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Data Visualization With Python
Choosing the Right Chart, Plotting Libraries, Clear Design, Dashboards and Reports People Actually Understand
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64 b
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Most charts fail before anyone reads them: the wrong chart type, a misleading axis, colours that vanish in print or for colour-blind readers. This book shows Python users how to fix that. You will turn a vague request into a clear question, pick the right chart from a decision table, and build bar charts, line charts, histograms, box plots, scatter plots and heatmaps with matplotlib, pandas and seaborn. Dedicated chapters cover titles and annotation, accessible colour, honest axes, static dashboards and a repeatable reporting workflow. Every code example was run, and every chart is described in words so you can check your result. A complete end-to-end example takes raw orders to a finished PDF. Written for analysts, developers and students who already know the basics of pandas.
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