Top 20 Python libraries for Data Science. –

Python language is already assisting developers in creating standalone, PC, games, mobile and other enterprise applications. Python with more than 137,000 libraries helps in various ways. In this data-centric world, where consumers demand relevant information in their buying journey, companies also require data scientists to avail valuable insights by processing massive data sets.

This information guides them in critical decision making, streamlining business operations and thousands of other tasks which require valuable information to accomplish efficiently. Thus, with this increased demand for data scientists, beginners and professionals are looking for resources to learn this art of analyzing and representing data. One can go through the Simplilearn’s online Data Science Certification Training, blogs, videos and other resources accessible over the internet. Once they understand how to deal with this unstructured information, they are good to grab from millions of flowing opportunities.

Here below, I’m discussing a few Python libraries which are very helpful in this whole data science-related operations:

Through NumPy, you can leverage n-dimensional array objects, C, C++, Fortran program based integration tools, functions for performing complex mathematical operations like Fourier transformation, linear algebra, random number etc. One can also use NumPy as a multi-dimensional container to treat generic data. Thus, you can effectively integrate your database by choosing varieties of operations to perform with.

NumPy is installed under the TensorFlow and other complex machine learning platforms empowering their operations internally. Since it is an Array interface, it allows us multiple options to reshape large datasets. It can be used for treating images, sound waves representations, and other binary operations. If you have just marked your presence in this data science or ML field, you must have a great understanding of NumPy to process your real-world data sets.

2. Theano Theano is another useful Python library assists data scientists in performing large multi-dimensional arrays related computing operations. It is more like TensorFlow but the only difference is, it is not that efficient.

It is getting used for distributed and parallel computing based tasks. Through it, you can optimize, express or evaluate you array-enabled mathematical operations. It is tightly coupled with NumPy powered by implemented numpy.ndarray function.

Due to GPU based infrastructure, it holds the capability to process operations in faster ways than CPU. It stands fit for speed and stability optimizations delivering us the expected outcomes.

For faster evaluation, its dynamic C code generator is popular among data scientists. Here, they can perform unit-testing to identify flaws in the whole model.

3. Keras Keras is one of the most powerful Python libraries which allow high-level neural networks APIs for integration. Theses APIs execute over the top of TensorFlow, Theano and CNTK. Keras was created for reducing challenges faced in complex researches allowing them to compute faster. For one who is using deep learning libraries for their work, Keras is the best option.

It allows fast prototyping, supports recurrent and convolution networks individually and also their combination, execution over GPU and CPU.

Keras provides a user-friendly environment reducing your effort in cognitive load with simple APIs giving us the required results. Due to its modular nature, one can use varieties of modules from neural layers, optimizers, activation functions etc.., for developing a new model.

It is an open source library written in Python. For data scientists having trouble adding new modules, Keras is a good option where they can simply add a new module as classes and functions

4. PyTorch PyTorch is considered one of the largest machine learning libraries for data scientists and researchers.