Building Dictionary Comprehensions in Python

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PyViz: Python Tools for Data Visualization

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Building AI Agents With Your Enterprise Data: A Developer’s Guide

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Catching Memory Leaks With Your Test Suite

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10 Ways to Work With Large Files in Python

“Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge.
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Investigating the Popularity of Python Build Backends

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Testing Your Python Package Releases

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How I Automated Data Cleaning in Python

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The “Active Enum” Pattern

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A Guide to JAX for PyTorch Developers

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Creating a Fitness Tracker App With Python Reflex

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Database Indexing in Django

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Python’s range() Function

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Getter and setter methods allow you to access and mutate non-public attributes while maintaining encapsulation. In Python, you’ll typically expose attributes as part of your public API and use properties when you need attributes with functional behavior. This tutorial guides you through writing getter and setter methods, replacing them with properties, and exploring alternatives like descriptors for optimized attribute management.

By the end of this tutorial, you’ll understand that:

• Getter and setter methods allow you to access and modify data attributes while maintaining encapsulation.
• Python properties can replace getters and setters, providing a more Pythonic way to manage attributes with functional behavior.
• You typically avoid using getters and setters in Python unless necessary, as properties and descriptors offer more flexible solutions.
• Descriptors are an advanced Python feature that enable reusable attributes with attached behaviors across different classes.
• In some scenarios, inheritance limitations may make traditional getters and setters preferable over properties.

Even though properties are the Pythonic way to go, they can have some practical drawbacks. Because of this, you’ll find some situations where getters and setters are preferable over properties. This tutorial will delve into the nuances of using different approaches, equipping you with the knowledge to make informed decisions about attribute management in your classes.

To get the most out of this tutorial, you should be familiar with Python object-oriented programming. It’ll also be a plus if you have basic knowledge of Python properties and descriptors.

Take the Quiz: Test your knowledge with our interactive “Getters and Setters: Manage Attributes in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Getters and Setters: Manage Attributes in Python

In this quiz, you'll test your understanding of Python's getter and setter methods, as well as properties. You'll learn when to use these tools and how they can help maintain encapsulation in your classes.

Getting to Know Getter and Setter Methods

When you define a class in object-oriented programming (OOP), you’ll likely end up with some instance and class attributes. These attributes are just variables that you can access through the instance, the class, or both.

Attributes hold the internal state of objects. In many cases, you’ll need to access and mutate this state, which involves accessing and mutating the attributes. Typically, you’ll have at least two ways to access and mutate attributes. You can either:

1. Access and mutate the attribute directly
2. Use methods to access and mutate the attribute

If you expose the attributes of a class to your users, then those attributes automatically become part of the class’s public API. They’ll be public attributes, which means that your users will directly access and mutate the attributes in their code.

Having an attribute that’s part of a class’s API will become a problem if you need to change the internal implementation of the attribute itself. A clear example of this issue is when you want to turn a stored attribute into a computed one. A stored attribute will immediately respond to access and mutation operations by just retrieving and storing data, while a computed attribute will run computations before such operations.

The problem with regular attributes is that they can’t have an internal implementation because they’re just variables. So, changing an attribute’s internal implementation will require converting the attribute into a method, which will probably break your users’ code. Why? Because they’ll have to change attribute access and mutation operations into method calls throughout their codebase if they want the code to continue working.

To deal with this kind of issue, some programming languages, like Java and C++, require you to provide methods for manipulating the attributes of your classes. These methods are commonly known as getter and setter methods. You can also find them referred to as accessor and mutator methods.

What Are Getter and Setter Methods?

Getter and setter methods are quite popular in many object-oriented programming languages. So, it’s pretty likely that you’ve heard about them already. As a rough definition, you can say that getters and setters are:

• Getter: A method that allows you to access an attribute in a given class
• Setter: A method that allows you to set or mutate the value of an attribute in a class

In OOP, the getter and setter pattern suggests that public attributes should be used only when you’re sure that no one will ever need to attach behavior to them. If an attribute is likely to change its internal implementation, then you should use getter and setter methods.

Implementing the getter and setter pattern requires:

1. Making your attributes non-public
2. Writing getter and setter methods for each attribute

For example, say that you need to write a Label class with text and font attributes. If you were to use getter and setter methods to manage these attributes, then you’d write the class like in the following code:

class Label:
    def __init__(self, text, font):
        self._text = text
        self._font = font

    def get_text(self):
        return self._text

    def set_text(self, value):
        self._text = value

    def get_font(self):
        return self._font

    def set_font(self, value):
        self._font = value

In this example, the constructor of Label takes two arguments, text and font. These arguments are stored in the ._text and ._font non-public instance attributes, respectively.

Then you define getter and setter methods for both attributes. Typically, getter methods return the target attribute’s value, while setter methods take a new value and assign it to the underlying attribute.

In Python, constants are identifiers for values that don’t change during a program’s execution. Unlike some other languages, Python lacks built-in syntax for constants, treating them as variables that should remain unchanged. You define constants by following a naming convention: use all uppercase letters with underscores separating words. This signals to other developers that these variables should not be reassigned.

By the end of this tutorial, you’ll understand that:

• Constants in Python are variables that should remain unchanged throughout execution.
• Python lacks built-in syntax for constants, relying on conventions to signal immutability.
• Defining a constant involves using uppercase letters with underscores for clarity.
• Best practices include defining constants at the top of a file and using descriptive names.
• Built-in constants like math.pi offer predefined, reliable values, unlike user-defined ones.

To learn the most from this tutorial, you’ll need basic knowledge of Python variables, functions, modules, packages, and namespaces. You’ll also need to know the basics of object-oriented programming in Python.

Understanding Constants and Variables

Variables and constants are two historical and fundamental concepts in computer programming. Most programming languages use these concepts to manipulate data and work in an effective and logical fashion.

Variables and constants will probably be present in each project, app, library, or other piece of code that you’ll ever write. The question is: what are variables and constants in practice?

What Variables Are

In math, a variable is defined as a symbol that refers to a value or quantity that can change over time. In programming, a variable is also a symbol or name typically associated with a memory address containing a value, object, or piece of data. Like in math, the content of a programming variable can change during the execution of the code that defines it.

Variables typically have a descriptive name that’s somehow associated with a target value or object. This target value can be of any data type. So, you can use variables to represent numbers, strings, sequences, custom objects, and more.

You can perform two main operations on a variable:

1. Access its value
2. Assign it a new value

In most programming languages, you can access the value associated with a variable by citing the variable’s name in your code. To assign a new value to a given variable, you’ll use an assignment statement, which often consists of the variable’s name, an assignment operator, and the desired value.

In practice, you’ll find many examples of magnitudes, data, and objects that you can represent as variables. A few examples include temperature, speed, time, and length. Other examples of data that you can treat as variables include the number of registered users in a web app, the number of active characters in a video game, and the number of miles covered by a runner.

What Constants Are

Math also has the concept of constants. The term refers to a value or quantity that never changes. In programming, constants refer to names associated with values that never change during a program’s execution.

Just like variables, programming constants consist of two things: a name and an associated value. The name will clearly describe what the constant is all about. The value is the concrete expression of the constant itself.

Like with variables, the value associated with a given constant can be of any of data type. So, you can define integer constants, floating-point constants, character constants, string constants, and more.

After you’ve defined a constant, it’ll only allow you to perform a single operation on it. You can only access the constant’s value but not change it over time. This is different from a variable, which allows you to access its value, but also reassign it.

You’ll use constants to represent values that won’t change. You’ll find lots of these values in your day-to-day programming. A few examples include the speed of light, the number of minutes in an hour, and the name of a project’s root folder.

Why Use Constants

In most programming languages, constants protect you from accidentally changing their values somewhere in the code when you’re coding at two in the morning, causing unexpected and hard-to-debug errors. Constants also help you make your code more readable and maintainable.

Some advantages of using constants instead of using their values directly in your code include:

Creating a class constructor in Python involves understanding the instantiation process, which consists of two steps: instance creation and instance initialization. You start this process by calling the class like a function, which triggers the .__new__() method to create an instance and the .__init__() method to initialize it. Mastering these methods allows you to customize how Python constructs and initializes objects of your classes.

By the end of this tutorial, you’ll understand that:

• A class constructor in Python triggers the instantiation process, creating and initializing objects.
• Python handles instantiation internally with .__new__() for creation and .__init__() for initialization.
• You can customize object initialization by overriding the .__init__() method in your class.
• The difference between .__new__() and .__init__() is that .__new__() creates the instance, while .__init__() initializes it.
• Common use cases for overriding .__new__() include subclassing immutable types or implementing singletons.

To better understand the examples and concepts in this tutorial, you should be familiar with object-oriented programming and special methods in Python.

Take the Quiz: Test your knowledge with our interactive “Python Class Constructors: Control Your Object Instantiation” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Python Class Constructors: Control Your Object Instantiation

In this quiz, you'll test your understanding of class constructors in Python. By working through this quiz, you'll revisit the internal instantiation process, object initialization, and fine-tuning object creation.

Python’s Class Constructors and the Instantiation Process

Like many other programming languages, Python supports object-oriented programming. At the heart of Python’s object-oriented capabilities, you’ll find the class keyword, which allows you to define custom classes that can have attributes for storing data and methods for providing behaviors.

Once you have a class to work with, then you can start creating new instances or objects of that class, which is an efficient way to reuse functionality in your code.

Creating and initializing objects of a given class is a fundamental step in object-oriented programming. This step is often referred to as object construction or instantiation. The tool responsible for running this instantiation process is commonly known as a class constructor.

Getting to Know Python’s Class Constructors

In Python, to construct an object of a given class, you just need to call the class with appropriate arguments, as you would call any function:

>>> class SomeClass:
...     pass
...

>>> # Call the class to construct an object
>>> SomeClass()
<__main__.SomeClass object at 0x7fecf442a140>

In this example, you define SomeClass using the class keyword. This class is currently empty because it doesn’t have attributes or methods. Instead, the class’s body only contains a pass statement as a placeholder statement that does nothing.

Then you create a new instance of SomeClass by calling the class with a pair of parentheses. In this example, you don’t need to pass any argument in the call because your class doesn’t take arguments yet.

In Python, when you call a class as you did in the above example, you’re calling the class constructor, which creates, initializes, and returns a new object by triggering Python’s internal instantiation process.

A final point to note is that calling a class isn’t the same as calling an instance of a class. These are two different and unrelated topics. To make a class’s instance callable, you need to implement a .__call__() special method, which has nothing to do with Python’s instantiation process.

Understanding Python’s Instantiation Process

You trigger Python’s instantiation process whenever you call a Python class to create a new instance. This process runs through two separate steps, which you can describe as follows:

1. Create a new instance of the target class
2. Initialize the new instance with an appropriate initial state

To run the first step, Python classes have a special method called .__new__(), which is responsible for creating and returning a new empty object. Then another special method, .__init__(), takes the resulting object, along with the class constructor’s arguments.

The .__init__() method takes the new object as its first argument, self. Then it sets any required instance attribute to a valid state using the arguments that the class constructor passed to it.

In short, Python’s instantiation process starts with a call to the class constructor, which triggers the instance creator, .__new__(), to create a new empty object. The process continues with the instance initializer, .__init__(), which takes the constructor’s arguments to initialize the newly created object.

To explore how Python’s instantiation process works internally, consider the following example of a Point class that implements a custom version of both methods, .__new__() and .__init__(), for demonstration purposes:

 1class Point:
 2    def __new__(cls, *args, **kwargs):
 3        print("1. Create a new instance of Point.")
 4        return super().__new__(cls)
 5
 6    def __init__(self, x, y):
 7        print("2. Initialize the new instance of Point.")
 8        self.x = x
 9        self.y = y
10
11    def __repr__(self) -> str:
12        return f"{type(self).__name__}(x={self.x}, y={self.y})"

Creating and modifying PDF files in Python is straightforward with libraries like pypdf and ReportLab. You can read, manipulate, and create PDF files using these tools. pypdf lets you extract text, split, merge, rotate, crop, encrypt, and decrypt PDFs. ReportLab enables you to create new PDFs from scratch, allowing customization with fonts and page sizes.

By the end of this tutorial, you’ll understand that:

• You can read and modify existing PDF files using pypdf in Python.
• You can create new PDF files from scratch with the ReportLab library.
• Methods to encrypt and decrypt a PDF file with a password are available in pypdf.
• Concatenating and merging multiple PDF files can be done using pypdf.
• You can add custom fonts to a PDF using ReportLab.
• Python can create interactive PDFs with forms using ReportLab.

To follow along with this tutorial, you should download and extract to your home folder the materials used in the examples. To do this, click the link below:

Extracting Text From PDF Files With pypdf

In this section, you’ll learn how to read PDF files and extract their text using the pypdf library. Before you can do that, though, you need to install it with pip:

$ python -m pip install pypdf

With this command, you download and install the latest version of pypdf from the Python package index (PyPI). To verify the installation, go ahead and run the following command in your terminal:

$ python -m pip show pypdf
Name: pypdf
Version: 3.8.1
Summary: A pure-python PDF library capable of splitting,
 merging, cropping, and transforming PDF files
Home-page:
Author:
Author-email: Mathieu Fenniak <biziqe@mathieu.fenniak.net>
License:
Location: .../lib/python3.10/site-packages
Requires:
Required-by:

Pay particular attention to the version information. At the time of publication for this tutorial, the latest version of pypdf was 3.8.1. This library has gotten plenty of updates lately, and cool new features are added quite frequently. Most importantly, you’ll find many breaking changes in the library’s API if you compare it with its predecessor library PyPDF2.

Before diving into working with PDF files, you must know that this tutorial is adapted from the chapter “Creating and Modifying PDF Files” in Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you’ll find occasional references to IDLE throughout this tutorial. However, you should have no problems running the example code from the editor and environment of your choice.

Reading PDF Files With PdfReader

To kick things off, you’ll open a PDF file and read some information about it. You’ll use the Pride_and_Prejudice.pdf file provided in the downloadable resources for this tutorial.

Open IDLE’s interactive window and import the PdfReader class from pypdf:

>>> from pypdf import PdfReader

To create a new instance of the PdfReader class, you’ll need to provide the path to the PDF file that you want to open. You can do that using the pathlib module:

>>> from pathlib import Path

>>> pdf_path = (
...     Path.home()
...     / "creating-and-modifying-pdfs"
...     / "practice_files"
...     / "Pride_and_Prejudice.pdf"
... )

The pdf_path variable now contains the path to a PDF version of Jane Austen’s Pride and Prejudice.

Now create the PdfReader instance by calling the class’s constructor with the path to your PDF file as an argument:

>>> pdf_reader = PdfReader(pdf_path)

If you’ve been following along in Python Basics, then you’ll remember from Chapter 12, “File Input and Output,” that all open files should be closed before a program terminates. The PdfReader object does all of this for you, so you don’t need to worry about opening or closing the PDF file!

Now that you’ve created a PdfReader instance, you can use it to gather information about the PDF file. For example, to get the number of pages contained in the PDF file, you can use the built-in len() function like in the code below:

Installing Python on your system involves a few straightforward steps. First, check if Python is already installed by opening a command-line interface and typing python --version or python3 --version. You can install Python on Windows using the official installer from Python.org or through the Microsoft Store. On macOS, you can use the official installer or Homebrew. For Linux, use your package manager or build Python from source.

By the end of this tutorial, you’ll understand how to:

• Check if Python is installed by running python --version or python3 --version in a command-line interface.
• Upgrade Python by downloading and installing the latest version from Python.org.
• Install and manage multiple Python versions with pyenv to keep them separate.

This tutorial covers installing the latest Python on the most important platforms or operating systems, such as Windows, macOS, Linux, iOS, and Android. However, it doesn’t cover all the existing Linux distributions, as that would be a massive task. Nevertheless, you’ll find instructions for the most popular distributions available today.

To get the most out of this tutorial, you should be comfortable using your operating system’s terminal or command line.

Take the Quiz: Test your knowledge with our interactive “Python Installation and Setup” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Python Installation and Setup

In this quiz, you'll test your understanding of how to install or update Python on your computer. With this knowledge, you'll be able to set up Python on various operating systems, including Windows, macOS, and Linux.

Windows: How to Check or Get Python

In this section, you’ll learn to check whether Python is installed on your Windows operating system (OS) and which version you have. You’ll also explore three installation options that you can use on Windows.

For a more comprehensive guide on setting up a Windows machine for Python programming, check out Your Python Coding Environment on Windows: Setup Guide.

Checking the Python Version on Windows

To check whether you already have Python on your Windows machine, open a command-line application like PowerShell or the Windows Terminal.

Follow the steps below to open PowerShell on Windows:

1. Press the Win key.
2. Type PowerShell.
3. Press Enter.

Alternatively, you can right-click the Start button and select Windows PowerShell or Windows PowerShell (Admin). In some versions of Windows, you’ll find Terminal or Terminal (admin).

With the command line open, type in the following command and press the Enter key:

PS> python --version
Python 3.x.z

Using the --version switch will show you the installed version. Note that the 3.x.z part is a placeholder here. In your machine, x and z will be numbers corresponding to the specific version you have installed.

Alternatively, you can use the -V switch:

PS> python -V
Python 3.x.z

Using the python -V or python—-version command, you can check whether Python is installed on your system and learn what version you have. If Python isn’t installed on your OS, you’ll get an error message.

Knowing the Python Installation Options on Windows

You’ll have different options to install Python if you’re on a Windows machine. Here are three popular ones:

The pandas .groupby() method allows you to efficiently analyze and transform datasets when working with data in Python. With df.groupby(), you can split a DataFrame into groups based on column values, apply functions to each group, and combine the results into a new DataFrame. This technique is essential for tasks like aggregation, filtering, and transformation on grouped data.

By the end of this tutorial, you’ll understand that:

• Calling .groupby("column_name") splits a DataFrame into groups, applies a function to each group, and combines the results.
• To group by multiple columns, you can pass a list of column names to .groupby().
• Common aggregation methods in pandas include .sum(), .mean(), and .count().
• You can use custom functions with pandas .groupby() to perform specific operations on groups.

This tutorial assumes that you have some experience with pandas itself, including how to read CSV files into memory as pandas objects with read_csv(). If you need a refresher, then check out Reading CSVs With pandas and pandas: How to Read and Write Files.

You can download the source code for all the examples in this tutorial by clicking on the link below:

Prerequisites

Before you proceed, make sure that you have the latest version of pandas available within a new virtual environment:

• Windows
• Linux + macOS

PS> python -m venv venv
PS> venv\Scripts\activate
(venv) PS> python -m pip install pandas

$ python3 -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install pandas

In this tutorial, you’ll focus on three datasets:

1. The U.S. Congress dataset contains public information on historical members of Congress and illustrates several fundamental capabilities of .groupby().
2. The air quality dataset contains periodic gas sensor readings. This will allow you to work with floats and time series data.
3. The news aggregator dataset holds metadata on several hundred thousand news articles. You’ll be working with strings and doing text munging with .groupby().

You can download the source code for all the examples in this tutorial by clicking on the link below:

Once you’ve downloaded the .zip file, unzip the file to a folder called groupby-data/ in your current directory. Before you read on, ensure that your directory tree looks like this:

./
│
└── groupby-data/
    │
    ├── legislators-historical.csv
    ├── airqual.csv
    └── news.csv

With pandas installed, your virtual environment activated, and the datasets downloaded, you’re ready to jump in!

Example 1: U.S. Congress Dataset

You’ll jump right into things by dissecting a dataset of historical members of Congress. You can read the CSV file into a pandas DataFrame with read_csv():

import pandas as pd

dtypes = {
    "first_name": "category",
    "gender": "category",
    "type": "category",
    "state": "category",
    "party": "category",
}
df = pd.read_csv(
    "groupby-data/legislators-historical.csv",
    dtype=dtypes,
    usecols=list(dtypes) + ["birthday", "last_name"],
    parse_dates=["birthday"]
)

The dataset contains members’ first and last names, birthday, gender, type ("rep" for House of Representatives or "sen" for Senate), U.S. state, and political party. You can use df.tail() to view the last few rows of the dataset:

>>> from pandas_legislators import df
>>> df.tail()
      last_name first_name   birthday gender type state       party
11970   Garrett     Thomas 1972-03-27      M  rep    VA  Republican
11971    Handel      Karen 1962-04-18      F  rep    GA  Republican
11972     Jones     Brenda 1959-10-24      F  rep    MI    Democrat
11973    Marino        Tom 1952-08-15      M  rep    PA  Republican
11974     Jones     Walter 1943-02-10      M  rep    NC  Republican

The DataFrame uses categorical dtypes for space efficiency:

>>> df.dtypes
last_name             object
first_name          category
birthday      datetime64[ns]
gender              category
type                category
state               category
party               category
dtype: object

You can see that most columns of the dataset have the type category, which reduces the memory load on your machine.

Mocking in Python with unittest.mock allows you to simulate complex logic or unpredictable dependencies, such as responses from external services. You create mock objects to replace real ones in your tests, ensuring that your tests are isolated. The Mock class allows you to imitate real objects, and the patch() function lets you temporarily substitute mocks for real objects in your tests.

By the end of this tutorial, you’ll understand that:

• A mock in Python is a substitute object that simulates a real object in a testing environment.
• Mock differs from MagicMock in that MagicMock includes implementations of most magic methods.
• The patch() function replaces real objects with mock instances, controlling the scope of mocking.
• You can assert if a Mock object was called with methods like .assert_called().
• You set a mock’s return value by assigning a value to the mock’s .return_value attribute.

In this tutorial, you’ll explore how mocking enhances your testing strategy by enabling controlled and predictable test environments for your Python code. When you can control the behavior of your code during testing, you can reliably test that your application logic is correct.

Take the Quiz: Test your knowledge with our interactive “Understanding the Python Mock Object Library” quiz. You’ll receive a score upon completion to help you track your learning progress:

---

Interactive Quiz

Understanding the Python Mock Object Library

In this quiz, you'll test your understanding of Python's unittest.mock library. With this knowledge, you'll be able to write robust tests, create mock objects, and ensure your code is reliable and efficient.

What Is Mocking?

A mock object substitutes and imitates a real object within a testing environment. Using mock objects is a versatile and powerful way to improve the quality of your tests. This is because by using Python mock objects, you can control your code’s behavior during testing.

For example, if your code makes HTTP requests to external services, then your tests execute predictably only so far as the services are behaving as you expected. Sometimes, a temporary change in the behavior of these external services can cause intermittent failures within your test suite.

Because of this, it would be better for you to test your code in a controlled environment. Replacing the actual request with a mock object would allow you to simulate external service outages and successful responses in a predictable way.

Sometimes, it’s difficult to test certain areas of your codebase. Such areas include except blocks and if statements that are hard to satisfy. Using Python mock objects can help you control the execution path of your code to reach these areas and improve your code coverage.

Another reason to use mock objects is to better understand how you’re using their real counterparts in your code. A Python mock object contains data about its usage that you can inspect, such as:

• If you called a method
• How you called the method
• How often you called the method

Understanding what a mock object does is the first step to learning how to use one. Next, you’ll explore the Python mock object library to see how to use Python mock objects.

The Python Mock Library

Python’s built-in mock object library is unittest.mock. It provides an easy way to introduce mocks into your tests.

$ python -m pip install mock

unittest.mock provides a class called Mock, which you’ll use to imitate real objects in your codebase. Mock, along with its subclasses, offers incredible flexibility and insightful data that will meet most of your Python mocking needs.

The library also provides a function called patch(), which replaces the real objects in your code with Mock instances. You can use patch() as either a decorator or a context manager, giving you control over the scope in which the object will be mocked. Once the designated scope exits, patch() will clean up your code by replacing the mocked objects with their original counterparts.

Finally, unittest.mock provides solutions for some of the issues inherent in mocking objects, which you’ll explore later in this tutorial.

Now that you have a better understanding of what mocking is and the library you’ll be using, it’s time to dive in and explore the features and functionalities unittest.mock has to offer.

The Mock Object

unittest.mock offers a base class for mocking objects called Mock. The use cases for Mock are practically limitless because Mock is so flexible.

Begin by instantiating a new Mock instance:

>>> from unittest.mock import Mock
>>> mock = Mock()
>>> mock
<Mock id='4561344720'>

NumPy’s max() function efficiently finds maximum values within an array, making it a key tool for data analysis in Python. This tutorial guides you through using max() and maximum(), handling missing values, and explores advanced features like broadcasting for comparing arrays of different shapes.

By the end of this tutorial, you’ll understand that:

• NumPy’s max() function finds the maximum value within a single array, working with both one-dimensional and multi-dimensional arrays.
• Conversely, np.maximum() compares two arrays element-wise to find the maximum values.
• np.amax() and max() are equivalent in NumPy.
• You can use np.nanmax() to find the maximum value in an array while ignoring nan values, preventing them from affecting the result.

This tutorial includes a very short introduction to NumPy, so even if you’ve never used NumPy before, you should be able to jump right in.

With the background provided here, you’ll be ready to continue exploring the wealth of functionality to be found in the NumPy library.

NumPy: Numerical Python

NumPy is short for Numerical Python. It’s an open source Python library that enables a wide range of applications in the fields of science, statistics, and data analytics through its support of fast, parallelized computations on multidimensional arrays of numbers. Many of the most popular numerical packages use NumPy as their base library.

Introducing NumPy

The NumPy library is built around a class named np.ndarray and a set of methods and functions that leverage Python syntax for defining and manipulating arrays of any shape or size.

NumPy’s core code for array manipulation is written in C. You can use functions and methods directly on an ndarray as NumPy’s C-based code efficiently loops over all the array elements in the background. NumPy’s high-level syntax means that you can simply and elegantly express complex programs and execute them at high speeds.

You can use a regular Python list to represent an array. However, NumPy arrays are far more efficient than lists, and they’re supported by a huge library of methods and functions. These include mathematical and logical operations, sorting, Fourier transforms, linear algebra, array reshaping, and much more.

Today, NumPy is in widespread use in fields as diverse as astronomy, quantum computing, bioinformatics, and all kinds of engineering.

NumPy is used under the hood as the numerical engine for many other libraries, such as pandas and SciPy. It also integrates easily with visualization libraries like Matplotlib and seaborn.

NumPy is easy to install with your package manager, for example pip or conda. For detailed instructions plus a more extensive introduction to NumPy and its capabilities, take a look at NumPy Tutorial: Your First Steps Into Data Science in Python or the NumPy Absolute Beginner’s Guide.

In this tutorial, you’ll learn how to take your very first steps in using NumPy. You’ll then explore NumPy’s max() and maximum() commands.

Creating and Using NumPy Arrays

You’ll start your investigation with a quick overview of NumPy arrays, the flexible data structure that gives NumPy its versatility and power.

The fundamental building block for any NumPy program is the ndarray. An ndarray is a Python object wrapping an array of numbers. It may, in principle, have any number of dimensions of any size. You can declare an array in several ways. The most straightforward method starts from a regular Python list or tuple:

>>> import numpy as np
>>> A = np.array([3, 7, 2, 4, 5])
>>> A
array([3, 7, 2, 4, 5])

>>> B = np.array(((1, 4), (1, 5), (9, 2)))
>>> B
array([[1, 4],
       [1, 5],
       [9, 2]])

You’ve imported numpy under the alias np. This is a standard, widespread convention, so you’ll see it in most tutorials and programs. In this example, A is a one-dimensional array of numbers, while B is two-dimensional.

Notice that the np.array() factory function expects a Python list or tuple as its first parameter, so the list or tuple must therefore be wrapped in its own set of brackets or parentheses, respectively. Just throwing in an unwrapped bunch of numbers won’t work:

>>> np.array(3, 7, 2, 4, 5)
Traceback (most recent call last):
...
TypeError: array() takes from 1 to 2 positional arguments but 5 were given

With this syntax, the interpreter sees five separate positional arguments, so it’s confused.

In your constructor for array B, the nested tuple argument needs an extra pair of parentheses to identify it, in its entirety, as the first parameter of np.array().

Django is a powerful Python web framework for creating complex applications. It follows the Model-View-Template (MVT) architecture and includes built-in features like authentication, an admin interface, and database management.

In this tutorial, you’ll create a portfolio app step by step, gaining hands-on experience with Django’s core features. Along the way, you’ll work with models, views, templates, and the admin interface to build a fully functional web application. This hands-on approach will demystify Django’s structure and functionality.

By the end of this tutorial, you’ll understand that:

• Django projects begin with setting up a development environment and creating a project structure.
• Learning HTML and CSS before Django can help with styling templates, but it’s not mandatory.
• Django is used for building web applications with Python, offering built-in features and scalability.
• A real-life example of Django is a portfolio website that showcases projects and manages content.

At the end of this tutorial, you’ll have a working portfolio website to showcase your projects. If you’re curious about how the final source code looks, then you can click the link below:

Take the Quiz: Test your knowledge with our interactive “Get Started With Django: Build a Portfolio App” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Get Started With Django: Build a Portfolio App

In this quiz, you'll test your understanding of Django, a fully featured Python web framework. By working through this quiz, you'll revisit the steps to create a fully functioning web application and learn about some of Django's most important features.

Learn Django

There are endless web development frameworks out there, so why should you learn Django over any of the others? First of all, it’s written in Python, one of the most readable and beginner-friendly programming languages out there.

The second reason you should learn Django is the scope of its features. When building a website, you don’t need to rely on any external libraries or packages if you choose Django. This means that you don’t need to learn how to use anything else, and the syntax is seamless because you’re using only one framework.

There’s also the added benefit that Django is straightforward to update, since the core functionality is in one package. If you do find yourself needing to add extra features, there are several external libraries that you can use to enhance your site.

One of the great things about the Django framework is its in-depth documentation. It has detailed documentation on every aspect of Django and also has great examples and even a tutorial to get you started.

There’s also a fantastic community of Django developers, so if you get stuck, there’s almost always a way forward by either checking the docs or asking the community.

Django is a high-level web application framework with loads of features. It’s great for anyone new to web development due to its fantastic documentation, and it’s especially great if you’re also familiar with Python.

Understand the Structure of a Django Website

A Django website consists of a single project that’s split into separate apps. The idea is that each app handles a self-contained task that the site needs to perform. As an example, imagine an application like Instagram. There are several different tasks that it needs to perform:

• User management: Logging in and out, registering, and so on
• The image feed: Uploading, editing, and displaying images
• Private messaging: Sending messages between users and providing notifications

These are each separate pieces of functionality, so if this example were a Django site, then each piece of functionality would be a different Django app inside a single Django project.

The Django project holds some configurations that apply to the project as a whole, such as project settings, URLs, shared templates and static files. Each application can have its own database, and it’ll have its own functions to control how it displays data to the user in HTML templates.

Each application also has its own URLs as well as its own HTML templates and static files, such as JavaScript and CSS.

Django apps are structured so that there’s a separation of logic. It supports the model-view-controller pattern, which is the architecture for most web frameworks. The basic principle is that each application includes three separate files that handle the three main pieces of logic separately:

• Model defines the data structure. This is usually the database description and often the base layer to an application.
• View displays some or all of the data to the user with HTML and CSS.
• Controller handles how the database and the view interact.

If you want to learn more about the MVC pattern, then check out Model-View-Controller (MVC) Explained – With Legos.

Python’s built-in functions max() and min() allow you to find the largest and smallest values in a dataset. You can use them with iterables, such as lists or tuples, or a series of regular arguments. They can handle numbers, strings, and even dictionaries. Plus, with the optional arguments key and default, you can customize their behavior to suit your needs.

By the end of this tutorial, you’ll understand that:

• Python’s max() and min() can find the largest and smallest values in a dataset.
• min() and max() can handle string inputs by comparing their alphabetical order.
• The key argument modifies comparison criteria by applying a function to each element before comparison.
• You can use min() and max() with generator expressions for memory-efficient value comparison.

This tutorial explores the practical use cases for min() and max(), such as removing outliers from lists and processing strings. By the end, you’ll also know how to implement your own versions of min() and max() to deepen your understanding of these functions.

To get the most out of this tutorial, you should have some previous knowledge of Python programming, including topics like for loops, functions, list comprehensions, and generator expressions.

Getting Started With Python’s min() and max() Functions

Python includes several built-in functions that make your life more pleasant and productive because they mean you don’t need to reinvent the wheel. Two examples of these functions are min() and max(). They mostly apply to iterables, but you can use them with multiple regular arguments as well. What’s their job? They take care of finding the smallest and largest values in their input data.

Whether you’re using Python’s min() or max(), you can use the function to achieve two slightly different behaviors. The standard behavior for each is to return the minimum or maximum value through straightforward comparison of the input data as it stands. The alternative behavior is to use a single-argument function to modify the comparison criteria before finding the smallest and largest values.

To explore the standard behavior of min() and max(), you can start by calling each function with either a single iterable as an argument or with two or more regular arguments. That’s what you’ll do right away.

Calling min() and max() With a Single Iterable Argument

The built-in min() and max() have two different signatures that allow you to call them either with an iterable as their first argument or with two or more regular arguments. The signature that accepts a single iterable argument looks something like this:

min(iterable, *[, default, key]) -> minimum_value

max(iterable, *[, default, key]) -> maximum_value

Both functions take a required argument called iterable and return the minimum and maximum values respectively. They also take two optional keyword-only arguments: default and key.

Here’s a summary of what the arguments to min() and max() do:

Later in this tutorial, you’ll learn more about the optional default and key arguments. For now, just focus on the iterable argument, which is a required argument that leverages the standard behavior of min() and max() in Python:

>>> min([3, 5, 9, 1, -5])
-5

>>> min([])
Traceback (most recent call last):
    ...
ValueError: min() arg is an empty sequence

>>> max([3, 5, 9, 1, -5])
9

>>> max([])
Traceback (most recent call last):
    ...
ValueError: max() arg is an empty sequence

In these examples, you call min() and max() with a list of integer numbers and then with an empty list. The first call to min() returns the smallest number in the input list, -5. In contrast, the first call to max() returns the largest number in the list, or 9. If you pass an empty iterator to min() or max(), then you get a ValueError because there’s nothing to do on an empty iterable.

An important detail to note about min() and max() is that all the values in the input iterable must be comparable. Otherwise, you get an error. For example, numeric values work okay:

>>> min([3, 5.0, 9, 1.0, -5])
-5

>>> max([3, 5.0, 9, 1.0, -5])
9

These examples combine int and float numbers in the calls to min() and max(). You get the expected result in both cases because these data types are comparable.

However, what would happen if you mixed strings and numbers? Check out the following examples:

>>> min([3, "5.0", 9, 1.0, "-5"])
Traceback (most recent call last):
    ...
TypeError: '<' not supported between instances of 'str' and 'int'

>>> max([3, "5.0", 9, 1.0, "-5"])
Traceback (most recent call last):
    ...
TypeError: '>' not supported between instances of 'str' and 'int'