Awesome Python Data Science libraries and frameworks – Python is now, despite its age, one of the most popular programming languages. The entry barrier is comparatively low due to its easy syntax. Python offers a modular character through its many, very easy to implement libraries. The programming language is cross-platform and free. You are offered a variety of programming paradigms. You can do object-oriented and functional programming. But the most important thing is that Python is easy to learn and user-friendly.
Many performant Big Data frameworks now offer a Python API. So entire data pipeline systems, from the data mining process, to the persistence and analytics, to a user interface can be developed in pure Python. Python shows its strengths especially in the implementation of machine learning methods and the execution of mathematical operations on large data sets. Libraries provide powerful, predefined algorithms and object types optimized for big data. Python is one of the most widely used programming languages for Data Science.
As a result, Python is now used in almost every scientific discipline.
Creating complex data and analysis pipelines has never been easier. You’ll be inundated with tutorials online. You can learn the language at every turn. Keeping track of it all is not so easy. Learning the programming basics is easy, but keeping track of the technological possibilities only grows with experience.
Here we introduce you to the most important Python libraries and frameworks you won’t be able to avoid as a Data Scientist.
Python Data Science Libraries – Big Data handling through custom data types
Especially in data science, where very complex algorithms are applied to very large amounts of data, high-performance data formats are crucial.
One of the most popular open source Python libraries developed specifically for scientific calculations and forms the basis for many specific frameworks and libraries is NumPy.
It is a library that simplifies large multidimensional array-based calculations and makes them very performant. It is these performance advantages over simple Python lists that make NumPy indispensable for data science. Many popular deep learning framework computations, such as TensorFlow or PyTorch, are based on tensors, multidimensional matrices. These can be processed permanently and easily via NumPy arrays and then passed via suitable APIs.
Another open source library that is indispensable for the analysis and manipulation of Big Data in Python is Pandas. It is based on NumPy and defines its own multidimensional data objects on its basis, which, in contrast to NumPy arrays, can also accommodate different data types. Thus Pandas shows strength just in the processing and analysis of very large table data and time series.
Pandas is therefore particularly suitable for preprocessing data that can be readily translated into NumPy arrays for analysis by machine learning or deep learning algorithms.
If you want to know more about pandas and NumPy, check out this article from us. Here we explained both libraries and their data types in detail and showed you all the differences.
Python Data Science Libraries – Visualization tools
Due to the ever broadening application areas of Python, the importance of fully compatible visualization methods is also increasing.
The best known open source Python libraries for the representation of mathematical calculations is probably Matplotlib. A very important part and also tool of data analysis is visualization.
Beside the usual static representations also the expiration of complex algorithms can be represented as animation.
Matplotlib is modularly extendable by external packages and is able to display more complex graphics.
Matplotlib can often produce appealing plots only with very specialized code. Seaborn is another Python visualization library that builds on the functionality and data structures of Matplotlib and provides its own graph library on top of it, with many complementary features.
Especially when it comes to the visualization of Pandas and NumPy data structures, Seaborn offers many optimizations compared to Matplotlib.
Here in this article you can learn more about both visualization libraries and their advantages and disadvantages.
Python Data Science Libraries and Frameworks for data analysis and machine learning solutions
AI solutions are increasingly being developed in data analysis. Intelligent programs should be able to solve problems creatively and recognize patterns in large data sets independently.
Python is the most popular programming language for AI and offers a variety of modeling tools.
SciPy is an open source Python library and provides a large collection of mathematical algorithms and convenience functions.
It is mainly used by scientists, analysts and engineers for scientific calculations, visualizations and related activities and is built on the NumPy array datatype.
A popular Python library based on both the NumPy arrays and SciPy is scikit-learn. With this library, it is easy to implement both unsupervised and supervised machine learning algorithms with high performance.
scikit-learn offers extensive possibilities for recognizing patterns and data relationships in a data set.
Read more about this library here.
Keras is one of the most popular open source deep learning libraries in Python and corresponds to a kind of deep learning front-end.
It focuses on being user-friendly, modular and extensible, as well as enabling fast and easy prototyping of neural networks via ready-made building blocks. Keras is thus a unified interface for various backend libraries, such as TensorFlow or PyTorch.
The top dog among the deep learning frameworks is Google’s TensorFlow. This tool is implemented in Python as well as C++ and corresponds to a deep learning backend and can communicate with Keras, for example, via a high-level API.
With TensorFlow, custom models can be developed and processed and dynamically distributed to hardware resources.
You can read more about TensorFlow and where the framework differs from Theano here.
While TensorFlow is still the most popular deep learning backend framework, PyTorch has been catching up in leaps and bounds for the past few years. This framework from Facebook is trying to overtake TensorFlow with increased ease of use and an expanded feature set.
It is a NumPy-like tensor library that provides extensive GPU support to enable accelerated neural network learning.
By accelerating tensor analysis via allocation to GPUs, PyTorch achieves high flexibility and high speed for deep learning algorithms. In addition, PyTorch’s Python base provides unlimited compatibilities with powerful Python libraries such as NumPy and SciPy and the Cython programming language.
Here you can read more about PyTorch and which framework will own the throne in the future.
Theano is an open source Python library for machine learning and neural network programming, as well as a compiler for computing mathematical expressions.
It is particularly suitable for the definition, optimization and evaluation of mathematical expressions involving multidimensional arrays. To do this, Theano draws on the NumPy program library for dealing with matrices, large multidimensional arrays and vectors.
Mathematical expressions are programmed and symbolized in Theano using a NumPy-like syntax and, like TensorFlow, can also be used as a backend for the Keras framework.
However, unlike TensorFlow, Theano focuses on supporting symbolic matrix expressions rather than tensors as the base data type.
Python Data Science Libraries for Graph processing
In times of Big Data, the graph has become a popular data structure due to its flexible and clear relationship-oriented structure. Even entire database systems are now designed according to the graph principle.
PyGraph is a powerful graph manipulation open source library in Python
It supports the use of many well-known graph computation operations and fast algorithm-based search functions.
For more information, see here.
PyTorch BigGraph (PBG)
With PyTorch BigGraph, Facebook offers an open source library that can be used to create very performant graph embeddings for extremely large graphs (billions of nodes and trillions of edges).
How this is done is explained here.
More awesome Python Data Science Libraries and Frameworks – Apache projects
Almost all major Apache Big Data projects also offer Python interfaces. This means that over 350 open source projects are available, with which very high-performance and modular data systems can be easily developed. These software solutions for different applications and individual user comfort are developed by experts from all over the world and maintained by a huge community.
Especially for the relatively new but already very popular data stream analysis, Apache projects such as Kafka and Spark offer basic solutions.
Since this is a huge field with great software solutions, we have summarized the most important Apache Big data projects for you here in this article.