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

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

A simple guide to use Python library tarfile to work with tape archives (tarfile). Tutorial explains how to read existing tar files, create tar files, add files to tar archives, extract contents of tar archives, compress contents using different algorithms, etc.

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

ZipFile - Simple Guide to Work with Zip Archives in Python

A simple guide on how to use Python module zipfile to handle Zip archives in Python. Tutorial covers topics like reading existing zip archives, creating new archives, appending new files to existing archives, compressing contents using different algorithms, etc.

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

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

A complete guide on how to use Python module multiprocessing to create processes and assign tasks to them for parallel execution with simple and easy-to-understand examples. Tutorial covers topics like how to create processes, how to make processes wait for other processes, how to kill or terminate processes, how to create a pool of processes and submit tasks to it, etc.

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

asyncio - Concurrent Programming using Async / Await Syntax in Python

A detailed guide on how to use Python Module "asyncio" and keywords "async" & "await" for concurrent programming in Python. Tutorial covers how to create coroutines using "async/await" syntax and run them in parallel using "asyncio" module. Tutorial covers topics like creating coroutines, collecting results once coroutines are complete, waiting for coroutines, canceling coroutines, retrieving pending coroutines, etc in detail.

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

email - How to Represent an Email Message in Python?

A complete guide on how to use Python library "email" to represent emails with simple examples. Tutorial covers topics like creating a simple email message, emails with CC / BCC, emails with diff types of attachments, setting HTTP headers in emails, combining different content types in emails, etc.

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

smtplib - Simple Guide to Sending Mails using Python

A detailed guide on how to use Python library "smtplib" to send emails (Gmail, Yahoo, etc) with simple examples. Tutorial covers various operations with mailbox servers like login / logout, verifying email ids, sending emails with CC / BCC, sending mails with attachments, etc. It uses SMTP protocol behind the scene to send emails.

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

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

A comprehensive guide on how to use Python module "signal" to send, receive and handle system (Unix/Windows) signals to signal some event. The library lets us catch signal and run a handler (callback) based on event represented by signal. The signal can be sent to different threads and processes to inform them about event and execute handler by them accordingly.

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

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

A simple guide on how to use Python module “difflib” to compare sequences and find out differences between them. Tutorial explains whole API of a module to explain different ways of comparing sequences and format results in different ways. It can be very useful to compare file contents to see differences.

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

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

A simple guide on how to use Python library "configparser" to create, read, parse, and modify config files (Generally ending with .conf, .ini, etc) in Python. Apart from basics, tutorial covers how to add/remove sections, create configuration from Python dictionary or strings and specify section details as formatted Python strings. All topics are covered with simple and easy-to-understand examples.

Sunny Solanki  Sunny Solanki
Aug 26, 2022 ~30 mins
Tags events, scheduler, sched
sched - How to Schedule Events in Python?
Python

sched - How to Schedule Events in Python?

A simple guide on how to use Python module "sched" to schedule events. It let us run a function/callable in future. We can run a task at a specified time in future or after a specified time interval has passed. Apart from scheduling tasks, we have covered how to cancel tasks, assign priorities, create a scheduler with different time units, etc.

Sunny Solanki  Sunny Solanki
Aug 25, 2022 ~15 mins

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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 (69)
Digital Marketing (8)
Machine Learning (38)
Python (130)

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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