How to Publish Python Packages to PyPI

How to Publish Python Packages to PyPI. In this step-by-step tutorial, you'll learn how to create a Python package for your project and how to publish it to PyPI, the Python Package Repository. Why should you package your code for PyPI? How to structure your project and your code, including why you need a codesrc/code folder!

Always wanted to publish a package on PyPI, but didn't know where to start? This talk is for you! Starting with nothing, we'll build a package and publish it on PyPI using current best practices.

Learn how to structure your project and your code. Discover what goes in your coderequirements.txt/code and your codesetup.py/code. Ensure your code will work in different on different platforms! Document your code so people won't ask you loads of questions! Finally, learn how avoid doing any of this yourself.

This talk will cover:

Why should you package your code for PyPI?
How to structure your project and your code, including why you need a codesrc/code folder!
Discover what goes in your codePipfile/code and your codesetup.py/code, and why. Learn the difference between installing your library to use it, and installing it to develop on it.
Write tests for your project, and run them using Tox.
Ensure your code will work in different on different platforms with
Continuous Integration!
Document your code so people won't ask you loads of questions!
How to actually get your code on PyPI using Twine.
Finally, learn how avoid doing any of this yourself (or avoid doing it twice) using CookieCutter templates.

Python Tutorial - Learn Python for Machine Learning and Web Development

Python tutorial for beginners - Learn Python for Machine Learning and Web Development. Can Python be used for machine learning? Python is widely considered as the preferred language for teaching and learning ML (Machine Learning). Can I use Python for web development? Python can be used to build server-side web applications. Why Python is suitable for machine learning? How Python is used in AI? What language is best for machine learning?

Python tutorial for beginners - Learn Python for Machine Learning and Web Development

TABLE OF CONTENT

00:00:00 Introduction
00:01:49 Installing Python 3
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:13:03 Variables
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:29:31 Strings
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
01:55:50 Lists
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:13:25 Tuples
02:15:34 Unpacking
02:18:21 Dictionaries
02:26:21 Emoji Converter
02:30:31 Functions
02:35:21 Parameters
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
02:53:42 Exceptions
02:59:14 Comments
03:01:46 Classes
03:07:46 Constructors
03:14:41 Inheritance
03:19:33 Modules
03:30:12 Packages
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django

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Top Python Development Companies | Hire Python Developers

After analyzing clients and market requirements, TopDevelopers has come up with the list of the best Python service providers. These top-rated Python developers are widely appreciated for their professionalism in handling diverse projects. When...

After analyzing clients and market requirements, TopDevelopers has come up with the list of the best Python service providers. These top-rated Python developers are widely appreciated for their professionalism in handling diverse projects. When you look for the developer in hurry you may forget to take note of review and ratings of the company's aspects, but we at TopDevelopers have done a clear analysis of these top reviewed Python development companies listed here and have picked the best ones for you.

List of Best Python Web Development Companies & Expert Python Programmers.

Using Scikit-Learn for Machine Learning Application Development in Python

Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers don’t know where to start...

They should look into the scikit-learn library, which is one of the best for developing machine learning applications. It is free and relatively easy to install and learn.

Why Machine Learning Programmers Should Be Familiar With Scikit-Learn

If you are trying to develop machine learning applications, then you were going to need a robust toolkit. Scikit-learn is just the solution that you need. This library was developed in 2007 as part of a Google project. Three years later, the code was released as hey solution for machine learning algorithms in conjunction with Google and several other major companies.

Scikit-learn is a library that contains several implementations of machine learning algorithms. There are two essential classifiers for developing machine learning applications with this library: a supervised learning model known as an SVM and a Random Forest (RF).

There are numerous reasons that scikit-learn is one of the preferred libraries for developing machine learning solutions. Some of the Premier benefits include:

Regression modeling
Unsupervised classification and clustering
Decision tree pruning and induction
Comprehensive and neural network training with regression and classification algorithms
Decision boundary learning with SVMs
Advanced probability modeling
Feature analysis and selection
Reduction of dimensionality
Outlier detection and rejection

Scikit-learn has been used in a number of applications by J.P. Morgan, Spotify, Inria, and other major companies. Machine learning applications built with scikit-learn include financial cybersecurity analytics, product development, neuroimaging, barcode scanner development, medical modeling and help with handling Shopify inventory issues.

The wide range of decision modeling features makes scikit-learn. One of the most versatile machine learning environments available in any programming language. Intermediate and advanced Python programmers should be able to master the nuances of this sophisticated library in a matter of hours.

The scikit-learn library is not installed by default. Fortunately, you should be able to set it up quickly. Here are some guidelines for installation and creating the foundation for your first machine learning project.

Installation of Scikit-Learn

If you already have pip installed, it's very easy to install the scikit-learn library. The instructions are available on this page.

Data for Audio

The purpose of using classification is to create a model based on the representation of a phenomenon in vector form (i.e. as a vector) and its corresponding class. This model will then be used to assign a class to an unknown vector. MFCCs can be used for approximations of sound vectors. MFCC provides 13 values per window. One option is to try classifying the class of a sound using those values. However, the sequences of the sound are very important.

This approach resolves some vector problems. The first approach we can follow is to take a segment of MFCCs and average them. Rather than having 13 values for the size of the segment, we end up with thirteen values. Averaging them is very simple, but we can get other statistics, such as: standard deviations and quartiles. This strategy provides statistical representations of all variables.

Loading Data From a CSV File

You will save your scikit-learn data in CSV files. Each line represents a line and each regular column represents a dimension of the vector. In general, the latter represents the class. Rows are separated by a line break and columns by a column. An illustrative example would be as follows:

#!events
event_1,event_2, event_label
1,2,3
11.1,1221,11341
1322,1422,320
330,222,121

To upload a file you can execute the following code:

import numpy as np
.loadtxt('scikit_1.csv',)
data.shape

At the end of this code, the variable data contains our data. The file scikit_1.csv contains segment data..

Separating Different Data Types

In order to learn a model, we need to follow the methodology presented at the beginning. We are not going to be able to follow it to the letter, but we are going to do our best to make our model the best. The first step is to hide some examples to consider them as evidence.

scikit learn prefers separate data between dimensions and classes.

Here is the code that accomplishes this step:

[:,:2233]
[:,-3]

The first line brings $2233$ dimensions of our vectors (in this case we are ignoring those derived from these data). The data will be stored in the variable $First_variable$. The variable $Second_variable$ stores the classes (all lines, last column).

Scikit learn contains a function that allows separating the training data from the test data, and this is done automatically and shuffles the data randomly that supports our methodology.

We have four sets; two versions of the dimension data we generally call features and two versions of the classes. One version is for training (train), and another for testing (test). The train versions have half of the original data, while testing the other half.

How to Publish Python Packages to PyPI

Python Tutorial - Learn Python for Machine Learning and Web Development

Further reading

Top Python Development Companies | Hire Python Developers

Using Scikit-Learn for Machine Learning Application Development in Python