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Applied Data Science Coding in Python: scatter plots

A scatter plot is a graphical representation of two-dimensional data, where each point on the plot represents a pair of (x,y) values. It is used to visualize the relationship between two continuous variables. Scatter plots can be used to identify patterns in the data, such as linear or non-linear relationships, clusters, and outliers.

In Python, the most common library to generate scatter plots is matplotlib. The pyplot sublibrary of matplotlib provides a function called scatter() that can be used to generate scatter plots. This function takes in two arrays, one representing the x-coordinates of the points and the other representing the y-coordinates of the points.

The scatter() function has a few parameters that can be used to customize the plot. For example, the c parameter can be used to specify the color of the points. The s parameter can be used to specify the size of the points. The alpha parameter can be used to specify the transparency of the points.

Another library that can be used for scatter plots is seaborn library, it has a function scatterplot() can be used to generate scatter plots.

In summary, scatter plots are used to visualize the relationship between two continuous variables. In Python, the most common library to generate scatter plots is matplotlib with pyplot sublibrary, it provides a function called scatter() that can be used to generate scatter plots. The seaborn library also provides a function scatterplot() to generate scatter plots. These functions can be customized using parameters such as color, size, and transparency of the points.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to generate scatter plots.

Applied Data Science Coding in Python: scatter plots

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