Python is a very popular programming language among developers and coders. The syntax of Python is very close to mathematical syntax which makes it very popular among data scientists. Furthermore, extensive third-party tools and libraries make Python development a perfect programming language for scientific computing and data science. Even in the field of machine learning, Python is considered to be more user friendly as compared to Java.
Here are some new tools which improve the inherent capabilities of Python.
Scikit-Learn is an open-source tool used for data mining and data science apart from machine learning. The major features of the tool include classification, regression, clustering, dimensionality reduction, model selection, and pre-processing. The API is consistent and user friendly. The tool also offers grid and random searches. The biggest beneficial feature of this tool is the very high speed of performing various benchmarks on toy datasets.
Keras is an open-source high-level neural network API library written in Python. It offers a very easy way to express neural networks which makes it ideal for beginners of machine learning. By being written in Python, Keras runs on popular neural network frameworks such as CNTK, TensorFlow and Theano. The mainstays of this tool are modularity, user-friendliness, working with Python and easy extensibility.
SciPy is a Python-based open-source library designed for computing requirements in the fields of Mathematics, Science, and Engineering. SciPy uses many Python packages for programming tasks in Maths and Science. These packages include IPython, NumPy, and Pandas. SciPy is used to manipulate figures for mathematical and scientific applications and also visualize the results on the computer.
Theano is a Python deep learning library. This tool is used to optimize, define and evaluate mathematical computations involving multidimensional arrays. Many other libraries can be built upon Theano. Theano’s features include tight integration with NumPy, efficient symbolic differentiation, transparent use of GPU, speed and stability optimizations, dynamic C code generation, self-verification and extensive unit testing.
5- Robot Framework
Robot Framework is an open-source generic test automation framework. It is used for acceptance testing and acceptance test-driven development. This framework integrates with Python libraries. It uses a keyword-driven tabular test data syntax. A robot is widely used for Android and iOS test automation and web app development.
6- Beautiful Soup
Beautiful Soup is a Python library that is used to dig out data from HTML and XML files. It can be used for navigating, searching and modifying a parse tree by integrating it with any parser by leveraging Pythonic idioms. The tool converts incoming documents to Unicode and outgoing documents to UTF-8 automatically.
TestComplete provides automation testing for web, mobiles, and desktops. It supports various languages such as Python, C++, and VBScript. Keyword-driven testing is enabled by TestComplete. This tool can detect and update UI objects due to its GUI object recognition feature. Record and Playback feature is also present in the tool.
These are just some of the numerous Python tools suited for a plethora of functions for diverse needs. They are beneficial for data scientists, software developers, and other kinds of techies well versed in Python.