data visualization in python

Data Visualizations in Python and R: Python V/S R

Data Visualizations in Python and R: Python V/S R: We all know the significance of data in formulating business strategies. However, with the growing complexities of the data sets, only AI and a more sophisticated coding language will be able to unveil the information hidden in it. Access to the right tools will help in getting clear insights from complex data sets. Data visualization helps in a more effective presentation of the inferred information. With data visualization in Python and data visualization in R, the data scientist can explore the details and the information in the datasets.

Python and R are advanced programming languages used to get glean insights into complex datasets. The human brain can process visual data much better than any other information. Around 90% of our brain processes visual data. Hence it is more impactful as compared to hearing. As machine learning and advanced analytics continue to intervene in our lives, large amounts of structured and unstructured data are created daily. With the help of the right programming language and coding, it becomes easier to unfold the details of this data.

In this article, we take you through the key aspects of Python and R. Here we will be unfolding how data visualization in R and data visualization in python work. 

Overview of Data Visualizations

Data visualization allows humans to explore data in different ways. The human brain carves out narrative and visualization to derive strategies from information stored in the data. Today, we are generating more data than ever. The work of a data scientist is to work on both structured and unstructured data. With the help of the right data tools, it becomes easier to explore the meaningful information stored in the datasets.

Elements of Data Visualization

You can explore the data using the following elements:

  •  Charts
  • Graphs
  • Plots
  • Maps
  • And other visualizations tools

Data visualization in Python

Python is one of the most recognized and universally used programming languages for data visualization. It is an easy-to-use programming language and hence finds application in data visualization. When dealing with the problems of Machine learning or Deep learning, Python proves helpful.

There is a range of libraries that you can use to create Data visualization in Python. Some of the popular options are Plotly, seaborn, Matploylib, and others. With the help of Python data visualization, one can understand:


  • Mean
  • Median
  • Outlier
  • Correlation
  • Skewness
  • Spread measurements

Python provides various libraries that come with different features for visualizing data. All these libraries come with different features and can support various types of graphs.

Exploring in-depth the different libraries used in Python Data Visualization:

1. Matplotlib It is an easy-to-use library. But it has low-level data visualization. This library has different options to visualize the data like histogram, line plot, scatters plot, etc.

The command for installing Matplotlib:

pip install matplotlib

2. Bokeh This library is famous for its interactive chart visualization. And it provides lots using HTML and JavaScript that uses a modern web browser to present the graphics. Also, it offers higher


The command for installing Bokeh:

pip install bokeh

3. Seaborn It is a high-level interface that has been built on Matplotlib. This library provides beautiful designs, colors, and styles that make data visualization more effective and impactful.

The command for installing Seaborn.

pip installs seaborn

4. Plotly- It has unique capabilities that help identify anomalies in the different data points. Moreover, this library is flexible and gives you the leverage of customization. All this makes data visualization more attractive.

The command for installing Plotly:

pip install plotly

Data Vizualization in R

It is also a popularly used programming language for data visualization.  There are several libraries are available in R, which makes data visualization easy. Some of the popular R libraries for data collection are:

  •           Curl
  •           Crawler
  •           readxl,
  •           eadrl, etc.

Beginners usually prefer R for its ease of use. The popular visualization libraries in R are :

 ggplot2: It allows customization along with free chart preparation.

 plotly: This library finds application when one has to establish the correlation between variables

 Esquisse: You can explore data interactively by visualizing it. 

 Shiny: It turns analytics into an interactive web application.

Why R is used for Data Visualization?

  • Ease of comprehension- Understanding the graphics and the chart is much easier than going through a detailed document or report. Thus it can attract more audience. Also, it promotes the widespread use of business insights to make better decisions
  •  Efficiency- One of the major advantages of IR is that it allows the display of more information in a smaller space. Deciding on the business strategies and the business decision is complex, but displaying it via graph can simplify the task.
  • Location– It uses features like geographic maps and GIS. The map is used to show business insights from different locations and helps the company understand the gravity of the issue and the reason behind it.

Understanding the difference between Python and R on the parameters of Syntax and operability and data collection:

  • Syntax- Python is famous for its easy-to-read Syntax. It is easy to understand and apply. R is also similar to Python, but it becomes a bit complex in advanced stages.

R programming language has been specially designed to work with data, whereas Python is a general-purpose programming language used for different applications, data science, and visualization.  Data science experts are well versed in R for some tasks and Python for others.

  • Operability- we have already discussed that python is an easy-to-use programming language, and it is also used for accessing data from local devices. Thus it takes up less space and makes the task simpler.

The programmer can use Google Collab when dealing with higher volume data. Once they have extracted the data from the website and they can use it with code. This code is designed so that if there are any changes in the data protection the code automatically changes. This change is deployed online with the support of web application frameworks like JavaScript, HTML, and CSS.

R has different platforms to run the test code. It uses RStudio and Spyder platforms. The data miners’ user is too sure whether structured and unstructured data is comprehensible by the user. The complex data is divided into different modules with some calculations and then put together to create a bigger picture. It takes lesser time and space.

  • Data Collection: Python has an edge over R because of its versatility in data collection. Python supports different data formats like XML, CSV, and others. You can also get live data from the websites and import SQL tables. Coming to R is not as easy as Python when it comes to fetching data from websites.

Python vs R

  •           R programming language focuses more on handling statistical data than on the software implementation part.
  •           If your focus is more on production or deployment, then Python is the right choice
  •           If you are keen on research, R is the right programming language. This is especially important when dealing with higher                         volumes of statistical data.
  •           If you are looking for a quality product in lesser time, then Python takes the lead over R.
  •           Advanced visualization capabilities of the programming language make it suitable for analysis projects.
  •           Regarding machine learning, Python is the right choice.
  •           R programmers do not require computer programming skills
  •           R has an eco-structure that focuses on data science and a large number of libraries.
  •           Where development using Python is easy to release and maintain.

Pros of Python

  •           It is open source and easy to learn and read
  •           Python is easy to embed with web applications
  •           The last programming community that helps in troubleshooting
  •           As a higher number of libraries and packages for data analysis

 Cons of Python

  •           Regarding the operating level, Python is a bit slow.
  •           It also requires higher memory consumption

Pros of R

  •           It is apt for complex calculations that have packages and libraries built to support analytical visualization
  •           R is a good choice for statistical analysis
  •           Several libraries are dedicated to statistics and analysis of data

Cons of R

  •           It cannot be embedded with web applications
  •           Party is also one of the concern

Summing it up!!!

Both, R and Python have supporting libraries that assist in data analysis and visualization. Picking up the best one can be challenging.

The programmer has to pick the ideal programming language to visualize the data. To make this decision, the user must look into the data type. One can use histograms, line graphs, and other 2-D charts from Python if the data is continuous.

However, when dealing with discrete data, you can consider using columns, pie charts, and bars.

R makes it easy with built-in functions. When it comes to scalability or visual representation, then it is not possible with R. To make the best decision, the user should have complete knowledge and expertise in both these programming languages and their features.   

The above discussion would have given you a brief insight into python and R programming languages. If you two are willing to learn more about Python, then Pickl’s Python programming language course helps you understand it well.



  • Neha Singh

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    I’m a full-time freelance writer and editor who enjoys wordsmithing. The 8 years long journey as a content writer and editor has made me relaize the significance and power of choosing the right words. Prior to my writing journey, I was a trainer and human resource manager. WIth more than a decade long professional journey, I find myself more powerful as a wordsmith. As an avid writer, everything around me inspires me and pushes me to string words and ideas to create unique content; and when I’m not writing and editing, I enjoy experimenting with my culinary skills, reading, gardening, and spending time with my adorable little mutt Neel.