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Data Visualization Techniques - Scatter, Line, and Histogram

Explore fundamental data visualization methods to understand data relationships and distributions. Learn to create scatter plots showing multiple data dimensions, line plots for trend comparison, and histograms to analyze data distributions using Python tools.

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Visualization Techniques

1. Scatter Plots

Scatter plots are deceptively simple and commonly used, but simple doesn’t mean that they aren’t useful!

In a scatter plot, data points are represented individually with a dot, circle, or some other shape. These plots are great for showing the relationship between two variables as we can directly see the raw distribution of the data.

To create a scatter plot in Matplotlib we can simply use the scatter method. Let’s see how by creating a scatter plot with randomly generated data points of many colors and sizes.

First, let’s generate some random data points, x and y, and set random values for colors and sizes because we want a pretty plot:

A complete “runnable” example is at the end.

Python 3.5
# Generating Random Data
x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)

Now that we have our two variables (x, y) and the colors and sizes of the points, we can call the scatter method like this:

plt.scat
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