R Vs. Python ⚔️ - Difference Between R and Python ⚖

R Vs. Python ⚔️ - Difference Between R and Python ⚖

⚔️ The big question is which one should we learn as for someone who is interested in machine learning or large datasets – Python or R? ⚔️ In this article, we will answer this question considering all the aspects of both the languages. ⚖

⚔️ The big question is which one should we learn as for someone who is interested in machine learning or large datasets – Python or R? ⚔️ In this article, we will answer this question considering all the aspects of both the languages. ⚖

For a large number of people, data analysis is one of the most important parts of their jobs. The increased availability of data has made computing more powerful and the need for an analytics-driven decision in businesses has brought data science into the limelight. According to a report by IBM, in 2015, there were 2.35 million openings for data analytics jobs in the US. It is expected and estimated that by 2020, the number will rise to 2.72 million. IBM likes to call it “The Quant Crunch”.

In the current era, programming languages like R and Python have been in much demand especially in this quest for data science. Both were developed in the early 1990s. R was mainly for statistical analysis and Python was rather a general-purpose language. Now the big question is which one should we learn as for someone who is interested in machine learning or large datasets – Python or R? In this article, we will answer this question considering all the aspects of both the languages.

Introducing Python and R

Python and R are both open-source, state-of-the-art programming languages. Both languages are oriented toward data science. Learning both of them would be an ideal solution. But since we are to make a comparison let us segregate both the language modules based on their respective qualities.

Python

Python, which is also called the Swiss army knife of coding, is a general-purpose, high-level programming language which focuses on versatility and cleaner programming.

It is easy-to-use and makes replicability and accessibility easier than R. Python is primarily used in the field of Artificial Intelligence and game development.

R

It is basically a low-level programming language used by statisticians and data miners for developing statistical software, graphical representations, and for data analysis. R Foundation for Statistical Computing has been supporting it. R has one of the richest ecosystems of around 12000 packages in the open-source repository for performing data analysis.

History

Python

Python is not named after the snake, but rather after the British TV show Monty Python. Influenced by Modula-3 and successor of the ABC programming language, Python was implemented in the year 1989 by Guido van Rossum.

It was initially released in the year 1991 as Python 0.9.0. Python 2.0 and Python 3.0 were released in the year 2000 and 2008 respectively (the latest version of Python is 3.7.3).

R

Ross Ihaka and Robert Gentleman were the developers of R, which is an implementation of the S programming language created by John Chambers in 1976. Ihaka and Gentleman developed it while working together in New Zealand.

When R was released in 1990, many joined the project to make improvements. It was declared “open-source” in the year 1995. The first version of R was released to the public in the year 2000.

Features

R

R is a free programming language and is considered to be the best since most statistical languages are not priceless.

It covers a wide range of packages which are used in various fields starting from statistical computing, genomics, machine learning, finance, medicine and so on.

Let us list some key features of R -

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.

Python

Python is an interpreted high-level language and it is extremely versatile. It’s a name you can hear among people who love working with data.

According to the TIOBE Programming Community Index, Python is the 3rd most popular language of 2019 after Java and C.

Let us list five significant reasons why Python is the language for all.

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.

Below are two images which show the difference in the code for displaying “Hello World” in Python and R.

Code for displaying “Hello World” in Python

Code for displaying “Hello World” in R

Setup Instructions and Installation

Python

For Windows—

**Step 1: **Open any browser and go to https://www.python.org/

Step 2: Click on the Downloads option. You will see the latest version of Python(which is Python 3.7.3 and stable too).

**Step 3: **Click on ” Download Python 3.7.x ” option.

**Step 4: **The file named “Python-3.7.x.exe” should start downloading into your standard download folder.

Step 5: After it is downloaded, go to the specified folder and run it. Proceed with the Installation process. After a few minutes or so, you will have your Python IDLE running in your computer.

For MacOS—

**Step 1: **Open any browser and go to https://www.python.org/

Step 2: Click on the Downloads option. You will see the latest version of Python(Python 3.7.3).

**Step 3: **Click on “Download Python 3.7.x” option.

**Step 4: **The file named “Python-3.7.x.pkg” should start downloading into your standard download folder.

**Step 5: **After it is downloaded, go to the specified folder and run it. Proceed with the Installation process. After a few minutes or so, you will have your Python IDLE running in your computer.

R

For Windows—

Step 1: Open any internet browser and go to www.r-project.org.

Step 2: Click on the ”download R” link in the middle of the page under "Getting Started."

Step 3: Select a CRAN location and click the corresponding link.

Step 4: Click on the "install R for the first time" link at the top of the page.

Step 5: Click on "Download R for Windows" and save the file on your computer. Run the .exe file and follow the installation instructions thereafter.

For MacOS—

Step 1: Open any internet browser and go to www.r-project.org.

Step 2: Click the "download R" link in the centre of the page under "Getting Started".

Step 3: Select a CRAN location (a mirror site) and click the corresponding link.

Step 4: Click on the "Download R for (Mac) OS X" link at the top of the page.

Step 5: Click on the file which contains the latest version of R under "Files".

Step 6: Save the .pkg file, double-click it to open, and follow the installation instructions thereafter.

Distributions

Both R and Python have a common free and open-source distribution— Anaconda. Its main functions include applications of machine learning, large-scale data processing, predictive analysis, and data science.

The Anaconda distribution consists around 1400 popular data science packages including Anaconda Navigator, a desktop Graphical User Interface(GUI) which allows users to launch applications and manage the conda package.

Some of the commonly used IDEs of Python are -

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.

Some of the commonly used IDEs of R are -

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.
Which language to choose to learn out of these two?

If you have programming experience, which is better to learn, R or Python?

If you have gathered some knowledge about programming, Python is the language for you. The syntax of Python is much analogous to other languages in comparison to R’s syntax.

R has a non-standardized kind of code which might be a difficulty for people who are new to programming. On the other hand, Python is much readable and focuses on development fruitfulness.

Which is better, R or Python, if you want to go into industry or academia?

R is a statistical programming language which is mainly used in the academic sector. But the real question is which one is industry-ready?

If we consider this, Python would be a better option. Organizations use Python extensively to develop their production systems.

But since some time now, R has updated their libraries to open-source, industries are also considering it for their work and is being largely used.

Which is better for data analysis?

This is the most common question which is lurking around everyone for some time. But before settling to the conclusion, let me provide you with two examples.

Consider a situation where we need to cover election data. This is a relatively repetitive and predictable process where we need to collect data and make recurrent analysis and make pies and charts based on that. In this case, Python will provide ease of work.

Now, if we take text analysis, for example, where we need to break paragraphs into phrases and words and analyze patterns, it is better to make use of R.

Conclusively, we can say Python is used for repeated jobs and data manipulation whereas R for heavy statistical projects and situations where we need to dive into one-time datasets.

What do you want to learn, “statistical learning” or “machine learning”?

Machine learning comes in the category of Artificial Intelligence while Statistical learning is a subfield of Statistics. Machine learning focuses on the development of real-world applications and predictive models; while Statistical learning mainly emphasizes on preciseness and uncertainty.

Since R was developed by statisticians, people who have a background in statistics, R would be easier to work with.

Python, on the other hand, is a better choice for those in the data department where they need to perform analysis and also for those in the machine learning sector, especially because of its flexibility.

Which language to learn if you want to do a lot of web development and software engineering?

R would be your choice if you want to go for web development. Though it is not the best in comparison to JavaScript or CSS. R provides you with the Shiny library by which websites can be developed which will be powered by R.

For software engineering, Python is the one. For an engineering environment, Python is better than R in the larger spectrum. However, you might need to make use of a low-level module like C++ or Java for really efficient coding.

Which language helps to create beautiful and interactive data visualizations, R or Python?

R is always a better option for continuous prototyping and handling datasets. Data visualizations can be performed with R with library packages like ggplot2, HTML widgets, Leaflet. Though Python has made some advances with Matplotlib but still lags behind R in this area.

What are the libraries R and Python offers?

**For data collection **

Python

The data you seek, python has it for you. It contains CSV(comma-separated value documents) and JSON(JavaScript Object Notation) sourced from the web. SQL tables can also be inserted in the code.

Python has a special library called the Python requests library which simplifies HTTP requests into a line of code by allowing data from websites. It also contains libraries for organizing data and making an in-depth analysis.

R

R is not very efficient in collecting information from websites as compared to Python. However, packages like Rvest and magrittr can be used for web scraping, cleaning and breaking down information. You can also insert data from CSV, Excel and from text files into R.

**For data exploration **

Python

Pandas is the data analysis library of Python. It can work easily with large amounts of data. It allows the user to filter, arrange and display the data in minimal time.

While working with projects, Pandas allows the construction and reconstruction of frameworks. Invalid values like Nan(not a number) can be replaced with a value(such as 0) which will allow ease in numerical analysis. You can scan and clean the illogical data.

R

Since R was made by statisticians to perform statistical and numerical analysis, data exploration is a privilege to those using R. You can make probability distributions, perform statistical tests and make standard machine learning models.

Optimization techniques, statistical processing, random number generation, signal processing, and machine learning are some basic functionalities of R.

For data modelling

Python

Ask a question and Python is there to help you out. Numerical modelling analysis? There’s Numpy.

Scientific computation and calculation? SciPyi is there.

And for Machine learning algorithms? It is a scikit-learn. By using scikit-learn you can use all the machine learning library packages contained in Python without worrying about the inside complexities.

R

If you want to perform some particular modeling analysis, you have to go outside of R’s basic library functions.

Poisson’s distribution and mixtures of probability laws are some of the outside library packages used for some specific data modeling analysis.

For data visualization

Python

For data visualization, we can use Python’s distribution—Anaconda.

Matplotlib is used to create graphs and charts using the data stored in Python and for advanced ones and better design, Plot.ly is used.

You might have seen online tutorials on how to learn Python. People use the nbconvert function to create it. With this function, you can convert your snippets of code to HTML documents.

R

R contains packages for scientific visualization techniques which allows the results to be displayed graphically.

You can create elementary graphs and plots from data matrices and save them in .jpg or PDF formats. This can be done from the basic R libraries.

However, for advance plots or graphs, you can use the ggplot2 function.

Topographic hill shading using Matplotlib

Plot.ly correlation points of the Iris dataset

Advantages of using R and Python in Data Science and Machine Learning
  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.
The popularity of Python vs R

Both R and Python have become stars in the field of Data Science and Machine Learning.

R had its popularity in the year 2015 – 2016. But in recent years, Python has become more popular.

Python’s popularity has been because of its multi-programming paradigms, easy readability, availability of vast library, and community support. While other programming languages like C, C++ or Java takes around 5 to 7 lines code to print “hello world”, Python saves your time and effort because a single line of code is more than enough to execute it.

Some of the sectors where both R and Python have gained popularity in recent years are –

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.

In the above chart, we can see that gradually other sectors are also adapting R and Python as a preference. Organizations like financial firms, retail organizations, banks and healthcare institutions have started offering job roles in R.

The Growing Rate of R and Python

Python

Python is considered to be the fastest growing programming language in the world. According to Stack Overflow developer survey, in 2013, Python overtook R as the most popular language for data science.

According to Forbes, a data scientist is the “sexiest job of the 21st century”. Python is real-life implemented. Basic data science operations are easier in Python as compared to R. In addition to its versatility and easier to code features, developers tend to use it more.

R

In the year 2016, R was used by 55% data scientists while Python stood at 51%. In the following 2 years, Python increased by 33% and R got reduced by 25%.

So the question is will the slope of R continue going downwards? I guess it will, but not in practice.

R is the statistician’s language. People having mathematics and statistics as their background will never neglect R while creating a data science model. R would be easy and simple to them rather than Python.

So how will we choose?

Since the popularity of R is down-swinging, using R as complementary to Python will be a good combination. This way R would always have a role to play in a data scientist’s toolbox.

Below is a Python’s Jupyter Notebook’s percentage of Monthly Active Users (MAU) on Github survey by Ben Frederickson which shows a sharp increase after 2015.

“Ranking programming languages by Github users” – Ben Frederickson

Career Opportunities

Python

According to IEEE, which tracks the programming languages by its popularity, Python is currently considered to be the most popular language for Data Scientists worldwide.

Some of the regions in which Python is widely used are mentioned below:

Some of the organizations which use Python language—

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.

Some of the Python job profiles with their basic salary package—

According to Payscale.com, below is a graph depicting average Python salary for India and US.

You can also take up the Python training to learn the basics of the world’s fastest growing and most popular programming language used by data scientists, software engineers, machine learning engineers. This training will be a great introduction to both fundamental programming concepts and the programming language and will also enhance your skill sets.

R

The graph below highlights the jobs of R programmers from the year 2009 – 2017.

Source: Stackoverflow

Some of the organizations which use R as a tool for analytics—

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.

R job roles with their basic salary package—

  • A lot of Techniques - It is a well-developed programming language which encompasses a wide range of techniques such as linear and non-linear modelling, clustering, classification, etc.
  • Matrix and vectors computations - R supports matrix arithmetic and its data structures contain lists, matrices, vectors, and arrays.
  • Compliance - It complies with other programming languages like C, C++ or Java and allows communication with statistical packages(SAS and SPSS).
  • **Large Community - **R has a progressive community that influences its modifications, which allows R to run on almost any operating system including Windows and Linux.
PROS and CONS

Python

Pros —

**1) All-in-one language - **Python is an interpreted, interactive, modular, dynamic, portable, object-oriented, high-level programming language which is accessible and easy to learn and has a gentle learning curve.

**2) A handful of Support Libraries - **Python boasts a high number of standard libraries for string operations, operating system interfaces, data manipulation, data collection, machine learning, Internet and so on.

Scikit-learn and Pandas are two tools for data analysis and high-performance structures respectively. If you want to include R-like functions, you have the RPy2 package.

3) Integration - Python has better integration features than R. It can develop Web Services by integrating with Enterprise Application Integration.

Though developers prefer low-level languages like C, C++ or Java, if Python gets integrated with them, the control capabilities of Python gets boosted.

4) Productivity - Python is extremely productive to the programmer and also in the development area. Due to its integration feature, framework and increased control abilities, it speeds up the development process.

Cons—

**1) Difficulty in going to other languages - **If you work with Python for a span of time, I would warn you not to fall in blind love. Declaring values and variables would stand as insecurity thereafter.

**2) Weak computation in mobile - **Though Python has made its name in most of desktop and server platforms, mobile computation is still a dream.

3) Speed reduction - Since Python executes using an interpreter rather than a compiler, the time needed for execution and compilation is a bit higher than expected.

**4) Run-time errors - **Testing time, run-time errors and design restrictions are some common problems since Python was initially dynamically typed.

R

Pros—

**1) Data and visualization - **R would be your choice if data analytics and data visualization are priorities for your project.

**2) Wealthy with libraries and tools - **R has a rich ecosystem of statistical libraries which makes it a better tool for statistical computations.

Caret is a machine learning library which is capable of creating effective prediction models.

R contains advanced data analysis packages which can control the pre-modeling, modeling and post-modeling phases and can also perform particular tasks like data visualization and model validation.

3) Good Explorations - If you are work is about statistical models and you are just in phase 1 of your exploratory project, consider R to be that friend of yours who explains concepts in simple and brief just before the exam.

Cons—

**1) Steep learning curve - **R is definitely a challenging programming language and few developers work with it for building projects.

**2) Inconsistency - **The pace of development of R is decreased due to the inconsistency of the language because most algorithms in R are provided by third parties.

Every time you have a new algorithm in hand, it needs to learn new ways to model it.

Conclusion and Summary

Here’s a brief summary of all the important aspects of comparison between the two most important languages for Data Science and Machine Learning - Python and R.

After understanding the whole scenario, we can draw a conclusion that the entire decision whether R is better than Python, is up to us. It is the users’ requirement which makes a programming language like R and Python popular than the other. It is our choice, based on the features, to select the programming language to work on Data Science or Machine learning or Predictive models or data manipulation and so on. On the other hand, it might be possible for a third language as a conjunction of both R and Python. Till then let us merge our creativity and the machine and develop models that could nearly be a betterment for the human race.

Python Tutorial: Data Science vs. Web Development

Python Tutorial: Data Science vs. Web Development

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
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.

Flask
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.

CherryPy
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 $180,000

Why 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.

  1. Python has a unified design philosophy that focuses on ease of use, readability and easy learning curve for data science.
  2. Python has high scalability and is much faster when compared to other languages like Stata, Matlab.
  3. There are more and more data visualization libraries and cool application programming interfaces being added for inclusion of graphics to depict the results of data analysis.
  4. Python has a large community with good number of data science or data analytics libraries like Sci-Kit learn, NumPy, Pandas, and Statsmodels, SciPy etc. which have rich functionality and have been tested extensively. Data analysis libraries in Python language are growing over time.
Python Programming for Number Crunching and Scientific Computing in Data Science

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-

NumPy
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
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.

Pandas
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.

SciKit
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.

Matplotlib
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.

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development




TABLE OF CONTENT

00:00:00 Introduction

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

00:13:03 Variables

00:18:21 Receiving Input

00:22:16 Python Cheat Sheet

00:22:46 Type Conversion

00:29:31 Strings

00:37:36 Formatted Strings

00:40:50 String Methods

00:48:33 Arithmetic Operations

00:51:33 Operator Precedence

00:55:04 Math Functions

00:58:17 If Statements

01:06:32 Logical Operators

01:11:25 Comparison Operators

01:16:17 Weight Converter Program

01:20:43 While Loops

01:24:07 Building a Guessing Game

01:30:51 Building the Car Game

01:41:48 For Loops

01:47:46 Nested Loops

01:55:50 Lists

02:01:45 2D Lists

02:05:11 My Complete Python Course

02:06:00 List Methods

02:13:25 Tuples

02:15:34 Unpacking

02:18:21 Dictionaries

02:26:21 Emoji Converter

02:30:31 Functions

02:35:21 Parameters

02:39:24 Keyword Arguments

02:44:45 Return Statement

02:48:55 Creating a Reusable Function

02:53:42 Exceptions

02:59:14 Comments

03:01:46 Classes

03:07:46 Constructors

03:14:41 Inheritance

03:19:33 Modules

03:30:12 Packages

03:36:22 Generating Random Values

03:44:37 Working with Directories

03:50:47 Pypi and Pip

03:55:34 Project 1: Automation with Python

04:10:22 Project 2: Machine Learning with Python

04:58:37 Project 3: Building a Website with Django


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Further reading

Complete Python Bootcamp: Go from zero to hero in Python 3

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python and Django Full Stack Web Developer Bootcamp

Complete Python Masterclass

Python Programming Tutorial | Full Python Course for Beginners 2019 👍

Top 10 Python Frameworks for Web Development In 2019

Python for Financial Analysis and Algorithmic Trading

Building A Concurrent Web Scraper With Python and Selenium

Best Python Libraries For Data Science & Machine Learning

Best Python Libraries For Data Science & Machine Learning

Best Python Libraries For Data Science & Machine Learning | Data Science Python Libraries

This video will focus on the top Python libraries that you should know to master Data Science and Machine Learning. Here’s a list of topics that are covered in this session:

  • Introduction To Data Science And Machine Learning
  • Why Use Python For Data Science And Machine Learning?
  • Python Libraries for Data Science And Machine Learning
  • Python libraries for Statistics
  • Python libraries for Visualization
  • Python libraries for Machine Learning
  • Python libraries for Deep Learning
  • Python libraries for Natural Language Processing

Thanks for reading

If you liked this post, share it with all of your programming buddies!

Follow us on Facebook | Twitter

Further reading about Python

Complete Python Bootcamp: Go from zero to hero in Python 3

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python and Django Full Stack Web Developer Bootcamp

Complete Python Masterclass

Python Tutorial - Python GUI Programming - Python GUI Examples (Tkinter Tutorial)

Computer Vision Using OpenCV

OpenCV Python Tutorial - Computer Vision With OpenCV In Python

Python Tutorial: Image processing with Python (Using OpenCV)

A guide to Face Detection in Python

Machine Learning Tutorial - Image Processing using Python, OpenCV, Keras and TensorFlow

PyTorch Tutorial for Beginners

The Pandas Library for Python

Introduction To Data Analytics With Pandas