By Kirthi Raman
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Additional resources for Mastering Python Data Visualization
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.