" Data visualization is the process of translating large data sets and metrics into charts, graphs and other visuals. The resulting visual representation of data makes it easier to identify and share real-time trends, outliers, and new insights about the information represented in the data. "
There’s a whole selection of visualization methods to present data in effective and interesting ways. Common general types of data visualization:
- Charts
- Tables
- Graphs
- Maps
- Infographics
- Dashboards
Python offers multiple great graphing libraries that come packed with lots of different features. No matter if you want to create interactive, live or highly customized plots python has an excellent library for you. To get a little overview here are a few popular plotting libraries:
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Matplotlib: low level, provides lots of freedom
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Pandas Visualization: easy to use interface, built on Matplotlib
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Seaborn: high-level interface, great default styles
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ggplot: based on R’s ggplot2, uses Grammar of Graphics
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Plotly: can create interactive plots