In this article, I will be listing down the top 10 reasons to learn Python.
Programming languages have been around for ages, and every decade sees the launch of a new language sweeping developers off their feet. Python is considered as one of the most popular and in-demand programming language. A recent Stack Overflow survey showed that Python has taken over languages such as Java, C, C++ and has made its way to the top. This makes Python certification one of the most sought-after programming certifications.
Below are the major features and applications due to which people choose Python as their first programming language:
Python’s popularity & high salaryPython is used in Data Science Python’s scripting & automation Python used with Big DataPython supports TestingComputer Graphics in PythonPython used in Artificial IntelligencePython in Web DevelopmentPython is portable & extensiblePython is simple & easy to learn
If you are planning to start your career in Python and wish to know the skills related to it, now is the right time to dive in, when the technology is in its nascent state.
Now, let me help you to understand these in more detail.10. Simple & Easy To Learn Python
So at number 10, Python is extremely simple and easy to learn. It is a very powerful language and it closely resembles the English language!
So, what contributes to its simplicity? Python is
Free & open sourceHigh-levelInterpretedBlessed with large community
Furthermore, in Python, you don’t have to deal with complex syntax, you can refer to the below image:
If you have to print ‘hello world’, you have to write above three lines whereas in Python, just one line is sufficient to print “hello world”. It’s that SIMPLE guys!
So the 10th reason lies in the simplicity of the code which makes the best suit for beginners.9. Portable & Extensible
The portable and extensible properties of Python allow you to perform cross-language operations seamlessly. Python is supported by most platforms present in the industry today ranging from Windows to Linux to Macintosh, Solaris, Play station, among others.
Python’s extensibility features allow you to integrate Java as well as .NET components. You can also invoke C and C++ libraries.8. Web Development
Python has an array of frameworks for developing websites. The popular frameworks are Django, Flask, Pylons etc. Since these frameworks are written in Python, its the core reason which makes the code a lot faster and stable.
You can also perform web scraping where you can fetch details from any other websites. You will also be impressed as many websites such as Instagram, bit bucket, Pinterest are build on these frameworks only.7. Artificial Intelligence
AI is the next huge development in the tech world. You can actually make a machine mimic the human brain which has the power to think, analyze and make decisions.
Furthermore, libraries such as Keras and TensorFlow bring machine learning functionality into the mix. It gives the ability to learn without being explicitly programmed. Also, we have libraries such as openCv that helps computer vision or image recognition.6. Computer Graphics
Python is largely used in small, large, online or offline projects. It is used to build GUI and desktop applications. It uses ‘Tkinter‘ library to provide fast & easy way to create applications.
It is also used in game development where you can write the logic of using a module ‘pygame’ which also runs on android devices.5. Testing Framework
Python is great for validating ideas or products for established companies. Python has many built-in testing frameworks that covers debugging & fastest workflows. There are a lot of tools and modules to make things easier such as *Selenium *and Splinter.
It supports testing with cross-platform & cross-browser with frameworks such as PyTest and Robot Framework. Testing is a tedious task and Python is the booster for it, so every tester should definitely go for it!4. Big Data
Python handles a lot of hassles of data. It supports parallel computing where you can use Python for Hadoop as well. In Python, you have a library called “Pydoop” and you can write a MapReduce program in Python and process data present in the HDFS cluster.
There are other libraries such as ‘Dask‘ and ‘Pyspark‘ for big data processing. Therefore, Python is widely used for Big Data where you can easily process it!3. Scripting & Automation
Many people only knows that Python is a programming language, but Python can also be used as Scripting language. In scripting:
The code is written in the form of scripts and get executedMachine reads and interprets the codeError checking is done during Runtime
Once the code is checked, it can be used several times. So by automation, you can automate certain tasks in a program.
Python is the leading language of many data scientist. For years, academic scholars and private researchers were using the MATLAB language for scientific research but it all started to change with the release of Python numerical engines such as ‘Numpy’ and ‘Pandas’.
Python also deals with the tabular, matrix as well as statistical data and it even visualizes it with popular libraries such as ‘Matplotlib’ and ‘Seaborn‘.1. Python’s Popularity & High Salary
Python engineers have some of the highest salaries in the industry. The average Python Developer salary in the United States is approximately $116,028 per year.
Also, Python has a strong spike in popularity over the last 1 year. Refer the below screenshot taken from Google Trends.
In this "Python Tutorial: Data Science vs. Web Development" to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.
Python programming has various frameworks and features to expand in web application development, graphical user interfaces, data analysis, data visualization, etc. Python programming language might not be an ideal choice for web application development, but is extensively used by many organizations for evaluating large datasets, for data visualization, for running data analysis or prototyping. Python programming language is gaining traction amongst users for data science whilst being outmoded as a web programming language. The idea of this blog post is to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.Python for Data Science :
Organizations of all sizes and industries — from the top financial institutions to the smallest big data start-ups are using Python programming language to run their business.
Python language is among the popular data science programming languages not only with the top big data companies but also with the tech start up crowd. Python language ranks among the top 10 programming languages to learn in 2019.
Python language comes in the former category and is finding increased adoption in numerical computations, machine learning and several data science applications. Python language can do anything, excluding performance dependent and low level stuff. The best bet to use Python programming language is for data analysis and statistical computations. Learning Python programming for web development requires programmers to master various web frameworks like Django that can help the build websites whereas learning Python for data science requires data scientists to learn the usage of regular expressions, get working with the scientific libraries and master the data visualization concepts. With completely different purposes, programmers or professionals who are not knowledgeable about web programming concepts with Python language can easily go ahead and pursue data science in Python programming language without any difficulty.
Python is a 23-year-old powerful expressive dynamic programming language where a programmer can write the code once and execute it without using a separate compiler for the purpose. Python in web development supports various programming paradigms such as structured programming, functional programming and object oriented programming. Python language code can be easily embedded into various existing web application that require a programming interface. However, Python language is a preeminent choice for academic, research and scientific applications which need faster execution and precise mathematical calculations.
Python web programming requires programmers to learn about the various python web development frameworks, which can be intimidating because the documentation available for the python web development frameworks might be somewhat difficult to understand. However, it is undeniable that to develop a dynamic website or a web application using Python language, learning a web framework is essential.Python Web Development Frameworks
There are several Python web application frameworks available for free like-
Django is the python web development framework for perfectionists with deadlines. Python web development with django is best suited for developing database driven web applications with attractive features like automatic admin interface and a templating system. For web development projects that don’t require extensive features, Django may be an overkill because of its confusing file system and strict directory structure. Some companies that are using python web development with django are The New York Times, Instagram, and Pinterest.
It is a simple and lightweight solution for beginners who want to get started with developing single-page web applications. This framework does not support for validation, data abstraction layer and many other components that various other frameworks include. It is not a full stack framework and is used only in the development of small websites.
It emphasizes on Pythonic conventions so that programmers can build web applications just the way they would do it using object oriented Python programming. CherryPy is the base template for other popular full stack frameworks like TurboBears and Web2py.
There are so many other web frameworks like Pyramid, Bottle, and Pylons etc. but regardless of the fact, whichever web framework a python programmer uses, the challenge is that he/she needs to pay close attention to detailing on the tutorials and documentation.Why Web Development with Python is an impractical choice?
Python programming language probably is an impractical choice for being chosen as a web programming language –
Python for web development requires non-standard and expensive hosting particularly when programmers use popular python web frameworks for building websites. With PHP language being so expedient for web programming, most of the users are not interested in investing in Python programming language for web development.
Python language for web development is not a commonly demanded skill unlike demand for other web development languages like PHP, Java or Ruby on Rails. Python for Data science is gaining traction and is the most sought after skill companies are looking for in data scientists, with its increased adoption in machine learning and various other data science applications.
Python for web development has come a long way but it does not have a steep learning curve as compared to other web programming languages like PHP.
Why Python for Data Science is the best fit?
Python programming is the core technology that powers big data, finance, statistics and number crunching with English like syntax. The recent growth of the rich Python data science ecosystem with multiple packages for Machine learning, natural language processing, data visualization, data exploration, data analysis and data mining is resulting in Pythonification of the data science community. Today, Python data science language has all the nuts and bolts for cleaning, transforming, processing and crunching big data. Python is the most in-demand skill for data scientist job role. A data scientist with python programming skills in New York earns an average salary of $180,000Why data scientists love doing data science in Python language?
Data Scientists like to work in a programming environment that can quickly prototype by helping them jot down their ideas and models easily. They like to get their stuff done by analysing huge datasets to draw conclusions. Python programming is the most versatile and capable all-rounder for data science applications as it helps data scientists do all this productively by taking optimal minimal time for coding, debugging, executing and getting the results.
The real value of a great enterprise data scientist is to use various data visualizations that can help communicate the data patterns and predictions to various stakeholders of the business effectively, otherwise it is just a zero-sum game. Python has almost every aspect of scientific computing with high computational intensity which makes it a supreme choice for programming across different data science applications, as programmers can do all the development and analysis in one language. Python for data science links between various units of a business and provides a direct medium for data sharing and processing language.
Data analysis and Python programming language go hand in hand. If you have taken a decision to learn Data Science in Python language, then the next question in your mind would be –What are the best data science in Python libraries that do most of the data analysis task? Here are top data analysis libraries in Python used by enterprise data scientists across the world-
It is the foundation base for the higher level tools built in Python programming language. This library cannot be used for high level data analysis but in-depth understanding of array oriented computing in NumPy helps data scientists use the Pandas library effectively.
SciPy is used for technical and scientific computing with various modules for integration, special functions, image processing, interpolation, linear algebra, optimizations, ODE solvers and various other tasks. This library is used to work with NumPy arrays with various efficient numerical routines.
This is the best library for doing data munging as this library makes it easier to handle missing data, supports automatic data alignment, supports working with differently indexed data gathered from multiple data sources.
This is a popular machine learning library with various regression, classification and clustering algorithms with support for gradient boosting, vector machines, naïve Bayes, and logistic regression. This library is designed to interoperate with NumPy and SciPy.
It is a 2D plotting library with interactive features for zooming and panning for publication quality figures in different hard copy formats and in interactive environments across various platforms.
A step-by-step guide to setting up Python for Deep Learning and Data Science for a complete beginner
A step-by-step guide to setting up Python for Deep Learning and Data Science for a complete beginner
You can code your own Data Science or Deep Learning project in just a couple of lines of code these days. This is not an exaggeration; many programmers out there have done the hard work of writing tons of code for us to use, so that all we need to do is plug-and-play rather than write code from scratch.
You may have seen some of this code on Data Science / Deep Learning blog posts. Perhaps you might have thought: “Well, if it’s really that easy, then why don’t I try it out myself?”
If you’re a beginner to Python and you want to embark on this journey, then this post will guide you through your first steps. A common complaint I hear from complete beginners is that it’s pretty difficult to set up Python. How do we get everything started in the first place so that we can plug-and-play Data Science or Deep Learning code?
This post will guide you through in a step-by-step manner how to set up Python for your Data Science and Deep Learning projects. We will:
Once you’ve set up the above, you can build your first neural network to predict house prices in this tutorial here:
The main programming language we are going to use is called Python, which is the most common programming language used by Deep Learning practitioners.
The first step is to download Anaconda, which you can think of as a platform for you to use Python “out of the box”.
Visit this page: https://www.anaconda.com/distribution/ and scroll down to see this:
This tutorial is written specifically for Windows users, but the instructions for users of other Operating Systems are not all that different. Be sure to click on “Windows” as your Operating System (or whatever OS that you are on) to make sure that you are downloading the correct version.
This tutorial will be using Python 3, so click the green Download button under “Python 3.7 version”. A pop up should appear for you to click “Save” into whatever directory you wish.
Once it has finished downloading, just go through the setup step by step as follows:
Click “I Agree”
Choose a destination folder and click Next
Click Install with the default options, and wait for a few moments as Anaconda installs
Click Skip as we will not be using Microsoft VSCode in our tutorials
Click Finish, and the installation is done!
Once the installation is done, go to your Start Menu and you should see some newly installed software:
You should see this on your start menu
Click on Anaconda Navigator, which is a one-stop hub to navigate the apps we need. You should see a front page like this:
Anaconda Navigator Home Screen
Click on ‘Launch’ under Jupyter Notebook, which is the second panel on my screen above. Jupyter Notebook allows us to run Python code interactively on the web browser, and it’s where we will be writing most of our code.
A browser window should open up with your directory listing. I’m going to create a folder on my Desktop called “Intuitive Deep Learning Tutorial”. If you navigate to the folder, your browser should look something like this:
Navigating to a folder called Intuitive Deep Learning Tutorial on my Desktop
On the top right, click on New and select “Python 3”:
Click on New and select Python 3
A new browser window should pop up like this.
Browser window pop-up
Congratulations — you’ve created your first Jupyter notebook! Now it’s time to write some code. Jupyter notebooks allow us to write snippets of code and then run those snippets without running the full program. This helps us perhaps look at any intermediate output from our program.
To begin, let’s write code that will display some words when we run it. This function is called print. Copy and paste the code below into the grey box on your Jupyter notebook:
Your notebook should look like this:
Entering in code into our Jupyter Notebook
Now, press Alt-Enter on your keyboard to run that snippet of code:
Press Alt-Enter to run that snippet of code
You can see that Jupyter notebook has displayed the words “Hello World!” on the display panel below the code snippet! The number 1 has also filled in the square brackets, meaning that this is the first code snippet that we’ve run thus far. This will help us to track the order in which we have run our code snippets.
Instead of Alt-Enter, note that you can also click Run when the code snippet is highlighted:
Click Run on the panel
If you wish to create new grey blocks to write more snippets of code, you can do so under Insert.
Jupyter Notebook also allows you to write normal text instead of code. Click on the drop-down menu that currently says “Code” and select “Markdown”:
Now, our grey box that is tagged as markdown will not have square brackets beside it. If you write some text in this grey box now and press Alt-Enter, the text will render it as plain text like this:
If we write text in our grey box tagged as markdown, pressing Alt-Enter will render it as plain text.
There are some other features that you can explore. But now we’ve got Jupyter notebook set up for us to start writing some code!
Now we’ve got our coding platform set up. But are we going to write Deep Learning code from scratch? That seems like an extremely difficult thing to do!
The good news is that many others have written code and made it available to us! With the contribution of others’ code, we can play around with Deep Learning models at a very high level without having to worry about implementing all of it from scratch. This makes it extremely easy for us to get started with coding Deep Learning models.
For this tutorial, we will be downloading five packages that Deep Learning practitioners commonly use:
The first thing we will do is to create a Python environment. An environment is like an isolated working copy of Python, so that whatever you do in your environment (such as installing new packages) will not affect other environments. It’s good practice to create an environment for your projects.
Click on Environments on the left panel and you should see a screen like this:
Click on the button “Create” at the bottom of the list. A pop-up like this should appear:
A pop-up like this should appear.
Name your environment and select Python 3.7 and then click Create. This might take a few moments.
Once that is done, your screen should look something like this:
Notice that we have created an environment ‘intuitive-deep-learning’. We can see what packages we have installed in this environment and their respective versions.
Now let’s install some packages we need into our environment!
The first two packages we will install are called Tensorflow and Keras, which help us plug-and-play code for Deep Learning.
On Anaconda Navigator, click on the drop down menu where it currently says “Installed” and select “Not Installed”:
A whole list of packages that you have not installed will appear like this:
Search for “tensorflow”, and click the checkbox for both “keras” and “tensorflow”. Then, click “Apply” on the bottom right of your screen:
A pop up should appear like this:
Click Apply and wait for a few moments. Once that’s done, we will have Keras and Tensorflow installed in our environment!
Using the same method, let’s install the packages ‘pandas’, ‘scikit-learn’ and ‘matplotlib’. These are common packages that data scientists use to process the data as well as to visualize nice graphs in Jupyter notebook.
This is what you should see on your Anaconda Navigator for each of the packages.
Installing pandas into your environment
Installing scikit-learn into your environment
Installing matplotlib into your environment
Once it’s done, go back to “Home” on the left panel of Anaconda Navigator. You should see a screen like this, where it says “Applications on intuitive-deep-learning” at the top:
Now, we have to install Jupyter notebook in this environment. So click the green button “Install” under the Jupyter notebook logo. It will take a few moments (again). Once it’s done installing, the Jupyter notebook panel should look like this:
Click on Launch, and the Jupyter notebook app should open.
Create a notebook and type in these five snippets of code and click Alt-Enter. This code tells the notebook that we will be using the five packages that you installed with Anaconda Navigator earlier in the tutorial.
import tensorflow as tf import keras import pandas import sklearn import matplotlib
If there are no errors, then congratulations — you’ve got everything installed correctly:
A sign that everything works!
If you have had any trouble with any of the steps above, please feel free to comment below and I’ll help you out!
*Originally published by Joseph Lee Wei En at *medium.freecodecamp.org
Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development
TABLE OF CONTENT
00:01:49 Installing Python
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:26:21 Emoji Converter
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django
Thanks for reading ❤
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