Hits: 37

# 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

#### Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

**Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!**

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**