Top 5 Python Libraries for Data Science in 2018

Python has gained immense popularity as a general-purpose, high-level back-end programming language for the creation of the prototype and developing applications. Python’s readability, flexibility, and suitability to data science operations have made it one of the most preferred languages among developers. It is extensively used by developers who need to apply statistical techniques or data analysis in their work. Data scientists use Python to integrate their tasks with web apps or production environments.

Python libraries simplify complex jobs and make data integration much easier with fewer codes and in lesser time. In this article, I will discuss the salient features of some the top Python libraries for Data Science in 2018, and how to use them for work.

1) NumPy and SciPy

NumPy provides you with fast precompiled functions for mathematical and numerical routines. In addition, NumPy optimizes Python programming with powerful data structures for efficient computation of multi-dimensional arrays and matrices.

Scientific Python also is known as SciPy is inextricably linked with NumPy. Using SciPy you can lend a competitive edge to NumPy, by enhancing useful functions for regression, minimization, Fourier-transformation, and many more. You need to install NumPy first, and then SciPy

2) PANDAS for Data Analysis Library

 It can be used to add data structures and tools designed for practical data analysis in multiple streams such as finance, statistics, social sciences, and engineering. The best part of Pandas is its easy adaptability, which makes it one of the top Python Libraries for Data Science. It can work perfectly well with incomplete, unstructured, messy, and uncategorized data. 
Pandas, one of the Top Python Libraries for Data Science, come with several unique features such as:

• Pandas python can reshape the data structures
• Pandas can label series and tabular data to facilitate automatic alignment of data
• Heterogeneous indexing of data along with systematic labeling
• Capable of identifying and fixing missing data
• Ability to load and save data from multiple formats
• Easy Conversion from NumPy and Python data structures to Pandas objects

3) Matplotlib:

Matplotlib a Python 2D plotting library, capable of producing publication quality figures in a wide variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.

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4) SciKit-Learn:

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. It is one of the best-known machine-learning libraries for python. The Scikit-learn package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. The primary emphasis is upon ease of use, performance, documentation, and API consistency

5)Tensorflow:

Tensor, one of the top Python Libraries for Data Science for a job, is Google Brain’s second-generation system. Written mostly written in C++, it includes the Python bindings, performance is not a matter of worry. One of my favorite features is the flexible architecture, which allows me to deploy it to one or more CPUs or GPUs in a desktop, server, or mobile device all with the same API. Not many, if any, libraries can make that claim. It was developed for the Google Brain project and is now extensively used. However, you must dedicate some time to learn its API, but the time spent is worth it. Within the first few minutes of playing around with the core features, I could already tell TensorFlow would allow me to spend more time implementing my network designs and not fighting through the API.