Tableau vs Python

tableau vs python

Tableau vs Python

As a business analyst, here I explain how we can make use of Tableau and Python for the purposes of business intelligence.


Tableau is a rapidly growing data visualization tool widely used in the business intelligence industry. It’s regarded as the best solution to transform the unprocessed set of data into an easily comprehensible format. You can handle it without any technical skills or knowledge in coding.

As soon as you launch Tableau, you can make use of the built-in data connectors to get connected to any database.

You can extract the data easily and connect to the Tableau data engine i.e. Tableau Desktop. Data analyt and data engineer here deal with the data procured and convert them to visualizations. You can convert them to static files and share them with the user. These dashboards are viewed by the users through Tableau Reader. Collected data is published in the enterprise platform Tableau Server. Users anywhere can open these files even in their smartphones. Tableau Server supports collaboration and distribution as well as security model and automation.

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Python has established itself as a dynamic programming language in many areas of software development worldwide.

Python is a dynamic, interpretive script programming language. Python was developed at the beginning of the 1990s by the Dutchman Guido van Rossum. Today, this language is being developed as an open-source project by many developers worldwide. for the further development of Python in the foreground.

The Python programming language supports the major programming paradigms of today’s software development methods: structured programming, object-oriented programming (OOP), and aspect-oriented programming (AOP). Due to the availability of powerful add-on packages for specific applications, you can develop high-performance applications in Python.

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Python language constructs also allow the selective implementation of the principles in the software development as code ReUse, KISS( Keep it short and- simple) and( Don’t repeat yourself), In particular, the improvement of ReUse concepts is an important topic in research into more effective software development. This led to research activities at universities around the world leading to the formulation of so-called patterns.

Python program code is transparently translated by an interpreter into an intermediate code, the so-called byte code, and then executed. Python interpreter C Python, developed in the programming language C, is available for all common operating systems such as Linux, Mac OS, Windows, and others. Therefore, programs developed in Python are platform-independent.

Many companies including Google have chosen the programming language Python as their in-house scripting language for the development of web applications. A powerful example of web applications is Google’s App Engine. Guido van Rossum has been working for Google since December 2005. In a lecture available on Youtube in 2008, he explains the topic of rapid development with Python on Google’s application platform. The standard solution for data persistence of Python applications is called ZODB, an object-oriented database often referred to as an object store. Even large organizations and companies like NASA or even the CIA use these powerful and secure web platforms.

In 2018, Python obtained a share of 22.8%, just taking the first place in Java which was content with 22.5%. The month before, Python was in second place with a share of 22.2%. Moreover, Python was the only language in the top 20 to have experienced significant growth (5.5%). The TIOBE index has just designated the Python language, winner of the programming language title of the year 2018. The TIOBE index, a bit like PyPL, is based on a formula that examines language research in search engines such as Google, Bing and Wikipedia. The formula measures the number of qualified engineers, courses and third-party suppliers in relation to a language.


  • A general purpose programming language that can also be used for Data Analytics & Visualization.
  • It was designed by Guido van Rossum and first published in 1991.
  • Python is known for its code readability, with ample whitespace. It consists of constructs with which it’s easy to execute clear programming on small and large scales.
  • Seaborn library in Python provides a high level of graphics interface for designing platform identifying patterns and emphasizing key element.
  • Python is liked by software developers for its clean and short syntax.
  • Python is an open-source portable language supported by a huge standard library.Python is developed under an open source license approved by the Open Source Initiative. So  it is freely usable and distributable also for commercial purposes.  Python Software Foundation administers its license.
  • If you have an IT team with notable programming skills you can make use of the several packages in Python like matplotlib Basemap, mplot3, Pandas,  ggPlot, Plotly, and Seaborn. With these, you can make highly complex data simple with graphs.
  • Multiple graphing libraries.
  • Is very good at dealing with streaming data.
  • Difficult to do quick, out of the box data visuals
  • Easy to parse data of obscure types
  • Python is known for·         Intuitive and readable program code·         as powerful as other established programming languages·         suitable for daily programming tasks

    ·         it should, is and remains an open source language

  •  Python is workable in Windows, AIX, IBM I (formerly AS/400, iSeries), iOS, OS/390, and z/OS, Solaris, MS, HP-UX,  Linux.
  •  Python has an ecosystem of modules and tools to collect data from multiple sources.
  • To handle data, no other software can vie with Python. If you have to deal with streaming data, Python is the best to handle it. Python with its big user data, you can easily find a package to parse the data you’ve collected, even if it’s of an obscure type.If you require to do divergent data visualization in a rapid manner, Python is not your cup of tea. It’ll be difficult to make a good visual with Python.Python’s Pandas library will help you in extracting data from the web, and to manipulate and visualize it.
  • Tableau is an interactive data visualization product mainly used in Business Intelligence.
  • Chris Stolte and Christian Chabot developed it 16 years ago.
  • Tableau is used to explore and analyze relational databases and data which include products, location and years.  Tableau extracts a large amount of data and keeps and regains from its exclusive in-memory data engine.
  • Tableau helps to create wonderful user interfaces.
  • Software developers prefer Tableau for its mapping functionality.  It’s quite easy to narrate longitudes and  latitudes and bond to spatial files such as zipped Esri File Geodatabases GeoJSON files, Shapefiles, Keyhole Markup Language files,  MapInfo tables, and  TopoJSON files
  • Tableau helps self-service Business Intelligence. Businesses need not employ expert IT personnel.But, unlike Python, it may be unaffordable for small and medium enterprises as the price for its professional edition is $1,999. $999 for a user per year for personal use. Detailed pricing information on Tableau desktop, Tableau online, and Tableau server is available on their official website.But its version Tableau Public is free. If you need to connect only to flat files like Microsoft Excel or .CSV spreadsheets, Tableau Public is sufficient for you.
  • Very minimal programming  skills needed
  • Can instantly consume various file types
  • Can instantly consume multiple file types
  • Can connect to various types of databases like relational databases, cloud databases, flat files, and spreadsheets.
  • With web scraping,  NoSQL databases and  nested data sources it may be a bit difficult with Tableau to convert  data into a viable formatTableau is for trouble-free creation and distribution of interactive data dashboards. You can narrate dynamics, trends of change, and data density distributions with easy-to-understand visuals.
  •  With Tableau·         You can analyze data in real-time·         You can blend data quickly·          Data collaboration
  • With Tableau, you can collect data from various places. It can source data from all the platforms irrespective the of the databases.
  • Tableau is known for its out of the box connection potentials. It can instantly consume multiple file types. It can also connect to various types of databases. You can access multiple services thanks to it is as pre-built connections. It’s astonishingly flexible but has robust functionality.But with web scraping,  NoSQL databases and nested data sources it may be a bit difficult with Tableau to convert data into a viable format. Tableau can’t read XML data too.


With the Python package TabPy, you can carry out Python soft code on the Tabfly and exhibit outcomes in Tableau picturing, helping you to swiftly arrange cutting edge analytics applications. Excellent data visualization capabilities, powered by potent data science algorithms of TabPy gives a split approach. A great advantage of Python algorithms in Tableau is that it allows users to synchronize parameters and assess their effect on data analysis in real-time with every dashboard updates.

When you combine the capabilities of both Python and Tableau, immense powerful possibilities are there. You can use Python code within Tableau to create an interactive dashboard putting into service a time-series forecast. Or you can create Interactive user-input-based dashboard like Tableau using Python Plotly Dash. In the field of data science, integrating Tableau with Python can do wonders in any business.


Tableau is a business intelligence and data visualization tool while Python is a widely used programming language that supports a variety of statistical and machine learning techniques. Tableau’s data visualization and Python machine’s learning capabilities, when combined, help developers rapidly create advanced data analysis applications for a variety of business applications.

If you learn how to assimilate Tableau and Python making use of the TabPy API, your chances of getting highly paid jobs in the lucrative Business Intelligence field will be high. Joining a relevant in the course in prominent IT institutions like Besant Technologies will enable you to become an expert in Business Intelligence.

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