python multithreading in 3 minutes

### python multithreading tutorial

**introduction**

multithreading is a programming technique that allows concurrent execution of two or more threads (smaller units of a process). python provides the `threading` module to create and manage threads. this can be particularly useful for i/o-bound tasks, where the program spends a lot of time waiting for external events, such as network responses or file i/o.

### key concepts

1. **thread**: a thread is a separate flow of execution. the main program can continue running while a thread is working.
2. **global interpreter lock (gil)**: python has a gil which allows only one thread to execute at a time. this means that multithreading is not always beneficial for cpu-bound tasks (heavy computations).
3. **thread lifecycle**: threads can be in various states: new, runnable, waiting, and terminated.

### creating threads

you can create threads in python by subclassing the `thread` class or using the `thread` class directly with a target function.

### example code

here’s a simple example demonstrating multithreading in python:

### explanation of the code

1. **importing the module**: we import the `threading` and `time` modules.

2. **defining functions**: we define two functions, `print_numbers()` and `print_letters()`, which will run in separate threads.

3. **creating threads**: we create two threads, `thread1` and `thread2`, specifying the target functions.

4. **starting threads**: the `start()` method begins the thread's activity. this method invokes the thread's `run()` method, which calls the target function.

5. **joining threads**: the `join()` method blocks the calling thread (main program) until the thread whose `join()` method is called is terminated.

6. **output**: the output will interleave numbers and letters, demonstrating concurrent execution.

### when to use multithreading

- **i/o-bound operations**: when your program is waiting for external operations (like file i/o, network requests).
- **use ...

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