Python is a high-level, object-oriented, general-purpose programming language developed by Guido van Rossum in 1991. You can use Python for web development, software development, intense mathematical problems, and system scripting. It can work on various platforms, such as Windows, Mac, Linux, Raspberry Pi, etc. Python also supports rapid application development.

The programming language offers a syntax closely related to the English language, making it easy to use and understand even for a new programmer. The platform also allows multitasking and provides a set of in-built libraries and functions, making the job of developers very easy. Due to these benefits, Python has gained popularity among the global developer community.

Companies like Amazon, Dropbox, Facebook, Google, IBM, Instagram, etc., are using Python as a core part of their tech stack. As per the Stack Overflow Developer Survey 2022, Python (48.07%) is the fourth most popular technology in the world. In addition, 49% of people use Python for web development, 45% for data analysis, and 40% for machine learning.

So, the question is, what makes Python an apt choice for data analytics and machine learning? Let’s explore that in our upcoming section.

Why Python for data analytics and machine learning?

Here are some reasons why Python is well-suited for data analytics and machine learning-based solutions:

Python supports rapid application development. Hence, Python developers working on data analytics or ML have little trouble comprehending or changing anything in code.

There are no ambiguities and inconsistencies, so AI, ML specialists, and data analysts can easily share important data.

There is a large and active community of developers, so while working with complex ML or data analytics problems, you can ask for a solution from the global community.

It has many ready-to-use libraries for AI, ML, and data science-related projects.

Python is easy to learn and understand, so developers can spend more time solving complex business problems rather than learning the technicalities of the language.

It has stunning visualization and plotting capabilities which is essential while working with data science of ML-related projects.

So far, you’ve understood that Python can solve intense mathematical problems and has many ready-to-use libraries for data science-related projects. The following section will highlight some of the top Python-based frameworks for data science.

Top 9 Python-based Frameworks for Data Science

1) NumPy

NumPy is one of the first names that comes to mind regarding Python-based frameworks for data science. The framework provides multidimensional array support and functions for dealing with linear algebra, Fourier series, and random number generation. NumPy also provides integration with C and Fortran, making it easier for developers to use the legacy code in Python-based projects. NASA and Google are two renowned names that are using NumPy.

Key features:

• Supports object-oriented programming approach.
• Provides fast and pre-compiled functions for numerical routine.
• In-built tools for reading and writing array data to disk.
• Integrations with other scientific Python libraries.
• A wide range of mathematical functions for array operations.

2) TensorFlow

TensorFlow is one of the most popular Python-based frameworks for data science that is useful for high-performance numeric computations. With 35K comments and 1.5K contributions, TensorFlow has a vibrant community of developers helping to solve various scientific problems. Created by Google Brain Team, TensorFlow helps you quickly build data science and machine learning apps. The framework is highly scalable, flexible, and easy to use and understand.

Key features:

• Automatic differentiation makes it easy to implement complex algorithms.
• Support for GPU assists in the training of deep learning models.
• A rich vein of libraries for data manipulation, preprocessing, and visualization.
• Supports various data types such as images, text, and time series data.
• Seamless library management backed by Google Brain Team.

3) Pattern

‘Pattern’ is a unique Python-based framework that provides a complete set of tools to solve data mining, machine learning, and natural language processing problems. Fast and intuitive user interfaces are the USP of Pattern framework with easy-to-understand syntax. The framework supports parallel and vector processing, which is essential for working with large datasets. Pattern balances out powerful computations with ease of use, making it a prevalent choice.

Key features:

• Simple, intuitive, and consistent user interface.
• In-built support for popular machine learning algorithms.
• Highly flexible and extensible framework.
• Provides support for rapid application development.
• A large and active community of developers.

4) Theano

Theano is a robust and powerful data science framework for Python developers that helps optimize and evaluate mathematical operations on multidimensional arrays. You can create unique machine learning models as per the need with Theano. Theano can optimize the code for speed, meaning it has extensive computational power for repetitive operations. Furthermore, the framework ensures the efficient execution of operations on the CPU and GPU architectures.

Key features:

• Plenty of built-in mathematical functions for array operations.
• Highly portable and extensible framework.
• Provides support for automatic differentiation.
• Support for NVIDIA CUDA GPUs.
• Suitable for high-intensive calculations.

5) Keras

Keras is a top-level Python framework that helps you to build complex deep-learning models. Within a few lines of code, you can train complex neural networks. Keras is a flexible and highly extensible framework so that you can add new layers, models, and optimizers. The framework takes its basis from Tensor, a multidimensional array necessary for building deep learning networks. You can use Keras with TensorFlow or Theano to create a powerful combination.

Key features:

• Runs seamlessly on CPU and GPU architectures.
• Comes with 10+ prebuilt image classification models.
• Plenty of in-built industry-standard machine learning datasets.
• Allows quick and easy prototyping.
• Modular, extensible, and user-friendly framework.

6) Shogun

Shogun is one of the most potent machine-learning libraries that assist Python developers in data analysis and predictive modeling. The library relies on C-programming language and provides support for several other languages. Shogun is scalable and efficient for both linear and non-linear models. Preprocessing is an essential aspect of data science, and Shogun offers functionalities such as feature selection and dimensionality reduction.

Key features:

• Support for data streaming and online learning.
• A rich vein of classification, regression, and clustering algorithms.
• Allows users to extend their toolbox with their algorithms and data types.
• Support for various data types such as real-valued, sequence, graph, and text.
• Fantastic set of methods for preprocessing and visualizing data.

7) Pandas

Pandas is an acronym for Python Data Analysis, used heavily by developers for data analysis and cleaning. The framework provides fast and flexible data structures that conveniently work with structured data formats. Pandas rely on NumPy and two robust data structures;

(i) Series: one-dimensional lists of items, and
(ii) DataFrames: a two-dimensional table with multiple columns.

The framework also offers different methods for simplified data filtering activities.

Key features:

• Eloquent syntax and rich functionalities to work with.
• Allows you to create your function and run it through a series of data.
• It comes with high-level data structure and manipulation tools.
• Useful for data wrangling and data cleaning.
• Can read data from CSV, TSV, and XLSX files.

8) Matplotlib

Matplotlib is a powerful data visualization and plotting library that helps Python developers to make static, animated, and interactive graphs and plots. You can use Matplotlib, which is free and open-source, as a MATLAB replacement. In addition, the library is highly flexible and easily configurable with NumPy, SciPy, and IPython. So whether it’s a line plot, scatter plot, bar chart, pie chart, histogram, stem plot, contour plot, or spectrogram, Matplotlib covers all these aspects.

Key features:

• Support for rich visualization and dynamic charts.
• Highly scalable, flexible, and extensible library.
• A Vibrant and active community of developers.
• Supports dozens of backends and output types.
• Low memory consumption and better runtime behavior.

9) SciPy

SciPy is a robust Python-based framework that helps you in data-intensive tasks such as statistical modeling, data visualization, and machine learning. It’s a collection of modules that comprises standard functions for scientific computation. These modules are related to linear algebra, statistics, optimization, integration, and more. Lastly, SciPy also provides a fancy data visualization facility through which you can plot your results through various graphs and charts.

Key features:

• Free and open-source Python-based framework.
• A well-developed ecosystem of third-party packages.
• Various tools for scientific computing and numerical analysis.
• High-level commands for data manipulation and visualization.
• Built-in functions for solving differential equations.

Wrapping up

Python is one of the most popular and widely used programming languages, providing extensive data analytics and machine learning support. Plenty of libraries and frameworks are available in the Python ecosystem that developers can use to their advantage. However, the challenge is finding the ones that fit into the list of requirements and problems you want to solve.

Here, we have compiled a list of the top 9 Python-based frameworks for data science that may help you. We have tried to highlight the USPs and core features of every framework so that you can choose the best one. However, the list is not limited to these tools. There are plenty more tools available in the market. It all boils down to your understanding of the problem.