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Applied Data Science Coding in Python: How to generate density plots

Density plots, also known as probability density plots, are used to visualize the probability density function of a continuous random variable. It gives an idea of the distribution of the data and helps to identify patterns, such as skewness or outliers.

In Python, there are several libraries that can be used to generate a density plot, such as matplotlib, seaborn, and plotly. The most common method is using the densityplot() function from the seaborn library. It takes a DataFrame or a Series as an input and returns a density plot, where the x-axis represents the values of the variable, and the y-axis represents the probability density.

The matplotlib library also provides a method to generate density plots, the hist() function. It takes a 1D array as an input and returns a histogram, which is a type of density plot.

Another method is using the violinplot() function from the seaborn library. It takes a DataFrame or a Series as an input and returns a violin plot, which is a combination of a box plot and a density plot. It shows the distribution of the data across all levels of a categorical variable.

In summary, Density plots are used to visualize the probability density function of a continuous random variable. In Python, the most common method is using the densityplot() function from the seaborn library, hist() function from the matplotlib library, and violinplot() function from the seaborn library to generate density plots.

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

Applied Data Science Coding in Python: How to generate density plots

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