New PDF release: Mastering Python Data Visualization

By Kirthi Raman

Approximately This e-book discover a number of instruments and their strengths whereas development significant representations which can enable you comprehend information jam-packed with computational tools and algorithms in different fields of technology Written in an easy-to-follow specific variety, this ebook discusses a few area of interest strategies that may make your code more straightforward to paintings with and reuse Who This ebook Is For when you are a Python developer who plays facts visualization and needs to enhance your current Python wisdom, then this publication is for you. A easy wisdom point and realizing of Python libraries is thought. What you are going to research assemble, cleanse, entry, and map facts to a visible framework realize which visualization strategy is appropriate and examine top practices for information visualization Get conversant in reader-driven narratives, author-driven narratives, and the rules of belief comprehend why Python is a good instrument for numerical computation very like MATLAB, and discover a few attention-grabbing info constructions that include it Use a number of visualization options to discover how Python might be very priceless for monetary and statistical computations examine Python with different visualization methods utilizing Julia and a JavaScript-based framework akin to D3.js realize how Python can be utilized together with NoSQL, akin to Hive, to provide effects successfully in a disbursed surroundings intimately Python has a handful of open resource libraries for numerical computations that contain optimization, linear algebra, integration, interpolation, and different specified capabilities utilizing array gadgets, computing device studying, info mining, and plotting. This booklet bargains useful advice that will help you at the trip to potent facts visualization. beginning with a bankruptcy at the facts framework, the ebook covers the total visualization approach, utilizing the preferred Python libraries with operating examples. you are going to how to use NumPy, SciPy

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However, if the data is not well understood, you may not frame the right questions about the data. When creating visualizations, the first step is to be clear on the question to be answered. In other words, how is visualization going to help? There is another challenge that follows this—knowing the right plotting method. Some visualization methods are as follows: • Bar graph and pie chart • Box plot • Bubble chart • Histogram • Kernel Density Estimation (KDE) plot • Line and surface plot • Network graph plot • Scatter plot • Tree map • Violin plot In the course of identifying the message that the visualization should convey, it makes sense to look at the following questions: • How many variables are we dealing with, and what are we trying to plot?

26 ] Chapter 1 With bar graphs, each column represents a group defined by a specific category; with histograms, each column represents a group defined by a quantitative variable. With bar graphs, the x axis does not have a low-end or a high-end value, because the labels on the x axis are categorical and not quantitative. On the other hand, in a histogram, there is going to be a range of values. ". There are critics who come crashing down on pie charts, and one of the main reasons, for that is that when there are numerous categories, it becomes very hard to get the proportions and compare those categories to gain any insight.

33 ] A Conceptual Framework for Data Visualization The following scatter plot is the result of the example using color map: [ 34 ] Chapter 1 Bubble charts are extremely useful for comparing relationships between data in three numeric-data dimensions: the x axis data, the y axis data, and the data represented by the bubble size. Bubble charts are like XY scatter plots, except that each point on the scatter plot has an additional data value associated with it that is represented by the size of the circle or "bubble" centered on the XY point.

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