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What is PyPI & How To Publish An Open-Source Python Package to PyPI

The Python Standard Library comprises of sophisticated and robust capabilities for working with larger packages. You will find modules for working with sockets and with files and file paths.Though there might be great packages that Python comes with, there are more exciting and fantastic projects outside the standard library which are mostly called the Python Packaging Index (PyPI). It is nothing but a repository of software for the Python programming language.The PyPI package is considered as an important property for Python being a powerful language. You can get access to thousands of libraries starting from Hello World to advanced deep learning libraries.What is PyPI"PyPI" should be pronounced like "pie pea eye", specifically with the "PI" pronounced as individual letters, but rather as a single sound. This minimizes confusion with the PyPy project, which is a popular alternative implementation of the Python language.The Python Package Index, abbreviated as PyPI is also known as the Cheese Shop. It is the official third-party software repository for Python, just like CPAN is the repository for  Perl.  Some package managers such as pip, use PyPI as the default source for packages and their dependencies. More than 113,000 Python packages can be accessed through PyPI.How to use PyPITo install the packages from PyPI you would need a package installer. The recommended package installer for PyPI is ‘pip’. Pip is installed along when you install Python on your system. To learn more about ‘pip’, you may go through our article on “What is pip”. The pip command is a tool for installing and managing Python packages, such as those found in the Python Package Index. It is a replacement for easy_install.To install a package from the Python Package Index, just open up your terminal and type in a search query using the PIP tool. The most common usage for pip is to install, upgrade or uninstall a package. Starting with a Small Python PackageWe will start with a small Python package that we will use as an example to publish to PyPI. You can get the full source code from the GitHub repository. The package is called reader and it is an application by which you can download and read articles. Below shows the directory structure of reader :reader/  │  ├── reader/  │   ├── config.txt  │   ├── feed.py  │   ├── __init__.py  │   ├── __main__.py  │   └── viewer.py  │  ├── tests/  │   ├── test_feed.py  │   └── test_viewer.py  │  ├── MANIFEST.in  ├── README.md  └── setup.py The source code of the package is in a reader subdirectory that is bound with a configuration file. The GitHub repository also contains few tests in a separate subdirectory. In the coming sections, we will discuss the working of the reader package and also take a look at the special files which include setup.py, README.md, MANIFEST.in, and others. Using the Article ReaderThe reader is a primitive data format used for providing users with the latest updated content. You can download the frequent articles from the article feed with the help of reader. You can get the list of articles using the reader:$ python -m reader The latest tutorials from Real Python (https://realpython.com/)   0 How to Publish an Open-Source Python Package to PyPI   1 Python "while" Loops (Indefinite Iteration)   2 Writing Comments in Python (Guide)   3 Setting Up Python for Machine Learning on Windows   4 Python Community Interview With Michael Kennedy   5 Practical Text Classification With Python and Keras   6 Getting Started With Testing in Python   7 Python, Boto3, and AWS S3: Demystified   8 Python's range() Function (Guide)   9 Python Community Interview With Mike Grouchy  10 How to Round Numbers in Python  11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2  12 Splitting, Concatenating, and Joining Strings in Python  13 Image Segmentation Using Color Spaces in OpenCV + Python  14 Python Community Interview With Mahdi Yusuf  15 Absolute vs Relative Imports in Python  16 Top 10 Must-Watch PyCon Talks  17 Logging in Python  18 The Best Python Books  19 Conditional Statements in PythonThe articles in the list are numbered. So if you want to read a particular article, you can just write the same command along with the number of the article you desire to read.For reading the article on “How to Publish an Open-Source Python Package to PyPI”, just add the serial number of the article:$ python -m reader 0  # How to Publish an Open-Source Python Package to PyPI  Python is famous for coming with batteries included. Sophisticated  capabilities are available in the standard library. You can find modules  for working with sockets, parsing CSV, JSON, and XML files, and  working with files and file paths. However great the packages included with Python are, there are many  fantastic projects available outside the standard library. These are  most often hosted at the Python Packaging Index (PyPI), historically  known as the Cheese Shop. At PyPI, you can find everything from Hello  World to advanced deep learning libraries.  ...  ...  ...You can read any of the articles in the list just by changing the article number with the command. Quick LookThe package comprises of five files which are the working hands of the reader. Let us understand the implementations one by one: config.txt -  It is a text configuration file that specifies the URL of the feed of articles. The configparser standard library is able to read the text file. This type of file contains key-value pairs that are distributed into different sections.  # config.txt [feed] url=https://realpython.com/atom.xml__main__.py - It is the entry point of your program whose duty is to control the main flow of the program. The double underscores denote the specialty of this file. Python executes the contents of the __main__.py file. # __main__.py from configparser import ConfigParser  from importlib import resources  import sys from reader import feed  from reader import viewer def main(): # Read URL of the Real Python feed from config file  configure=ConfigParser() configure.read_string(resources.readtext("reader","config.txt"))  URL=configure.get("feed","url") # If an article ID is given, show the article  if len(sys.argv) > 1:  article = feed.getarticle(URL, sys.argv[1])  viewer.show(article) # If no ID is given, show a list of all articles else: site = feed.getsite(URL)  titles = feed.gettitles(URL)  viewer.showlist(site,titles)  if __name__ == "__main__": main() __init__.py - It is also considered a special file because of the double underscore. It denotes the root of your package in which you can keep your package constants, your documentations and so on. # __init__.py # Version of the realpython-reader package  __version__= "1.0.0"__version__ is a special variable in Python used for adding numbers to your package which was introduced in PEP 396. The variables which are defined in __init__.py are available as variables in the namespace also. >>> import reader >>> reader.__version__ '1.0.0'feed.py - In the __main__.py, you can see two modules feed and viewer are imported which perform the actual work. The file feed.py  is used to read from a web feed and parse the result.  # feed.py import feedparser import html2text Cached_Feeds = dict() def _feed(url):  """Only read a feed once, by caching its contents""" if url not in _CACHED_FEEDS: Cached_Feeds[url]=feedparser.parse(url) return Cached_Feeds[url]viewer.py -  This file module contains two functions show() and show_list(). # viewer.py def show(article):  """Show one article""" print(article) def show_list(site,titles):  """Show list of articles""" print(f"The latest tutorials from {site}") for article_id,title in enumerate(titles): print(f"{article_id:>3}{title}")The function of show() is to print one article to the console. On the other hand, show_list prints a list of titles.Calling a Package You need to understand which file you should call to run the reader in cases where your package consists of four different source code files. The Python interpreter consists of an -m option that helps in specifying a module name instead of a file name.An example to execute two commands with a script hello.py:$ python hello.py Hi there! $ python -m hello Hi there!The two commands above are equivalent. However, the latter one with -m has an advantage. You can also call Python built-in modules with the help of it: $ python -m antigravity Created new window in existing browser session.The -m option also allows you to work with packages and modules:$ python -m reader ...The reader only refers to the directory. Python looks out for the file named __main__.py, if the file is found, it is executed otherwise an error message is printed: $ python -m math python: No code object available for mathPreparing Your PackageSince now you have got your package, let us understand the necessary steps that are needed to be done before the uploading process. Naming the Package Finding a good and unique name for your package is the first and one of the most difficult tasks. PyPI has more than 150,000 packages already in their list, so chances are that your favorite name might be already taken. You need to perform some research work in order to find a perfect name. You can also use the PyPI search to verify whether it is already used or not.  We will be using a more descriptive name and call it realpython-reader so that the reader package can be easily found on PyPI and then use it to install the package using pip:$ pip install realpython-readerHowever, the name we have given is realpython-reader but when we import it, it is still called as reader:>>> import reader >>> help(reader) >>> from reader import feed >>> feed.get_titles() ['How to Publish an Open-Source Python Package to PyPI', ...]You can use a variety of names for your package while importing on PyPI but it is suggested to use the same name or similar ones for better understanding. Configuring your PackageYour package should be included with some basic information which will be in the form of a setup.py file. The setup.py is the only fully supported way of providing information, though Python consists of initiatives that are used to simplify this collection of information.The setup.py file should be placed in the top folder of your package. An example of a setup.py  for reader: import pathlib from setuptools import setup # The directory containing this file HERE = pathlib.Path(__file__).parent # The text of the README file README = (HERE/"README.md").read_text() # This call to setup() does all the work setup( name="realpython-reader",  version="1.0.1",  descp="The latest Python tutorials",  long_descp=README, long_descp_content="text/markdown",  URL="https://github.com/realpython/reader",  author="Real Python",  authoremail="office@realpython.com",  license="MIT",  classifiers=[  "License :: OSI Approved :: MIT License",  "Programming Language :: Python :: 3",  "Programming Language :: Python :: 3.7",  ],  packages=["reader"],  includepackagedata=True,  installrequires=["feedparser","html2text"],  entrypoints={  "console_scripts":[  "realpython=reader.__main__:main",  ]  },  ) The necessary parameters available in setuptools in the call to setup() are as follows: name - The name of your package as being appeared on PyPI version - the present version of your package packages - the packages and subpackages which contain your source code You will also have to specify any subpackages if included. setuptools contains find_packages() whose job is to discover all your subpackages. You can also use it in the reader project:from setuptools import find_packages,setup  setup(  ... packages=find_packages(exclude=("tests",)), ... ) You can also add more information along with name, version, and packages which will make it easier to find on PyPI.Two more important parameters of  setup() : install_requires - It lists the dependencies your package has to the third-party libraries. feedparser and html2text are listed since they are the dependencies of reader.entry_points - It creates scripts to call a function within your package. Our script realpython calls the main() within the reader/__main__.py file.Documenting Your PackageDocumenting your package before releasing it is an important step. It can be a simple README file or a complete tutorial webpage like galleries or an API reference.  At least a README file with your project should be included at a minimum which should give a quick description of your package and also inform about the installation process and how to use it. In other words, you need to include your README as the long_descp argument to setup() which will eventually be displayed on PyPI. PyPI uses Markdown for package documentation. You can use the setup() parameter long_description_content_type to get the PyPI format you are working with. When you are working with bigger projects and want to add more documentation to your package, you can take the help of websites like GitHub and Read the Docs. Versioning Your Package Similarly like documentation, you need to add a version to your package. PyPI promises reproducibility by allowing a user to do one upload of a particular version for a package. If there are two systems with the same version of a package, it will behave in an exact manner. PEP 440 of Python provides a number of schemes for software versioning. However, for a simple project, let us stick to a simple versioning scheme. A simple versioning technique is semantic versioning which has three components namely MAJOR, MINOR, and PATCH and some simple rules about the incrementation process of each component: Increment the MAJOR version when you make incompatible API changes. Increment the MINOR version when you add functionality in a backward-compatible manner. Increment the PATCH version when you make backward-compatible bug fixes. (Source) You need to specify the different files inside your project. Also, if you want to verify whether the version numbers are consistent or not, you can do it using a tool called Bumpversion: $ pip install bumpversionAdding Files To Your PackageYour package might include other files other than source code files like data files, binaries, documentation and configuration files. In order to add such files, we will use a manifest file. In most cases, setup() creates a manifest that includes all code files as well as README files.   However, if you want to change the manifest, you can create a manifest template of your own. The file should be called MANIFEST.in and it will specify rules for what needs to be included and what needs to be excluded: include reader/*.txtThis will add all the .txt files in the reader directory. Other than creating the manifest, the non-code files also need to be copied. This can be done by setting the include_package_data toTrue: setup(  ... include_package_data=True,  ... )Publishing to PyPI For publishing your package to the real world, you need to first start with registering yourself on PyPI and also on TestPyPI, which is useful because you can give a trial of the publishing process without any further consequences. You will have to use a tool called Twine to upload your package ton PyPI: $ pip install twineBuilding Your PackageThe packages on PyPI are wrapped into distribution packages, out of which the most common are source archives and Python wheels. A source archive comprises of your source code and other corresponding support files wrapped into one tar file. On the other hand, a Python wheel is a zip archive that also contains your code. However, the wheel can work with any extensions, unlike source archives. Run the following command in order to create a source archive and a wheel for your package: $ python setup.py sdist bdist_wheelThe command above will create two files in a newly created directory called dist, a source archive and a wheel: reader/ │  └── dist/      ├── realpython_reader-1.0.0-py3-none-any.whl      └── realpython-reader-1.0.0.tar.gz The command-line arguments like the sdist and bdist_wheel arguments are all implemented int the upstream distutils standard library. Using the --help-commands option, you list all the available arguments: $ python setup.py --help-commands  Standard commands:    build             build everything needed to install    build_py          "build" pure Python modules (copy to build directory)    < ... many more commands ...>Testing Your Package In order to test your package, you need to check whether the distribution packages you have newly created contain the expected files. You also need to list the contents of the tar source archive on Linux and macOS platforms: $ tar tzf realpython-reader-1.0.0.tar.gz  realpython-reader-1.0.0/  realpython-reader-1.0.0/setup.cfg  realpython-reader-1.0.0/README.md  realpython-reader-1.0.0/reader/  realpython-reader-1.0.0/reader/feed.py  realpython-reader-1.0.0/reader/__init__.py  realpython-reader-1.0.0/reader/viewer.py  realpython-reader-1.0.0/reader/__main__.py  realpython-reader-1.0.0/reader/config.txt  realpython-reader-1.0.0/PKG-INFO  realpython-reader-1.0.0/setup.py  realpython-reader-1.0.0/MANIFEST.in  realpython-reader-1.0.0/realpython_reader.egg-info/  realpython-reader-1.0.0/realpython_reader.egg-info/SOURCES.txt  realpython-reader-1.0.0/realpython_reader.egg-info/requires.txt  realpython-reader-1.0.0/realpython_reader.egg-info/dependency_links.txt  realpython-reader-1.0.0/realpython_reader.egg-info/PKG-INFO  realpython-reader-1.0.0/realpython_reader.egg-info/entry_points.txt  realpython-reader-1.0.0/realpython_reader.egg-info/top_level.txt On Windows, you can make use of the utility tool 7-zip to look inside the corresponding zip file. You should make sure that all the subpackages and supporting files are included in your package along with all the source code files as well as the newly built files. You can also run twine check on the files created in dist to check if your package description will render properly on PyPI: $ twine check dist/* Checking distribution dist/realpython_reader-1.0.0-py3-none-any.whl: Passed  Checking distribution dist/realpython-reader-1.0.0.tar.gz: Passed Uploading Your PackageNow you have reached the final step,i.e. Uploading your package to PyPI. Make sure you upload your package first to TestPyPI to check whether it is working according to your expectation and then use the Twine tool and instruct it to upload your newly created distribution: $ twine upload --repository-url https://test.pypi.org/legacy/ dist/* After the uploading process is over, you can again go to TestPyPI and look at your project being displayed among the new releases.  However, if you have your own package to publish, the command is short: $ twine upload dist/* Give your username and password and it’s done. Your package has been published on PyPI. To look up your package, you can either search it or look at the Your projects page or you can just directly go to the URL of your project: pypi.org/project/your-package-name/. After completing the publishing process, you can download it in your system using pip: $ pip install your-package-nameMiscellaneous Tools There are some useful tools that are good to know when creating and publishing Python packages. Some of these are mentioned below. Virtual Environments Each virtual environment has its own Python binary and can also have its own set of installed Python packages in its directories. These packages are independent in nature. Virtual environments are useful in situations where there are a variety of requirements and dependencies while working with different projects. You can grab more information about virtual environments in  the following references: Python Virtual Environments Pipenv It is recommended to check your package inside a basic virtual environment so that to make sure all necessary dependencies in your setup.py file are included. Cookiecutter Cookiecutter sets up your project by asking a few questions based on a template. Python contains many different templates. Install Cookiecutter using pip: $ pip install cookiecutterTo understand cookiecutter, we will use a template called pypackage-minimal. If you want to use a template, provide the link of the template to the cookiecutter: $ cookiecutter https://github.com/kragniz/cookiecutter-pypackage-minimal  author_name [Louis Taylor]: Real Python  author_email [louis@kragniz.eu]: office@realpython.com  package_name [cookiecutter_pypackage_minimal]: realpython-reader  package_version [0.1.0]:  package_description [...]: Read Real Python tutorials  package_url [...]: https://github.com/realpython/reader  readme_pypi_badge [True]:  readme_travis_badge [True]: False  readme_travis_url [...]: Cookiecutter sets up your project after you have set up answered a series of questions. The template above will create the following files and directories: realpython-reader/  │  ├── realpython-reader/  │   └── __init__.py  │  ├── tests/  │   ├── __init__.py  │   └── test_sample.py  │  ├── README.rst  ├── setup.py  └── tox.ini You can also take a look at the documentation of cookiecutter for all the available cookiecutters and how to create your own template. Summary Let us sum up the necessary steps we have learned in this article so far to publish your own package - Finding a good and unique name for your packageConfiguring your package using setup.py Building your package Publishing your package to PyPI Moreover, you have also learned to use a few new tools that help in simplifying the process of publishing packages.  You can reach out to Python’s Packaging Authority for more detailed and comprehensive information. To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 
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What is PyPI & How To Publish An Open-Source Python Package to PyPI

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What is PyPI & How To Publish An Open-Source Python Package to PyPI

The Python Standard Library comprises of sophisticated and robust capabilities for working with larger packages. You will find modules for working with sockets and with files and file paths.

Though there might be great packages that Python comes with, there are more exciting and fantastic projects outside the standard library which are mostly called the Python Packaging Index (PyPI). It is nothing but a repository of software for the Python programming language.

The PyPI package is considered as an important property for Python being a powerful language. You can get access to thousands of libraries starting from Hello World to advanced deep learning libraries.

What is PyPI

"PyPI" should be pronounced like "pie pea eye", specifically with the "PI" pronounced as individual letters, but rather as a single sound. This minimizes confusion with the PyPy project, which is a popular alternative implementation of the Python language.

The Python Package Index, abbreviated as PyPI is also known as the Cheese Shop. It is the official third-party software repository for Python, just like CPAN is the repository for  Perl.  Some package managers such as pip, use PyPI as the default source for packages and their dependencies. More than 113,000 Python packages can be accessed through PyPI.

How to use PyPI

To install the packages from PyPI you would need a package installer. The recommended package installer for PyPI is ‘pip’. Pip is installed along when you install Python on your system. To learn more about ‘pip’, you may go through our article on “What is pip”. The pip command is a tool for installing and managing Python packages, such as those found in the Python Package Index. It is a replacement for easy_install.

To install a package from the Python Package Index, just open up your terminal and type in a search query using the PIP tool. The most common usage for pip is to install, upgrade or uninstall a package. 

Starting with a Small Python Package

We will start with a small Python package that we will use as an example to publish to PyPI. You can get the full source code from the GitHub repository. The package is called reader and it is an application by which you can download and read articles. 

Below shows the directory structure of reader :

reader/ 
│ 
├── reader/ 
│   ├── config.txt 
│   ├── feed.py 
│   ├── __init__.py 
│   ├── __main__.py 
│   └── viewer.py 
│ 
├── tests/ 
│   ├── test_feed.py 
│   └── test_viewer.py 
│ 
├── MANIFEST.in 
├── README.md 
└── setup.py 

The source code of the package is in a reader subdirectory that is bound with a configuration file. The GitHub repository also contains few tests in a separate subdirectory. 

In the coming sections, we will discuss the working of the reader package and also take a look at the special files which include setup.py, README.md, MANIFEST.in, and others. 

Using the Article Reader

The reader is a primitive data format used for providing users with the latest updated content. You can download the frequent articles from the article feed with the help of reader

You can get the list of articles using the reader:

$ python -m reader
The latest tutorials from Real Python (https://realpython.com/)
  0 How to Publish an Open-Source Python Package to PyPI
  1 Python "while" Loops (Indefinite Iteration)
  2 Writing Comments in Python (Guide)
  3 Setting Up Python for Machine Learning on Windows
  4 Python Community Interview With Michael Kennedy
  5 Practical Text Classification With Python and Keras
  6 Getting Started With Testing in Python
  7 Python, Boto3, and AWS S3: Demystified
  8 Python's range() Function (Guide)
  9 Python Community Interview With Mike Grouchy
 10 How to Round Numbers in Python
 11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2
 12 Splitting, Concatenating, and Joining Strings in Python
 13 Image Segmentation Using Color Spaces in OpenCV + Python
 14 Python Community Interview With Mahdi Yusuf
 15 Absolute vs Relative Imports in Python
 16 Top 10 Must-Watch PyCon Talks
 17 Logging in Python
 18 The Best Python Books
 19 Conditional Statements in Python

The articles in the list are numbered. So if you want to read a particular article, you can just write the same command along with the number of the article you desire to read.

For reading the article on “How to Publish an Open-Source Python Package to PyPI”, just add the serial number of the article:

$ python -m reader 0 
# How to Publish an Open-Source Python Package to PyPI 

Python is famous for coming with batteries included. Sophisticated 
capabilities are available in the standard library. You can find modules 
for working with sockets, parsing CSV, JSON, and XML files, and 
working with files and file paths.

However great the packages included with Python are, there are many 
fantastic projects available outside the standard library. These are 
most often hosted at the Python Packaging Index (PyPI), historically 
known as the Cheese Shop. At PyPI, you can find everything from Hello 
World to advanced deep learning libraries. 
... 
... 
...

You can read any of the articles in the list just by changing the article number with the command. 

Quick Look

The package comprises of five files which are the working hands of the reader. Let us understand the implementations one by one: 

  • config.txt -  It is a text configuration file that specifies the URL of the feed of articles. The configparser standard library is able to read the text file. This type of file contains key-value pairs that are distributed into different sections.  
# config.txt
[feed]
url=https://realpython.com/atom.xml
  • __main__.py - It is the entry point of your program whose duty is to control the main flow of the program. The double underscores denote the specialty of this file. Python executes the contents of the __main__.py file. 
# __main__.py
from configparser import ConfigParser 
from importlib import resources 
import sys

from reader import feed 
from reader import viewer

def main():
    # Read URL of the Real Python feed from config file 
    configure=ConfigParser()
    configure.read_string(resources.readtext("reader","config.txt")) 
    URL=configure.get("feed","url")

    # If an article ID is given, show the article 
    if len(sys.argv) > 1: 
        article = feed.getarticle(URL, sys.argv[1]) 
        viewer.show(article)

    # If no ID is given, show a list of all articles 
    else:
      site = feed.getsite(URL) 
      titles = feed.gettitles(URL) 
      viewer.showlist(site,titles) 
if __name__ == "__main__":
   main() 
  • __init__.py - It is also considered a special file because of the double underscore. It denotes the root of your package in which you can keep your package constants, your documentations and so on. 
# __init__.py

# Version of the realpython-reader package 
__version__= "1.0.0"

__version__ is a special variable in Python used for adding numbers to your package which was introduced in PEP 396. The variables which are defined in __init__.py are available as variables in the namespace also. 

>>> import reader
>>> reader.__version__
'1.0.0'
  • feed.py - In the __main__.py, you can see two modules feed and viewer are imported which perform the actual work. The file feed.py  is used to read from a web feed and parse the result.  
# feed.py

 import feedparser
 import html2text

Cached_Feeds = dict()

def _feed(url): 
   """Only read a feed once, by caching its contents"""
  if url not in _CACHED_FEEDS:
      Cached_Feeds[url]=feedparser.parse(url)
  return Cached_Feeds[url]
  • viewer.py -  This file module contains two functions show() and show_list()
# viewer.py

def show(article): 
"""Show one article""" 
print(article)

 def show_list(site,titles): 
"""Show list of articles"""
print(f"The latest tutorials from {site}")
for article_id,title in enumerate(titles):
print(f"{article_id:>3}{title}")

The function of show() is to print one article to the console. On the other hand, show_list prints a list of titles.

Calling a Package 

You need to understand which file you should call to run the reader in cases where your package consists of four different source code files. The Python interpreter consists of an -m option that helps in specifying a module name instead of a file name.

An example to execute two commands with a script hello.py:

$ python hello.py
Hi there!

$ python -m hello
Hi there!

The two commands above are equivalent. However, the latter one with -m has an advantage. You can also call Python built-in modules with the help of it: 

$ python -m antigravity
Created new window in existing browser session.

The -m option also allows you to work with packages and modules:

$ python -m reader
...

The reader only refers to the directory. Python looks out for the file named __main__.py, if the file is found, it is executed otherwise an error message is printed: 

$ python -m math
python: No code object available for math

Preparing Your Package

Since now you have got your package, let us understand the necessary steps that are needed to be done before the uploading process. 

Naming the Package 

Finding a good and unique name for your package is the first and one of the most difficult tasks. PyPI has more than 150,000 packages already in their list, so chances are that your favorite name might be already taken. 

You need to perform some research work in order to find a perfect name. You can also use the PyPI search to verify whether it is already used or not.  

We will be using a more descriptive name and call it realpython-reader so that the reader package can be easily found on PyPI and then use it to install the package using pip:

$ pip install realpython-reader

However, the name we have given is realpython-reader but when we import it, it is still called as reader:

>>> import reader
>>> help(reader)

>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]

You can use a variety of names for your package while importing on PyPI but it is suggested to use the same name or similar ones for better understanding. 

Configuring your Package

Your package should be included with some basic information which will be in the form of a setup.py file. The setup.py is the only fully supported way of providing information, though Python consists of initiatives that are used to simplify this collection of information.

The setup.py file should be placed in the top folder of your package. An example of a setup.py  for reader

import pathlib
from setuptools import setup

# The directory containing this file
HERE = pathlib.Path(__file__).parent

# The text of the README file
README = (HERE/"README.md").read_text()

# This call to setup() does all the work
setup(
   name="realpython-reader", 
   version="1.0.1", 
   descp="The latest Python tutorials", 
   long_descp=README,
   long_descp_content="text/markdown", 
   URL="https://github.com/realpython/reader", 
   author="Real Python", 
   authoremail="office@realpython.com", 
   license="MIT", 
   classifiers="License :: OSI Approved :: MIT License""Programming Language :: Python :: 3""Programming Language :: Python :: 3.7", 
   ], 
   packages=["reader"], 
   includepackagedata=True, 
   installrequires=["feedparser","html2text"], 
   entrypoints="console_scripts":[ 
           "realpython=reader.__main__:main", 
       ] 
   }, 
 ) 

The necessary parameters available in setuptools in the call to setup() are as follows: 

  • name - The name of your package as being appeared on PyPI 
  • version - the present version of your package 
  • packages - the packages and subpackages which contain your source code 

You will also have to specify any subpackages if included. setuptools contains find_packages() whose job is to discover all your subpackages. You can also use it in the reader project:

from setuptools import find_packages,setup 
setup( 
    ...
    packages=find_packages(exclude=("tests",)),
    ...
) 

You can also add more information along with name, version, and packages which will make it easier to find on PyPI.

Two more important parameters of  setup() : 

  • install_requires - It lists the dependencies your package has to the third-party libraries. feedparser and html2text are listed since they are the dependencies of reader.
  • entry_points - It creates scripts to call a function within your package. Our script realpython calls the main() within the reader/__main__.py file.

Documenting Your Package

Documenting your package before releasing it is an important step. It can be a simple README file or a complete tutorial webpage like galleries or an API reference.  

At least a README file with your project should be included at a minimum which should give a quick description of your package and also inform about the installation process and how to use it. In other words, you need to include your README as the long_descp argument to setup() which will eventually be displayed on PyPI. 

PyPI uses Markdown for package documentation. You can use the setup() parameter long_description_content_type to get the PyPI format you are working with. 

When you are working with bigger projects and want to add more documentation to your package, you can take the help of websites like GitHub and Read the Docs

Versioning Your Package 

Similarly like documentation, you need to add a version to your package. PyPI promises reproducibility by allowing a user to do one upload of a particular version for a package. If there are two systems with the same version of a package, it will behave in an exact manner. 

PEP 440 of Python provides a number of schemes for software versioning. However, for a simple project, let us stick to a simple versioning scheme. 

A simple versioning technique is semantic versioning which has three components namely MAJOR, MINOR, and PATCH and some simple rules about the incrementation process of each component: 

  • Increment the MAJOR version when you make incompatible API changes. 
  • Increment the MINOR version when you add functionality in a backward-compatible manner. 
  • Increment the PATCH version when you make backward-compatible bug fixes. (Source

You need to specify the different files inside your project. Also, if you want to verify whether the version numbers are consistent or not, you can do it using a tool called Bumpversion

$ pip install bumpversion

Adding Files To Your Package

Your package might include other files other than source code files like data files, binaries, documentation and configuration files. In order to add such files, we will use a manifest file. In most cases, setup() creates a manifest that includes all code files as well as README files.   

However, if you want to change the manifest, you can create a manifest template of your own. The file should be called MANIFEST.in and it will specify rules for what needs to be included and what needs to be excluded: 

include reader/*.txt

This will add all the .txt files in the reader directory. Other than creating the manifest, the non-code files also need to be copied. This can be done by setting the include_package_data toTrue

setup( 
    ...
    include_package_data=True...
)

Publishing to PyPI 

For publishing your package to the real world, you need to first start with registering yourself on PyPI and also on TestPyPI, which is useful because you can give a trial of the publishing process without any further consequences. 

You will have to use a tool called Twine to upload your package ton PyPI: 

$ pip install twine

Building Your Package

The packages on PyPI are wrapped into distribution packages, out of which the most common are source archives and Python wheels. A source archive comprises of your source code and other corresponding support files wrapped into one tar file. On the other hand, a Python wheel is a zip archive that also contains your code. However, the wheel can work with any extensions, unlike source archives. 

Run the following command in order to create a source archive and a wheel for your package: 

$ python setup.py sdist bdist_wheel

The command above will create two files in a newly created directory called dist, a source archive and a wheel: 

reader/
│ 
└── dist/ 
    ├── realpython_reader-1.0.0-py3-none-any.whl 
    └── realpython-reader-1.0.0.tar.gz 

The command-line arguments like the sdist and bdist_wheel arguments are all implemented int the upstream distutils standard library. Using the --help-commands option, you list all the available arguments: 

$ python setup.py --help-commands 
Standard commands: 
  build             build everything needed to install 
  build_py          "build" pure Python modules (copy to build directory) 
  < ... many more commands ...>

Testing Your Package 

In order to test your package, you need to check whether the distribution packages you have newly created contain the expected files. You also need to list the contents of the tar source archive on Linux and macOS platforms: 

$ tar tzf realpython-reader-1.0.0.tar.gz 
realpython-reader-1.0.0/ 
realpython-reader-1.0.0/setup.cfg 
realpython-reader-1.0.0/README.md 
realpython-reader-1.0.0/reader/ 
realpython-reader-1.0.0/reader/feed.py 
realpython-reader-1.0.0/reader/__init__.py 
realpython-reader-1.0.0/reader/viewer.py 
realpython-reader-1.0.0/reader/__main__.py 
realpython-reader-1.0.0/reader/config.txt 
realpython-reader-1.0.0/PKG-INFO 
realpython-reader-1.0.0/setup.py 
realpython-reader-1.0.0/MANIFEST.in 
realpython-reader-1.0.0/realpython_reader.egg-info/ 
realpython-reader-1.0.0/realpython_reader.egg-info/SOURCES.txt 
realpython-reader-1.0.0/realpython_reader.egg-info/requires.txt 
realpython-reader-1.0.0/realpython_reader.egg-info/dependency_links.txt 
realpython-reader-1.0.0/realpython_reader.egg-info/PKG-INFO 
realpython-reader-1.0.0/realpython_reader.egg-info/entry_points.txt 
realpython-reader-1.0.0/realpython_reader.egg-info/top_level.txt 

On Windows, you can make use of the utility tool 7-zip to look inside the corresponding zip file. 

You should make sure that all the subpackages and supporting files are included in your package along with all the source code files as well as the newly built files. 

You can also run twine check on the files created in dist to check if your package description will render properly on PyPI: 

$ twine check dist/*
Checking distribution dist/realpython_reader-1.0.0-py3-none-any.whl: Passed 
Checking distribution dist/realpython-reader-1.0.0.tar.gz: Passed 

Uploading Your Package

Now you have reached the final step,i.e. Uploading your package to PyPI. Make sure you upload your package first to TestPyPI to check whether it is working according to your expectation and then use the Twine tool and instruct it to upload your newly created distribution: 

$ twine upload --repository-url https://test.pypi.org/legacy/ dist/* 

After the uploading process is over, you can again go to TestPyPI and look at your project being displayed among the new releases.  

However, if you have your own package to publish, the command is short: 

$ twine upload dist/* 

Give your username and password and it’s done. Your package has been published on PyPI. To look up your package, you can either search it or look at the Your projects page or you can just directly go to the URL of your project: pypi.org/project/your-package-name/

After completing the publishing process, you can download it in your system using pip: 

$ pip install your-package-name

Miscellaneous Tools 

There are some useful tools that are good to know when creating and publishing Python packages. Some of these are mentioned below. 

Virtual Environments 

Each virtual environment has its own Python binary and can also have its own set of installed Python packages in its directories. These packages are independent in nature. Virtual environments are useful in situations where there are a variety of requirements and dependencies while working with different projects. 

You can grab more information about virtual environments in  the following references: 

It is recommended to check your package inside a basic virtual environment so that to make sure all necessary dependencies in your setup.py file are included. 

Cookiecutter 

Cookiecutter sets up your project by asking a few questions based on a template. Python contains many different templates. 

Install Cookiecutter using pip: 

$ pip install cookiecutter

To understand cookiecutter, we will use a template called pypackage-minimal. If you want to use a template, provide the link of the template to the cookiecutter: 

$ cookiecutter https://github.com/kragniz/cookiecutter-pypackage-minimal 
author_name [Louis Taylor]: Real Python 
author_email [louis@kragniz.eu]: office@realpython.com 
package_name [cookiecutter_pypackage_minimal]: realpython-reader 
package_version [0.1.0]: 
package_description [...]: Read Real Python tutorials 
package_url [...]: https://github.com/realpython/reader 
readme_pypi_badge [True]: 
readme_travis_badge [True]: False 
readme_travis_url [...]: 

Cookiecutter sets up your project after you have set up answered a series of questions. The template above will create the following files and directories: 

realpython-reader/ 
│ 
├── realpython-reader/ 
│   └── __init__.py 
│ 
├── tests/ 
│   ├── __init__.py 
│   └── test_sample.py 
│ 
├── README.rst 
├── setup.py 
└── tox.ini 

You can also take a look at the documentation of cookiecutter for all the available cookiecutters and how to create your own template. 

Summary 

Let us sum up the necessary steps we have learned in this article so far to publish your own package - 

  • Finding a good and unique name for your package
  • Configuring your package using setup.py 
  • Building your package 
  • Publishing your package to PyPI 

Moreover, you have also learned to use a few new tools that help in simplifying the process of publishing packages.  

You can reach out to Python’s Packaging Authority for more detailed and comprehensive information. To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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How to Round Numbers in Python

While you are dealing with data, sometimes you may come across a biased dataset. In statistics, bias is whereby the expected value of the results differs from the true underlying quantitative parameter being estimated. Working with such data can be dangerous and can lead you to incorrect conclusions. To learn more about various other concepts of Python, go through our Python Tutorials or enroll to our Python Certification course online.There are many types of biases such as selection bias, reporting bias, sampling bias and so on. Similarly, rounding bias is related to numeric data. In this article we will see:Why is it important to know the ways to round numbersHow to use various strategies to round numbersHow data is affected by rounding itHow to use NumPy arrays and Pandas DataFrames to round numbersLet us first learn about Python’s built-in rounding process.About Python’s Built-in round() FunctionPython Programming offers a built-in round() function which rounds off a number to the given number of digits and makes rounding of numbers easier. The function round() accepts two numeric arguments, n and n digits and then returns the number n after rounding it to ndigits. If the number of digits are not provided for round off, the function rounds off the number n to the nearest integer.Suppose, you want to round off a number, say 4.5. It will be rounded to the nearest whole number which is 5. However, the number 4.74 will be rounded to one decimal place to give 4.7.It is important to quickly and readily round numbers while you are working with floats which have many decimal places. The inbuilt Python function round() makes it simple and easy.Syntaxround(number, number of digits)The parameters in the round() function are:number - number to be roundednumber of digits (Optional) - number of digits up to which the given number is to be rounded.The second parameter is optional. In case, if it is missing then round() function returns:For an integer, 12, it rounds off to 12For a decimal number, if the last digit after the decimal point is >=5 it will round off to the next whole number, and if =5 print(round(5.476, 2))     # when the (ndigit+1)th digit is  1 print(round("x", 2)) TypeError: type str doesn't define __round__ methodAnother example,print(round(1.5)) print(round(2)) print(round(2.5))The output will be:2 2 2The function round() rounds 1.5 up to 2, and 2.5 down to 2. This is not a bug, the round() function behaves this way. In this article you will learn a few other ways to round a number. Let us look at the variety of methods to round a number.Diverse Methods for RoundingThere are many ways to round a number with its own advantages and disadvantages. Here we will learn some of the techniques to rounding a number.TruncationTruncation, as the name means to shorten things. It is one of the simplest methods to round a number which involves truncating a number to a given number of digits. In this method, each digit after a given position is replaced with 0. Let us look into some examples.ValueTruncated ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34The truncate() function can be used for positive as well as negative numbers:>>> truncate(19.5) 19.0 >>> truncate(-2.852, 1) -2.8 >>> truncate(2.825, 2) 2.82The truncate() function can also be used to truncate digits towards the left of the decimal point by passing a negative number.>>> truncate(235.7, -1) 230.0 >>> truncate(-1936.37, -3) -1000.0When a positive number is truncated, we are basically rounding it down. Similarly, when we truncate a negative number, the number is rounded up. Let us look at the various rounding methods.Rounding UpThere is another strategy called “rounding up” where a number is rounded up to a specified number of digits. For example:ValueRound Up ToResult12.345Tens place2018.345Ones place1918.345Tenths place18.418.345Hundredths place18.35The term ceiling is used in mathematics to explain the nearest integer which is greater than or equal to a particular given number. In Python, for “rounding up” we use two functions namely,ceil() function, andmath() functionA non-integer number lies between two consecutive integers. For example, considering a number 5.2, this will lie between 4 and 5. Here, ceiling is the higher endpoint of the interval, whereas floor is the lower one. Therefore, ceiling of 5.2 is 5, and floor of 5.2 is 4. However, the ceiling of 5 is 5.In Python, the function to implement the ceiling function is the math.ceil() function. It always returns the closest integer which is greater than or equal to its input.>>> import math >>> math.ceil(5.2) 6 >>> math.ceil(5) 5 >>> math.ceil(-0.5) 0If you notice you will see that the ceiling of -0.5 is 0, and not -1.Let us look into a short code to implement the “rounding up” strategy using round_up() function:def round_up(n, decimals=0):     multiplier = 10 ** decimals     return math.ceil(n * multiplier) / multiplierLet’s look at how round_up() function works with various inputs:>>> round_up(3.1) 4.0 >>> round_up(3.23, 1) 3.3 >>> round_up(3.543, 2) 3.55You can pass negative values  to decimals, just like we did in truncation.>>> round_up(32.45, -1) 40.0 >>> round_up(3352, -2) 3400You can follow the diagram below to understand round up and round down. Round up to the right and down to the left.Rounding up always rounds a number to the right on the number line, and rounding down always rounds a number to the left on the number line.Rounding DownSimilar to rounding up we have another strategy called rounding down whereValueRounded Down ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34In Python, rounding down can be implemented using a similar algorithm as we truncate or round up. Firstly you will have to shift the decimal point and then round an integer. Lastly shift the decimal point back.math.ceil() is used to round up to the ceiling of the number once the decimal point is shifted. For “rounding down” we first need to round the floor of the number once the decimal point is shifted.>>> math.floor(1.2) 1 >>> math.floor(-0.5) -1Here’s the definition of round_down():def round_down(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n * multiplier) / multiplierThis is quite similar to round_up() function. Here we are using math.floor() instead of math.ceil().>>> round_down(1.5) 1 >>> round_down(1.48, 1) 1.4 >>> round_down(-0.5) -1Rounding a number up or down has extreme effects in a large dataset. After rounding up or down, you can actually remove a lot of precision as well as alter computations.Rounding Half UpThe “rounding half up” strategy rounds every number to the nearest number with the specified precision, and breaks ties by rounding up. Here are some examples:ValueRound Half Up ToResult19.825Tens place1019.825Ones place2019.825Tenths place19.819.825Hundredths place19.83In Python, rounding half up strategy can be implemented by shifting the decimal point to the right by the desired number of places. In this case you will have to determine whether the digit after the shifted decimal point is less than or greater than equal to 5.You can add 0.5 to the value which is shifted and then round it down with the math.floor() function.def round_half_up(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n*multiplier + 0.5) / multiplierIf you notice you might see that round_half_up() looks similar to round_down. The only difference is to add 0.5 after shifting the decimal point so that the result of rounding down matches with the expected value.>>> round_half_up(19.23, 1) 19.2 >>> round_half_up(19.28, 1) 19.3 >>> round_half_up(19.25, 1) 19.3Rounding Half DownIn this method of rounding, it rounds to the nearest number similarly like “rounding half up” method, the difference is that it breaks ties by rounding to the lesser of the two numbers. Here are some examples:ValueRound Half Down ToResult16.825Tens place1716.825Ones place1716.825Tenths place16.816.825Hundredths place16.82In Python, “rounding half down” strategy can be implemented by replacing math.floor() in the round_half_up() function with math.ceil() and then by subtracting 0.5 instead of adding:def round_half_down(n, decimals=0):     multiplier = 10 ** decimals return math.ceil(n*multiplier - 0.5) / multiplierLet us look into some test cases.>>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0 >>> round_half_down(2.25, 1) 2.2In general there are no bias for both round_half_up() and round_half_down(). However, rounding of data with more number of ties results in bias. Let us consider an example to understand better.>>> data = [-2.15, 1.45, 4.35, -12.75]Let us compute the mean of these numbers:>>> statistics.mean(data) -2.275Now let us compute the mean on the data after rounding to one decimal place with round_half_up() and round_half_down():>>> rhu_data = [round_half_up(n, 1) for n in data] >>> statistics.mean(rhu_data) -2.2249999999999996 >>> rhd_data = [round_half_down(n, 1) for n in data] >>> statistics.mean(rhd_data) -2.325The round_half_up() function results in a round towards positive infinity bias, and round_half_down() results in a round towards negative infinity bias.Rounding Half Away From ZeroIf you have noticed carefully while going through round_half_up() and round_half_down(), neither of the two is symmetric around zero:>>> round_half_up(1.5) 2.0 >>> round_half_up(-1.5) -1.0 >>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0In order to introduce symmetry, you can always round a tie away from zero. The table mentioned below illustrates it clearly:ValueRound Half Away From Zero ToResult16.25Tens place2016.25Ones place1616.25Tenths place16.3-16.25Tens place-20-16.25Ones place-16-16.25Tenths place-16.3The implementation of “rounding half away from zero” strategy on a number n is very simple. All you need to do is start as usual by shifting the decimal point to the right a given number of places and then notice the digit d immediately to the right of the decimal place in this new number. Here, there are four cases to consider:If n is positive and d >= 5, round upIf n is positive and d < 5, round downIf n is negative and d >= 5, round downIf n is negative and d < 5, round upAfter rounding as per the rules mentioned above, you can shift the decimal place back to the left.There is a question which might come to your mind - How do you handle situations where the number of positive and negative ties are drastically different? The answer to this question brings us full circle to the function that deceived us at the beginning of this article: Python’s built-in  round() function.Rounding Half To EvenThere is a way to mitigate rounding bias while you are rounding values in a dataset. You can simply round ties to the nearest even number at the desired precision. Let us look at some examples:ValueRound Half To Even ToResult16.255Tens place2016.255Ones place1616.255Tenths place16.216.255Hundredths place16.26To prove that round() really does round to even, let us try on a few different values:>>> round(4.5) 4 >>> round(3.5) 4 >>> round(1.75, 1) 1.8 >>> round(1.65, 1) 1.6The Decimal ClassThe  decimal module in Python is one of those features of the language which you might not be aware of if you have just started learning Python. Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – except from the decimal arithmetic specification. Some of the benefits of the decimal module are mentioned below -Exact decimal representation: 0.1 is actually 0.1, and 0.1 + 0.1 + 0.1 - 0.3 returns 0, as expected.Preservation of significant digits: When you add 1.50 and 2.30, the result is 3.80 with the trailing zero maintained to indicate significance.User-alterable precision: The default precision of the decimal module is twenty-eight digits, but this value can be altered by the user to match the problem at hand.Let us see how rounding works in the decimal module.>>> import decimal >>> decimal.getcontext() Context(     prec=28,     rounding=ROUND_HALF_EVEN,     Emin=-999999,     Emax=999999,     capitals=1,     clamp=0,     flags=[],     traps=[         InvalidOperation,         DivisionByZero,         Overflow     ] )The function decimal.getcontext() returns a context object which represents the default context of the decimal module. It also includes the default precision and the default rounding strategy.In the above example, you will see that the default rounding strategy for the decimal module is ROUND_HALF_EVEN. It allows to align with the built-in round() functionLet us create a new Decimal instance by passing a string containing the desired value and declare a number using the decimal module’s Decimal class.>>> from decimal import Decimal >>> Decimal("0.1") Decimal('0.1')You may create a Decimal instance from a floating-point number but in that case, a floating-point representation error will be introduced. For example, this is what happens when you create a Decimal instance from the floating-point number 0.1>>> Decimal(0.1) Decimal('0.1000000000000000055511151231257827021181583404541015625')You may create Decimal instances from strings containing the decimal numbers you need in order to maintain exact precision.Rounding a Decimal using the .quantize() method:>>> Decimal("1.85").quantize(Decimal("1.0")) Decimal('1.8')The Decimal("1.0") argument in .quantize() allows to determine the number of decimal places in order to round the number. As 1.0 has one decimal place, the number 1.85 rounds to a single decimal place. Rounding half to even is the default strategy, hence the result is 1.8.Decimal class:>>> Decimal("2.775").quantize(Decimal("1.00")) Decimal('2.78')Decimal module provides another benefit. After performing arithmetic the rounding is taken care of automatically and also the significant digits are preserved.>>> decimal.getcontext().prec = 2 >>> Decimal("2.23") + Decimal("1.12") Decimal('3.4')To change the default rounding strategy, you can set the decimal.getcontect().rounding property to any one of several  flags. The following table summarizes these flags and which rounding strategy they implement:FlagRounding Strategydecimal.ROUND_CEILINGRounding updecimal.ROUND_FLOORRounding downdecimal.ROUND_DOWNTruncationdecimal.ROUND_UPRounding away from zerodecimal.ROUND_HALF_UPRounding half away from zerodecimal.ROUND_HALF_DOWNRounding half towards zerodecimal.ROUND_HALF_EVENRounding half to evendecimal.ROUND_05UPRounding up and rounding towards zeroRounding NumPy ArraysIn Data Science and scientific computation, most of the times we store data as a  NumPy array. One of the most powerful features of NumPy is the use of  vectorization and broadcasting to apply operations to an entire array at once instead of one element at a time.Let’s generate some data by creating a 3×4 NumPy array of pseudo-random numbers:>>> import numpy as np >>> np.random.seed(444) >>> data = np.random.randn(3, 4) >>> data array([[ 0.35743992,  0.3775384 ,  1.38233789,  1.17554883],        [-0.9392757 , -1.14315015, -0.54243951, -0.54870808], [ 0.20851975, 0.21268956, 1.26802054, -0.80730293]])Here, first we seed the np.random module to reproduce the output easily. Then a 3×4 NumPy array of floating-point numbers is created with np.random.randn().Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.To round all of the values in the data array, pass data as the argument to the  np.around() function. The desired number of decimal places is set with the decimals keyword argument. In this case, round half to even strategy is used similar to Python’s built-in round() function.To round the data in your array to integers, NumPy offers several options which are mentioned below:numpy.ceil()numpy.floor()numpy.trunc()numpy.rint()The np.ceil() function rounds every value in the array to the nearest integer greater than or equal to the original value:>>> np.ceil(data) array([[ 1.,  1.,  2.,  2.],        [-0., -1., -0., -0.], [ 1., 1., 2., -0.]])Look at the code carefully, we have a new number! Negative zero! Let us now take a look at Pandas library, widely used in Data Science with Python.Rounding Pandas Series and DataFramePandas has been a game-changer for data analytics and data science. The two main data structures in Pandas are Dataframe and Series. Dataframe works like an Excel spreadsheet whereas you can consider Series to be columns in a spreadsheet. Series.round() and DataFrame.round() methods. Let us look at an example.Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.>>> import pandas as pd >>> # Re-seed np.random if you closed your REPL since the last example >>> np.random.seed(444) >>> series = pd.Series(np.random.randn(4)) >>> series 0    0.357440 1    0.377538 2    1.382338 3    1.175549 dtype: float64 >>> series.round(2) 0    0.36 1    0.38 2    1.38 3    1.18 dtype: float64 >>> df = pd.DataFrame(np.random.randn(3, 3), columns=["A", "B", "C"]) >>> df           A         B         C 0 -0.939276 -1.143150 -0.542440 1 -0.548708  0.208520  0.212690 2  1.268021 -0.807303 -3.303072 >>> df.round(3)        A      B      C 0 -0.939 -1.143 -0.542 1 -0.549  0.209  0.213 2  1.268 -0.807 -3.303 The DataFrame.round() method can also accept a dictionary or a Series, to specify a different precision for each column. For instance, the following examples show how to round the first column of df to one decimal place, the second to two, and the third to three decimal places: >>> # Specify column-by-column precision with a dictionary >>> df.round({"A": 1, "B": 2, "C": 3})      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 >>> # Specify column-by-column precision with a Series >>> decimals = pd.Series([1, 2, 3], index=["A", "B", "C"]) >>> df.round(decimals)      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 If you need more rounding flexibility, you can apply NumPy's floor(), ceil(), and print() functions to Pandas Series and DataFrame objects: >>> np.floor(df)      A    B    C 0 -1.0 -2.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -4.0 >>> np.ceil(df)      A    B    C 0 -0.0 -1.0 -0.0 1 -0.0  1.0  1.0 2  2.0 -0.0 -3.0 >>> np.rint(df)      A    B    C 0 -1.0 -1.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -3.0 The modified round_half_up() function from the previous section will also work here: >>> round_half_up(df, decimals=2)       A     B     C 0 -0.94 -1.14 -0.54 1 -0.55  0.21  0.21 2 1.27 -0.81 -3.30Best Practices and ApplicationsNow that you have come across most of the rounding techniques, let us learn some of the best practices to make sure we round numbers in the correct way.Generate More Data and Round LaterSuppose you are dealing with a large set of data, storage can be a problem at times. For example, in an industrial oven you would want to measure the temperature every ten seconds accurate to eight decimal places, using a temperature sensor. These readings will help to avoid large fluctuations which may lead to failure of any heating element or components. We can write a Python script to compare the readings and check for large fluctuations.There will be a large number of readings as they are being recorded each and everyday. You may consider to maintain three decimal places of precision. But again, removing too much precision may result in a change in the calculation. However, if you have enough space, you can easily store the entire data at full precision. With less storage, it is always better to store at least two or three decimal places of precision which are required for calculation.In the end, once you are done computing the daily average of the temperature, you may calculate it to the maximum precision available and finally round the result.Currency Exchange and RegulationsWhenever we purchase an item from a particular place, the tax amount paid against the amount of the item depends largely on geographical factors. An item which costs you $2 may cost you less (say $1.8)  if you buy the same item from a different state. It is due to regulations set forth by the local government.In another case, when the minimum unit of currency at the accounting level in a country is smaller than the lowest unit of physical currency, Swedish rounding is done. You can find a list of such rounding methods used by various countries if you look up on the internet.If you want to design any such software for calculating currencies, keep in mind to check the local laws and regulations applicable in your present location.Reduce errorAs you are rounding numbers in a large datasets used in complex computations, your primary concern should be to limit the growth of the error due to rounding.SummaryIn this article we have seen a few methods to round numbers, out of those “rounding half to even” strategy minimizes rounding bias the best. We are lucky to have Python, NumPy, and Pandas already have built-in rounding functions to use this strategy. Here, we have learned about -Several rounding strategies, and how to implement in pure Python.Every rounding strategy inherently introduces a rounding bias, and the “rounding half to even” strategy mitigates this bias well, most of the time.You can round NumPy arrays and Pandas Series and DataFrame objects.If you enjoyed reading this article and found it to be interesting, leave a comment. To learn more about rounding numbers and other features of Python, join our Python certification course.
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How to Round Numbers in Python

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Swift Vs Python

Programming Languages: Their popularity Every passing year witnesses changes in the preferences of programming languages. Some of them get knocked off the perch, while others continue growing. In recent years, two programming languages stand out from the rest and are rapidly growing in popularity. Those two are Swift and Python. In this article, we will talk about the attributes of Swift and Python, their pros and cons and how they are similar to each other. Read along to know more. What they are  Swift and Python. One is a general-purpose, multi-paradigm, object-oriented, functional, imperative and block-structured language while the latter is a widely-used general-purpose, high-level programming language.  Python was originally designed by Guido van Rossum in 1991 and further developed by Python Software Foundation. It was developed to stress code readability along with its syntax enables programmers to code less to express their concepts. It helps coders to speed up the workflow and integrate systems more efficiently. In a survey by Stack Overflow in 2017, Python was the fastest-growing programming language. This resulted in numerous companies prominently using Python as their programming language, the list including Quora, Netflix, Dropbox, Reddit, Facebook, Spotify, Instagram, etc.  In terms of Python’s usability shown above, Data analysis goes first, followed by web development, machine learning, and DevOps. However, Python is less used for educational purposes, prototyping, and Quality Assurance Services. Now talking about Swift, it was designed and released in 2014 after conducting fresh research on programming languages and by using a modern approach to safety, software design patterns by Apple Inc. It is a completely new programming language for the iOS application, macOS application, watchOS application, tvOS application. Needless to say, it quickly grew to be one of the top 5 programming languages and became the most used programming language among the Apple developer community within a short span of than 5 years, also effectively replacing the previously used Objective C. Let us share an important piece of information with you. According to a survey done on the most popular programming languages, Python takes the first spot with overwhelming popularity with a share of 25.36%, whereas Swift is climbing up the ladder at the 9th spot with 2.69% share. The table is mentioned below:Advantages and disadvantages of using Python  Advantages In this section, we will focus on the criteria that make Python a truly developer-friendly language. As we learnt that Python has its uses in numerous lines of work, we will find out how it ticks the checkboxes of the required criteria. Simplicity and readability One of the prime benefits of using Python is that it is simple to code and read. Of course, it is not a repetitive language to follow but is very similar to English and hence is easy to follow. Moreover, Python is a good choice for beginners in programming Multi-paradigm It is a programming language that is object-oriented as well as procedural. Its procedural paradigm allows reuse code and object-oriented methodology allows varied inheritances and summarising data and functions as one Open-source As Python is open source, you can download and modify its source code. This versatile feature led to the formation of a strong community that keeps growing stronger Integration with other languages Being an extensible and embeddable language, programmers can easily integrate Python to Java applications, C, and C++ Portability and compatibility Python is compatible with various platforms. If required, users do not require to change the code before moving the project. to be moved to another platform Vast collection of libraries Being in the game for a long time, Python boasts having a strong community with a vast range of libraries and frameworks for different purposes. providing programmers with a wide spectrum of opportunities. Additionally, libraries like Pandas, Plotly, NumPy, Pipenv, and others and are included as well. Django, Flask, CherryPy, and PyTorch are.among the most famous frameworks. Disadvantages With the pros come the cons. There is also the other side of a coin that needs attention. In spite of having a long history in the programming world, Python still has several weak sides. Not ideal for Mobile development Python is not a good solution for mobile developers. However, a come-around solution with a few challenges is Kivy - a cross-platform Python framework for developing mobile apps Design restrictions There are specific design limitations in Python. Being dynamically typed language using duck typing, Python automatically identifies a type of a variable and can cause runtime errors Although it is not frequent, it does make errors at times.  Memory consumption Python consumes high memory and is definitely not a good option to run intensive memory tasks. Swift pros and cons Being a relatively new programming language, Swift was launched at the WWDC conference in 2014. According to Apple, the primary features of Swift is that it is fast, modern and interactive. Swift's creator Chris Lattner his creation was a result of ideas inspired by different languages such as C#, Ruby, and especially by Python. That's why we can easily find a couple of similarities between Swift and Python. Nevertheless, let’s see what the pros and cons of Swift are. Advantages Easy to read and maintain The Swift program codes are based on English as it acquired syntaxes from other programming languages, thus making the language more expressive Scalable More features to Swift, so it is a scalable programming language. Swift has already replaced Objective C and Swift is what Apple is relying on Concise Swift does not include long lines of code and that favours the developers who want a concise syntax, thus increasing the development and testing rate of the program Safety and improved performance Almost 40% better than the Objective-C, Swift is handier to tackle the bugs that lead to safer programming when speed and performance is concerned Cross-device support This language can handle a wide range of Apple platforms such as iOS, iOS X, macOS, tvOS, and watchOS Automatic Memory Management This feature prevents memory leaks and helps in optimising the application’s performance that is done by using Automatic Reference Counting. Disadvantages Compatibility issues The updated versions Swift is observed to be a bit unstable with the newer versions of Apple leading to a few issues. Switching to a newer version of Swift is the fix but that is costly Speed Issues This is relevant to the earlier versions of the Swift programming language Less in number: The number of Swift developers is limited as Swift is a new programming language Delay in uploading apps Developers will be facing delays over their apps written in Swift to be uploaded to the App Store only after iOS 8 and Xcode 6 are released. The estimated time for release is reported to be September-October, 2014. Common attributes of Swift and Python Swift and Python are predominantly contrasting languages. Despite that, the do possess some common traits. Let’s see what they are. Both Swift and Python have a distinct syntax and are very similar to the English language. Missing semicolons while coding in either Swift or Python will not result in errors. Both languages have a REPL environment that aids in detecting errors in code and debugging Both are multi-paradigm programming languages They have additional tools to facilitate learning. What makes Swift and Python different from each other? From the previous discussions in this article, it is crystal clear that Swift and Python are fundamentally different from each other. Apple’s Swift is ideal for developing software for the Apple ecosystem while Python can be utilised for use cases but is mainly applied in back-end development. Moreover, as Apple claims, Swift is 8.4x faster than Python in terms of performance. Choosing between Swift and Python depends on the intent of the programmer. If the purpose is developing mobile applications that need to work flawlessly in the Apple platforms, then Swift is the ultimate choice. However, if the intentions are to develop artificial intelligence, design a prototype or build the backend, then Python is the one. In the end, what matters is the intent So now we see that in fact choosing Python or Swift for coding mostly depends on your purpose. If you are fond of developing mobile applications that will work seamlessly on Apple operating systems, you should definitely choose Swift. Python is good in case you want to develop your own artificial intelligence, build the backend or create a prototype. There is no hiding the fact that both Swift and Python are good at what they do. While Python has been a game-changer for years, Swift has been rapidly rising up the ranks. Comparing the two directly is a bit unjust as each one of the two has their own uses. The best person to select the right programming language is you! So be the judge of your decision. Good luck! 
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Swift Vs Python

Programming Languages: Their popularity Every pas... Read More

Top 10 Python IDEs and Code Editors

Over the years, Python language has evolved enormously with the contribution of developers. Python is one of the most popular programming languages. It was designed primarily for server-side web development, software development, evaluation, scripting and artificial intelligence. For this feature Python encloses certain code editors and IDEs that are used for software development say, Python itself. If you are new to programming, learning Python is highly recommended as it is fast, efficient and easy to learn. Python interpreters are available on various operating systems such as Windows, Linux, Mac OS. This article provides a look into code editors and IDEs along with their features, pros and cons and talks about which are the best suited for writing Python codes. But first let us see what are code editors and IDEs. What is a Code Editor? A code editor is built for editing and modifying source code. A standalone text editor is used for writing and editing computer programs. Excellent ones can execute code as well as control a debugger as well as interact with source control systems. Compared to an IDE, a good dedicated code editor is usually smaller and quicker, but is less functional. Typically they are optimized for programming languages. One major feature of a text editor is that they are designed to modify various files and work with whatever language or framework you choose. What is IDE? IDE (Integrated Development Environment) understands the code significantly better than a text editor. It is a program exclusively built for software development. It is designed with a set of tools that all work together:  Text editor  Compiler Build automation Debugging Libraries, and many more to speed up the work.  These tools integrate: An editor designed to frame codes with text formatting, auto-completionetc., build, execution, debugging tools, file management and source and version control. It reduces manual efforts and combines all the equipment in a typical framework. IDE comes with heavy files. Hence, the downloads and installation is quite tedious. IDE requires expertise along with a lot of patience.  How does an IDE and Code editor differ from each other? An IDE is distinctive from code editors in the following ways: Integrated build process:The user does not have to write his own scripts to build apps in an IDE.  File management: IDE has an integrated file management system and deployment tool. It provides support to other framework as well. On the other hand, a Text editor is a simple editor where source code can be edited and it has no other formatting or compiling options. Development Environment: An IDE is mainly used for development purposes as it provides comparatively better features than a text editor. It allows you to write, compile and debug the entire script.  Syntax Highlighting:The editor displays the text message and puts the source code in different colours to improve its readability. Even error messages are displayed in different colours so that the user understands where he has written the wrong code.  Auto completion:It identifies and inserts a common code for the user instantly. This feature acts as an assistance for the programmer. The code suggestion automatically gets displayed.  Debugger: This tool helps the programmer to test and debug the source code of the main program.  Although IDEs have far better features than a Text editor one major significance of Text editor is that it allows modifying all types of files rather than specifying any definite language or types. Features For a good software development, we need code editors and IDEs which help the developer to automate the process of editing, compiling, testing, debugging and much more. Some of the features of these editors are listed below: Good user interface: They allow users to interact and run programs easily. Incredibly fast: Although these IDEs need to import heavy libraries, compile and debug, they offer fast compilation and run time.  Syntax stylizing: Codes are colorized automatically and syntax is highlighted.    Debugging tool: Itruns the code, set breakpoints, examine the variables. Provides good language syntax: IDEs usually work on a specific language but the others are designed for multi-language support. Code editors are designed with multi-language support.  Good source and version control environment: IDEs come with source control feature to keep a track of changes made in source code and other text files during the development of any software. Intelligent code completion:This feature speeds up the coding process by automatically suggesting for incomplete codes. It reduces typos and other common mistakes. Why do we need a good coding environment? For a good software development one seeks a better coding environment. Although features vary from app to app, a definite set of features is required for one. There are many other things involved such as source code control, extension tools, language support etc. Listed below are the core features which make a good coding environment : Retrieve files: All the codes written in an IDE get saved. Also, the programmer can retrieve his code file at the same state where the work is left off. Run within the environment: It should be able to compile and run within the environment where the codes are written. No external file shall be needed to be downloaded for the execution of the programs.  Good Debugging Tool: An IDE or editor should be able to diagnose and  troubleshoot the programmer’s works and highlight the lines with errors if any. A pop-up window should display the error message. This way the programmer can keep a track of his errands and diagnose them.   Automatic formatting tool: Indentation is done automatically as soon as the programmer moves onto the next line. It keeps the code clean and readable. Quick highlighting: keywords, variables and symbols are highlighted. This feature keeps the code clean and easy to understand. Also, pops up the variables making them easy to spot. This makes it a whole lot easier to pick out portions of code than simply looking at a wall of undifferentiated text. Some of the IDEs and code editors There are various Python IDEs and text editors. Some of the IDEs and text editors along with their features and pros and cons are mentioned below: IDLEKey Features: It is an open source IDE entirely written in Python. It is mainly supported by WINDOWS, LINUX, MAC OS etc.. IDLE is a decent IDE for learning because it is lightweight and quite simple to use. IDLE is installed by default as soon as installation of Python is complete. This makes it easier to get started in Python. IDLE features include the Python shell window(interactive interpreter), auto-completion, syntax highlighting, smart indentation, and a basic integrated debugger. It is however not suitable for the completion of larger projects and best suitable for educational purposes only.  Pros A cross-platform where a developer can search within any window, search through multiple files and replace within the windows editor  Supports syntax highlighting, auto code completion, smart indentation and editable configurations Includes Python shell with highlighter Powerful Integrated Debugger with continuous breakpoints, global view, and local spaces Improves the performance  Call stack visibility Increases the flexibility for developers Cons Used for programming just for beginners Limited to handle normal usage issues. Supports basic design  Large software development cannot be handled  Sublime text Key Features: It is a source code editor, supported on all platforms. It is a very popular cross-platform  and a better text editor. It possesses a built-in support for Python for code editing and packages to extend the syntax and editing features. All Sublime Text packages are written in Python and also a Python API. Installation of the packages often requires you to execute scripts directly in Sublime Text. it is designed to support huge programming and markup languages. Additional functions can be applied by the user with the help of plugins.  Pros More reliable for developers and is cross-platform Supports GOTO anything to access files  Generates wide index of each method, class, and function. AllowsUser interface toolkit Easy navigation to words or symbols Multiple selections to change things at one time Offers command palette to sort, edit and modify the syntax and maintain the indentation.  Offers powerful API and package ecosystem Great performance Highly customizable Allows split editing and instant project switch  Better compatibility with language grammar Custom selection on specific projects Cons Not free Installation of extensions is quite tricky Does not support for direct executing or debugging code from within the editor Less active GIT plugin AtomKey Features: It is an open source code editor developed by Github. It is supported on all platforms. It has features similar to that of Python. It has a framework based on atom shells which help to achieve cross platform functionality. With a sleek interface, file system browser, and marketplace for extensions, it offers a framework for creating desktop applications using JavaScript, HTML, CSS . Extensions can be installed when Atom is running.It enables support for third party packages. Its major feature is that although it is a code editor,it can also be used as an IDE. It is also used for educational purposes. Atom is being improvised day by day, striving to make the user experience rewarding and not remain confined to beginners use only.  Pros Cross-platform  Smooth editing Improves performance of its users Offers built-in package manager and file system browser Faster scripting  Offers smart auto-completion  Smart and flexible Supports multiple pane features Easy navigation across an application Simple to use Allows user interface customization Full support from GitHub Quick access to data and information Cons For beginners only Tedious for sorting configurations and plugins Clumsy tabs reduce performance  Slow loading Runs on JavaScript process  Built on Electron, does not run as a native application VimKey Features: Categorized as a stable open source code editor, VI and VIM are modal editors. As it is supported on almost every platform such as: Windows, LINUX, MAC OS, IOS, Android, UNIX, AmigaOS, MorphOS etc. it is highly configurable. Because of its modal mode of operation, it differs from most other text editors. It possesses three basic modes: insert mode, normal or command mode and command line mode. It is easily customized by the addition of extensions and configuration which makes it easily adaptable for Python development.  Pros Free and easily accessible Customizable and persistent  Has a multi-level undo tree  Extensions are added manually Configuration file is modified Multi-buffers support simultaneous file editing Automated indentation  Good user interface Recognition and conversion of file formats Exclusive libraries including wide range of languages Comes with own scripting language with powerful integration, search and replace functionality Extensive system of plugins Allows debugging and refactoring  Provides two different modes to work: normal and editing mode Strings in VIM can be saved and reused  Cons Used as a text editor only No different color for the pop-up option Not good for beginners PyDev Key Features: It is also categorized as an open source IDE mainly written with JAVA.Since it is an eclipse plugin, the Java IDE is transformed into Python IDE. Its integration with Django gives a Python framework. It also has keyword auto-completion, good debugging tool, syntax highlighting and indentation. Pros Free open source Robust IDE feature set Auto-completion of codes and analysis Smart indentation Interactive console shortcuts Integrated with Django configuration  Platform independent Cons: User interface is not great  Visual studioKey Features: It is categorized as an IDE, is a full-featured IDE developed by Microsoft. It is compatible with Windows and Mac OS only and comes with free as well as paid versions. It has its own marketplace for extensions. PTVS(Python Tools for Visual Studio) offers various features as in coding for Python development, IntelliSense, debugging, refactoring etc. Pros Easy and less tedious installation for development purposes Cons Spacious files  Not supported on Linux Visual studio code Key Features: VS code is a code editor and is way more different from VS. It is a free open source code editor developed by Microsoft can be run on platforms such as Windows, Linux and Mac OS X.  It has a full-featured editor that is highly configurable with Python compatibility for software development. Python tools can be added to enable coding in Python.VS code is integrated with Git which promotes it to perform operations like push, commit directly from the editor itself. It also has electron framework for Node JS applications running on the Blink browser engine. It is enclosed with smart code completion with function definition, imported modules and variable types. Apart from these, VS code also comes with syntax highlighting, a debugging console and proprietary IntelliSense code auto completion. After installing Python, VS code recognizes Python files and libraries immediately.  Pros Free and available on every platform  Small, light-weight but highly extensible Huge compatibility Has a powerful code management system Enables debugging from the editor Multi-language support  Extensive libraries Smart user interface and an acceptable layout Cons Slow search engine Tedious launch time Not a native app just like Atom WingKey Features: Wing is also one of the powerful IDEs today and comes with a lot of good features. It is an open source IDE used commercially. It also is constituted with a strong framework and has a strong debugger and smart editor for Python development making it fast, accurate and fun to perform. It comes with a 30 day trial version. It supports text driven development with unit test, PyTest and Django testing framework.  Pros Open source Find and go-to definition Customizable and extensible Auto-code completion Quick Troubleshoot  Source browser shows all the variables used in the script Powerful debugger  Good refactoring  Cons Not capable of supporting dark themes Wing interface is quite intimidating Commercial version is expensive Python-specific IDEs and Editors Anaconda - Jupyter NotebooksKey Features: It is also an open source IDE with a server-client structure, used to create and edit the codes of a Python. Once it is saved, you can share live code equations, visualizations and text. It has anaconda distribution i.e., libraries are preinstalled so downloading the anaconda itself does the task. It supports Python and R language which are installed by default at installation.  This IDE is again used for data science learning. Quite easy to use, it is not just used as an editor but also as an educational tool or presentation. It supports numerical simulation, machine  learning visualization and statistical modelling. Pros Free Open source  Good user interface Server-client structure Educational tool- Data science, Machine learning  Supports numerical simulation  Enables to create, write, edit and insert images Combines code, text and images Integrated libraries - Matplotlib, NumPy, Pandas Multi-language support Auto code completion Cons Sometimes slow loading is experienced Google Colaboratory Key Features: It is the simplest web IDE used for Python. It gives a free GPU access. Instead of downloading heavy files and tedious launch time, one can directly update the files from Colab to the drive. All you need to do is log in to your google account and open Colab. There is no need for extra setup. Unlike other IDEs no files are required to download. Google provides free computation resources with Colaboratory. It is designed for creating machine learning models. For compilation and execution, all you need to do is to update Python package and get started.   Pros Available to all Code can be run without any interruption Highly user interactive No heavy file downloads Integrated libraries Multi-language support Updated in google drive Update the Python package for execution  Runs on cloud Comments can be added in cells Can import Jupiter or IPython notebooks Cons  All colaboratory files are to be stored in google drive Install all specific libraries No access to unsaved files once the session is over Pycharm Key Features: Developed by Jet Brains and one of the widely used full-featured Python IDE, this is a cross-platform IDE for Python programming and  is well-integrated with Python console and IPython Notebook. It is supported by Windows, Linux, Mac OS and other platforms as well. It has massive productivity and saves ample amount of time. It comes with smart code navigation, code editor, good debugging tool, quick refactoring etc. and supports Python web development frameworks such as Angular JS, JavaScript, CSS, HTML  and live editing functions. The paid version offers advanced features such as full database management and a multitude Framework than the community version such as Django, Flask, Google App, Engine, Pyramid and web2py. Pros Great supportive community Brilliant performance. Amazing editing tools Robust debugging tool Smart code navigation Quick and safe refactoring  Built in developer tools Error detection and fix up suggestions Customizable interface Available in free and paid version Cons Slow loading  Installation is quite difficult and may hang up in between SpyderKey Features: It is an open source IDE supported on all platforms. Ranked as one of the best Python compilers, it supports syntax highlighting, auto completion of codes just like Pycharm. It offers an advanced level of editing, debugging, quick diagnose, troubleshoot and many data exploration features. To get started with Spyder, one needs to install anaconda distribution which is basically used in data science and machine learning. Just like Pycharm it has IntelliSense auto-completion of code. Spyder is built on a structured and powerful framework which makes it one of the best IDE used so far. It is most commonly used for scientific development. Pros Free open source IDE Quick troubleshoot Active framework Smart editing and debugging Syntax is automatically highlighted Auto completion of codes Good for data science and machine learning Structured framework Integrates common Python data science libraries like SciPy, NumPy, and Matplotlib Finds and eliminates bottlenecks Explores and edits variables directly from GUI  Performs well in multi-language editor and auto completion mode Cons Spyder is not capable to configure a specific warning Too many plugins degrades its performance ThonnyKey Features: Thonny is another IDE best suited for beginners for Python development and provides a good virtual environment. It is supported on all platforms. It gives a simple debugger with F5, F6 and F7 keys for debugging. Also, Thonny supports highlighting errors, good representation of function calls, auto code completion and smart indentation. It even allows the developers to configure their code and shell commands. by default,  in Thonny Python is pre-installed as it downloads with its own version of Python.  Pros Simple Graphical user interface.  Free open source IDE Best for beginners Simple debugger with F5, F6, F7 Keys Tackles issues with Python interpreters Highlights syntax error Auto-completion of code Good representation of function calls User can change reference mode easily Step through expression evaluation Reply and resolve to comments Cons Interface is not that good for developers Confined to text editing No template support Slow plugin creation Too basic IDE for software development Which Python IDE is right for you? Requirements vary from programmer to programmer. It is one’s own choice to pick the right tool that is best suited for the task at hand. Beginners need to use a simple tool with few customizations whereas experts require tools with advanced features to bring new updates. Few suggestions are listed below:- Beginners should start with IDLE and Thonny as they do not have complex features and are pretty easy to learn. For data science learners Jupyter Notebooks and Google Colaboratory is preferred. Generally, large scale enterprises prefer the paid versions of IDEs like PyCharm, Atom, Sublime Text etc. in order to get extensive service support from the company. Also, they provide easy finance options and manpower. On the other hand, middle and small scale enterprises tend to look for open source tools which provides them with excellent features. Some of such IDEs are Spyder, Pydev, IDLE and Visual Studio. Conclusion Today, Python stands out as one of the most popular programming languages worldwide. IDE being a program dedicated to software development has made it easier for developers to build, execute, and debug their codes. Code editors can only be used for editing codes whereas an IDE is a feature rich editor which has inbuilt text editor, compiler, debugging tool and libraries. Different IDEs and code editors are detailed in this article along with their merits and demerits. Some are suitable for beginners because of their lightweight nature and simplicity like IDLE, Thonny whereas experts require advance featured ones for building software.  For learning purposes say data science, machine learning Jupyter and Google Colaboratory are strongly recommended. Again there are large scale enterprises who prefer PyCharm, Atom, Sublime Text for software development. On the other hand, small scale enterprises prefer Spyder, Pydev, IDLE and Visual Studio. Hence,the type of IDE or code editor that should be used completely depends upon the requirement of the programmer . To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 
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Top 10 Python IDEs and Code Editors

Over the years, Python language has evolved enormo... Read More