CoderzColumn : Python Tutorials (Page: 1)

Python Tutorials

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.

  • Parallel Computing
  • Concurrent Programming
  • Speed Up Python Code
  • Working with Emails
  • Profiling
  • Jupyter Notebook Helpers
  • Web Scraping
  • File & Directory Access
  • Work With Archives
  • Generic OS Services
  • Python Runtime Services
  • Text Processing Services
  • File Formats
  • Networking & Interprocess Communication
  • Image Processing

For an in-depth understanding of the above concepts, check out the sections below.

Recent Python Tutorials

Transforming Numbers, Dates, and File Sizes with Humanize in Python

Transforming Numbers, Dates, and File Sizes with Humanize in Python

Sunny Solanki  Sunny Solanki
Jan 16, 2024 ~10 mins
tarfile - Simple Guide to Work with Tape Archives in Python

tarfile - Simple Guide to Work with Tape Archives in Python

Sunny Solanki  Sunny Solanki
Oct 04, 2022 ~30 mins
ZipFile - Simple Guide to Work with Zip Archives in Python

ZipFile - Simple Guide to Work with Zip Archives in Python

Sunny Solanki  Sunny Solanki
Oct 02, 2022 ~30 mins
Python multiprocessing Module - Simple Guide to Work with Processes in Python

Python multiprocessing Module - Simple Guide to Work with Processes in Python

Sunny Solanki  Sunny Solanki
Sep 15, 2022 ~45 mins
asyncio - Concurrent Programming using Async / Await Syntax in Python

asyncio - Concurrent Programming using Async / Await Syntax in Python

Sunny Solanki  Sunny Solanki
Sep 11, 2022 ~30 mins
email - How to Represent an Email Message in Python?

email - How to Represent an Email Message in Python?

Sunny Solanki  Sunny Solanki
Aug 30, 2022 ~20 mins
smtplib - Simple Guide to Sending Mails using Python

smtplib - Simple Guide to Sending Mails using Python

Sunny Solanki  Sunny Solanki
Aug 29, 2022 ~20 mins
signal - Simple Guide to Send, Receive and Handle System Signals in Python

signal - Simple Guide to Send, Receive and Handle System Signals in Python

Sunny Solanki  Sunny Solanki
Aug 28, 2022 ~20 mins
difflib - Simple Way to Find Out Differences Between Sequences / File Contents using Python

difflib - Simple Way to Find Out Differences Between Sequences / File Contents using Python

Sunny Solanki  Sunny Solanki
Aug 27, 2022 ~25 mins
configparser - Simple Guide to Create and Parse Configuration Files in Python

configparser - Simple Guide to Create and Parse Configuration Files in Python

Sunny Solanki  Sunny Solanki
Aug 26, 2022 ~30 mins

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Parallel Computing using Python

Parallel Computing using Python

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.

joblib

joblib
ipyparallel

ipyparallel
dask.distributed

dask.distributed
dask.delayed

dask.delayed
dask.bag

dask.bag
Concurrent Programming in Python

Concurrent Programming in Python

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.

threading

threading
multiprocessing

multiprocessing
concurrent.futures

concurrent.futures
asyncio

asyncio
asyncio - Synchronization Primitives

asyncio - Synchronization Primitives
queue

queue
Speed Up Python Code

Speed Up Python 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.

Numba @jit Decorator

Numba @jit Decorator
Numba @vectorize Decorator

Numba @vectorize Decorator
Numba @stencil Decorator

Numba @stencil Decorator
Numba @guvectorize Decorator

Numba @guvectorize Decorator
Numba with Pandas

Numba with Pandas
Working with Emails in Python

Working with Emails in Python

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'.

imaplib

imaplib
smtplib

smtplib
email

email
mimetypes

mimetypes
Profile Python Code / Script / Program

Profile Python Code / Script / Program

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.

cProfile & profile

cProfile & profile
line_profiler

line_profiler
memory_profiler

memory_profiler
tracemalloc

tracemalloc
Snakeviz

Snakeviz