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    Ian Talks Python A-Z
    Ian Talks Python A-Z
    Ian Talks Python A-Z
    Ebook419 pages5 hours

    Ian Talks Python A-Z

    By Ian Eress

    ()

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    About this ebook

    Unlock the mysteries of Python programming with this guide to the key concepts and definitions. From famous programmers and libraries to data science and web development, this book provides a clear and accessible reference to the field of Python programming. Written for beginners, it is the perfect resource for anyone looking to deepen their understanding of this rapidly evolving field. With clear explanations, this book is your go-to reference for all things Python.


     

    LanguageEnglish
    PublisherIan Eress
    Release dateFeb 9, 2023
    ISBN9798215369296
    Ian Talks Python A-Z
    Author

    Ian Eress

    Born in the seventies. Average height. Black hair. Sometimes shaves. Black eyes. Nearsighted. Urban. MSc. vim > Emacs. Mac.

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      Ian Talks Python A-Z - Ian Eress

      Ian Talks Python A-Z

      Ian Eress

      Published by Ian Eress, 2023.

      While every precaution has been taken in the preparation of this book, the publisher assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

      IAN TALKS PYTHON A-Z

      First edition. February 9, 2023.

      Copyright © 2023 Ian Eress.

      ISBN: 979-8215369296

      Written by Ian Eress.

      Table of Contents

      A

      B

      C

      D

      E

      F

      G

      H

      I

      J

      K

      L

      M

      N

      O

      P

      Q

      R

      S

      T

      U

      V

      W

      X

      Y

      Z

      INDEX

      For Caitlyn

      A

      ADOdb: adodbapi is a Python DB-API 2.0 (PEP-249) module that makes it easy to use Microsoft ADO for connecting with databases and other data sources. It is designed to be used with either CPython or IronPython. adodbapi provides an interface for accessing SQL server databases using ADO. It works with SQL2005, SQL2008, SQL2012, and SQL2014 databases. The library also includes a code fragment that accesses an ADO 2.1 recordset.

      AGG, Matplotlib: Matplotlib is a plotting library in Python used for data visualization. It provides functions for creating a wide range of plots, including line plots, scatter plots, bar plots, histograms, and more. Matplotlib has a number of built-in plotting styles and also supports customization through the use of stylesheets.

      AGG is a high-quality rendering engine for Matplotlib that uses Anti-Grain Geometry (AGG) to render the plots. AGG is a C++ library that provides high-quality rendering with low overhead, making it a good choice for rendering large amounts of data. It is also highly optimized, which makes it ideal for use in real-time plotting applications.

      Matplotlib with AGG can be used to create publication-quality plots, with the option to export the plots in various formats, including PNG, PDF, and SVG. The library supports interactive plotting, with the ability to zoom, pan, and select data points. This makes it a popular choice for data exploration and analysis, as well as for creating visualizations for presentations and reports.

      Abstract base class: An abstract base class (ABC) in Python is a class that is used as a blueprint for other classes. It provides an infrastructure for defining abstract base classes, as outlined in PEP 3119, and allows you to create a set of methods that must be created within the child classes. ABCs don't contain implementation, but instead provide an interface that must be implemented by the child classes. This helps to ensure consistency across related classes and can help reduce code duplication.

      Abstract class: An abstract class in Python is a special kind of class that cannot be instantiated. It acts as a blueprint for other classes and defines common methods that must be implemented by its subclasses.

      An abstract class is defined using the abc module and the ABC class, and it can contain both abstract methods (methods with no implementation) and concrete methods (methods with implementation).

      Subclasses of an abstract class must override its abstract methods and provide their own implementation. This allows for code reuse and enforces a common interface among subclasses.

      Abstract classes are useful for creating a common interface for a set of related classes, and for encapsulating common behavior and properties in a single place. They are also used to define contracts for classes that will be used as a base for multiple implementations.

      Abstract method: An abstract method in Python programming is a method that is declared, but contains no implementation. Abstract classes cannot be instantiated and require subclasses to provide implementations for the abstract methods. Abstraction is implemented using the abstract classes, which are created to declare a set of methods that must be overridden. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods and properties are overridden.

      Advanced indexing, NumPy: Advanced indexing in NumPy refers to the use of slicing, index arrays or boolean masks to extract elements from arrays or modify elements in arrays. This enables us to access elements or subarrays from arrays that are non-contiguous, non-sequential, or of different shapes.

      NumPy is a library in Python used for scientific computing and data analysis. It provides multi-dimensional arrays and functions to perform mathematical operations on those arrays. Advanced indexing is one of the features of NumPy that makes it highly versatile and useful in many data analysis applications.

      For example, to extract elements that meet a certain condition, you can use a boolean mask, where the mask is an array of True and False values that correspond to the elements in the array. The masked elements can then be extracted using the array and the mask.

      Advanced indexing also supports accessing elements from arrays with different shapes, for example, indexing a 2D array with a 1D array. This can be useful in a number of data analysis scenarios where you want to access elements from different arrays based on certain conditions.

      Along an axis, NumPy: In NumPy, an axis refers to a dimension of an array. When you perform operations on arrays, you can specify whether you want the operation to be performed along an axis or across all axes.

      For example, you might have a 2D array and want to calculate the sum of each row. To do this, you would perform the sum operation along axis 0, which corresponds to the rows of the array. This would result in a 1D array, where each element is the sum of the values in a row of the original 2D array.

      Similarly, you might have a 3D array and want to calculate the sum of each plane. To do this, you would perform the sum operation along axis 2, which corresponds to the planes of the array.

      The concept of operating along an axis is useful in many data analysis and manipulation scenarios, as it allows you to perform operations on a specific dimension of the array, rather than across the entire array. This can be useful for reducing the dimensionality of the data, for summarizing the data along a specific axis, or for reshaping the data in a specific way.

      Annotation: Annotation in Python programming refers to the process of adding metadata or additional information to the code. This information can be used for documentation purposes, for example to explain the purpose of a function or a variable, or to provide information about the expected inputs and outputs.

      In Python, annotations are added as comments to the source code and have a specific syntax. For example, a function annotation can be added like this:

      def add(a: int, b: int) -> int:

      return a + b

      In this example, the : int after a and b is an annotation indicating that these parameters are expected to be integers. The -> int after the parameter list is an annotation indicating that the function returns an integer.

      Annotations are optional in Python, and they are not used by the Python interpreter to enforce any constraints. However, they can be useful for other tools, like code editors, to provide better code completion suggestions, or for documentation tools, to generate more accurate and complete documentation.

      Annotations were added to Python in version 3.0, and they are not supported in earlier versions of the language.

      AppJar: AppJar is a Python library for building graphical user interfaces (GUIs). It provides a simple and easy-to-use interface for creating windows, buttons, labels, and other GUI components, without the need for extensive knowledge of the underlying GUI toolkit.

      AppJar is built on top of the Tkinter library, which is the standard GUI library for Python. It provides a higher-level interface for creating GUIs, abstracting away some of the complexities of Tkinter.

      One of the key features of AppJar is its simplicity. It has a small number of core components and a straightforward API, making it easy to get started with building GUIs. It also supports a range of common GUI components, including buttons, labels, checkboxes, and more.

      AppJar is well-suited for creating simple, single-window applications, and it can be especially useful for educational purposes, where the focus is on teaching basic programming concepts rather than the details of GUI development.

      While AppJar is a useful library for creating simple GUIs, it may not be suitable for more complex applications that require a more sophisticated user interface. In these cases, you may want to consider using a more powerful GUI library, like PyQt or wxPython.

      Argument: An argument in Python programming refers to the value passed to a function when it is called. Functions can be defined to accept one or more arguments, which are used to perform specific operations within the function.

      For example, consider the following function definition:

      def add(a, b):

      return a + b

      This function takes two arguments, a and b, which are used to perform the addition operation. When the function is called, you must pass two values to it, like this:

      result = add(3, 4)

      In this example, the values 3 and 4 are passed as arguments to the add function. The function uses these values to perform the addition operation and returns the result, which is then stored in the variable result.

      There are several types of arguments in Python, including positional arguments, keyword arguments, and default arguments. Positional arguments are passed to a function in the order in which they are defined, while keyword arguments are passed by explicitly specifying the argument name and its value. Default arguments are arguments that have a default value specified in the function definition, and they are used when no value is provided for that argument.

      By using arguments in functions, you can write reusable and flexible code that can be adapted to different inputs and use cases.

      Arithmetic operator: Arithmetic operators are symbols used in Python programming to perform arithmetic operations like addition, subtraction, multiplication, division, and modulo. The following table summarizes the most common arithmetic operators in Python:

      For example, the following code performs arithmetic operations using the +, -, *, and / operators:

      a = 3

      b = 4

      c = a + b

      d = a - b

      e = a * b

      f = a / b

      In this example, the values of a and b are assigned to 3 and 4, respectively. The addition operation a + b results in 7, which is assigned to c. The subtraction operation a - b results in -1, which is assigned to d. The multiplication operation a * b results in 12, which is assigned to e. The division operation a / b results in 0.75, which is assigned to f.

      Note that in Python 3, division between two integers returns a float, while in Python 2, division between two integers returns an integer. To get the floor division between two integers in Python 2, use the // operator. To get the floor division between two integers in both Python 2 and 3, use the // operator.

      Array: An array in Python programming is a data structure used to store a collection of elements of the same type. In Python, arrays can be implemented using the array module. The array module provides an array() constructor, which can be used to create arrays of different types, like integers, floating-point numbers, and characters.

      Here is an example of how to create an array of integers in Python:

      import array

      # Create an array of integers

      arr = array.array('i', [1, 2, 3, 4, 5])

      # Print the array

      print(arr)

      This code imports the array module and creates an array of integers using the array() constructor. The first argument to the array() constructor is the type code for the array, in this case 'i' for integers. The second argument is a list of values, which are stored in the array.

      Arrays in Python are similar to lists in that they are ordered collections of elements. However, unlike lists, arrays have a fixed size and the elements must all be of the same type. The array module provides a number of methods for manipulating arrays, like adding and removing elements, and converting arrays to other data structures.

      For most purposes, lists are a more flexible and convenient data structure in Python. However, arrays can be useful in certain performance-critical situations, where the fixed size and homogeneous type of the elements can lead to more efficient memory usage and faster processing.

      Array scalar, NumPy: An Array Scalar in NumPy is a scalar value that is assigned to an array with a single element. It is used to represent scalar values within the NumPy array framework, and it enables the use of scalar operations that are broadcast across the entire array.

      For example, consider the following code:

      import numpy as np

      # Create an array scalar

      scalar = np.array(5)

      # Create an array

      arr = np.array([1, 2, 3, 4])

      # Add the array scalar to the array

      result = scalar + arr

      In this example, np.array(5) creates an array scalar with a single element. The value 5 is broadcast to all elements of the array arr, resulting in an array with the same shape as arr, but with each element increased by 5. The result of the addition is assigned to the variable result.

      Array scalars are similar to scalar values in other programming languages, but they are implemented as arrays with a single element in NumPy. This allows scalar operations to be broadcast across arrays and enables the use of scalar values within the NumPy framework.

      Array, NumPy: An array in NumPy is a multi-dimensional data structure for storing homogeneous data, like numbers or strings. NumPy arrays are similar to lists in Python, but they have a number of advantages over lists, including more efficient memory usage and faster computation.

      NumPy arrays are created using the numpy.array() function. Here is an example of how to create a 1-dimensional array in NumPy:

      import numpy as np

      # Create a 1-dimensional array

      arr = np.array([1, 2, 3, 4, 5])

      # Print the array

      print(arr)

      In this example, the np.array() function is used to create an array with the values [1, 2, 3, 4, 5]. The resulting object is a 1-dimensional array with the shape (5,).

      NumPy arrays have a number of useful attributes and methods, including:

      shape: returns the dimensions of the array

      ndim: returns the number of dimensions of the array

      size: returns the number of elements in the array

      dtype: returns the data type of the elements in the array

      NumPy also provides a wide range of functions for manipulating arrays, like reshaping, sorting, and aggregating data.

      NumPy arrays are an essential data structure for many scientific and mathematical applications in Python. They provide a flexible and efficient way to store and manipulate large amounts of data, and they are widely used in a variety of fields, including machine learning, computer vision, and scientific computing.

      Array_like, NumPy: In NumPy, the term array-like refers to objects that can be converted into arrays, like lists, tuples, and other arrays. Array-like objects have a similar structure to NumPy arrays, but they may have different properties, like being mutable or having different data types.

      NumPy provides a number of functions that can convert array-like objects into NumPy arrays, like numpy.array() and numpy.asarray(). For example:

      import numpy as np

      # Create a list

      list_ = [1, 2, 3, 4, 5]

      # Convert the list to a NumPy array

      arr = np.array(list_)

      # Print the array

      print(arr)

      In this example, the np.array() function is used to convert the list list_ into a NumPy array. The resulting object is a 1-dimensional array with the shape (5,) and the values [1, 2, 3, 4, 5].

      In some cases, it may be necessary to use numpy.asarray() instead of numpy.array() to convert array-like objects into NumPy arrays. The difference between the two functions is that numpy.asarray() does not make a copy of the input data, whereas numpy.array() always returns a copy of the input data.

      The term array-like in NumPy refers to objects that can be converted into arrays, like lists and tuples. NumPy provides a number of functions for converting array-like objects into NumPy arrays, which can then be used for a wide range of data processing and analysis tasks.

      Assertion: An assertion in Python is a statement used to check whether a condition is true, and if it is not, raise an exception (an error). The syntax for an assertion is as follows:

      assert condition, message

      The condition is the expression that you want to check, and the message is an optional string that will be displayed if the assertion fails (i.e., if condition is False).

      Assertions are used to check the validity of input data, and they are used in functions and methods to ensure that the inputs are within the expected range. For example, you might use an assertion to check that a function argument is a positive integer:

      def factorial(n):

      assert n >= 0, Input must be a positive integer

          # ...

      If the input to the function factorial() is a negative integer, the assertion will raise an AssertionError with the message Input must be a positive integer.

      Assertions should not be used to handle runtime errors in your code. Instead, they should be used to check for programming errors, like incorrect function arguments or invalid data.

      Assertions are used in Python to check whether a condition is true and raise an error if it is not. They are an important tool for catching programming errors, but they should not be used to handle runtime errors.

      Asynchronous context manager: In Python, asynchronous context managers are a type of context manager that can be used with asynchronous programming. A context manager is an object that defines the behavior for entering and exiting a context, like with a with statement.

      Asynchronous context managers are used to manage resources that can be used asynchronously, like sockets, files, and database connections. For example, here's an asynchronous context manager that opens a file and reads its contents asynchronously:

      import asyncio

      async def read_file(file):

      async with open(file, r) as f:

      return await f.read()

      In this example, the async with statement is used to open the file file for reading. The async keyword is used to indicate that the context manager should be used asynchronously, and the await keyword is used to wait for the contents of the file to be read.

      The benefit of using asynchronous context managers is that they can simplify the management of resources that need to be used asynchronously. For example, you can use the same async with statement to manage a variety of resources, like sockets and databases, without having to write custom code for each resource.

      Asynchronous context managers are a type of context manager that can be used with asynchronous programming. They simplify the management of resources that need to be used asynchronously, and they can be used with a variety of resources, like sockets and databases.

      Asynchronous generator: An asynchronous generator in Python is a special type of generator that can be used in asynchronous programming. A generator is a special type of iterator that can be used to produce a sequence of values.

      An asynchronous generator is similar to a regular generator, but it can be used to generate values asynchronously. For example, you might use an asynchronous generator to read data from a file asynchronously:

      import asyncio

      async def read_file(file):

      async def generator():

      with open(file, r) as f:

      while True:

                      line = await f.readline()

      if not line:

      break

      yield line

      return generator()

      In this example, the asynchronous generator generator is defined inside the read_fileO function. The generator uses the yield statement to produce the lines of the file as they are read. The await keyword is used to wait for the next line of the file to be read.

      The benefit of using asynchronous generators is that they allow you to generate values asynchronously, which can simplify the management of asynchronous resources, like files and sockets. For example, you can use an asynchronous generator to read data from a file in chunks, which can help to avoid blocking the event loop.

      An asynchronous generator in Python is a special type of generator that can be used in asynchronous programming. It allows you to generate values asynchronously, which can simplify the management of asynchronous resources, like files and sockets.

      Asynchronous generator iterator: An asynchronous generator iterator in Python is a special type of iterator that is used to iterate over the values produced by an asynchronous generator. An asynchronous generator is a special type of generator that can be used to produce values asynchronously.

      An asynchronous generator iterator is similar to a regular iterator, but it can be used to iterate over the values produced by an asynchronous generator asynchronously. For example, you might use an asynchronous generator iterator to read data from a file asynchronously:

      import asyncio

      async def read_file(file):

      async def generator():

      with open(file, r) as f:

      while True:

                      line = await f.readline()

      if not line:

      break

      yield line

      async for line in generator():

      print(line)

      In this example, the async for statement is used to iterate over the values produced by the asynchronous generator generator. The await keyword is used to wait for the next value to be produced by the generator.

      The benefit of using asynchronous generator iterators is that they allow you to iterate over values asynchronously, which can simplify the management of asynchronous resources, like files and sockets. For example, you can use an asynchronous generator iterator to read data from a file in chunks, which can help to avoid blocking the event loop.

      An asynchronous generator iterator in Python is a special type of iterator that is used to iterate over the values produced by an asynchronous generator. It allows you to iterate over values asynchronously, which can simplify the management of asynchronous resources, like files and sockets.

      Asynchronous iterable: An asynchronous iterable in Python is a special type of iterable that can be used in asynchronous programming. An iterable is any object in Python that can be iterated over, like lists, tuples, and dictionaries.

      An asynchronous iterable is similar to a regular iterable, but it can be used to iterate over its elements asynchronously. For example, you might use an asynchronous iterable to read data from a file asynchronously:

      import asyncio

      async def read_file(file):

      async def generator():

      with open(file, r) as f:

      while True:

                      line = await f.readline()

      if not line:

      break

      yield line

      return generator()

      async def main():

      async for line in read_file(file.txt):

      print(line)

      await main()

      In this example, the asynchronous iterable read_file returns an asynchronous generator. The async for statement is used to iterate over the values produced by the asynchronous generator. The await keyword is used to wait for the next value to be produced by the generator.

      The benefit of using asynchronous iterables is that they allow you to iterate over values asynchronously, which can simplify the management of asynchronous resources, like files and sockets. For example, you can use an asynchronous iterable to read data from a file in chunks, which can help to avoid blocking the event loop.

      In summary, an asynchronous iterable in Python is a special type of iterable that can be used in asynchronous programming. It allows you to iterate over values asynchronously, which can simplify the management of asynchronous resources, like files and sockets.

      Asynchronous iterator: An asynchronous iterator in Python is a special type of iterator that can be used in asynchronous programming. An iterator is an object that implements the __next__ method and the __iter__ method, and is used to iterate over a collection of values.

      An asynchronous iterator is similar to a regular iterator, but it can be used to iterate over values asynchronously. For example, you might use an asynchronous iterator to read data from a file asynchronously:

      import asyncio

      async def read_file(file):

      with open(file, r) as f:

      while True:

                  line = await f.readline()

      if not line:

      break

      yield line

      async def main():

      async for line in read_file(file.txt):

      print(line)

      await main()

      In this example, the read_file() function returns an asynchronous generator, which is an asynchronous iterator. The async for statement is used to iterate over the values produced by the asynchronous generator. The await keyword is used to wait for the next value to be produced by the generator.

      The benefit of using asynchronous iterators is that they allow you to iterate over values asynchronously, which can simplify the management of asynchronous resources, like files and sockets. For example, you can use an asynchronous iterator to read data from a file in chunks, which can help to avoid blocking the event loop.

      In summary, an asynchronous iterator in Python is a special type of iterator that can be used in asynchronous programming. It allows you to iterate over values asynchronously, which can simplify the management of asynchronous resources, like files and sockets.

      Attribute: An attribute in Python is a named value or property of an object. It is used to store information about an object, and it is accessed using dot notation.

      For example, consider the following class definition:

      class Person:

      def __init__(self, name, age):

              self.name = name

              self.age = age

      person = Person(John Doe, 30)

      In this example, the Person class has two attributes: name and age. These attributes are set using the self.name and self.age assignments in the __init__ method. To access these attributes, you can use dot notation:

      print(person.name)  # Output: John Doe

      print(person.age)   # Output: 30

      You can also access attributes dynamically using the getattr() function:

      print(getattr(person, name))  # Output: John Doe

      print(getattr(person, age))   # Output: 30

      In addition to instance attributes, classes can also have class attributes. Class attributes are shared by all instances of a class, and they are defined outside of any method:

      class Person:

          species = Homo sapiens

      def __init__(self, name, age):

              self.name = name

              self.age = age

      person = Person(John Doe, 30)

      print(person.species)  # Output: Homo sapiens

      In summary, an attribute in Python is a named value or property of an object. It is used to store information about an object, and it is accessed using dot notation. Class attributes are shared by all instances of a class, while instance attributes are unique to each instance.

      Augmented assignment operator: In Python, the augmented assignment operator is a shorthand for updating a variable's value with an operation. Instead of writing x = x + 1, you can write x += 1.

      Here are some examples of the augmented assignment operator in Python:

      x = 5

      x += 1  # x is now 6

      y = 8

      y -= 3  # y is now 5

      z = 9

      z *= 2  # z is now 18

      w = 10

      w /= 5  # w is now 2.0

      The augmented assignment operator can be used with any arithmetic operator (+, -, *, /, %, //, **), as well as with the bitwise operators (&, |, ^, <<, >>). It can also be used with the assignment of other compound data types like lists and dictionaries.

      numbers = [1, 2, 3, 4]

      numbers += [5, 6, 7]  # numbers is now [1, 2, 3, 4, 5, 6, 7]

      person = {'name': 'John Doe', 'age': 30}

      person['age'] += 1  # person is now {'name': 'John Doe', 'age': 31}

      In summary, the augmented assignment operator is a shorthand for updating a variable's value with an operation in Python. It can be used with any arithmetic operator, as well as with the assignment of other compound data types like lists and dictionaries.

      Augmented assignment statement: The augmented assignment statement in Python is a shorthand for updating a variable's value with an operation. It's a combination of the assignment operator (=) and an arithmetic or bitwise operator. Instead of writing x = x + 1, you can write x += 1.

      Here are some examples of the augmented assignment operator in Python:

      x = 5

      x += 1  # x

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