Must-Know Python Libraries for Every Data Scientist
Python is THE language to learn for people who want to pursue Data Science. It is among the first few skills a recruiter searches in a resume when one applies to become a data scientist(“The Sexiest Job of the 21st century”).
A lot of you might ask why Python is so widely used in the Data Science community and the answer to that is that python is a very easy to code object oriented high level language! . It consists of an ocean of libraries for doing Data mining, Data Exploration ,visualization and so on.
There are 10 libraries that are a must-know for every Data scientist and they must have a sound knowledge on how to implement these in their projects.
- NumPy
- SciPy
- Pandas
- Matplotlib
- SciKit-Learn
- TensorFlow
- Scrapy
- Pytorch
- Keras
- LightGBM
NumPy (Numerical Python):
NumPy is a perfect tool for scientific computing and performing basic and advanced array operations. It is an extension module for Python, mostly written in C. This makes sure that the precompiled mathematical and numerical functions and functionalities of NumPy guarantee great execution speed.
SciPy:
SciPy includes modules for linear algebra, integration, optimization, and statistics. Its main functionality was built upon NumPy, so its arrays make use of this library.It contains useful functions for minimization, regression, Fourier-transformation and many others.
Pandas:
Pandas is one of the most important packages available in Python. The package is known for a very useful data structure called the pandas DataFrame. Pandas also allows Python developers to easily deal with tabular data (like spreadsheets) within a Python script.
Matplotlib:
Matplotlib is a 2-D plotting library which produces publication quality figures in a variety of hard-copy formats and interactive environments across platforms.It is probably the most famous library on python for Data Visualization as the flexibility and agility it offers is unrivaled.
SciKit-Learn:
SciKit-Learn is a machine learning library for the Python programming language. It features a lot of tools for various types of classification, regression,clustering and dimensionality reduction.The package is extensive and easy to implement as well.
Tensorflow:
TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications. It was developed by the Google Brain team and has become the most popular library for Deep learning.
Scrapy:
Scrapy is a framework built to build web scrapers more easily and relieve the pain of maintaining them. Basically, it allows you to focus on the data extraction using CSS selectors and choosing XPath expressions.Web Scraping is one of the easiest and inexpensive ways to gain access to a large amount of data available on the internet. One can easily build structured datasets and that can be used for different types of data analysis.
Pytorch:
PyTorch is a Python machine learning package based on Torch(“Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPU’s first”).PyTorch has two main features:
- Tensor computation like numPy with strong GPU acceleration
- Automatic differentiation for building and training neural networks.
Keras:
Keras is an open-source Python library used widely by artificial intelligence, deep learning, and data science professionals. Neural networks are also used in Data Science for analyzing observational data like audio and pictures. Developers use Keras because of it’s minimalistic design approach.
LightGBM:
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient so that it has a faster training speed, lower memory usage, better accuracy and so on. LightGBM is almost 7 times faster than XGBoost and is a much better approach when dealing with large datasets.
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