Abstraction

1.6

In computing, abstraction is a fundamental concept that involves simplifying complex systems by hiding the underlying technical details and exposing only the necessary aspects to the user or programmer. It allows for focusing on higher-level functionality without getting bogged down in the intricacies of lower-level operations. This is achieved by creating more straightforward and generic representations of processes or entities, which can be used to manage and manipulate complex systems more efficiently. Abstraction is evident in various forms, such as programming languages that abstract away machine code, software development methodologies that encapsulate details within objects or functions, and hardware design where complex circuitry is represented as simple, reusable components. By providing a way to manage complexity, abstraction makes it possible to handle large and intricate computing systems, enabling developers and users to interact with technology efficiently and effectively.

Algorithm

1.6

An algorithm is a step-by-step procedure or a set of rules designed to perform a specific task or solve a particular problem. It is a fundamental concept in computer science and mathematics, acting as a blueprint for the actions needed to achieve a desired outcome. Algorithms are used in many applications, from simple tasks like sorting a list of numbers to complex operations like data encryption or machine learning. They are characterized by clarity, efficiency, and finite nature, meaning they must have a clear starting point, a definitive set of instructions, and an endpoint. The effectiveness of an algorithm is often measured by its speed and the minimal resources it requires to complete its task. In the digital world, algorithms form the backbone of computer programs, enabling computers to process data and make decisions based on predefined logical sequences. We should also realize that algorithms are not exclusive to computing or technology; we (humans) use algorithms all the time in daily life. For example, a cake recipe is an algorithm, the sequence of steps to tie your shoes is an algorithm, etc.

Arguments

1.8.2

In programming, we often pass data and objects between different segments of our programs. For example, when you call a function, like print(), you can often pass values to that function for it to perform its tasks. These values that we pass are called arguments.

Case Sensitive

1.8.2

Case sensitivity in Python refers to the way the language distinguishes between uppercase and lowercase letters in identifiers, such as variable names, function names, and class names. In Python, case matters; for example, Variable, VARIABLE, and variable are recognized as three distinct identifiers. This means that Python treats these as completely different entities, and each can be associated with different values or functionalities. The case sensitivity in Python is crucial for maintaining clarity and consistency in code. It enforces good coding practices and helps avoid conflicts or confusions that might arise from inadvertently using the wrong case. For instance, Python's built-in names and keywords are always in lowercase (like int, float, if, else, etc.), and this convention is followed consistently throughout the language's syntax and standard library. Developers often adopt naming conventions, such as using all caps for constants or camelCase/PascalCase for class names, to enhance readability and maintainability of the code.

Casting

1.8.2, 2.4

In programming, we often need to transform (convert) data from one type to another type. In Python, this is accomplished using casting functions. Here is a list of the casting functions:

• int() - Converts a value to an integer.
• float() - Converts a value to a floating point number.
• str() - Converts a value to a string.
• bool() - Converts a value to a boolean.
• list() - Converts a value to a list.
• tuple() - Converts a value to a tuple.
• set() - Converts a value to a set.
• dict() - Converts a value to a dictionary.

And an example:

Because the print() function's object need to be strings, it requires us to cast (convert) the numeric values to strings to concatenate those values with our string labels Age and Sum. In this case, we do this using the str() function, which casts numeric values to string values.\

Comments

1.8.3

Comments in programming are annotations in the source code that the compiler or interpreter does not execute. They are used to provide explanations or clarifications about the code, making it easier for others (and the programmers themselves) to understand the purpose, functionality, and logic of the code at a later time. Comments are crucial for maintaining and updating code, especially in collaborative projects. They can also be used to temporarily turn off code during debugging or development without actually deleting the code.

Concatenation

1.8.2

Concatenation refers to the act of combining or linking things together in a sequence or series. In programming or computer science, concatenation often refers explicitly to joining strings together to create a single, long string. This is accomplished by placing one string after another, effectively merging their characters or contents. String concatenation is commonly used when working with text or manipulating data in programming languages.

Development Environment

1.8.1

A development environment is a set of software (tools) and hardware used by programmers to create programs and applications. These enviroments can vary widely depending on a number of factors, such as the overall goals of the development projects, the configuration of people who will work on the projects (one programmer, a team of programmers, etc.), the size of the projects, the type and availability of the programming tools involved, the personal preferences of the programmers, and other factors.

Escape Sequences

1.8.2

In Python programming, escape sequences are special characters used within strings to represent characters that can't be easily typed or have special meaning in Python. Prefaced by a backslash (\), these sequences are interpreted by Python to perform specific functions or represent specific characters. Common escape sequences include \n for a newline, \t for a tab, \\ for a literal backslash, \' for a single quote, and \" for a double quote. These sequences are essential for including characters in a string that would otherwise be interpreted differently by Python. For instance, the newline escape sequence (\n) is widely used to break text into multiple lines. Escape sequences enable more precise control over the formatting and representation of strings in Python code, allowing developers to include a range of characters that are not easily typable or would disrupt the string's syntax if used directly.

Formal Language

1.6

A formal language, in the context of mathematics, computer science, and linguistics, is a set of strings of symbols governed by specific grammatical rules or a formal system. Unlike natural languages, which are used for everyday human communication and are rich in ambiguity and idiomatic expressions, formal languages are constructed with a precise, unambiguous structure. They consist of an alphabet – a finite set of symbols – and rules that define valid strings or expressions in the language. Formal languages are essential for various applications, such as programming languages that provide instructions to computers, mathematical notation that precisely conveys mathematical concepts, and theoretical models in linguistics. They enable clear communication of instructions and information in systems where ambiguity and misinterpretation must be minimized, making them indispensable in fields like computer science, logic, and mathematics.

High-Level & Low-Level Languages

1.6

Programming languages are often categorized as high-level or low-level, based on how close they are to human language or machine language. High-level languages, like Python, JavaScript, or Java, are closer to human language. They are more abstract, easier to read, and write, and are generally more intuitive for beginners. High-level languages handle a lot of complexity for you, such as memory management, which makes them more efficient for developing complex applications. On the other hand, low-level languages, like C or assembly language, are closer to machine language. They provide more control over what's happening at the hardware level, such as direct memory management. This can lead to more efficient programs in terms of execution speed and resource usage but often at the cost of longer and more complex code. While high-level languages are great for general-purpose programming, low-level languages are often used in systems programming, where direct interaction with the hardware is necessary.

Input ⇨ Processing ⇨ Output

1.6

In computing, we frequently use the terms input, processing, and output. Input is anything that enters the computer, such as characters typed on a keyboard, mouse clicks, taps on the screen, recording our voice, recording video, information from sensors, and many others. Processing is what happens to those inputs inside the computer, including calculations, storing values in memory or on hard drives, uploading input data to the Internet, etc. Output is the result of processing, including displaying results on the screen, printing on paper, playing video or sounds, etc.

Natural Language

1.6

Natural language refers to any language that has evolved organically among humans for the purpose of communication. It is characterized by its complex structure and use in everyday spoken and written communication by people. Unlike artificial or constructed languages, natural languages develop over time through use in a community of speakers, and are subject to the dynamics of cultural evolution and human interaction. They consist of intricately structured rules for grammar, syntax, semantics, and phonetics, which enable a wide range of expressions, emotions, and ideas to be communicated. Natural languages, such as English, Mandarin, Spanish, and thousands of others, are central to human identity and social interaction, forming the basis for the vast majority of human-to-human communication, literature, and cultural expression.

Object

1.8.2

Objects are an important concept in Python programming, but can be difficult to understand. Let's use the concept of a noun in human language. A noun is a person, place or thing. We often refer to things as objects. Further, we often describe objects by their attributes (color, shape, size, etc.) and by their behaviors, or actions (fly, roll, spin, etc.). For example, the word bicycle is a noun that represents a thing (an object) that we are familiar with. We could say "that bike is red (attribute) and it has two wheels that roll (behavior)."

In many programming languages, like Python, we have a similar conceptual approach and terminology. There are many things (objects) in Python, such as date objects, video, audio, data, people, bicycles, etc. You may be wondering how that's possible, a bicycle in Python? As we proceed, I'll point out objects that already exist in Python, their attributes and behaviors. Also, we'll learn to create our own objects as well. We will also see tools in Python, such as functions, that help us work with objects. The first of these functions that we'll learn is the print function.

Printing

1.8.2

Printing in Python refers to the action of displaying text or other data on the screen. It's one of the most basic and essential functions in Python programming, especially useful for beginners. When you 'print' something in Python, you are asking the computer to output information to the console or terminal. This is done using the print() function, which takes the data you want to display (like a string of text, a number, or the value of a variable) and writes it to the screen. Printing is a vital tool for debugging, as it allows you to see what your code is doing, track variables' values at different stages, or simply display a message to the user. For instance, the line print("Hello, world!") in Python will display the text "Hello, world!" on your screen. This simplicity makes the print() function a fundamental building block in learning to code with Python.

Programming Tool

1.8.1

Programming tools are programs that programmers use to create, debug, maintain, and support other programs and applications. These tools include code editors (used by programmers to write computer source code), debuggers (used to test code to find and remove mistakes in code), compilers and/or interpreters (used to convert source code into software (executables) useable by others), source code sharing tools (used by programmers to share their code with others, often other programmers who are working on the same overall software systems in a team-based development group), and many others.

Production System

A production system combines hardware, software, data, networks, Internet, security, devices, manufacturing, shipping, etc., used by "users" to run a business, manage customers, process transactions, etc. The data involved in a product system is often referred to as "live" data, meaning actual product and customer information.

Semantics

1.6

Semantics in language is the study of meaning and interpretation of words, phrases, sentences, and larger units of text. It delves into how human beings comprehend and attribute meaning to language, bridging the gap between the abstract or symbolic representations of words and their real-world implications. Semantics considers the connotations and implications of language, including the literal meaning of words (denotation), the implied or suggested meanings (connotations), and how context influences the interpretation of language. This field is crucial for understanding not just what is explicitly stated, but also the nuances and complexities inherent in human communication. Semantics plays a pivotal role in linguistics, philosophy of language, and computational linguistics, where understanding and replicating human language comprehension is a key goal. It is fundamental in determining how language conveys different types of information, such as facts, beliefs, commands, desires, and questions, and in distinguishing between different meanings of words that appear similar.

Strings

1.8.2

In Python, we call text strings. Strings are characters, words, or phrases and contain from one to any number of valid alphanumeric symbols surrounded by quotes (" "). Alphanumeric symbols include the letters of the alphabet, numeric digits (when included in a string, numbers are not numeric but characters of the string), all of the other symbols on the keyboard (~!@#$%^&*()_+=-`{}|[]\:";'<>?,./'`), and extended characters from non-English languages, etc.

Syntax

1.6

Syntax in language refers to the set of rules, principles, and processes that govern the structure of sentences in a given language, specifically the arrangement of words and phrases to create well-formed sentences. It acts as a guideline for constructing meaningful and grammatically correct sentences, determining how different parts of speech, such as nouns, verbs, adjectives, and adverbs, are combined. Syntax varies widely across languages, and it encompasses various components such as word order, agreement (e.g., between subjects and verbs, in number or gender), and the hierarchical structure of sentences. In essence, syntax is not about the meaning of individual words, but rather how these words are organized and interact with each other to convey complex ideas and relationships in a clear and logical manner. Understanding syntax is crucial in linguistics for analyzing sentence structure, in language learning for mastering a new language, and in computational linguistics for programming computers to process human language.

Alphanumeric

2.2

Alphanumeric characters include the alphabetic letters A thru Z, both upper case and lowercase, as well as the digits 0 thru 9. When a digit is within an alphanumeric value, it is not considered a number but rather a textual representation of the digit. Alphanumeric also includes other characters found in keyboards, such as @ $ # & * ( ) { } [ ] , = - _ . + ; ' /. Also, a blank space (created on the keybard by the spacebar) is considered alphanumeric

American Standard Code for Information Interchange (ASCII)

2.2.1

A set of characters used in computers for electronic communication. The table presented below is the complete ASCII character set. Also, you can find a more thorough listing of ASCII values here. There are other encoding systems as well, such as EBCDIC.

Every character in the set (see the chart below) has a code number associated with it that computers can interpret and produce the associated character on a computer screen, printer, etc. Some of the ASCII characters are called control characters, which do not have a visual printable representation, but they have a function related to electronic communications. For example, character 09 is a tab, the same as if you pressed the Tab key on your keyboard. So, if I put that character in the middle of a string in a Python print statement when it is printed on the console a tab would appear at the position in the string. Other characters are printable, meaning they are visible on a computer screen, printer, etc. The standard range of characters you see on most keyboards is between 32 and 127. Beyond 127 is referred to as the extended ASCII set, however, today more systems use the Unicode standard for extended character representation (see the Unicode discussion below).

Black Box

2.1

In programming and software engineering, the term "black box" refers to a system or component whose internal workings are not known or accessible to the user or developer. In a black box approach, the focus is on the inputs and outputs of the system, without any concern for its internal implementation. This concept is widely used in testing (black box testing), where the tester evaluates the system based solely on its functionality and response to inputs, without any knowledge of the internal code structure. Black box systems are also common in software components or APIs, where the user interacts with a well-defined interface but has no access to or knowledge of the underlying code. This abstraction allows users to utilize complex systems without needing to understand or manage the intricate details of their operations, promoting modularity and ease of use. However, it also means that troubleshooting or optimizing such systems can be challenging since their internal mechanisms are hidden.

Boolean

2.2.4

A boolean is a binary state that can only be one of two values, True or False. Boolean variables in Python can only hold one of those two values and conditions can only result in one of those two values. We use boolean in many scenarios in programming, such as repeating an action until some condition becomes True. We often associate boolean result values of True and False to Yes or No questions.

Data

2.1

Data is the foundation upon which programming languages operate and is essentially information that can be processed or manipulated by a computer. In its simplest form, data can be numbers, words, measurements, or observations. It's important to note that 'data' is actually the plural form of the word 'datum,' although 'data' is often used to refer both to single and multiple items. For example, when we have a single piece of information, it's a datum, but as soon as we have more than one, it's referred to as data. Throughout this book, when we discuss data, we're talking about the raw material that you, as a budding Python programmer, will learn to manipulate, analyze, and transform into meaningful insights or actions." Data refers to raw facts, figures, and details collected from various sources, which, when processed and analyzed, becomes meaningful information. It can exist in various forms, such as numbers, text, bits and bytes, images, and sounds. Data is a fundamental element in computing and information sciences, used for analysis, decision-making, and creating strategies in various fields like business, science, technology, and social studies. In its raw form, data might seem random or disconnected, but through processing and analysis, it can reveal patterns, insights, and trends. The significance of data has grown immensely in the digital age, where vast amounts of it are generated and used every day, leading to the development of fields like data science and big data analytics, which focus on extracting meaningful information and knowledge from large and complex datasets. See related concept Datum. Also see the related concept information.

Data File

2.1

In programming, a data file is a file that primarily contains data used or generated by a software application. Unlike executable files, which contain code that is run by the computer, data files are designed to be read from or written to by programs. They come in various formats, depending on the type and use of the data, such as text files (.txt), comma-separated values files (.csv), JavaScript Object Notation files (.json), and eXtensible Markup Language files (.xml), among others. Data files can hold a wide array of information, ranging from simple text or numbers to complex structured data like user settings, program state, or large datasets used in data analysis. The way these files are structured and accessed depends on the requirements of the application and the specific programming language being used. Efficient handling and manipulation of data files are often crucial in software development, especially in fields like data science, database management, and web development.

Data Structure

2.5.1

In programming, data structures are specialized formats for organizing, processing, retrieving, and storing data. They provide the means to manage large amounts of data and are essential for designing efficient algorithms. Data structures are critical in the field of computing and are fundamental in the development of algorithms and the functioning of software systems. They are categorized broadly into linear data and non-linear. The choice of a data structure for a specific problem can have a profound impact on the efficiency of the solution, as each structure has its own set of strengths and weaknesses in terms of data access, processing speed, and memory usage. Understanding data structures and their applications is crucial for solving complex computational problems and for the effective processing and storage of data.

Data Type

2.2

A data type in programming is a classification that specifies the type of value a variable can hold and how the computer interprets that value. Think of data types as different kinds of containers, each designed to hold a specific type of item. Common data types in Python include integers (whole numbers like 3 or -42), floating-point numbers (numbers with a decimal point like 2.5 or 3.14), strings (sequences of characters like "hello" or "1234"), and booleans (true or false values). Each data type in Python has its own set of operations that can be performed on it. For example, you can add and multiply integers or concatenate (join together) strings. Understanding data types is crucial in programming because it affects what operations you can perform with your variables and how you can manipulate your data. For instance, you can't perform mathematical operations on strings, even if they contain numbers. Knowing the data types helps you structure your code logically and prevent errors.

Database

2.7

In programming, a database is a systematic collection of data that is organized and stored in a way that facilitates efficient access, retrieval, management, and updating of information. Databases are essential for handling large amounts of data, providing a structured framework to store data in tables, records, and fields. This structure allows for easy querying and manipulation of the data using specialized database management systems (DBMS), such as SQL (Structured Query Language), which is used to interact with relational databases. There are various types of databases, including relational databases, which organize data into interrelated tables, and NoSQL databases, which are designed for unstructured data and offer more flexibility in terms of data models (like document, key-value, graph, or wide-column stores). Databases are fundamental in a wide range of applications, from simple websites to complex enterprise systems, as they enable efficient data handling, consistency, security, and scalability. They are integral to tasks such as data analysis, customer relationship management, inventory tracking, and beyond, serving as the backbone for data-driven decision-making and operations.

Dataset

2.6.4

In programming, a dataset is a collection of data that is typically organized and structured in some manner. It serves as the raw material from which information can be extracted, processed, analyzed, and interpreted. Datasets can come in various forms and sizes, ranging from simple arrays of numbers to complex, multi-dimensional structures containing diverse types of data. They can be sourced from various places like databases, file systems, APIs, or generated through simulations and experiments. In the context of data analysis, machine learning, or scientific research, a dataset is crucial as it forms the basis upon which algorithms operate and insights are drawn. The quality, relevance, and integrity of a dataset greatly influence the outcomes of any computational process applied to it. Good dataset management practices involve cleaning, organizing, and sometimes transforming data to make it suitable for specific tasks. The representation and handling of datasets are often facilitated by programming libraries and tools tailored to the specific needs of the data and the objectives of the project.

Datum

2.1

Datum refers to a single piece of information or a unit of data. It is the most elemental form of data that a computer can process and manipulate. A datum can be as simple as a single number, a character, or a boolean value, and it represents the basic building block of larger data structures. In programming languages like Python, each datum is typically associated with a specific data type, which defines the nature and operations that can be performed on it. For instance, a datum could be an integer value '5', a floating-point number '3.14', or a string 'hello'. Understanding the concept of a datum is crucial for programmers, as it underpins how larger and more complex data structures like arrays, lists, and dictionaries are constructed and utilized in programming to store, organize, and process multiple data units efficiently. See related concept Data.

Dictionary

2.5.3

In Python, a dictionary is a collection data type that is mutable, dynamic, and can store an unordered set of key-value pairs. The key in a Python dictionary is unique and acts as an identifier for the associated value, making dictionaries optimized for retrieving data. The values in a dictionary can be of any data type, and a single dictionary can store values of different types. Dictionaries are defined with curly braces {}, with key-value pairs separated by colons, and each pair separated by commas. For example, my_dict = {'name': 'Alice', 'age': 30, 'city': 'New York'} is a dictionary that contains three key-value pairs. Keys are typically strings, but any immutable type can be used. The flexibility of dictionaries makes them ideal for tasks like data organization, fast data lookup, and data manipulation. They are widely used in applications such as data analysis, web development, and as a way to manage complex data structures in a structured manner.

Dynamic Typing

2.2

Dynamic typing in programming refers to a feature of languages like Python, where the type of a variable is determined at runtime, not in advance. This means that when you write code, you don't have to specify what type of data (like number, text, or something else) a variable will hold. Instead, Python figures it out automatically when the program is run. For example, in Python, you can create a variable x and assign it a number (x = 5), and later, you can assign a string to the same variable (x = "Hello"). The ability to change types dynamically makes Python and similar languages very flexible and user-friendly, especially for beginners. However, it also means that you need to be careful about how you use your variables, as incorrect types can lead to bugs in your programs. Dynamic typing is one of the features that makes Python so versatile and popular for a wide range of programming tasks.

Encoding & Decoding

2.6.1

Encoding is setting characters (alphabetic characters, numbers, and symbols) into specialized formats for efficient storage and transmission. Decoding is the opposite process of returning characters to their original sequence. There are many proprietary encoding schemes and formats, such as the encoding scheme of a Microsoft Word Document (.docx), an Adobe Photoshop image file (.psd), etc. Other encoding schemes are open standards such as ASCII, UTF-8, XML, and JSON, which are used as common formats for sharing and saving data.

Evaluate (Evaluation)

2.2.4

Evaluation in programming means that we examine the result of something and, usually, take some action based on the result of the evaluation. So, for example, if I write

x = 10
y = 2
z = x * y

if z > 15:
    # do something

File

2.6.1

A file in a computer is a collection of information stored on your computer's hard drive or other storage devices, such as a USB flash drive. This information can include text, pictures, music, videos, or other data. Each file is given a named, making it easy to find and access the file later on. Think of it like a physical file folder where you can store documents, except on a computer it is all digital. Files can be created, modified, deleted, and moved around on your computer's storage devices, making them a fundamental part of computer use.

File Storage

2.6.1

File Storage is the process and mechanism of storing and organizing digital files on storage devices. It involves allocating space, managing file structures, and facilitating access to stored files. File storage systems determine how data is physically stored, retrieved, and manipulated, ensuring efficient and reliable management of files within a computer system.

File Systems

2.6.1

File Systems control how data is stored and retrieved. There are various types of file systems, each designed for specific purposes, such as local storage (hard) drive file systems (like those used on Windows, Mac, and Linux computers for the local storage (hard) drives), network file systems, cloud-based file systems, etc. File systems control file names, file sizes, directories (folders), and access to those resources.

Hardcode

2.1

In programming, the term "hardcode" refers to the practice of embedding data directly into the source code of a program, rather than obtaining it from external sources or generating it dynamically. Hardcoded data is fixed, meaning that it does not change unless the source code itself is modified. This approach is often used for values that are unlikely to change, such as configuration settings, constants, or specific resource file paths. While hardcoding can simplify development by reducing complexity and dependencies, it also makes the program less flexible and more difficult to update. For instance, changing a hardcoded value requires modifying the code and redeploying the application, which can be inefficient for values that might need frequent updates. Moreover, hardcoding sensitive information, like passwords or API keys, is considered a bad practice due to security risks.

Information

2.1

Information is data that has been processed, organized, or structured in a way that adds context and meaning, making it useful and understandable to the person receiving it. Information arises from interpreting data, where the raw facts are analyzed to reveal patterns, relationships, or trends. This process involves sorting, aggregating, or transforming data to convey knowledge, solve problems, or make decisions. Information is meaningful and valuable because it provides insight, answers questions, or guides actions. For example, a collection of data points about weather patterns becomes information when it is analyzed and presented as a weather forecast, which people can use to plan their activities. Also see the related concept data.

Key-Value Pair

2.5.3

A key:value pair (also often called name:value pair, attribute:value pair or field:value pair) is a common representation of data in computer systems and applications. Programmers often work with this method of representing data. An example of a key:value pairs might be Make:Ford, Model:F150, where Make and Model are the keys and Ford and F150 are the corresponding values.

List

2.5.2

In Python, a list is a versatile, mutable data structure that allows you to store a collection of items. Lists are ordered, meaning that the items in the list appear in a specific sequence, and this order is maintained throughout the life of the list. One of the key characteristics of a list is its mutability: you can modify its content by adding, removing, or changing items. Lists are created by placing items (elements), which can be of any data type, inside square brackets [], separated by commas. For example, my_list = [1, 2, 'apple', 'banana'] is a list containing two integers and two strings. Python lists are also dynamic; they can grow or shrink as needed. They support a range of operations like slicing (to extract parts of the list), iterating (looping through each item in the list), and nested lists (lists within lists). Lists are one of the most commonly used data structures in Python due to their flexibility and ease of use, making them suitable for a wide range of applications, from simple data storage to complex data manipulations.

Method

2.2.1

In programming, a method is a function that is associated with an object and is defined within a class. Methods represent the behavior or actions that an object can perform, operating on the data contained within the object (known as attributes). When a method is called, it typically acts upon the attributes of the object it belongs to, either by modifying these attributes, performing a calculation, or executing any number of operations. For example, in a class Car, a method start_engine might change the state of a Car object from 'stopped' to 'running'. Methods are invoked on specific instances of a class (objects), using dot notation, like myCar.start_engine(). This concept is a cornerstone of Object-Oriented Programming, as it bundles data and behavior into discrete, self-contained units. Methods can also take arguments, like functions, and can return values. They provide a way to interact with an object, encapsulating the object's internal state and ensuring controlled access to its data.

Mutability

2.2.1

Mutability refers to the ability of an object (like a variable) to be changed after it has been created. Conversely, if an object cannot be changed and always retains its initial value, it is called immutable (like a string). If you remember from our discussion of mutability earlier, this means it cannot be changed. Any operation or function that appears to change a string is making a copy of the original string, modifying that copy, and returning it as a new string. This process happens automatically by the Python interpreter.

Precision

2.2

Precision in the context of numeric data types refers to the total number of significant digits in a number, regardless of where the decimal point is situated. It essentially measures the capacity of a data type in terms of how much information it can hold. In computer programming and database design, understanding precision is crucial for defining how accurately numbers are represented and how much space they occupy in memory. For instance, a numeric data type with a precision of 7 can hold seven digits, whether they are before or after the decimal point. This means a number like 12345.67 or 1234567 both fit within this precision requirement. The choice of precision directly impacts the accuracy of calculations and storage efficiency, particularly when dealing with large datasets or calculations requiring high accuracy. Note: See the related concept Scale.

Record

2.6.2, 2.6.3

In programming and database terms, a record is a set of data elements that, together, represent some entity, such as a customer, an order, a catalog product, etc. Each record contains from 1 to many attributes (columns) that describe the entity. For example, a customer record might include the following attributes:

• Customer ID
• First Name
• Last Name
• Address
• City
• State
• Zip Code
• Phone Number
• Email Address

Scale

2.2

Scale refers to the number of digits that can be stored to the right of the decimal point in a numeric data type. This aspect of a number is particularly important when dealing with fractional values, as it determines the fraction's accuracy or granularity. In many programming and database scenarios, specifying the scale of a numeric data type is essential to control the degree of precision of the fractional part of a number. For instance, a scale of 2, as in the number 12345.67, means that the number can have up to two digits following the decimal point. This is critical in financial calculations, measurements, and any other domain where the precision of fractional values can have significant implications. Understanding and correctly setting the scale ensures that numbers are not only accurately represented but also that they behave as expected in various operations and calculations. Note: See the related concept Precision.

Tabular Data

2.6.3

Tabular data is a fundamental format used in Python programming. It is often the format found in data files as explained below. These files represent data in a simple, table-like structure with rows and columns, where each row corresponds to a data record and each column represents a specific attribute or field of the record. The simplicity and universality of CSV files make them a popular choice for storing and exchanging data across various applications. In Python, libraries offer robust tools for reading, manipulating, and writing CSV files, allowing programmers to effortlessly handle large datasets, perform data cleaning, analysis, and visualization. The straightforward structure of tabular data in CSV files lends itself well to tasks ranging from basic data entry to complex machine learning algorithms, making it an essential format for Python programmers.

Truth Tables

2.2.4, 2.4.2

Truth Tables: are tables of rows and columns used to evaluate True/False conditions (often called propositions) of a logical problem. The table is intended to compare all possible propositions (combinations) that make up the resulting values of the problem. We can use truth tables in programming to aid in designing solutions.

Unicode

2.2.1

An information technology standard for representing text-based information. It is a worldwide standard used for electronic communication across human languages. Contrast this with the ASCII standard above which only represents English characters.

There are over 140,000 individual characters, across multiple human languages, available through Unicode, which is why it is often the preferred standard to use. You can find a list of the Unicode characters here. There's also a large list of Unicode emoji characters available here.

User Input

2.3

User input in Python is a way to interact with users by allowing them to enter data into your program. This interaction is crucial as it makes your programs dynamic and responsive to the user's needs. In Python, you can gather user input through the input() function. When input() is used, the program pauses and waits for the user to type something into the console and press Enter. The text entered by the user is then returned by input() as a string and can be stored in a variable for further use. For example, name = input("Enter your name: ") will display the prompt 'Enter your name: ' and allow the user to type their name. This makes your Python scripts more interactive, as they can now respond to user-provided information, leading to a more engaging experience. Whether it's asking for a name, a choice, or a specific piece of data, user input is a simple yet powerful tool in your Python programming toolkit.

Validation

2.4, 3.5.5

Data validation in programming is the process of ensuring that the input data a program receives meets specific criteria and is within acceptable parameters before it is processed or used in computations. This process is crucial for maintaining the integrity and reliability of a program. Validation can involve checking for data type correctness (ensuring an input is an integer when an integer is expected), range constraints (such as verifying dates fall within a certain period), format correctness (like validating email addresses or phone numbers against a specific pattern), and presence of necessary data (ensuring required fields are not empty). Data validation helps prevent errors, inconsistencies, and security vulnerabilities by catching incorrect or malicious data before it can cause problems in the system. It is an essential aspect of robust software development, particularly in applications involving user input, file handling, or network communications. By incorporating thorough data validation routines, programmers can ensure their applications behave predictably and handle invalid or unexpected inputs gracefully.

Variable

2.3

In programming, a variable is a fundamental concept that acts as a storage location in a computer's memory, holding data that can be accessed and manipulated by a program. Variables are named entities, and their names are used to refer to the stored data. The name of a variable acts as a symbolic representation of the data it contains. The type of data a variable can hold (such as integers, strings, objects) is determined by its data type. Variables are mutable, meaning their values can change throughout the execution of a program. They allow programmers to store temporary data and manipulate it, making programs dynamic and flexible. The declaration and naming of variables typically follow specific rules and conventions of the programming language being used. Proper use of variables is crucial for creating readable and maintainable code, allowing programmers to express operations in a clear and logical manner.

Accumulator (Accumlation)

3.3.1

In Python, accumulation refers to the process of repeatedly updating a variable by adding or appending values to it within a loop, thereby "accumulating" a final result. This concept is often used in scenarios such as summing a sequence of numbers, concatenating strings, or building up a list. For example, to calculate the sum of numbers in a list, you would initialize an accumulator variable (e.g., total = 0) and then iterate over the list, adding each element to total. Similarly, for concatenating strings, you would start with an empty string and append characters or other strings in a loop. The accumulator pattern is powerful in scenarios where a computation over a sequence of data is required, making it a fundamental technique in Python for processing collections of data, like lists, tuples, or even strings. The simplicity and versatility of this pattern make it a staple in Python programming, especially in data processing and manipulation tasks.

Arguments

3.6.1

In Python, function arguments are the values passed to a function at the time of its invocation, enabling functions to accept input data and behave differently based on that input. Python supports various types of arguments: positional arguments, which are mandatory and must be passed in the correct order; keyword arguments, where each argument is accompanied by a parameter name (e.g., func(param1=value)), offering more clarity and flexibility; default arguments, which have a default value and are optional during the function call; and variable-length arguments, allowing a function to accept an arbitrary number of arguments. These are denoted by *args for non-keyword variable-length arguments and **kwargs for keyword variable-length arguments. This flexibility in argument handling allows Python functions to be incredibly versatile, accommodating a wide range of use cases from simple to complex function calls.

Code Block

3.2.3

A code block is a group of one or more statements that are grouped together and executed as a single unit. Code blocks are defined by their indentation level, which is typically done using spaces or tabs. Proper indentation is crucial in Python because it determines the scope of statements within a block. For example, when writing an if statement or a loop, the indented code underneath it forms a code block. This indentation-based structure enhances code readability and ensures that Python interprets the code correctly. Understanding code blocks is fundamental for organizing and structuring Python programs, making them more organized and easier to maintain.

Decisions

3.2.1

Decisions in programming allow programs to respond differently under varying conditions, much like making choices in everyday life. This decision-making is achieved using conditional statements, primarily if, elif, and else. The if statement checks a condition: if the condition is True, the program executes a block of code; if False, it skips it. The elif (short for 'else if') provides additional conditions to check if the initial if condition is false. Lastly, the else statement catches anything not caught by the preceding conditions. This structure enables your program to handle different scenarios and data values, making your code versatile and dynamic. For example, in a simple temperature-checking program, you might use an if statement to decide whether the temperature is hot, cold, or moderate.

Delimiter

3.3.6

A delimiter is a character or sequence of characters used to specify the boundary between separate, independent regions in text or data streams. Delimiters play a crucial role in string data processing, particularly in parsing, where they help to split a string into components or tokens based on specific boundaries. Common examples of delimiters include commas (,), semicolons (;), spaces ( ), and newlines (\n). Python provides various methods to work with delimited string data, such as the split() method of string objects, which splits a string into a list of substrings based on a specified delimiter. For instance, using a comma as a delimiter, a CSV (Comma-Separated Values) string can be parsed into individual components. The choice of delimiter is crucial for accurately interpreting the intended structure of the data, and it often depends on the format and specifications of the input data being processed.

Functions

3.6.1

In Python, functions are fundamental building blocks that allow for the modularization and reuse of code. Defined using the def keyword, a function encapsulates a sequence of statements into a single unit that can be executed whenever the function is called. Functions can accept parameters, which are variables passed into the function, allowing for customization and flexibility in their operation. They often return a value using the return statement, but this is not mandatory; functions without a return statement implicitly return None. Functions in Python can perform a wide range of tasks—from simple operations like adding two numbers to complex logic like processing data or handling files. They support concepts like default arguments, variable-length arguments (*args and **kwargs), and recursion. The ability to define and use functions enables more organized, readable, and maintainable code, making them a cornerstone of Python programming.

Iterable

3.3.1

In Python programming, an iterable is an object that can be iterated over (sometimes called a container), meaning that you can traverse through all its elements in the object, one by one. Common examples of iterables include data structures like lists, tuples, dictionaries, and strings. The key characteristic of an iterable is that it implements the __iter__() method, which returns an iterator. This iterator then allows you to use a loop (like a for loop) to go through the elements of the iterable sequentially. Each iteration asks the iterator for the "next" item, and this continues until there are no more items, at which point the iteration stops. Iterables are a fundamental concept in Python, enabling a clean, readable, and efficient way to look at and process each item in a collection of elements, without the need for indexing or manual loop management. This feature underpins much of Python's simplicity and power in handling collections of data.

Library

3.5.1

Libraries are collections of pre-written code that you can include in your projects to add functionality without having to write that code from scratch. Think of libraries as toolkits filled with specialized tools you can use to perform specific tasks. These tasks can range from mathematical computations and data analysis to handling graphics or web development. For example, the math library provides various mathematical functions, Pandas offers extensive capabilities for data manipulation and analysis, and Matplotlib allows for creating a wide range of static, animated, and interactive visualizations. Using libraries not only saves time but also enhances the capability of your programs. Libraries are one of the reasons Python is so powerful and popular, as they provide a vast array of functionalities that can be easily integrated.

Parameters

3.6.1

In Python, function parameters are the variables listed inside the parentheses in the function definition. They act as placeholders for the values that the function can accept when it is called. There are several types of parameters: positional parameters, which are mandatory and are bound to arguments based on their order; default parameters, which have a predefined value and are optional in a function call; and keyword parameters, which are named and can be passed in any order as long as the name matches. Python also supports variable-length parameters, *args for accepting arbitrary numbers of positional arguments, and **kwargs for arbitrary numbers of keyword arguments. These parameters provide the functions with the flexibility to handle various inputs, enhancing the function's adaptability and reusability. The way these parameters are defined and used plays a crucial role in how a function processes input and produces output, making them a fundamental aspect of function design in Python.

Parsing

3.3.6

Parsing strings in Python involves analyzing a string's structure and extracting specific data from it according to a predefined pattern or structure. This process is essential in various applications, such as data analysis, web scraping, and configuration file management. Python offers multiple methods and libraries to facilitate string parsing, including built-in functions like split() for dividing a string into a list of substrings (called tokens) based on a delimiter, and strip() for trimming whitespace. For more complex parsing needs, Python provides the re module, which supports regular expressions allowing for sophisticated searching, matching, and manipulation of string patterns. This capability enables developers to extract specific pieces of information from strings, validate string formats, or transform strings in powerful ways, adapting to the diverse needs of different programming scenarios.

Here is a visual representation of parsing a string for the purposes of counting the number of words in the string:

Processing

Processing in programming refers to the series of actions or steps taken by a program to manipulate and analyze data to produce a desired outcome. Processing might involve reading data from files, performing calculations, sorting items in a list, or even making decisions based on certain conditions. These processes turns raw data into useful information or actions. For example, processing could involve taking user input (like a name or a date), calculating the number of days until a specific event, or generating a personalized message. Understanding how to process data effectively is key to building functional and efficient software. This fundamental concept is used in everything from simple scripts to complex applications in fields like web development, data science, and artificial intelligence.

Repetition

3.3.1

Repetition in programming, commonly referred to as looping, or iteration, allows us to execute a block of code multiple times. This is particularly useful when you need to perform repetitive tasks efficiently. Python provides several constructs for looping, the most common being the for loop and the while loop. The for loop is used to repeat a code block a specified number of times or iterate over a sequence (like a list, tuple, or string) in the order that the items appear. For instance, you can use a for loop to process each item in a list or each character in a string. The while loop, on the other hand, continues to execute as long as a specified condition is True. It's ideal for repeating an action when you don't know in advance how many times you'll need to repeat it. Both types of loops help in writing concise and effective code, eliminating the need to write the same code multiple times.

Token

3.3.6

Tokens in the context of string data refer to the individual components or pieces that result from dividing a string based on specific criteria, such as delimiters. Tokenization is the process of splitting a string into these smaller parts or tokens, which can be words, numbers, or symbols, depending on the content of the string and the rules applied for splitting. This concept is fundamental in text processing, parsing, and natural language processing (NLP) tasks, where analyzing and understanding the textual data at a granular level is essential. For example, when processing a sentence, tokenization might involve splitting the sentence into individual words or punctuation marks. Python's split() method on strings is a straightforward way to tokenize a string based on whitespace or other specified delimiters. Additionally, libraries like NLTK (Natural Language Toolkit) provide more sophisticated tools for tokenization, capable of handling complex patterns, such as separating contractions or distinguishing punctuation. Tokenization is a crucial first step in preparing text for further analysis, such as counting word frequencies, performing sentiment analysis, or building machine learning models for text classification.

Here is a visual representation of parsing a string for the purposes of counting the number of words in the string: