CoderzColumn : Python Tutorials (Page: 5)

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


Tags tape-archives, tar-files
tarfile - Simple Guide to Work with Tape Archives in Python
Python

tarfile - Simple Guide to Work with Tape Archives in Python

tarfile - Simple Guide to Work with Tape Archives in Python

Sunny Solanki  Sunny Solanki
Tags asyncio, synchronization-primitives
asyncio - Synchronization Primitives in Concurrent Programming using Async / Await Syntax
Python

asyncio - Synchronization Primitives in Concurrent Programming using Async / Await Syntax

asyncio - Synchronization Primitives in Concurrent Programming using Async-Await Syntax

Sunny Solanki  Sunny Solanki
Tags mimetypes
mimetypes - Guide to Determine MIME Type of File
Python

mimetypes - Guide to Determine MIME Type of File

mimetypes - Guide to Determine MIME Type of File

Sunny Solanki  Sunny Solanki
Tags textwrap
textwrap - Simple Guide to Wrap and Fill Text in Python
Python

textwrap - Simple Guide to Wrap and Fill Text in Python

textwrap - Simple Guide to Wrap and Fill Text in Python

Sunny Solanki  Sunny Solanki
Tags python-configuration-details
sysconfig - Access Python Configuration Details
Python

sysconfig - Access Python Configuration Details

sysconfig - Access Python Configuration Details

Sunny Solanki  Sunny Solanki
Tags platform-details
platform - Guide to Retrieve Underlying System's Details using Python
Python

platform - Guide to Retrieve Underlying System's Details using Python

platform - Guide to Retrieve Underlying System's Details using Python

Sunny Solanki  Sunny Solanki
Tags threads, threading, multithreading-using-python
threading - Guide to Multithreading in Python with Simple Examples
Python

threading - Guide to Multithreading in Python with Simple Examples

Python threading Module - Simple Guide to Create and Manage threads in Python

Sunny Solanki  Sunny Solanki
Tags file-comparison, directories-comparison
filecmp - Compare Files and Directories using Python
Python

filecmp - Compare Files and Directories using Python

filecmp - Compare Files and Directories using Python

Sunny Solanki  Sunny Solanki
Tags hmac, message-authentication-codes
hmac - Hash-based Message Authentication Code using Python
Python

hmac - Hash-based Message Authentication Code using Python

hmac - Hash-based Message Authentication Code using Python

Sunny Solanki  Sunny Solanki
Tags hashlib, secure-hash, message-digest
hashlib - Compute Secure Hashes (Message Digests) in Python
Python

hashlib - Compute Secure Hashes (Message Digests) in Python

hashlib - Compute Secure Hashes (Message Digests) in Python

Sunny Solanki  Sunny Solanki
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.

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.

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.

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'.

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.