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How to use distplot method in seaborn

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by vinitha@openweaver.com dot icon Updated: Sep 6, 2023

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The Seaborn library is a popular way to visualize data in Python. It is built on top of Matplotlib. It offers a high-level interface for building informative and attractive statistical graphics.  

 

The function distplot() plots univariate distributions are: 

  • The basic use of distplot() is to create histograms of a single variable. It shows the frequency distribution of the data points in bins. The data distribution gives you an idea.   
  • Kernel Density Estimation: It provides a smooth estimate of the data distribution. It can give you insights into the underlying distribution of the data points.   
  • Rug plot: It displays the density of each data variable and the overall distribution. It adds small vertical axes to each data axis along the y-axis.  

 

Seaborn distplots are versatile tools. Various levels of data analysis use it, from basic to more advanced techniques. Here's how you can use distplots for different variations of data analysis:   

  • Exploratory Data Analysis (EDA)  
  • Comparative Analysis  
  • Preprocessing and Data Transformation   
  • Modeling Techniques   
  • Interpretation of Results   

 

In conclusion, Seaborn distplot is a versatile and powerful tool for data analysis. It offers a range of applications, from basic analysis to sophisticated modeling techniques. To understand your data, it's important to see how one thing spreads out with these unique aspects.   

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we are using seaborn library

Seaborn distplot only whole numbers

PythondotLines of Code : 10dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import seaborn as sns # for data visualization
import numpy as np # for numeric computing
import matplotlib.pyplot as plt # for data visualization

arr = np.array([1,2,3,4,5,6,7,8,9])

sns.distplot(arr, bins = arr, kde = False)
plt.xticks(arr)
plt.show()

Instructions

Follow the steps carefully to get the output easily.


  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install seaborn - pip install seaborn
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your python file.
  6. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for ' Seaborn distplot only whole numbers ' in Kandi. You can try any such use case!

Environment tested


I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.11.1 Version
  3. Seaborn 1.0.7 Version


Using this solution, we can able to use distplot method in seaborn with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use distplot method in seaborn.

Dependency library

seabornby mwaskom

Python doticonstar image 10797 doticonVersion:v0.12.2doticon
License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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            seabornby mwaskom

            Python doticon star image 10797 doticonVersion:v0.12.2doticon License: Permissive (BSD-3-Clause)

            Statistical data visualization in Python
            Support
              Quality
                Security
                  License
                    Reuse

                      You can search for any dependent library on kandi like ' Seaborn '.

                      FAQ:  

                      1. How does Seaborn's distplot function help create a kernel density estimation plot?  

                      A KDE plot shows the density of a random variable without using specific parameters. It provides a smoothed representation of the data's distribution. Regular histograms don't show the shape and structure. These insights do show it.  

                       

                      You can create a KDE plot using Seaborn's distplot() function. Just set the kde parameter to True. Seaborn creates the KDE plot with other visuals, like histograms and rug plots.  

                       

                      2. How does a distribution plot differ from a histogram plot in Seaborn?  

                      Both histogram and distribution plots help you visualize data distributions,   

                      • A histogram provides a discrete representation using bins and bar heights  
                      • A distribution plot combines a histogram, KDE, and rug plot elements to give a complete view.   

                      Seaborn has new functions like displot and kdeplot that make specific distribution visualizations. 

                         

                      3. Can I use the displot function to create plots with matplotlib functions?  

                      Yes, you can use the displot function in Seaborn with Matplotlib functions. It allows you to customize and enhance the appearance of your plots. To add elements to the plot in Seaborn, you can access the Matplotlib Axes object created by displot.  

                       

                      4. Can you tell me where I can find information about the features of Seaborn distplot?  

                      Seaborn replaced the distplot function with displot, kdeplot, and histplot. The old distplot combined these newer functions, providing better flexibility.  

                       

                      If you want to know how to use the displot function, read the Seaborn documentation. The documentation provides comprehensive explanations, examples, and usage guidelines for each function.  

                       

                      5. How can displot create single and two-variable distributions? Which plot styles do they use?  

                      Seaborn's displot function has various plot styles for single and double distributions. The styles control how the data looks in your plots and let you customize it. Here are some of the available plot styles:  

                      • Histogram  
                      • Kernel Density Estimate  
                      • Empirical Cumulative Distribution Function  
                      • Summarized Data Distribution  
                      • Hexbin Plot  
                      • Two-Dimensional KDE Plot  
                      • Categorical Histograms and KDEs  

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