Python Mastery: From Beginner to Expert - Sykalo Eugene 2023
Modules
Functions and modules
Importing modules
One of the most powerful features of Python is its ability to import modules. A module is essentially a file containing Python definitions and statements. By importing a module, we can reuse code and organize our code logically. There are different ways to import modules, and we will explore each one of them.
Importing an entire module
The most straightforward way to import a module is to use the import statement followed by the name of the module. For example, to import the math module, we can use the following code:
import math
This statement imports the entire math module, which contains a collection of mathematical functions and constants. We can use the functions and constants defined in the math module by prefixing them with the module name, like this:
import math
print(math.pi) # Output: 3.141592653589793
print(math.sqrt(16)) # Output: 4.0
Importing specific objects from a module
Sometimes, we may only need to use a specific function or variable from a module. In such cases, we can import only the required objects from the module using the from keyword. For example, to import only the sqrt function from the math module, we can use the following code:
from math import sqrt
print(sqrt(16)) # Output: 4.0
This statement imports only the sqrt function from the math module, which means we can directly use the function without prefixing it with the module name.
Renaming imported objects
We can also rename the imported objects using the as keyword. This is useful when we want to give a shorter or more meaningful name to the imported object. For example, to import the sqrt function from the math module and rename it to square_root, we can use the following code:
from math import sqrt as square_root
print(square_root(16)) # Output: 4.0
Importing all objects from a module
It is also possible to import all objects from a module using the * wildcard. For example, to import all objects from the math module, we can use the following code:
from math import *
print(pi) # Output: 3.141592653589793
print(sqrt(16)) # Output: 4.0
However, it is generally not recommended to use the * wildcard, as it can make the code harder to read and maintain. It is usually better to import only the required objects explicitly.
In summary, there are different ways to import modules in Python, depending on our needs. We can import an entire module, import specific objects from a module, rename the imported objects, or import all objects from a module using the * wildcard. Knowing these techniques will allow us to use modules effectively in our Python programs.
Standard library modules
Python comes with a vast collection of standard library modules that provide a wide range of functionality that can be used in our programs without the need for any external dependencies. In this section, we will discuss some of the most commonly used standard library modules.
math
The math module provides a collection of mathematical functions and constants that we can use in our programs. Some of the commonly used functions include sqrt, log, exp, sin, cos, and tan. We can also use the pi and e constants defined in the math module.
import math
print(math.sqrt(16)) # Output: 4.0
print(math.log(10)) # Output: 2.302585092994046
print(math.pi) # Output: 3.141592653589793
os
The os module provides a way to interact with the underlying operating system. It provides functions for working with files and directories, accessing environment variables, and managing processes.
import os
print(os.getcwd()) # Output: /home/user
os.chdir('/path/to/new/directory')
print(os.listdir()) # Output: ['file1.txt', 'file2.txt', 'file3.txt']
sys
The sys module provides access to some variables and functions that interact with the Python interpreter. It provides functions to get and set the system path, access the standard input/output/error streams, and interact with the Python runtime environment.
import sys
print(sys.version) # Output: 3.9.7 (default, Sep 3 2021, 11:13:57)
print(sys.path) # Output: ['/path/to/script', '/usr/lib/python39.zip', ...]
datetime
The datetime module provides classes for working with dates and times. It provides classes for representing dates, times, date-times, and time intervals. It also provides functions for working with time zones and time deltas.
from datetime import datetime
now = datetime.now()
print(now) # Output: 2021-09-07 14:13:44.123456
print(now.year) # Output: 2021
print(now.month) # Output: 9
In summary, Python's standard library modules provide a wide range of functionality that can be used in our programs without requiring any external dependencies. The math, os, sys, and datetime modules are just a few examples of the many modules available in the standard library.
Third-party modules
In addition to the standard library modules, Python has a vast collection of third-party modules that we can use in our programs. Third-party modules are created by developers outside of the Python core development team and are not included in the standard Python distribution. These modules can provide additional functionality or extend the functionality provided by the standard library modules.
There are many third-party modules available for Python, and they can be installed using a package manager such as pip, which is included with Python by default. To install a third-party module using pip, we can use the following command in the command line:
pip install <module-name>
Once a third-party module is installed, we can import it into our Python programs and use it just like any other module. For example, let's say we want to use the requests module, which provides a way to send HTTP requests using Python. We can install the requests module using the following command:
pip install requests
Once the module is installed, we can import it into our Python program and use it like this:
import requests
response = requests.get('<https://www.google.com>')
print(response.status_code) # Output: 200
In this example, we import the requests module and use the get function provided by the module to send an HTTP GET request to Google's homepage. The response variable contains the response returned by the server, and we can access the status_code attribute of the response to check if the request was successful.
Some popular third-party modules for Python include:
numpy: A library for working with arrays and matrices in Python.pandas: A library for working with data in Python, particularly for data analysis and manipulation.matplotlib: A library for creating visualizations and plots in Python.scikit-learn: A library for machine learning and data mining in Python.django: A web framework for building web applications in Python.
In summary, third-party modules are a valuable resource for Python programmers, as they provide additional functionality that can be used to extend the capabilities of Python. By using a package manager like pip, we can easily install and use third-party modules in our Python programs.
Creating our own modules
In addition to using Python's built-in modules and third-party modules, we can also create our own modules in Python. A module is simply a Python file that contains definitions of functions, classes, and variables. We can then use these definitions in our Python programs by importing the module.
To create a module, we simply need to create a new Python file with a .py extension and define our functions, classes, and variables in the file. For example, let's create a module called my_module with a function add_numbers:
# Filename: my_module.py
def add_numbers(a, b):
return a + b
Now, we can use the add_numbers function in our Python program by importing the my_module module:
import my_module
result = my_module.add_numbers(3, 4)
print(result) # Output: 7
Note that we use the import statement to import the my_module module. We can then use the add_numbers function by prefixing it with the module name my_module.
We can also import specific functions or variables from a module using the from keyword. For example, to import only the add_numbers function from the my_module module, we can use the following code:
from my_module import add_numbers
result = add_numbers(3, 4)
print(result) # Output: 7
This statement imports only the add_numbers function from the my_module module, which means we can directly use the function without prefixing it with the module name.
In addition to functions and variables, we can also define classes in a module. For example, let's define a class Person in a module called person_module:
# Filename: person_module.py
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def get_info(self):
return f'{self.name} is {self.age} years old.'
Now, we can use the Person class in our Python program by importing the person_module module:
import person_module
person = person_module.Person('John', 30)
print(person.get_info()) # Output: John is 30 years old.
In this example, we import the Person class from the person_module module and create a new Person object with the name "John" and age 30. We then call the get_info method on the Person object to get a string representation of the object.
In summary, we can create our own modules in Python by defining functions, classes, and variables in a Python file with a .py extension. We can then use these definitions in our Python programs by importing the module.
Using modules in packages
In addition to organizing our code into modules, we can also organize our modules into packages to make our code more modular and reusable. A package is simply a directory containing a collection of modules that are related to each other in some way. By organizing our modules into packages, we can group related functionality together and make it easier to reuse our code in other projects.
Creating a package
To create a package, we need to create a directory with a special file called __init__.py. The __init__.py file tells Python that the directory should be treated as a package and can contain initialization code for the package.
For example, let's say we want to create a package called my_package that contains two modules, module1 and module2. We can create the following directory structure:
my_package/
__init__.py
module1.py
module2.py
The __init__.py file can be empty, or it can contain initialization code for the package. For example, we can define variables or functions in the __init__.py file that are used by the modules in the package.
Importing modules from a package
Once we have created a package, we can import modules from the package using the dot notation. For example, to import the module1 module from the my_package package, we can use the following code:
import my_package.module1
result = my_package.module1.add_numbers(3, 4)
print(result) # Output: 7
This statement imports the module1 module from the my_package package and uses the add_numbers function defined in the module.
We can also use the from keyword to import specific objects from a module in the package. For example, to import only the add_numbers function from the module1 module, we can use the following code:
from my_package.module1 import add_numbers
result = add_numbers(3, 4)
print(result) # Output: 7
This statement imports only the add_numbers function from the module1 module in the my_package package, which means we can directly use the function without prefixing it with the module name.
Importing all modules from a package
We can also import all modules from a package using the * wildcard. For example, to import all modules from the my_package package, we can use the following code:
from my_package import *
result = module1.add_numbers(3, 4)
print(result) # Output: 7
This statement imports all modules from the my_package package, which means we can directly use the functions and variables defined in the modules without prefixing them with the module name. However, it is generally not recommended to use the * wildcard, as it can make the code harder to read and maintain.
In summary, organizing our code into packages can make our code more modular and reusable. By creating a package and organizing related modules into the package, we can group related functionality together and make it easier to reuse our code in other projects. We can import modules from a package using the dot notation, import specific objects from a module in the package using the from keyword, or import all modules from a package using the * wildcard.
Best practices for using modules
When using modules in our Python programs, it is important to follow some best practices to make our code more maintainable and reusable. The following are some of the best practices for using modules in Python:
Use descriptive module names
When naming our modules, we should use descriptive names that reflect the functionality provided by the module. This makes it easier for other developers to understand what the module does and how it can be used. For example, if we create a module that provides functions for working with strings, we could name the module string_utils.
Follow naming conventions
Python has a set of naming conventions for modules, functions, and variables. For modules, the convention is to use all lowercase letters and separate words with underscores. For example, string_utils is a valid module name following this convention. For functions and variables, the convention is to use lowercase letters and separate words with underscores. For example, calculate_average is a valid function name following this convention.
By following these naming conventions, our code will be easier to read and understand for other developers.
Document the module
When creating a module, we should provide documentation that explains what the module does, how it can be used, and what functions and variables are provided by the module. This documentation can be included in the module file itself or in a separate documentation file. By providing clear documentation, other developers can understand how to use the module and what functionality it provides.
Avoid circular dependencies
Circular dependencies occur when two or more modules depend on each other, either directly or indirectly. This can cause problems when importing the modules, as Python may not be able to resolve the dependencies correctly. To avoid circular dependencies, we should design our modules to have clear boundaries and avoid importing modules that depend on each other.
Use virtual environments
Virtual environments are a way to isolate our Python environment and dependencies from other projects on the same system. By using virtual environments, we can ensure that our Python programs use the correct versions of the required modules and dependencies. This can prevent conflicts between different Python programs and make it easier to manage dependencies.
Use version control
Version control is a way to track changes to our code over time and collaborate with other developers. By using version control, we can keep track of changes to our modules and ensure that our code is always up-to-date. We can also collaborate with other developers by sharing our code on a version control platform such as GitHub.
By following these best practices, we can create maintainable and reusable Python code that can be easily understood and modified by other developers.