Tableau vs Python – Which One Is Better For Data Science?

A tableau is a tool for business intelligence and data visualization, while Python is a popular programming language that supports a number of techniques for statistical and machine learning.

In this article, we will cover all the aspects of Tableau vs Python. For building your solution, we provide you with both choices. You can read more about our Tableau services for data science transforming the data analysis and visualization process.
Mindbowser is considered among the top Python development companies.

After reading this article, you can make the right decision between Tableau or Python for your business.

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What Is Python?

In software circles, Python is a high-level programming language that is commonly used. To elaborate upon the same, computer language is highly contrasted to that of the human language.

The machine only understands the language of the basic level. For the purpose of interacting with the computer, we need to understand it. Because if not, then you would have to spend more time understanding the language rather than solving the problem.

For developers who code in these languages, learning and using the language and some other coding language along with the handover would be extremely tedious. So, as a workaround, software developers have come up with these higher-level languages.

The benefit of these kinds of languages is that in understandable syntaxes, the programmers can code, understand and evaluate to some degree.

How Does Python Work In Data Science?

One of the key reasons why Python is commonly used in scientific and academic communities is because of its ease of use and simple syntax, making it easy for individuals without an engineering background to adapt. It is more appropriate for rapid prototyping as well.

In addition to the data science packages, deep learning frameworks available with Python APIs, according to engineers from academia and industry, have made Python extremely productive and flexible. Read about Best Python IDEs and Code Editors here. 

Even ML scientists prefer Python for various works.

Developers have leaned towards Java in areas such as building fraud detection algorithms and network protection, while developers have opted for Python for applications such as natural language processing (NLP) and sentiment analysis since it offers a wide set of libraries that help to solve complex business problems easily and assists in the building of strong systems and data applications.

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What Is Tableau?

Tableau is the Business Intelligence Industry’s best and fastest-growing data visualization platform. It helps to simplify raw data into the form of dashboards and worksheets that make the process of understanding and comprehending them very easy and accurate.

It is a framework for visual analytics that transforms the way we use the information to solve problems, inspiring individuals and organizations to make the most of their information. Learn more about how to use data visualization to have data-driven decisions in business. 

A simple understanding of the pros and cons of Tableau can help you make better decisions. With intuitive, visual analytics for all, Tableau has disrupted business intelligence. Tableau also allows the use of fancier Python packages and by the use of Tableau’s SQL database connection, you can even drag and drop to describe or visualize the data that allows you to tell your story in a better, impactful manner.

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How Does Tableau Work In Data Science?

Since Tableau aims to make analytics simple for analysts, executives, IT departments, and everyone else, data scientists can perceive the program as underneath them or a challenge to their existence. Speaking of the latter first, Tableau is a popular and strong tool that is just one of several pieces of software used to gather data insights.

Even if anyone from the corner office to the reception desk is soon competent in Tableau, several programs will remain under the data scientist’s exclusive authority. Data science standbys as R and Python are complemented by Tableau tools.

While Tableau is not the best data cleaning or reshaping method. Its relational model does not enable procedural computations or offline algorithms. It still excels at data exploration and interactive analysis.

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Comparison Of  Tableau vs Python

This section of the article includes various points of differentiation between the two tools, Tableau vs Python. The two are differentiated on the basis of usage, data handling, integration, ease of learning, mobility and others. All these points are talked about in brief in the following section.

  • Usage

The high-level Python programming language is used to write software programs that solve computer problems. With enough whitespace, it is known for its code readability. It consists of constructions that make simple programming on small and large scales easy to execute.

Tableau, a tool for data visualization that helps to interpret information and develop efficient and meaningful business insights. It is used to evaluate and analyze the relationship between databases and the information containing items, locations and years. Tableau collects a significant volume of data from its exclusive in-memory data engine and stores and restores it. Tableau is also known for the creation of outstanding user interfaces.

  • Data Handling

If you have to deal with data streaming, it’s best for Python to manage it. With Python’s huge user data, even if it is of an obscure kind, you can easily find a package to parse the data you have gathered. Indirectly, different data types can be loaded to operate using the necessary libraries or packages that will handle the types of data.

Tableau is recognized for its out-of-the-box potential connections. Multiple file types can be consumed instantly. It can link to different database types as well. Thanks to its pre-built connections, you can access multiple services. It’s surprisingly versatile but has strong features. It can load data of different data types such as text file, JSON, xlsx, CSV, etc. and generate visualizations.

  • Visualization

Python is a programming language for general purposes that can also be used in data analytics services. Python can produce visualizations but the process for the same is very time-consuming and complex. Data visualizations through Python can be generated using open libraries such as MatPlotLib, SeaBorn, ggPlot, etc.

Tableau is a data visualization interactive product commonly used in Business Intelligence. Data visualization occurs from the word go with tools like drag/drop that are easy to use to construct high-value visuals. In essence, Tableau is a tool for data visualization and is user-friendly. You have almost all the visualizations that are needed for your traditional upfront business reporting.

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  • Integrations

Python is a portable, accessible language backed by an immense standard library. Python is built under an Open Source Initiative licenses open source license. It can thus be freely used and distributed for commercial purposes as well.

In the case of Tableau, it is possible to integrate the framework with the most common databases to import data and to work on making it extremely scalable. The databases include MySQL, Amazon Redshift, BigQuery from Google, etc.

  • Ease Of Learning

Python uses basic terms from the grammar of the English language. Due to the same, it is one of the world’s simplest programming languages at present that is facilitating programming. This is one of the prime reasons developers like it: it is clean and works in short syntaxes.

Using Tableau takes very little programming knowledge. This makes an ordinary specialist begin to analyze data from day zero. For its mapping feature, software developers prefer Tableau. Longitudes and latitudes are very simple to narrate and bond to spatial files such as zipped GeoJSON files of Esri File Geodatabases, Shapefiles, Keyhole Markup Language files, MapInfo tables.

  • Mobility

Online coding platforms that can be accessed via the Internet are available for running Python programs (especially for beginners). You can also load your Python 3 software on your laptop and run more complicated Python programs. Python can be used on Windows, AIX, IBM I (formerly AS/400, iSeries), iOS, OS/390, Solaris, MS, HP-UX, Linux, and z/OS.

Tableau, the platform, is available on all kinds and types of devices like smartphones, cell phones, laptops, tablets, etc. Moreover, Tableau can also be accessed through the internet.

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  • Analysis

When it comes to data analysis, Python does an exceeding amazing job with all its beauty. Data transformation & cleaning is important for any study. Data transformation and cleaning are vital elements of any analysis process and Python takes over these processes like no other.

The tool, Tableau is also an outstanding tool for data analysis but it is not very efficient in performing complex and intricate processes. Tableau also has a limited scope when it comes to data transformation and cleaning during analysis.

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Conclusion

After the above discussion, it is safe to say that comparing Tableau vs Python is like comparing apples and oranges. In their proprietary engine, which has a fixed number of things you can do with it, you analyze/visualize the data with Tableau. It then enables you to share outcomes with the rest of your business as dashboards.

With Python, with libraries like Bokeh, Altair, and Plotly, you can visualize data in a similar way to Tableau and create immersive visualizations, but you have a lot more flexibility.

While speaking of comparing and contrasting the two, it is also important to acknowledge the fact that the coming together of Tableau and Python as the TabPy API helps in the creation of rapid data analysis applications for various business use cases.

Sandeep Natoo

Head Of Emerging Trend

Sandeep is a highly vigorous Machine learning expert with over 12+ work of experience with developing heterogeneous systems in the IT sector. He is an expert in building Java integrated web applications and Python data analysis stack. He has been known for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing valuable prediction with a good eye for detail. He is highly optimistic and avid nature, for various challenges is his major strength.

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