Hits: 18

# Applied Data Science Coding in Python: histogram plots

A histogram is a graphical representation of the distribution of a dataset. It is an estimate of the probability distribution of a continuous variable. In other words, it shows how often certain values appear in a dataset. The histogram groups the values into bins, and the height of each bar represents the number of observations in that bin.

In Python, the most common library to generate histograms is `matplotlib`

. The `pyplot`

sublibrary of `matplotlib`

provides a function called `hist()`

that can be used to generate histograms. This function takes in a data array, and it returns a histogram plot.

The `hist()`

function has a few parameters that can be used to customize the plot. For example, the number of bins in the histogram can be changed by passing the `bins`

parameter to the function. The default value is 10, but it can be set to any integer value. The `range`

parameter can be used to specify the range of values to include in the histogram.

Another library that can be used for Histograms is `seaborn`

library, it has a function `displot()`

can be used to generate histograms.

In summary, histograms are used to visualize the distribution of a dataset. In Python, the most common library to generate histograms is `matplotlib`

with `pyplot`

sublibrary, it provides a function called `hist()`

that can be used to generate histograms. The `seaborn`

library also provides a function `displot()`

to generate histograms. These functions can be customized using parameters such as number of bins and range of values to include in the histogram.

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

## Applied Data Science Coding in Python: histogram plots

#### 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**