 'last_name', 'multiply']

Built in Modules

Python has many useful built-in modules that we can use to make coding easier.

Built-in modules can be imported without having to create them

In this example, we will import the sysconfig module and use its get_python_version() to return the Python version we’re using

import sysconfig

python_version = sysconfig.get_python_version()
print(python_version)

3.10

Math Module

The math module gives us access to mathematical functions

To use the math module, import it first, then we can start using it.

We can use the math module to find the square root of a number using the math.sqrt() method

import math

number = 16
number_sqrt = math.sqrt(number)

print('Number:', number)
print('Square root of number:', number_sqrt)

Number: 16
Square root of number: 4.0

We can use the math module to get the factorial of a number by using the math.factorial() method

import math

number = 5
number_factorial = math.factorial(number)

print('Number:', number)
print('Factorial:', number_factorial)

Number: 5
Factorial: 120

The math module also contains some constants like pi and e

import math

print('e:', math.e)
print('pi:', math.pi)

e: 2.718281828459045
pi: 3.141592653589793

The math module can do those and so much more

Random Module

The random module lets us generate a random number

As usual, to use the random module, import it first.

We can generate a random number that falls within a specified range by using the random.randint() method

import random

random_integer = random.randint(1,100)
print('Random Integer:', random_integer)

Random Integer: 4

We can generate numbers from a gaussian distribution with mean (mu) as 0 and standard deviation (sigma) as 1

numbers = []

counter = 0
while counter < 100:
    numbers.append(random.gauss(mu=0, sigma=1))
    counter += 1
    
print(numbers)

[0.8310128735410047, 2.402375340413018, -1.2769617295659348, 0.7569506717477539, 1.6026026122392498, 1.4142936594217554, -0.3169917649104485, -0.07305941097531603, -0.7885301448554015, -0.0674611332298377, 0.28288857512573684, 0.08844216926370602, -1.249987094506388, 0.870793290313952, -0.6607737394803138, 0.3780605189691181, 0.20288623881856632, 0.8439702923769746, 1.6500270929422152, -0.5579247768953991, -0.3076290349937902, 0.8927675985413197, -2.3716599434459114, 0.23253728473684382, 0.01698634011714592, -1.506684284668113, -1.516156046117149, -0.7549199652372819, 0.4855840249497611, -1.9426218553454226, -0.5672748318805165, 1.7849639815888045, -0.4223703532919884, -1.4182523392919628, 0.3817982448773813, -1.2151583559744263, 0.21736913499460964, 0.0743448686041854, -0.6217874541247053, -0.05369712902089164, 0.06560332100098984, 0.5791279113149166, 1.5329264216964942, -1.5523813284095307, 0.256018716284597, 1.498941708596562, 0.6484203278916434, 0.956658998431066, -0.7469607705965761, 0.9093585267915438, -0.3301676177291813, -2.1020486475752564, -0.6324768823835674, -0.2621489739923403, 0.36805271395009337, -0.1987104858441708, -0.20226660046300027, -1.0227302328088852, 0.9440428943259802, 1.3499647213634605, 0.28655811659281705, -0.48212404896946465, 1.5732404576352244, 1.7024230857294205, -0.32802550098029193, 2.0808443667109597, 2.2783854541239874, -0.265626754707208, -0.04641950638081212, 0.7941371582079103, -0.36860553191079254, -0.9098450679735101, 1.234946260813307, -2.835066105841072, 1.3883254119625694, 1.2853299658795028, 1.178005875662903, 0.3186472037221876, -1.0006920744966419, -2.3745959188263885, 1.8440465299894964, -0.35610549619690796, 0.5857012223823791, 0.7400382246661824, 0.07225122970263118, -0.5508995490344698, -0.038356750477046286, -0.040997463659922434, 0.6802546773316889, -1.3861271290488735, 0.7275261286416534, 0.3729374034245036, -0.013616473457934613, -0.7620103036607296, 0.15556952852877587, -1.7898533901375224, -1.137248630020012, -1.71518120153122, -0.5817297506694047, -0.4035542913039588]

Date and Time

The datetime module allows us to work with dates

As usual we have to import the datetime module to be able to use it.

Current Date and Time

The datetime.datetime.now() method returns the current date and time

import datetime

time_now = datetime.datetime.now()
print(time_now)

2024-05-01 08:18:09.070054

The date Object

The date object represents a date (year, month and day)

To create a date object, import it from the datetime module first.

from datetime import date

today = date.today()
print('Current date:', today)

Current date: 2024-05-01

JSON

JSON stands for JavaScript Object Notation.

JSON contains data that are sent or received to and from a server

JSON is simply a string, if follows a format similar to a Python dictionary

Example:

data = "{'first_name': 'Juma','last_name': 'Shafara', 'age': 39}"

print(data)

{'first_name': 'Juma','last_name': 'Shafara', 'age': 39}

JSON to Dictionary

Before we can individually access the data of a JSON, we need to convert it to a Python dictionary first.

To do that, we need to import the json module

import json 

data = '{"first_name": "Juma","last_name": "Shafara", "age": 39}'

# convert to dictionary
data_dict = json.loads(data)

print('Fist name:',data_dict['first_name'])
print('Last name:', data_dict['last_name'])
print('Age:', data_dict['age'])

Fist name: Juma
Last name: Shafara
Age: 39

Dictionary to JSON

To convert a dictionay to JSON, use the json.dumps() method.

import json

data_dict = {
    "first_name": "Juma",
    "last_name": "Shafara", 
    "age": 39
    }

data_json = json.dumps(data_dict)

What’s on your mind? Put it in the comments!