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What is pip, Getting Started with Python pip

Pip is a package manager for Python that allows you to install additional libraries and packages that are not part of the standard Python library such as the ones found in the Python Package Index. It is a replacement for easy install. If your version of Python is 2.7.9 (or greater) or Python 3.4 (or greater), then PIP comes pre-installed with Python, in other cases you will have to install it separately. PIP is a recursive acronym for “Preferred Installer Program” or “PIP Installs Packages”. It is a command-line utility that installs, reinstalls, or uninstalls PyPI packages with one simple command: pip. You may be familiar with the term package manager if you have used other languages like Ruby uses Gem, JavaScript uses npm for package management, and .NET uses NuGet. Pip has become the standard package manager for Python. The Python installer installs pip automatically, so it is ready for you to use, unless you have installed an older version of Python. You can also verify if pip is available on your Python version by running the command below:On running the command mentioned above, a similar output should be displayed which will show the pip version, along with the location and version of Python. If you are using an older version of Python, the pip version will not be displayed. Then you can install it separately. You can download pip from the following link: https://pypi.org/project/pip/ Installing pip in PythonFor WindowsFollow the instructions to install pip in Python on Windows 7, Windows 8.1, and Windows 10: Download get-pip.py installer script from https://bootstrap.pypa.io/get-pip.py. For Python 3.2, download from https://bootstrap.pypa.io/3.2/get-pip.py. After that, right-click on the link and select Save As and save it to any safe location on your computer. Open Command Prompt and navigate to the get-pip.py file where you saved it previously. Run the command: python get-pip.py For Mac Modern Mac systems have Python and pip pre-installed but the version of Python tends to be outdated and not the best choice for serious programming in Python. So, it’s highly recommended that you install a more updated version of Python and PIP. If you want to use the pre-installed Python application but don’t have PIP available, you can install PIP with the following commands in Terminal:sudo easy_install pipIf you want to install an updated version of Python, then you can use Homebrew. Installing Python with Homebrew requires a single command:brew install pythonInstalling Python with Homebrew will give you the latest version which should come packaged with PIP but if PIP is unavailable, you can re-link Python using the following commands in Terminal:brew unlink python && brew link pythonFor Linux If your Linux distribution came with Python pre-installed, using your system’s package manager you will be able to install PIP. This is preferable since pre-installed versions of Python do not work well with the get-pip.py script used on Windows and Mac. Given below are the commands you should run in order to install pip in your system depending on the version of Python you are using:Advanced Package Tool (Python 2.x):sudo apt-get install python-pip pacman Package Manager (Python 2.x):sudo pacman -S python2-pip Yum Package Manager (Python 2.x):sudo yum upgrade python-setuptools  sudo yum install python-pip python-wheel Dandified Yum (Python 2.x):sudo dnf upgrade python-setuptools  sudo dnf install python-pip python-wheel Zypper Package Manager (Python 2.x):sudo zypper install python-pip python-setuptools python-wheel Advanced Package Tool (Python 3.x): sudo apt-get install python3-pip pacman Package Manager (Python 3.x): sudo pacman -S python-pip Yum Package Manager (Python 3.x): sudo yum install python3 python3-wheel Dandified Yum (Python 3.x): sudo dnf install python3 python3-wheel Zypper Package Manager (Python 3.x): sudo zypper install python3-pip python3-setuptools python3-wheel For Raspberry Pi You are most likely running Raspbian if you are a Raspberry Pi user as it is the official operating system designated and provided by the Raspberry Pi Foundation. PIP comes pre-installed on with Raspbian Jessie. It is one of the biggest reasons to upgrade to Raspbian Jessie instead of using Raspbian Wheezy or Raspbian Jessie Lite. If you are using an older version of Raspbian, you can still manually install PIP. Given below are the commands you should run in order to install pip on your system depending on the version of Python you are using: On Python 2.x:sudo apt-get install python-pipOn Python 3.x:sudo apt-get install python3-pipRaspbian users, working with Python 2.x must use pip while Python 3.x users must use pip3 while running PIP commands.For Ubuntusudo apt-get install python-pipFor Fedorasudo yum install python-pipHow to use PIP and PyPI? PyPI - the Python Package Index After PIP is installed, we need to find a package to install. Packages are usually installed from the repository of software for the Python programming language which is the Python Package Index.Set environment variable for PIP:You won’t have to reference the pip install directory again and again if you set an environment variable.Set: (default = C:\Python27\Scripts) in your Windows/Linux “PATH” environment variable.Getting Started with PIP Now that we know what PIP is and have successfully installed it on our computer, let's get started on how to use it: Commands in PIP Enter pip in the command terminal and it will show the following output on the screen. Usage:pip [options] Commands:InstallInstall packagesDownloadDownload packagesuninstallUninstall packagesunzipUnzip individual packagesbundleCreate pybundleshelpShow help for commandsconfigManage local and global configurationfreezeOutput installed packages in required formatlistList installed packageswheelBuild wheels from your requirementshashCompute hashes of package archivescompletionA helper command used for command completioncheckVerify installed packages have compatible dependenciesshowShow information about installed packagessearchSearch PyPI for packageszipZip individual packagesCommonly used commands in pip are install, upgrade or uninstall. General Options: -h, --help: Shows help. --isolated: To run pip in an isolated mode by ignoring environment variables and user configuration. -v, --verbose: Give more output. Option is additive, and can be used up to 3 times. -V, --version: Show version and exit. -q, --quiet: Give less output. Option is additive, and can be used up to 3 times (corresponding to WARNING, ERROR, and CRITICAL logging levels). --proxy: Specify a proxy in the form [user:passwd@]proxy.server:port. --trusted-host: Mark this host as trusted, even though it does not have valid or any HTTPS. --cert: Path to alternate CA bundle. --client-cert: Path to SSL client certificate, a single file containing the private key and the certificate in PEM format. --retries: Maximum number of retries each connection should attempt(5 times by default). --timeout: Set the socket timeout(15 seconds by default). --exists-action: Default action when a path already exists: (s)witch,(i)gnore, (w)ipe, (b)ackup, (a)bort). --cache-dir: Store the cache data in <dir>.--no-cache-dir: Disable the cache. --disable-pip-version-check: Don't periodically check PyPI to determine whether a new version of pip is available for download. Implied with --no-index. Finding required packages:To search any package, i.e. Flask command will be as shown below: pip search Flask The following output will be displayed with all packages and description: Flask-Cache - Adds cache support to your Flask applicationFlask-SeaSurf - An update CSRF extension for FlaskFlask-Admin - Simple and extensible admin interface framework for FlaskFlask-Security - Simple security for Flask appsFlask - A microframework based on Werkzeug, Jinja2 and good intentions Installing a package: To install the required package, in our case it is Flask, enter the following command : pip install Flask Pip – Show information To check information about the newly installed packages enter: pip show Flask  --- Name: Flask  Version: 0.10.1  Location: /usr/local/lib/python2.7/dist-packages  Requires: Werkzeug, Jinja2, itsdangerousUninstalling a package:To uninstall any package installed by PIP, enter the command given below. pip uninstall Flask Uninstalling Flask: ... ..... Proceed (y/n)?  Successfully uninstalled Flask That’s all. The PIP application has been uninstalled. How to Upgrade PIP for Python Although PIP application doesn’t receive updates very often, it’s still important to keep the application up to date with the newer versions because there may be important fixes to bugs, compatibility, and security holes. Fortunately, upgrading to the latest versions of PIP is very fast and simple. On Windows python -m pip install -U pip On Mac, Linux, or Raspberry Pi pip install -U pip Certain versions of Linux and Raspberry Pi, pip3 needs to be entered instead of pip. Using Requirement FilesThe pip install command always installs the latest published version of a package, but you should install the particular version that suits your code. You would want to create a specification of the dependencies and versions that you have used while developing and running your application, so that there are no surprises when you use the application in production. Requirement files allow you to specify exactly the packages and versions that should be installed on your system. Executing pip help shows that there is a freeze command that displays the installed packages in requirements format. This command can be used to redirect the output to a file to generate a requirements file: The freeze command is used to dump all the packages and their versions to a standard output, so as to redirect the output to a file that can be used to install the exact requirements into another system. The general convention is to name this file requirements.txt, but it is completely up to you to name it whatever you want. If you want to replicate the environment in another system, run pip install specifying the requirements file using the -r switch:The versions listed in requirements.txt will match those of the packages: $ pip list Package    Version  ----------        ----------  certifi2018.11.29  chardet3.0.4  idna   2.8  pip    19.0.1  requests     2.21.0  setuptools  40.6.2  urllib31.24.1 You may submit the requirements.txt file to source control and can use it to create the exact environment in other machines. Fine-Tuning Requirements The problem with hardcoding the versions of your packages and their dependencies is that the packages receive frequent updates with bug and security fixes, and you probably want to update to them as soon as they are published. The requirements file format gives you a bit of flexibility to ensure that the packages are up to date by allowing you to enter dependency versions using logical operators, although specifying the base versions of a package. Make the following changes by opening the requirements.txt file in your editor: certifi>=2018.11.29  chardet>=3.0.4  idna>=2.8  requests>=2.21.0  urllib3>=1.24.1 Change the logical operator to >= to install an exact or greater version that has been published. While you set a new environment using the requirments.txt file, pip searches for the latest version that supports the requirement and installs it. The packages in your requirements file can be updated by running the install command with the --upgrade switch: In this case nothing was upgraded because latest versions have already been installed, but if a new version was published for a listed package, then the package would’ve been upgraded. New versions can introduce changes that fix bugs and will make or break your application. In order to fine-tune your requirements, the requirements file syntax supports additional version specifiers. Let us assume that a new version 3.0 of requests is published but it breaks your application as it introduces an incompatible change. In such a case, the requirements file can be modified to prevent 3.0 or higher versions from being installed: certifi>=2018.11.29 chardet>=3.0.4 idna>=2.8 requests>=2.21.0, <3.0 urllib3>=1.24.1Changing the version specifier for the requests package ensures that only the versions which are less than 3.0 get installed. Production vs Development DependenciesAll packages which are installed during the development of your applications are not going to be application dependencies. During the development process, there are certain packages published to PyPI that are development tools or libraries that can be useful to you. For example, you would require a unit test framework in order to unit test your application. Pytest is a popular framework for unit testing. You would want to install the unit testing framework in your development environment, but not in your production environment because it is not an application dependency. To set up a development environment, you need to create a second requirements file (requirements_file.txt) to list additional tools: # In requirements_file.txt  pytest>=4.2.0 To do this, you need to install both requirement files using pip: requirements.txt and requirements_file.txt. Pip allows for specifying additional parameters within a single requirements file. The requirements_file.txt can also be modified to install the requirements from the production requirements.txt file: # In requirements_file.txt  -r requirements.txt  pytest>=4.2.0 Notice that the exact same -r switch is being used in order to install the production requirements.txt file. The file format of the requirements file allows you to specify additional arguments right on a requirements file. Alternatives to pipPip is an essential tool for all Pythonistas which is used in developing many applications and projects for package management. This article gives you the basics of Pip for Python but the Python community is very active in providing great tools and libraries for developers using other applications as well. These include alternatives to pip that try to improve and simplify package management. Here are some package management tools other than pip which are available for Python: CondaPoetryPipenvSummaryBy now you know that pip is a package manager for Python that is used in many projects to manage dependencies. It is included with the Python installer, hence it is essential for all Python programmers to know how to use it. Although Python provides a wide range of standard libraries which are suitable for developing all types of applications, the active Python community provides more sets of tools and libraries that speed up the development process of a Python application. In this article, we have covered: The process of installing pip in Python Setting an environment variable for pip Commonly used commands in pip and their functions Finding and installing new packages using pip with requirement files in the command line and getting information about the newly installed package How to uninstall a package in pip? In addition to the above topics we have also covered the importance of keeping dependencies updated and a few alternatives to pip that can help managing those dependencies. 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 pip, Getting Started with Python pip

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What is pip, Getting Started with Python pip

Pip is a package manager for Python that allows you to install additional libraries and packages that are not part of the standard Python library such as the ones found in the Python Package Index. It is a replacement for easy install. If your version of Python is 2.7.9 (or greater) or Python 3.4 (or greater), then PIP comes pre-installed with Python, in other cases you will have to install it separately. 

PIP is a recursive acronym for “Preferred Installer Program” or “PIP Installs Packages”. It is a command-line utility that installs, reinstalls, or uninstalls PyPI packages with one simple command: pip. You may be familiar with the term package manager if you have used other languages like Ruby uses Gem, JavaScript uses npm for package management, and .NET uses NuGet. Pip has become the standard package manager for Python. 

The Python installer installs pip automatically, so it is ready for you to use, unless you have installed an older version of Python. You can also verify if pip is available on your Python version by running the command below:

PIP in Python

On running the command mentioned above, a similar output should be displayed which will show the pip version, along with the location and version of Python. If you are using an older version of Python, the pip version will not be displayed. Then you can install it separately. 

You can download pip from the following link:
https://pypi.org/project/pip/ 

Installing pip in Python

For Windows

Follow the instructions to install pip in Python on Windows 7, Windows 8.1, and Windows 10: 

For Mac 

Modern Mac systems have Python and pip pre-installed but the version of Python tends to be outdated and not the best choice for serious programming in Python. So, it’s highly recommended that you install a more updated version of Python and PIP. 

If you want to use the pre-installed Python application but don’t have PIP available, you can install PIP with the following commands in Terminal:

sudo easy_install pip

If you want to install an updated version of Python, then you can use Homebrew. Installing Python with Homebrew requires a single command:

brew install python

Installing Python with Homebrew will give you the latest version which should come packaged with PIP but if PIP is unavailable, you can re-link Python using the following commands in Terminal:

brew unlink python && brew link python

For Linux 

If your Linux distribution came with Python pre-installed, using your system’s package manager you will be able to install PIP. This is preferable since pre-installed versions of Python do not work well with the get-pip.py script used on Windows and Mac. Given below are the commands you should run in order to install pip in your system depending on the version of Python you are using:

Advanced Package Tool (Python 2.x):

sudo apt-get install python-pip 

pacman Package Manager (Python 2.x):

sudo pacman -S python2-pip 

Yum Package Manager (Python 2.x):

sudo yum upgrade python-setuptools 
sudo yum install python-pip python-wheel 

Dandified Yum (Python 2.x):

sudo dnf upgrade python-setuptools 
sudo dnf install python-pip python-wheel 

Zypper Package Manager (Python 2.x):

sudo zypper install python-pip python-setuptools python-wheel 

Advanced Package Tool (Python 3.x): 

sudo apt-get install python3-pip 

pacman Package Manager (Python 3.x): 

sudo pacman -S python-pip 

Yum Package Manager (Python 3.x): 

sudo yum install python3 python3-wheel 

Dandified Yum (Python 3.x): 

sudo dnf install python3 python3-wheel 

Zypper Package Manager (Python 3.x): 

sudo zypper install python3-pip python3-setuptools python3-wheel 

For Raspberry Pi 

You are most likely running Raspbian if you are a Raspberry Pi user as it is the official operating system designated and provided by the Raspberry Pi Foundation. PIP comes pre-installed on with Raspbian Jessie. It is one of the biggest reasons to upgrade to Raspbian Jessie instead of using Raspbian Wheezy or Raspbian Jessie Lite. If you are using an older version of Raspbian, you can still manually install PIP. Given below are the commands you should run in order to install pip on your system depending on the version of Python you are using: 

On Python 2.x:

sudo apt-get install python-pip

On Python 3.x:

sudo apt-get install python3-pip

Raspbian users, working with Python 2.x must use pip while Python 3.x users must use pip3 while running PIP commands.

For Ubuntu

sudo apt-get install python-pip

For Fedora

sudo yum install python-pip

How to use PIP and PyPI? 

PyPI - the Python Package Index 

After PIP is installed, we need to find a package to install. Packages are usually installed from the repository of software for the Python programming language which is the Python Package Index.

Set environment variable for PIP:

You won’t have to reference the pip install directory again and again if you set an environment variable.

Set: (default = C:\Python27\Scripts) in your Windows/Linux “PATH” environment variable.

Getting Started with PIP 

Now that we know what PIP is and have successfully installed it on our computer, let's get started on how to use it: 

Commands in PIP 

Enter pip in the command terminal and it will show the following output on the screen. 

Usage:
pip [options] 

Commands:

InstallInstall packages
DownloadDownload packages
uninstallUninstall packages
unzipUnzip individual packages
bundleCreate pybundles
helpShow help for commands
configManage local and global configuration
freeze
Output installed packages in required format
listList installed packages
wheelBuild wheels from your requirements
hashCompute hashes of package archives
completionA helper command used for command completion
checkVerify installed packages have compatible dependencies
showShow information about installed packages
searchSearch PyPI for packages
zipZip individual packages

Commonly used commands in pip are install, upgrade or uninstall. 

General Options: 

-h, --help: Shows help. 

--isolated: To run pip in an isolated mode by ignoring environment variables and user configuration. 

-v, --verbose: Give more output. Option is additive, and can be used up to 3 times. 

-V, --version: Show version and exit. 

-q, --quiet: Give less output. Option is additive, and can be used up to 3 times (corresponding to WARNING, ERROR, and CRITICAL logging levels). 

--proxy: Specify a proxy in the form [user:passwd@]proxy.server:port. 

--trusted-host: Mark this host as trusted, even though it does not have valid or any HTTPS. 

--certPath to alternate CA bundle. 

--client-cert: Path to SSL client certificate, a single file containing the private key and the certificate in PEM format. 

--retries: Maximum number of retries each connection should attempt(5 times by default). 

--timeout: Set the socket timeout(15 seconds by default). 

--exists-action: Default action when a path already exists: (s)witch,(i)gnore, (w)ipe, (b)ackup, (a)bort). 

--cache-dir: Store the cache data in <dir>.

--no-cache-dir: Disable the cache. 

--disable-pip-version-check: Don't periodically check PyPI to determine whether a new version of pip is available for download. Implied with --no-index. 

Finding required packages:

To search any package, i.e. Flask command will be as shown below: pip search Flask 

The following output will be displayed with all packages and description: 

Flask-Cache - Adds cache support to your Flask application
Flask-SeaSurf - An update CSRF extension for Flask
Flask-Admin - Simple and extensible admin interface framework for Flask
Flask-Security - Simple security for Flask apps
Flask - A microframework based on Werkzeug, Jinja2 and good intentions 

Installing a package: 

To install the required package, in our case it is Flask, enter the following command : 

pip install Flask 

Pip – Show information 

To check information about the newly installed packages enter: 

pip show Flask 
--- 
Name: Flask 
Version: 0.10.1 
Location: /usr/local/lib/python2.7/dist-packages 
Requires: Werkzeug, Jinja2, itsdangerous

Uninstalling a package:

To uninstall any package installed by PIP, enter the command given below. 

pip uninstall Flask
Uninstalling Flask:
...
.....
Proceed (y/n)? 
Successfully uninstalled Flask 

That’s all. The PIP application has been uninstalled. 

How to Upgrade PIP for Python 

Although PIP application doesn’t receive updates very often, it’s still important to keep the application up to date with the newer versions because there may be important fixes to bugs, compatibility, and security holes. Fortunately, upgrading to the latest versions of PIP is very fast and simple. 

On Windows 

python -m pip install -U pip 

On Mac, Linux, or Raspberry Pi 

pip install -U pip 

Certain versions of Linux and Raspberry Pi, pip3 needs to be entered instead of pip. 

Using Requirement Files

The pip install command always installs the latest published version of a package, but you should install the particular version that suits your code. 

You would want to create a specification of the dependencies and versions that you have used while developing and running your application, so that there are no surprises when you use the application in production. 

Requirement files allow you to specify exactly the packages and versions that should be installed on your system. Executing pip help shows that there is a freeze command that displays the installed packages in requirements format. This command can be used to redirect the output to a file to generate a requirements file: 

pip install Requirements in Python

The freeze command is used to dump all the packages and their versions to a standard output, so as to redirect the output to a file that can be used to install the exact requirements into another system. The general convention is to name this file requirements.txt, but it is completely up to you to name it whatever you want. 

If you want to replicate the environment in another system, run pip install specifying the requirements file using the -r switch:

The versions listed in requirements.txt will match those of the packages: 

$ pip list 

Package    Version 
----------        ---------- 
certifi2018.11.29 
chardet3.0.4 
idna   2.8 
pip    19.0.1 
requests     2.21.0 
setuptools  40.6.2 
urllib31.24.1 

You may submit the requirements.txt file to source control and can use it to create the exact environment in other machines. 

Fine-Tuning Requirements 

The problem with hardcoding the versions of your packages and their dependencies is that the packages receive frequent updates with bug and security fixes, and you probably want to update to them as soon as they are published. 

The requirements file format gives you a bit of flexibility to ensure that the packages are up to date by allowing you to enter dependency versions using logical operators, although specifying the base versions of a package. 

Make the following changes by opening the requirements.txt file in your editor: 

certifi>=2018.11.29 
chardet>=3.0.4 
idna>=2.8 
requests>=2.21.0 
urllib3>=1.24.1 

Change the logical operator to >= to install an exact or greater version that has been published. While you set a new environment using the requirments.txt file, pip searches for the latest version that supports the requirement and installs it. The packages in your requirements file can be updated by running the install command with the --upgrade switch: 

Fine-Tuning Requirements in Python

In this case nothing was upgraded because latest versions have already been installed, but if a new version was published for a listed package, then the package would’ve been upgraded. 

New versions can introduce changes that fix bugs and will make or break your application. In order to fine-tune your requirements, the requirements file syntax supports additional version specifiers. 

Let us assume that a new version 3.0 of requests is published but it breaks your application as it introduces an incompatible change. In such a case, the requirements file can be modified to prevent 3.0 or higher versions from being installed: 

certifi>=2018.11.29
chardet>=3.0.4
idna>=2.8
requests>=2.21.0, <3.0
urllib3>=1.24.1

Changing the version specifier for the requests package ensures that only the versions which are less than 3.0 get installed. 

Production vs Development Dependencies

All packages which are installed during the development of your applications are not going to be application dependencies. During the development process, there are certain packages published to PyPI that are development tools or libraries that can be useful to you. 

For example, you would require a unit test framework in order to unit test your application. Pytest is a popular framework for unit testing. You would want to install the unit testing framework in your development environment, but not in your production environment because it is not an application dependency. 

To set up a development environment, you need to create a second requirements file (requirements_file.txt) to list additional tools: 

# In requirements_file.txt 
pytest>=4.2.0 

To do this, you need to install both requirement files using pip: requirements.txt and requirements_file.txt. Pip allows for specifying additional parameters within a single requirements file. The requirements_file.txt can also be modified to install the requirements from the production requirements.txt file: 

# In requirements_file.txt 
-r requirements.txt 
pytest>=4.2.0 

Notice that the exact same -r switch is being used in order to install the production requirements.txt file. The file format of the requirements file allows you to specify additional arguments right on a requirements file. 

Alternatives to pip

Pip is an essential tool for all Pythonistas which is used in developing many applications and projects for package management. This article gives you the basics of Pip for Python but the Python community is very active in providing great tools and libraries for developers using other applications as well. These include alternatives to pip that try to improve and simplify package management. 

Here are some package management tools other than pip which are available for Python: 

  • Conda
  • Poetry
  • Pipenv

Summary

By now you know that pip is a package manager for Python that is used in many projects to manage dependencies. It is included with the Python installer, hence it is essential for all Python programmers to know how to use it. 

Although Python provides a wide range of standard libraries which are suitable for developing all types of applications, the active Python community provides more sets of tools and libraries that speed up the development process of a Python application. 

In this article, we have covered: 

  • The process of installing pip in Python 
  • Setting an environment variable for pip 
  • Commonly used commands in pip and their functions 
  • Finding and installing new packages using pip with requirement files in the command line and getting information about the newly installed package 
  • How to uninstall a package in pip? 

In addition to the above topics we have also covered the importance of keeping dependencies updated and a few alternatives to pip that can help managing those dependencies. 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

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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

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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

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