Programming FlashCards - Learn Coding Concepts

Custom Iterators

You can create your own iterators in Python by defining a class with __iter__() and __next__() methods. This allows for custom iteration logic, enabling you to traverse complex data structures easily.

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Iterators

Iterators are objects in Python that allow you to traverse through a collection, such as lists or tuples, without exposing the underlying structure. They implement two methods: __iter__() and __next__().

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Nested For Loops

Nested for loops allow you to iterate over multiple sequences or perform operations that require two levels of iteration. This is useful for working with multi-dimensional data structures like lists of lists.

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For Loops

For loops in Python are used to iterate over a sequence (like a list, tuple, or string) or other iterable objects. The loop executes a block of code for each item in the sequence, making it a powerful tool for repetitive tasks.

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POST Requests

Discover how to send data to a server using POST requests in Python with the 'requests' library. This is essential for creating or updating resources on a web server.

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HTTP Requests

Learn how to make HTTP requests in Python using the popular 'requests' library. This allows you to interact with web services and APIs easily.

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CSV File I/O

Learn how to read from and write to CSV files in Python using the `csv` module. This is useful for handling structured data easily.

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File Handling

Learn how to read from and write to files in Python using built-in functions. This is essential for data persistence and manipulation in applications.

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Default Values

In Python, you can define default values for keyword arguments in functions. If a keyword argument is not provided, the default value is used, making parameters optional.

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Keyword Arguments

Keyword arguments allow you to pass arguments to a function by explicitly naming them. This improves code readability and allows for optional parameters without needing to remember the order.

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Resource Management

Using a 'finally' block is crucial for resource management in Python. It ensures that resources like files or network connections are properly closed, even if an error occurs during processing, preventing resource leaks.

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Finally Block

The 'finally' block in Python is used for cleanup actions that must be executed under all circumstances, such as closing files or releasing resources, regardless of whether an exception was raised or not.

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Generator Function

A generator function in Python is defined using the 'yield' keyword. It allows you to iterate over a sequence of values without storing them all in memory at once, making it more memory efficient for large datasets.

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Yield Statement

The 'yield' statement in Python is used to create a generator function. Unlike 'return', 'yield' allows the function to return a value and pause its state, resuming from where it left off when called again.

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Type Conversion

In Python, you can convert between different numeric types using built-in functions. For example, you can convert an integer to a float or vice versa. This is useful for ensuring compatibility in mathematical operations.

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Numeric Types

In Python, numeric types include integers, floats, and complex numbers. Integers are whole numbers, floats are decimal numbers, and complex numbers have a real and imaginary part. This allows for versatile mathematical operations.

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String Slicing

String slicing in Python allows you to extract a portion of a string by specifying a start and end index. It’s a powerful way to manipulate and analyze strings.

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String Formatting

In Python, string formatting allows you to create strings dynamically by embedding variables or expressions within string literals. This can be done using f-strings, format method, or the % operator.

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List Comprehensions

A concise way to create lists in Python by applying an expression to each item in an iterable, optionally filtering items.

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Context Managers

Context managers handle resource setup and teardown, ensuring proper management of resources like files, network connections, and locks using the 'with' statement

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Decorators Basics

Decorators are a powerful way to modify or enhance functions without changing their source code, allowing for metaprogramming in Python.

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Decorators

Powerful Python feature that allows modifying or enhancing functions without directly changing their source code, using @ syntax to wrap functions

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Generator Expressions

Memory-efficient way to create iterators in Python, similar to list comprehensions but generating values on-the-fly instead of storing entire sequence in memory.

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List Comprehension Magic

Create new lists by applying an expression to each item in an iterable, with optional filtering in a single, readable line of code.

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List Comprehension

A concise way to create lists in Python by combining a for loop and conditional logic in a single line of code.

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Python Random Numbers

Learn how to generate random numbers in Python using the random module, including random integers, floating-point numbers, and making random selections.

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Random Seed

Learn how to set a random seed in Python to generate reproducible random numbers for testing and debugging purposes.

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Python Walrus Operator

Assignment expression (:=) that allows assignment and evaluation in a single line, useful for reducing code complexity and improving readability.

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Custom Iterators

Iterators

Nested For Loops

For Loops

POST Requests

HTTP Requests

CSV File I/O

File Handling

Default Values

Keyword Arguments

Resource Management

Finally Block

Generator Function

Yield Statement

Type Conversion

Numeric Types

String Slicing

String Formatting

List Comprehensions

Context Managers

Decorators Basics

Decorators

Generator Expressions

List Comprehension Magic

List Comprehension

Python Random Numbers

Random Seed

Python Walrus Operator