This repository contains some data visualizations done using Python libraries, such as NumPy, Pandas, Matplotlib, Seaborn, and Plotly.
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The datasets used, have been imported from Kaggle.com and their respective links have been provided below:
NumPy is an open-source Python library used for numerical manipulation of arrays. Python does utilize arrays however, they are slow to process. Numpy provides a 50 times faster efficiency as compared to Python lists. This library is very commonly used in the field of data science.
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Pandas is a Python library that is primarily used in data analysis and can be used to create database tables for the same. It has built-in functions for cleaning, manipulating, exploring, and analyzing datasets. Pandas dataframes can be used in exploratory data analysis and subsequently to produce a variety of plot diagrams.
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Matplotlib is an open-source tool, used to create primitive visualization plots. Certain kinds of plots such as histograms, Bar graphs, pie charts, line graphs, and more... can be created via this library
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Seaborn is a Python library which with the help of Matplotlib, is used to plot graphs of a distributed variance. It contains two subcategories: relational plots and categorical plots. Relational plots can be used in bivariate analysis or to plot frequency distribution. Categorical plots plot frequency distribution with respect to particular categories, whose respective data is to be analyzed.
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Plotly is a JavaScript-based visualization library, which contains 3D graphics charts. Dynamic plots such as horizontal bar plots, Pie plots, line plots, scatter plots, etc. can be created using this library.