Python As Fast as Possible - Learn Python in ~75 Minutes

This video aims to teach Python rapidly, focusing on delivering information quickly. It is not designed for absolute beginners; those seeking foundational tutorials can find resources linked in the description. Viewers are encouraged to provide feedback or corrections as needed.

To begin, download the latest version of Python from its official website, ensuring it's at least version 3.6 for compatibility with this tutorial. During installation, check the box to add Python to your system path for easier access later on. While IDLE comes bundled with Python as a coding environment, Visual Studio Code (VS Code) is recommended for a better experience; simply download it based on your operating system. After installing VS Code, install the Python extension by navigating to the extensions tab and searching for 'Python' in the marketplace.

Python is a versatile, general-purpose programming language known for its simplicity and readability. Its easy syntax allows for rapid development, making it accessible to beginners. Common applications of Python include web development, machine learning, artificial intelligence, and data science.

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Core data types in Python include integers, floats, strings, and booleans. An integer (int) is any whole number without a decimal point; for example, -9 or 73737 are ints. A float contains a decimal point—numbers like 272.00 or 9.7 qualify as floats due to their precision from the decimal notation. Strings consist of characters enclosed in single or double quotation marks; they can represent text or numbers but remain distinct based on their formatting—for instance, '4.6' is treated as a string despite resembling a float because it’s quoted.

Output in Python is primarily done using the print function, which requires strings to be enclosed in quotes. For example, printing 'hello world' works while just typing hello world results in an error due to lack of string notation. Numbers like 4.5 can be printed directly since they are recognized as floats without any issues. Multiple items can be printed together by separating them with commas; this automatically adds spaces between outputs unless specified otherwise through arguments such as end=''. By default, print statements move to a new line after execution but this behavior can be altered by changing the end parameter.

Variables in Python are created by assigning a name to a value using an equal sign. For example, setting the variable 'hello' to 10 allows access through print statements. Variables can also reference other variables; changing one does not affect another unless explicitly reassigned at that moment in time. Naming conventions dictate no special characters or starting with numbers, while underscores are used for multi-word names following snake_case format.

Getting user input in programming is straightforward using the input function. A prompt, which must be a string, guides users on what to enter; for example, 'name: '. It's essential to include a space after the colon so that typing begins neatly after it. The entered value can then be stored in a variable and printed out later as needed. Multiple inputs are possible too—like asking for age—and these values can be combined into meaningful output messages.

Understanding Arithmetic Operators in Python Arithmetic operators in Python include addition, subtraction, multiplication, division, exponentiation, floor division and modulus. It's crucial that operands are of the same data type; otherwise errors will occur. Division always returns a float to maintain precision even if both numbers are integers. The order of operations follows standard rules (brackets first), which can be managed using parentheses for clarity.

Handling User Input and Data Types When taking user input with the 'input' function in Python, values default to strings regardless of their appearance as numbers. To perform arithmetic operations on these inputs like subtraction or addition with numeric values requires conversion from string to integer using functions such as 'int()'. If any operand is a float during calculations involving floats or integers together it results automatically into a float output due to precision requirements.

Understanding String Methods and Their Functions When performing arithmetic operations, if one operand is a float, the result will also be a float. A method in programming refers to functions that can be called on objects using the dot operator. For example, calling 'hello.upper()' converts all characters of the string "hello" to uppercase. Similarly, 'hello.lower()' changes it to lowercase and methods like 'capitalize()' format strings properly by capitalizing only the first letter.

Counting Substrings Using Case-Sensitive Methods The count method identifies how many times a specific substring appears within another string while being case-sensitive; for instance, counting lowercase letters differs from uppercase ones. To accurately count occurrences regardless of case sensitivity, converting strings with '.lower()' before counting ensures correct results—like finding two instances of "o" in "Hello World." These methods can also be chained together for more complex manipulations or queries on strings.

Understanding String Operations and Boolean Logic String multiplication in Python allows a string to be repeated by an integer, while concatenation combines two strings. Conditions evaluate comparisons between variables or data types, yielding true or false results based on boolean logic. Core conditional operators include equality (==), inequality (!=), and relational operators (<, >). For example, comparing 'hello' with 'Hello' shows that they are not equal due to case sensitivity.

Combining Multiple Conditions Effectively Chained conditionals combine multiple conditions into one larger expression for evaluation. Each condition can involve arithmetic operations alongside logical comparisons; however, arithmetic takes precedence over conditional checks. This means expressions must be carefully structured using parentheses when necessary to ensure correct evaluations of both sides before applying the conditional operator.

Combining Boolean Results Using Logical Operators Chained conditionals allow for combining multiple boolean results using logical operators: 'and', 'or', and 'not'. The 'or' operator evaluates to true if at least one of the conditions is true, while the 'not' operator flips a boolean value. For example, if result 1 or result 2 is true, then their combined evaluation will also be true. This can be expanded by adding more conditions together with these operators.

Understanding Order of Operations in Logic Expressions The ‘and’ operator requires both sides to be true for the entire expression to evaluate as such; otherwise it returns false. Understanding order of operations among these logical operators—where not takes precedence over and which in turn precedes or—is crucial when constructing complex expressions. Evaluating an expression like false and true or true follows this hierarchy leading ultimately towards determining its final truth value based on individual evaluations.

Conditional Execution with If/Else Statements The if, else if (elif), and else statements allow for conditional execution in programming. An example demonstrates checking a user's name; if it matches 'Tim', the program responds positively. If not, an alternative message is printed using the else statement. The elif allows for additional conditions to be checked sequentially without needing multiple nested structures.

Understanding Collections Collections are groups of elements that can be ordered or unordered, essential for managing data efficiently in programming. Understanding how to loop over these collections will enhance data manipulation capabilities significantly.

Understanding Lists: Definition, Operations & Mutability Lists in Python are defined using square brackets and can contain elements of different data types, maintaining the order of insertion. The length of a list is determined by the `len` function, which also applies to strings. Elements can be added with `append`, extended from another list, or removed using `pop`, which returns and removes an element based on its index. Lists are mutable; changes made to one reference affect all references pointing to that same list unless explicitly copied.

Exploring Tuples: Characteristics & Limitations Tuples resemble lists but use round brackets and are immutable—once created, their contents cannot change or grow through appending or removing elements. Attempting modifications like item assignment will result in errors due to this immutability feature. While tuples aren't as commonly used as lists for dynamic collections because they can't be altered after creation, they still serve specific purposes where fixed groupings of values are needed.

Understanding For Loops and Range Function For loops allow iteration a defined number of times, unlike while loops which run based on conditions. A basic for loop can be created using the syntax 'for i in range(10)', printing numbers from 0 to 9. The range function accepts one to three arguments: start, stop, and step; with defaults starting at zero and incrementing by one unless specified otherwise. For example, 'range(1, 10)' starts at 1 and stops before reaching 10.

Iterating Through Lists Efficiently To iterate through lists using for loops directly prints each element when used as 'for i in list'. To track indices alongside values without manual indexing is possible with the enumerate function that pairs index positions with their corresponding elements efficiently. Using 'enumerate', you get both index (i) and value (element), allowing streamlined access to all items within a collection along with their respective positions.

While loops execute a block of code as long as a specified condition is true. For example, setting 'i' to zero and using the condition 'while i is less than 10', allows for repeated actions like printing "run" and incrementing 'i'. Various operations can be performed on variables within these loops, such as addition or multiplication with shorthand syntax (e.g., '+=' or '*='). Additionally, break statements can terminate the loop when certain conditions are met; for instance, if 'i' equals 10 in this case.

Mastering Python's Slice Operator The slice operator in Python allows for extracting portions of collections like strings, lists, or tuples using square brackets with a sequence of colons and numbers. It operates on three parameters: start, stop, and step—similar to the range function. For example, specifying '0:4:2' retrieves elements at indices 0 and 2 from the collection while excluding index 4. Omitting any parameter defaults it to either the beginning or end of the collection.

Versatility Across Data Types Reversing a list is easily achieved by using '::-1', which steps backwards through all elements from start to finish. This functionality extends beyond lists; strings can also be sliced similarly—for instance, stepping by two yields every second character in a string. The versatility of slicing applies across various data types including tuples as well.

Understanding Sets: Unique Elements Without Order Sets in Python are unordered collections of unique elements, meaning they do not allow duplicates and do not maintain order. This makes operations like lookups, additions, and removals extremely fast compared to lists since there’s no need for shifting positions when modifying the set. To create a set, use `set()` for an empty one or define it with curly braces containing initial elements; however, using just `{}` creates a dictionary instead.

Efficient Operations: Adding Items & Membership Checks Adding or removing items from sets is straightforward with methods like `.add()` and `.remove()`, which operate efficiently due to constant time complexity. Checking membership within a set (e.g., `4 in s`) is also quick compared to lists because of how sets manage their data structure. Additionally, various operations such as union and intersection can be performed on sets easily but require understanding their specific functions without delving into all details here.

Understanding Dictionaries: Key-Value Pairs and Operations Dictionaries, akin to hash tables or maps in other languages, consist of key-value pairs where each key corresponds to a specific value. These values can be various data types including lists or integers. Adding new keys is simple and allows for different data types as keys; operations such as checking existence within the dictionary are efficient due to hashing techniques that enable quick retrieval and modification.

Manipulating Dictionaries: Accessing Values, Deleting Keys To manipulate dictionaries further, one can retrieve all values using methods like x.values() which returns them in list form. Keys can also be accessed with x.keys(), while deletion involves specifying the key directly. Iteration over items provides both keys and their corresponding values through x.items(), allowing for flexible access during loops.

Python comprehensions allow for concise initialization of lists, tuples, dictionaries, and sets in a single line. For example, using `x = [x for x in range(5)]` generates a list of numbers from 0 to 4. More complex examples can include conditions like adding only multiples of five or initializing dictionary keys with zero values. While they are powerful tools that enhance code readability and efficiency, it's important to note that tuple comprehension requires the use of the tuple constructor due to its return type being a generator object when done incorrectly.

In Python, functions are defined using the 'def' keyword followed by a function name and optional parameters. The body of the function is indented and contains code that executes when called. Functions can return values, including multiple results in tuples which can be unpacked into separate variables for easier access. Optional parameters with default values allow flexibility in how functions are called without requiring all arguments to be specified each time.

Nested Functions as First-Class Objects Functions in Python can be nested, allowing one function to return another without executing it immediately. This demonstrates that functions are first-class objects and can be treated like variables. When a nested function is called later, it retains access to the outer function's scope.

Utilizing Unpack Operators in Function Calls The unpack operator (*) separates elements from collections into individual arguments for functions. For example, using * with print allows multiple values from a list or tuple to be printed separately instead of as a single collection item. The double unpack operator (**) works similarly for dictionaries by passing key-value pairs directly into functions regardless of their order.

Dynamic Argument Handling with Star Args and Quarks Star args (*) and star star quarks (**) enable flexible argument handling in functions by accepting an arbitrary number of positional and keyword arguments respectively. They allow developers to create more dynamic interfaces where the exact number or type of inputs isn't predetermined, enhancing code versatility while maintaining clarity on how these parameters are processed within the function context.

In Python, variables can be either local or global based on their scope. A variable defined within a function is local and cannot affect the same-named variable outside of it. To modify a global variable from within a function, you must declare it with the 'global' keyword; otherwise, any assignment creates a new local instance instead of changing the original value. While using 'global' allows access to change its value globally, it's generally discouraged due to potential confusion in code management.

In Python, exceptions are raised using the keyword 'raise', similar to 'throw' in Java. By invoking raise followed by an exception type and a message, you can trigger an error condition within your program. This allows for immediate feedback when something goes wrong; for example, raising a custom exception with descriptive text helps identify issues clearly. Additionally, since exceptions are base classes in Python's object-oriented programming paradigm, they can be extended to create custom exceptions tailored to specific needs.

In Python, exceptions are handled using a try-except-finally block. The code inside the try block is executed, and if an error occurs (like division by zero), it jumps to the except block where you can handle the exception without crashing the program. You can catch specific exceptions or use a general one for any type of error. Additionally, a finally block runs regardless of whether an exception occurred; it's typically used for cleanup operations like closing files after execution.

Lambdas in Python are one-line anonymous functions that do not require the 'def' keyword for definition. For example, a lambda can take an argument and return a value by simply using syntax like 'lambda x: x + 5'. When called with specific values, it performs calculations such as returning 7 when inputting 2 or yielding results from multiple arguments like adding two numbers together. Although lambdas may seem complex at first glance, they offer straightforward functionality once understood.

Transforming Lists with Map and Filter Functions Map and filter are essential Python functions that utilize lambda expressions to transform lists. The map function applies a specified operation, such as adding 2 or multiplying by 2, to each element in the list, generating a new list with modified values. In contrast, the filter function evaluates elements based on criteria defined in a lambda expression—returning only those that meet specific conditions like being even numbers.

The Power of Lambda Functions Lambda functions streamline coding by allowing quick definitions within map or filter statements without needing separate named functions. For instance, using 'i mod 2 == 0' filters for even numbers directly inline rather than defining an external function first. This flexibility makes lambdas particularly useful when concise operations are needed during data processing tasks.

F strings are a powerful feature for string manipulation in Python, allowing the embedding of expressions directly within strings. By using curly braces, you can include calculations or variables without needing to concatenate them manually. For example, writing `f'hello {6 + 8}'` evaluates the expression and outputs 'hello 14'. This method simplifies working with dynamic content in strings by automatically converting embedded values into their string representation.

The tutorial on f-strings concludes with a quick recap, emphasizing the importance of efficiency and respect for viewers' time. The speaker encourages feedback through likes and subscriptions while acknowledging that some topics, like object-oriented programming and advanced language features, were not covered in detail. Resources are provided for further learning: an hour-long video on object-oriented programming fundamentals and a six-part series exploring expert-level Python features such as meta classes, context managers, and generators.