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

os, useful-functions

Simple and Useful Functions of OS Module of Python

Python

Simple and Useful Functions of OS Module of Python

Simple and Useful Functions of "OS" Module of Python

Sep 01, 2021 ~20 mins

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

Mar 17, 2021 ~30 mins

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

Mar 10, 2021 ~15 mins

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

Mar 06, 2021 ~15 mins

python-configuration-details

sysconfig - Access Python Configuration Details

Python

sysconfig - Access Python Configuration Details

sysconfig - Access Python Configuration Details

Mar 02, 2021 ~10 mins

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

Mar 01, 2021 ~10 mins

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

Feb 28, 2021 ~30 mins

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

Feb 21, 2021 ~15 mins

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

Feb 20, 2021 ~15 mins

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

Feb 19, 2021 ~25 mins

Subscribe to Our YouTube Channel

YouTube SubScribe

Most Popular Python Tutorials

1. ipywidgets - An In-depth Guide to Interactive Widgets in Jupyter Notebook
2. Simple Guide to Style Display of Pandas DataFrames
3. imaplib - Simple Guide to Manage Mailboxes using Python
4. logging.config - Simple Guide to Configure Loggers from Dictionary and Config Files
5. line_profiler: Line by Line Profiling of Python Code

Tutorial Categories

Artificial Intelligence (83)
Data Science (84)
Digital Marketing (8)
Machine Learning (38)
Python (130)

Newsletter Subscription

First
...
4
5 (current)
6
...
Last

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

ipyparallel

dask.distributed

dask.distributed

dask.delayed

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

multiprocessing

multiprocessing

concurrent.futures

concurrent.futures

asyncio

asyncio - Synchronization Primitives

asyncio - Synchronization Primitives

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

smtplib

email

mimetypes

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

line_profiler

memory_profiler

memory_profiler

tracemalloc

Snakeviz

Overview

CoderzColumn is a place developed for the betterment of development. We provide a versatile platform to learn & code in order to provide an opportunity of self-improvement to aspiring learners.

Products & Services

Quick Links

Useful links

© 2023 Copyright: