If you are a rookie in the world of data science, these are some Python libraries you must learn in 2020
In the recent seasons, Python is one programming language that has transformed the way the data science community functions. It not only allows developers to design applications, but also supports data scientists and researchers in identifying explanations to different computational complications. Furthermore, the availability of several libraries along with a straightforward programming syntax compared to C, Java, and C++ makes it a prominent language.
If you have newly joined the data science universe, then the below mentioned Python libraries must be on your learning list in 2020.
TensorFlow
For everyone working on a machine learning project in Python, TensorFlow is an open-sourced library that could come in handy. For the training of neural networks, this library is an able tool as it empowers a developer to train neural networks on CPU and GPU. Not to mention, one can access a broad community for any kind of support.
Pandas
Released in 2008, Pandas can be used for data analysis. A free library, Pandas can be used to manipulate data in a time series and numerical tables format. For those who are into the field of data manipulating or data aggregation, Pandas can play a dynamic role as it allows a user to write and visualize data instantaneously. Also, Pandas accepts different files like excel and CSV.
Keras
Keras is a Python library that can put together a deep learning model in a swift and facile manner. By using Keras, one can experiment with Deep Neural Networks as it offers various APIs and enhanced solutions. The platform is written in Python and hence, ordinary debugging tools can be employed. The best feature of Keras is that it assists all kinds of neural networks and works on CPU as well as GPU.
PyTorch
If there’s a rival to TensorFlow, it’s PyTorch in terms of handling neural networks. Not to mention, it is widely utilized to deal with diverse natural language processing and computational vision task. Some highlighting features of PyTorch are access to ONNX-based frameworks, a dedicated module and it can be run with other libraries and packages like Cython and Numba.
