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Applied Data Science Coding in Python: How to visualise data with Boxplot

A boxplot, also known as a box-and-whisker plot, is a powerful tool for visualizing the distribution of a dataset. It is particularly useful for identifying outliers and understanding the spread and skewness of the data.

In Python, the matplotlib library provides several functions for creating boxplots. One of the most commonly used is the boxplot() function, which takes a dataset as an input and returns a boxplot representation of the data.

The boxplot visualizes the distribution of data by showing the median, first and third quartiles, and the maximum and minimum values of the data. The box in the middle of the plot represents the interquartile range, which is the range of data between the first and third quartiles.

The whiskers on either side of the box represent the minimum and maximum values of the data, except for outliers, which are plotted as individual dots. The length of the whiskers is determined by the interquartile range (IQR).

In addition to the boxplot() function, you can also use the violinplot() function from the seaborn library to visualise data with Boxplot in python. This function is similar to the boxplot, but it also shows the density of the data across the entire range of the dataset, which can be useful for identifying patterns and trends in the data.

In summary, boxplot is a powerful tool for visualizing the distribution of a dataset in python. You can use the boxplot() function from matplotlib library or violinplot() function from seaborn library to create boxplots, which can help to identify outliers and understand the spread and skewness of the data.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to visualise data with Boxplot.

Applied Data Science Coding in Python: How to visualise data with Boxplot

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