In recent years, Python has spread like wildfire, and many developers, from beginners to experts have taken a liking to it. Python open-source community is a group of maintainers and developers who work on software packages that rely on the Python language. Python packages streamline many significant processes, like analyzing and visualizing data, building ML models, capturing unstructured data from the web, and processing image and text information efficiently. There are more than 200,000 python packages across the world. Here are the top 10 most downloaded Python packages by developers.
NumPy: NumPy is the primary tool for scientific computing in Python. It combines the flexibility and simplicity of Python with the speed of languages like C and Fortran. No wonder there is a huge ecosystem of Python packages and libraries drawing on the power of NumPy.
SciPy: SciPy is an ML library for application developers and engineers. SciPy library contains modules for optimization, linear algebra, integration, and statistics. The main feature of SciPy is that it is developed using NumPy, and its array makes the most use of NumPy.
Pandas: Pandas is one of the most important Python packages built for working with complex datasets. It helps you work with large datasets and analyze them without learning any special language for data processing.
Pip: Pip is the standard way of installing and managing packages in Python. Pip comes standard with every Python distribution, allowing one to accomplish installs, uninstalls, updates, etc., from the command line.
Six: Six provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible with both Python versions. See the documentation for more information on what is provided.
Python-dateutil: Python-dateutil provides a number of date and time manipulation capabilities. It builds on the DateTime module that is built into Python and is simple and easy to use. The package is simple, but can dramatically improve your Python experience when handling time series data.
Requests: Requests are based on the most downloaded Python library urllib3. Requests make web requests as simple as possible while still being extremely versatile. This library is designed to make HTTP requests with Python more responsive and user-friendly.
Keras: Keras is a neural network library in Python. It works quickly with DL networks while being designed to be compact, modular, and extensible. It provides a simpler mechanism for expressing neural networks, Keras conjointly offers a number of the simplest options for compiling visualizing graphs, process datasets, and models.
LightGBM: LightGBM is one of the best and most popular ML libraries, which helps developers in building new algorithms by redefining elementary models and namely decision trees. It has very fast computation and ensures high production efficiency.
Theano: Theano is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays so efficiently. It is mostly used in building DL projects. It is mainly designed to handle the types of computation required for large neural network algorithms used in DL.
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