Why Python For Machine Learning and Data Science?

Why people are choosing Python for machine learning and data science? There can be many solid reasons for choosing Python for machine learning and data science over programming languages. Here we will discuss some basic and popular reasons.

In 2021, the worldwide machine learning market held an 88.71% market share. The reason why machine learning is so popular is that artificial intelligence may learn from its mistakes and enhance its functions, user interface, and forecasts with the use of machine learning. These days, many businesses may improve their business operations by using artificial intelligence and machine learning. 57% of customers indicated that their user experience has improved in 2021. Businesses began applying AI and ML to stay one step ahead of their rivals.

However, it is not just about machine learning. As a technology, ML also uses different languages and tools to be productive. One of the languages that best suits machine learning is Python. In this article, we will find out why we need Python for machine learning and data science and what the key features of Python make it best for machine learning and data science.

Why Choose Python for Machine Learning?

Many data science applications now only use Python. It blends the flexibility of domain-specific scripting languages like MATLAB or R with the power of general-purpose programming languages. Libraries for data loading, visualization, statistics, NLP, image processing, and other tasks are available in Python. Data scientists have access to a wide range of general- and specialized-purpose functionality thanks to this enormous toolkit.

The ability to interact directly with the code using a terminal or other tools like the Jupyter Notebook is one of the main advantages of using Python. Data drives machine learning and data analysis, which are fundamentally iterative processes. These processes must have access to technologies that enable quick iteration and simple engagement.

As a general-purpose programming language, Python also allows for the creation of
complex graphical user interfaces (GUIs) and web services, and for integration into
existing systems.

Here are some of the main and popular reasons for Python being a popular language.

  • It is easy to learn.
  • 100% compatible
  • The code is clear and short
  • Fast in development
  • It has a large number of libraries
  • It is an object-oriented language
  • It is open-source and is free and available
  • It is a high-level language
  • Data-structure is built-in

Python modules for machine learning and data science

As we discussed, Python has a large number of modules that can be very useful for machine learning and data science. That is why people prefer Python for machine learning and data science. The following are some of the popular Python modules that are useful for machine learning and data science.


Scikit-learn is an open-source project, so anyone may simply acquire the source code to examine what’s going on behind it. This means that it is free to use and share. The scikit-learn project has a very active user community and is constantly being developed and improved. Numerous cutting-edge machine learning methods are included, along with in-depth documentation for each algorithm. It is widely used in both business and academics, and there are a ton of tutorials and code samples online.

Some of the popular machine learning algorithms that are available in sklearn module are KNN, Linear regression, Boosting algorithms, decision trees, random forest, extra trees, SVM, etc.

Jupyter Notebook

An interactive environment for running code in the browser is the Jupyter Notebook.
It is a fantastic tool for exploratory data analysis, and data scientists frequently utilize it.
We just need Python support, even if the Jupyter Notebook has support for many more languages. The Jupyter Notebook makes it simple to combine text, code, and graphics; in fact, the entire book was created as a Jupyter Notebook.


One of the core Python packages for scientific computing is called NumPy. It has multidimensional array support, advanced mathematical features like the Fourier transform and linear algebra operations, and pseudorandom number generators.

The NumPy array is the core data structure in scikit-learn. NumPy arrays are the data type that scikit-learn accepts as input. You will need to convert any data you use into a NumPy array. The ndarray class, an n-dimensional multidimensional array, is the foundational component of NumPy. The array’s items must all be of the same type.


A Python library for manipulating and analyzing data is called pandas. It is constructed around the DataFrame, a data structure based on the R DataFrame. A pandas DataFrame is a table that resembles an Excel spreadsheet, to put it simply. This table can be modified and operated on using a wide variety of methods provided by pandas, including SQL-like queries and table joins.

Pandas permit each column to have a different type (for instance, integers, dates, floating-point numbers, and texts), in contrast to NumPy, which mandates that all items in an array be of the same type. The capability of pandas to ingest from a wide range of file formats and databases, including SQL, Excel files, and comma-separated values (CSV) files, makes it an additional useful tool. The scope of this book does not allow for a detailed discussion of pandas’ functionality.

Moreover, pandas can also be used to visualize the dataset. It has some basic plots that can be used to plot the data in various useful plots.


Python’s default scientific plotting library is matplotlib. It offers tools for creating publication-quality visualizations like scatter plots, line charts, and histograms. You can gain valuable insights by visualizing your data and various components of your investigation, and we’ll be using matplotlib for all of them.
When working inside a Jupyter Notebook, you can use the %matplotlib notebook and %matplotlib inline commands to display data directly in the browser.


Pycaret is an open-source (completely free) and low-code library in Python that aims to automate the development of machine-learning models. It supports supervised learning (classification and regression), clustering, anomaly detection, and natural language processing tasks. It contains 70+ automated open-source machine learning algorithms and over 25+ preprocessing techniques that help us build machine learning models with good performance.

PyCaret is essentially a Python wrapper around several machine-learning libraries and frameworks such as sklearn, XGBoost, LightGBMCatBoost, etc. Compared with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can replace hundreds of code lines with only a few lines. This makes experiments exponentially faster and more efficient.


Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language. In this short, article, we discussed why people choose Python for machine learning and data science. Moreover, we also discussed some Python modules that can be very useful for Machine learning and data science

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