Data Visualization in Python
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Data Driven Decision Science
Data visualization is the discipline of endeavoring to understand data by placing it in a visual context so that patterns, trends, and correlations that might not otherwise be detected can be exposed.
Python offers multiple great graphing libraries packed with lots of different features. Whether you optate to engender interactive 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 caliber, provides lots of liberation
Pandas Visualization: facile to utilize interface, built on Matplotlib
Seaborn: high-level interface, great default styles
plotnine: predicated on R’s ggplot2, uses Grammar of Graphics
Plotly: can engender interactive plots
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The Consummate Guide to Data Visualization in Python
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Table of Contents
What is Data Visualization?
Data visualization is a field in data analysis that deals with visual representation of data. It graphically plots data and is an efficacious way to communicate inferences from data.
Utilizing data visualization, we can get a visual summary of our data. With pictures, maps and graphs, the human mind has a more facile time processing and understanding any given data. Data visualization plays a consequential role in the representation of both minuscule and immensely colossal data sets, but it is especially utilizable when we have astronomically immense data sets, in which it is infeasible to visually perceive all of our data, let alone process and understand it manually.
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Data Visualization in Python
Python offers several plotting libraries, namely Matplotlib, Seaborn and many other such data visualization packages with different features for engendering informative, customized, and appealing plots to present data in the most simple and efficacious way.
Serviceable packages for visualizations in python
Matplotlib is a visualization library in Python for 2D plots of arrays. Matplotlib is indited in Python and makes utilization of the NumPy library. It can be utilized in Python and IPython shells, Jupyter notebook, and web application servers. Matplotlib comes with a wide variety of plots like line, bar, scatter, histogram, etc. which can avail us, deep-dive, into understanding trends, patterns, correlations. It was introduced by John Hunter in 2002.
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Seaborn is a dataset-oriented library for making statistical representations in Python. It is developed atop matplotlib and to engender different visualizations. It is integrated with pandas data structures. The library internally performs the required mapping and aggregation to engender informative visuals It is recommended to utilize a Jupyter/IPython interface in matplotlib mode.
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Bokeh is an interactive visualization library for modern web browsers. It is congruous for immensely colossal or streaming data assets and can be acclimated to develop interactive plots and dashboards. There is a wide array of intuitive graphs in the library which can be leveraged to develop solutions. It works proximately with PyData implements. The library is well-suited for engendering customized visuals according to required use-cases. The visuals can withal be made interactive to accommodate a what-if scenario model. All the codes are open source and available on GitHub.
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Altair is a declarative statistical visualization library for Python. Altair’s API is utilizer-convivial and consistent and built atop Vega-Lite JSON designation. Declarative library denotes that while engendering any visuals, we require to define the links between the data columns to the channels (x-axis, y-axis, size, color). With the avail of Altair, it is possible to engender informative visuals with minimal code. Altair holds a declarative grammar of both visualization and interaction.
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plotly.py is an interactive, open-source, high-level, declarative, and browser-predicated visualization library for Python. It holds an array of utilizable visualization which includes scientific charts, 3D graphs, statistical charts, financial charts among others. Plotly graphs can be viewed in Jupyter notebooks, standalone HTML files, or hosted online. Plotly library provides options for interaction and editing. The robust API works impeccably in both local and web browser mode.
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ggplot is a Python implementation of the grammar of graphics. The Grammar of Graphics refers to the mapping of data to aesthetic attributes (colour, shape, size) and geometric objects (points, lines, bars). The fundamental building blocks according to the grammar of graphics are data, geom (geometric objects), stats (statistical transformations), scale, coordinate system, and facet.
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Utilizing ggplot in Python sanctions you to develop informative visualizations incrementally, understanding the nuances of the data first, and then tuning the components to ameliorate the visual representations.