Why Python programming is essential for Data Analysis?

There are many popular programming languages used by data analysts such as Python, R, C++, etc. But Python holds a unique place among them. Python programming language is an OOP, open-source, adaptable, and easy to learn. It includes a rich group of libraries and tools that makes the tasks easier for Data scientists.

In addition to this, Python language has an enormous community base where engineers and data analysts can put in their queries and gets answer questions from others. Data science as services is using Python for quite some time and it will keep on being the top choice for many Data scientists and Developers.

Python programming has been around since the late 80s. Today, this outstanding programming language is useful for software development, mobile app development, web development, etc. Moreover, it is also useful in the examination and capturing of numeric and scientific data.

Anyone will be amazed to hear that major online platforms like Google, Dropbox, Instagram, and Spotify & YouTube — all had worked with this Programming language.

In the early days, this language was first used for automating repetitive tasks, prototyping apps, and the usage of those applications in multiple languages. It is relatively easier to learn and understand, on account of the spotless and straightforward syntax and extensive documentation.

Why learn Python for data analysis

Data analysis consulting companies are allowing their group of developers and data analysts to use Python as a programming language. Moreover, it has acquired well known and the most noteworthy prog language in an extremely small time frame.

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Data Scientists need to manage a huge amount of data such as big data. With simple usage and a huge organization of Python libraries; it becomes the most popular choice to deal with big data.

Easy to Use:

The framework is easy to use and includes a fast learning curve. New data analysts can easily learn this language with its simple to use syntax and better understandability. Moreover, it additionally provides a lot of data mining tools that help in better data handling. Ex;- Rapid Miner, Orange, etc.

This is noteworthy for data scientists since it has many useful and easy to use libraries. Such as; Pandas, Numpy, Tensorflow, and so on.

Python is Flexible:

It is very flexible as it not only lets you build software. But also allows you to deal with the analysis, numerical and logical data computing, and web development.

In addition to this, this language has become ever-present on the web, controlling different well-known websites using Web development frameworks. Such as TurboGears, Django, and Tornado.

Moreover, it is ideal for developers having the talent for web and app development

Best analytics platform

Data analytics is an important part of data science. Moreover, data analytics tools provide information about multiple frameworks important to assess the performance in any business. This programming language is the best choice for developing data analytics tools.

It can easily provide better knowledge and skills, get examples, and coordinate data from large datasets. In addition to this, the prog. Language is much noteworthy in self-service analytics.

Huge community base:

Python includes a large community base of engineers and data scientists like Python.org, realpython.com, etc. The program language developers can transmit their problems and thoughts to the community. Here, the Python Package Index (PPI) is an exceptional place to explore the various skylines of this Prog Language. Besides, the Python developers are continually making improvements in the language that is helping it to emerge to be better over time.

Advantages of using Python for data analysis

This programming language definitely has a bright future in the area of data science, especially when used in coincidence with powerful tools like Jupyter Notebooks, etc. These have become much popular in the data analyst community. The value proposition of Notebooks is that they are very easy to build and perfect for running experiments faster.


This technology’s feature is described earlier, not accidentally, but because it is closely connected with various options. Comparing with other programming languages like R, Go, and Rust, this language is much faster and has scalability. Therefore, this framework is good for multiple usages in various fields that can solve a wide range of issues. That’s why many companies prefer to migrate to Python.

Graphics and Visualization Tools

Any information that is visual in nature is much easier to understand, operate, and remember. Here is another portion of a good thing is that there is a pack of manifold visualization options available. Moreover, this makes this an important tool not only for data analysis but for all data science users. You can make the data more accessible and easier through building various charts and graphics including web-ready interactive scenarios.

Easy to learn

Python is a steadily typed language hence, the variables are defined automatically. This is more readable and uses minimal code to play out an equal task when compared with other program languages. It is particularly typed. C#, Ruby, Java, etc., program languages and others in the way are much harder to learn, particularly for entry-level programmers. It focuses on simplicity as well as readability by providing a host of useful options for data analysts in parallel.

Libraries collection

This is one of the most supportive languages today. It includes a long list of fully free libraries available for all the users. This is the key factor that gives a strong push for this programming language overall. Here are some of the popular useful libraries of this technology –

  • Matplotlib

This is a popular library for developing decorative displays. It covers a huge variety of features important in developing dynamic visualizations. Users can build bar plots, histograms, pi-plots, and even more complex displays of PCA with the help of this library.

  • Pandas

Pandas is a data squabble library that provides support for transforming information into arranged data-frames. This is essentially important for data modification and analysis.

  • Scikit-learn

This is one of the popular under these libraries and an ML library useful for classification and regression tasks.

Python programming: portable and extensible

This is one of the important reasons why it is so popular in the Data Analysis field. A lot of cross-platform operations are performed easily on this language because of its portable and extensive nature. Many data analysts prefer using Graphics Processing Units (GPUs) to train their ML models with the help of data on their machines and the portable nature of this prog language is well suited for this.

It’s possible to work as a data analyst using either Python or R. Both languages have their strengths and weaknesses, and both are extensively used in the industry. This prog language is more popular over the other, but R dominates in some areas.

To do data analysis work, you’ll definitely need to gain knowledge of at least one of these 2 languages. It doesn’t require being python, but it does have to be one of either Python or R.

Bottom Line

The success of any business entity directly depends on the ability to gather knowledge and skills. Moreover, it also depends on the insights from data to make effective and strategic decisions, stay competitive, and make progress. This is the internationally praised programming language to help in handling the user data in a better manner for several causes.

Moreover, it is one of the most easy-to-learn and finest languages, very simple in use, with the best price including an excellent pack of features. Though Python is an open-source/free language, it remains well-supported by a large community. Get more insights from Python Training.

Python certification training course will help you master the concepts and gain in-depth experience on writing Python code and packages like SciPy, Matplotlib,