Python top libraries for Data Science

In this article, I have shared some good python libraries for data science.....

Python is already a proven language in the data science industry. It has now taken the lead as the toolkit for scientific data analysis and modeling. In this blog, we would like to highlight some of the most popular and go-to Python libraries for data science. These are open-sourced libraries, offering alternate ways of deriving the same output. As the business world gets more and more competitive, data scientists and engineers are continually striving for ways to process information, extract insights and model, by processing massive datasets. So you need to be well versed in the various Python libraries that support your data science tasks and the benefits they offer to make your outputs more robust and speedy.

Here is a list of top 10 Python libraries that we expect will find prolific use across 2019:

CORE LIBRARIES

1. NumPy — The Core Numeric and Scientific Computation Library

NumPy or Numerical Python is a core library that forms the mainstay of the ecosystem of data science tools in Python. It supports scientific computing with high-qualitymathematical functions and logical operations on built-in multi-dimensional arrays and matrices. Besides n-dimensional array objects, NumPy provides functionality in basic algebraic functions, random numbers, basic Fourier transforms, sophisticated random number capabilities, tools for integrating Fortran code and C/C++ code. The Array interface of NumPy also allows multiple options to reshape large datasets.

NumPy ranks number one in the data science toolkit and is a must-know, not only to process real-world datasets but also because most other data science or machine learning Python packages (SciPy, MatplotLib, ScikitLearn, etc.) are built on it.

2. SciPy — The Numeric and Scientific Computation Library

SciPy or Scientific Python is another core library for scientific computing with algorithms and complex mathematical tools for Python. It contains tools for numerical integration, interpolation, optimization, etc., and helps to solve problems in linear algebra, probability theory, integral calculus, fast Fourier transform, signal processing, and other such tasks of data science. The SciPy key data structure is also a multidimensional array, implemented by Numpy.

It is set up after the NumPy installation and offers an edge to NumPy by improving useful functions for regression, minimization, Fourier-transformation, and more. SciPy is an important Python library for researchers, developers and data scientists.

3. Pandas — The Data Analysis Library

This is a dedicated library for data analysis, data cleaning, data handling, and data discovery, and steps executed prior to machine learning projects.

The Pandas library provides tools for shaping, merging, reshaping, and slicing of datasets. There are three types of data structures — “series” (single-dimensional, homogenous array), “data frames” (two-dimensional, heterogeneous columns) and “panel” (three-dimensional, size mutable array). These enable merging, grouping, filtering, slicing and combining data, besides providing a built-in time-series functionality. Data in multiple formats such as CSV, SQL, HDFS or excel can also be processed easily.

The Panda is the go-to library for data analysis in domains like finance, statistics, social sciences, and engineering. Its easy adaptability, ability to work well with incomplete, unstructured, and uncategorized data, makes it popular among data scientists.

VISUALISATION

4. Matplotlib — The Numerical Plotting Library

The Matplotlib is another core package for generating visualisations using fewer codes. It is a 2D plotting library for generating histograms, plots, bar charts, scatter plots, non-Cartesian coordinate graphs, etc., in multiple formats. The library is supported by various environments, platforms, and IDEs — Python script, Jupyter, IPython shells and application servers.

Matplotlib is a useful library for any data scientist as visualization helps identify the trends and patterns in order to make a data-driven decision.

MACHINE LEARNING

5. SciKit-Learn — The Data Analysis and Machine Learning Library

The SciKit-Learn library provides algorithms for the common machine learning and data mining tasks — clustering, regression, classification, dimensionality reduction, feature extraction, image processing, model selection and pre-processing. It is built on the top of SciPy, Numpy, and Matplotlib. SciKit-Learn has great supporting documentation that makes it user-friendly. The various functionalities of SciKit-Learn help data scientists in use cases like spam filters, image recognition, drug response, stock pricing, and customer segmentation.

DEEP LEARNING

6. TensorFlow — The Ultimate Machine Learning and Deep Learning Framework

This library uses a system of multi-layered nodes to enable setting up, training and deployment of artificial neural networks when working with large datasets. It was set up by Google Brain, and is written in C++ but can be called in Python. The most prolific applications of TensorFlow are object identification, speech recognition, word embedding, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. Besides, TensorFlow supports production prediction at scale, using the same models used for training.

TensorFlow has found popular use because of its high level of performance, flexible architecture, and the ability to run on any target like a local machine, a cluster in the cloud, iOS and Android devices, CPUs or GPUs.

7. Keras — The Library for Neural Networks

Keras is a high performing library for working with neural networks, running on top of TensorFlow, Theano, and CNTK (Microsoft’s Cognitive Toolkit). Keras is user-friendly, with simple APIs and easy fast experimentation, making it possible to work on more complex models. Its modular and extendable nature allows you to use varieties of modules from neural layers, optimizers, and activation functions to develop a new model. This makes Keras a good option for data scientists when they want to add a new module as classes and functions.

8. PyTorch — The Largest Machine Learning Framework

The PyTorch library has several features that make it the ultimate choice for data science. It is the largest machine learning library supporting complex tasks like dynamic computational graphs design and fast tensor computations with GPU acceleration. For applications calling for neural network algorithms, the PyTorch offers a rich API. It supports a cloud-based ecosystem for scaling of resources used in deployment and testing.

PyTorch allows you to define your computational graph dynamically and transitioning in graph mode for optimization. It is a great library for your deep learning research projects as it provides great flexibility and native support for establishing P2P communication.

DATA SCRAPING

9. Scrapy — The Online Data Crawler Library

The Scrapy library creates online crawling programs, or spider bots, that scan website pages and collects structured data from web applications or data from the API.

With this library, you can write codes, reuse the universal programs and create scalable large crawlers.

NATURAL LANGUAGE PROCESSING

10. NTLK — The Natural Language Library

NTLK or Natural Language Toolkit is the ultimate go-to set of libraries for natural language processing (NLP) tasks in data science. NTLK facilitates training, research, and prototyping of NLP and the related fields of linguistics or cognitive science artificial intelligence, that are driving advances in AI.

The features allow processing and analytic operations of text like text tagging, classification, tokenizing, name entities identification, parsing, stemming and semantic reasoning. NTLK is used by data scientists in tasks of sentiment analytics, chatbots, automatic summarization, and recommendations.