Python is one of the most widely used programming languages today. This section is a large archive of tutorials, based on Python programming language. We cover the basic and advanced technical aspects through coding examples and snippets.
For an in-depth understanding of the above concepts, check out the sections below.
warnings - Simple Guide to Handle Warning Messages in Python
argparse - Simple Guide to Command-Line Arguments Parsing in Python
traceback - How to Extract, Format, and Print Error Stack Traces in Python
Pyinstrument - Statistical Profiler for Python Code
pprofile - Deterministic & Statistical Profiling Of Python Code
Parallel Computing is a type of computation where tasks are assigned to individual processes for completion. These processes can be running on a single computer or cluster of computers. Parallel Computing makes multi-tasking super fast.
Python provides different libraries (joblib, dask, ipyparallel, etc) for performing parallel computing.
Concurrent computing is a type of computing where multiple tasks are executed concurrently. Concurrent programming is a type of programming where we divide a big task into small tasks and execute these tasks in parallel. These tasks can be executed in parallel using threads or processes.
Python provides various libraries (threading, multiprocessing, concurrent.futures, asyncio, etc) to create concurrent code.
Python is an interpreter-based language and hence is slow compared to compiler-based languages (C / C++ / Java, etc). But we can make it super fast (Almost as fast as C++).
Python has a library named 'Numba' that can help us with it. Numba is a JIT Compiler (Just-In-Time) of Python. It converts Python code to faster machine-level code using 'LLVM' compiler.
To convert python code to low-level machine code, it provides us with various decorators (@jit, @njit, @vectorize, @guvectorize, @stencil, etc.). We can decorate our Python functions using these decorators to speed up our Python functions.
Numba even let us parallelize compiled code on multiple CPUs / GPUs for even faster completion.
Python provides various libraries to work with emails. Libraries to send emails, manage mailboxes and represent emails are different based on HTTP protocol.
To send emails, Python provides a library named 'smtplib' (based on SMTP protocol).
To manage mailboxes (read emails, flag emails, move emails, create folder / directory, delete email / directory, etc), Python offers a library named 'imaplib' (based on IMAP protocol).
To represent emails, Python has a library named 'email'. To determine MIME Type of attachment file, Python offers a library named 'mimetypes'.
Profiling is the process of recording time taken by Python program / code / script / process. It measures the time complexity of a program.
Apart from time, we can also record memory usage (space complexity) by our code / program. The process of recording memory usage is referred to as 'memory profiling'. Profiling can help us make better decisions regarding resource allocation as well it presents many opportunities to optimize existing code for maximum resource utilization.
Python has many libraries (cProfile, profile, line_profiler, memory_profiler, tracemalloc, pprofile, scalene, yappi, guppy, py-spy, pyinstrument, etc.) to profile time and memory usage by our Python code.