Аннотация
You'll begin Data Visualization with Python with an introduction to data visualization and its importance. Then, you'll learn about statistics by computing mean, median, and variance for some numbers, and observing the difference in their values. You'll also learn about key NumPy and Pandas techniques, such as indexing, slicing, iterating, filtering, and grouping. Next, you'll study different types of visualizations, compare them, and find out how to select a particular type of visualization using this comparison. You'll explore different plots, including custom creations.
After you get a hang of the various visualization libraries, you'll learn to work with Matplotlib and Seaborn to simplify the process of creating visualizations. You'll also be introduced to advanced visualization techniques, such as geoplots and interactive plots. You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful and insightful visualizations. You'll study how to plot geospatial data on a map using Choropleth plot, and study the basics of Bokeh, extending plots by adding widgets and animating the display of information.
This book ends with an interesting activity in which you will be given a new dataset and you must apply all that you've learned to create an insightful capstone visualization.
![Practical Statistics for Data Scientists [50+ Essential Concepts Using R and Python]](https://www.rulit.me/data/programs/images/practical-statistics-for-data-scientists-50-essential-concep_607160.jpg "This book is aimed at the data scientist with some familiarity with the R and/or Python programming languages, and with some prior (perhaps spotty or ephemeral) exposure to statistics. Two of the authors came to the world of data science from the world of statistics, and have some appreciation of...")
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