CoderzColumn : Python Tutorials (Page: 4)

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

Pandas query(): Query Pandas DataFrame using Python Expressions

Pandas query(): Query Pandas DataFrame using Python Expressions

Nov 29, 2021 ~30 mins

Dask Array: Guide to Work with Large Arrays in Parallel [Python]

Dask Array: Guide to Work with Large Arrays in Parallel [Python]

Nov 26, 2021 ~30 mins

How to Speed up Code involving Pandas DataFrame using Numba?

How to Speed up Code involving Pandas DataFrame using Numba?

Nov 22, 2021 ~30 mins

Awkward Array: Guide to Work with JSON-like Nested, Variable-sized Datasets using Numpy-like Idioms

Awkward Array: Guide to Work with JSON-like Nested, Variable-sized Datasets using Numpy-like Idioms

Nov 20, 2021 ~30 mins

xarray.DataArray: Simple Guide to Labeled N-Dimensional Array

xarray.DataArray: Simple Guide to Labeled N-Dimensional Array

Nov 11, 2021 ~30 mins

Numba @vectorize Decorator: Convert Scaler Function to Universal Function (ufunc)

Numba @vectorize Decorator: Convert Scaler Function to Universal Function (ufunc)

Nov 06, 2021 ~30 mins

Dask DataFrames: Simple Guide to Work with Large Tabular Datasets [Python]

Dask DataFrames: Simple Guide to Work with Large Tabular Datasets [Python]

Nov 03, 2021 ~30 mins

JAX - (Numpy + Automatic Gradients) on Accelerators (GPUs / TPUs)

JAX - (Numpy + Automatic Gradients) on Accelerators (GPUs / TPUs)

Oct 30, 2021 ~30 mins

How to Draw Shapes on Images using Scikit-Image [Python]?

How to Draw Shapes on Images using Scikit-Image [Python]?

Sep 06, 2021 ~30 mins

Simple Guide to Style Pandas DataFrames

Simple Guide to Style Pandas DataFrames

Sep 03, 2021 ~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

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

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