Whenever we talk about Data Science, the first programming language that strikes our mind is Python. Data Science and Python are a deadly duo. And why not, Data Science is considered as the sexiest career of this century and Python as the most demanding programming language on the planet.
But have you ever wondered what has made Python the most preferred programming language among Data Scientists? The answer is its versatility and the robust libraries it has. Python libraries are tailor-made for Data Scientists. Python has a library for each stage of Data Science processing.
TOP PYTHON LIBRARIES
It is often said that, if you are an aspiring Data Scientist, then you must be well acquainted with Pandas. It is used to analyze structured as well as time-series data. The data analysis process is much easier with it as it provides fast, expressive, and flexible data structures for the same. We can use pandas to manipulate and analyze data. With the data structures and operations, it has to offer, you can play around with time series and numerical tables.
NumPy, an acronym for Numerical Python, is a perfect tool for dealing with huge, multidimensional matrices and arrays. Additionally, it offers many handy high-level functions to perform mathematical operations on these structures. Even the vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time.
It is a generalized Data Science library that allows us to generate interactive data visualizations such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs). It offers an object-oriented API for embedding plots into applications which makes it crucial for various Data Science projects. It is a Python alternative to MatLab.
TensorFlow is one of the most popularly used Python libraries for Machine Learning and Deep Learning, developed at Google Brain. It's considered as the best tool for tasks like object identification, speech recognition, and many others. It helps in working with artificial neural networks that demand to handle multiple data sets. TensorFlow has constantly expanded with its new releases – including fixes in potential security vulnerabilities or improvements in the integration of TensorFlow and GPU.
This is one of the most useful libraries for numerical routines. It includes separate modules for linear algebra, integration, optimization, and statistics. Since it was developed upon NumPy, it uses this library as well. Its extensive documentation is what makes working with this library quite easy.
This Python library is categorized among the most popular Data Science libraries. If you need fast, high-level screen scraping and web-crawling, then Scrapy is the ideal choice for you. It is a great tool for scraping data in Machine Learning models.
It is an ideal Python library for Data Science that is recommended by industry experts. Scikit-learn furnishes you with functions that facilitate easy classification, regression, and clustering techniques used for training Machine Learning models. Scikit-learn utilizes the math operations of SciPy to induce a concise interface to the most common Machine Learning algorithms.
Once you're well acquainted with all these Python libraries, the next and probably the most important step for you would be laying your hands on some top Data Science projects that would be implemented using all these libraries. These projects will surely help you in having deep insight into what these libraries are all about.
Thanks for your time.