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

Hits: 54

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

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

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

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

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!