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Univariate and Bivariate Analysis using Seaborn

Univariate and Bivariate

ARTICLE SUMMARY

Data Visualization is an important step in machine learning. This blog takes us on a deep dive into univariate and bivariate analysis using seaborn.

Data Visualization is an important step in machine learning. Data Visualization is used to visualize the distribution of data, the relationship between two variables, etc. 

So, let’s take a deep dive into univariate and bivariate analysis using seaborn. 

Univariate Analysis 

  1. Histogram 
  2. Distplot 
  3. Box plot 
  4. Countplot 

Bivariate Analysis on Categorical Variables 

  1. Barplot 

Bivariate Analysis on Continuous Variables 

  1. Scatterplot 
  2. lmplot 
  3. lineplot 
  4. regplot 

I have taken the Iris data set and have performed univariate and bivariate analysis of it. 

Univariate Analysis 

  1. Histogram 
    •  

A histogram is a visualisation tool that represents the distribution of one or more variables. 

sns.histplot(df[“sepal length”],color=‘darkorange’

Univariate and Bivariate Analysis

From the above histogram plot, we can infer that the sepal length ranges from 4 to 8. And also we can infer that more iris species have sepal length between 5.5 to 6.5.

To get vertical histogram plots, we can switch the axis. 

sns.histplot(y=“sepal length”,data=df,color=‘darkorange’

Univariate and Bivariate Analysis

Histogram for categorical variables 

To include ‘cat2egorical variables’, the hue parameter is used. The color encoding is done based on the categorical variable. 

sns.histplot(x=‘sepal length’,data=df,hue=df[‘iris’]) 

Univariate and Bivariate Analysis

From the plot, we can infer the sepal length of various ‘iris’ species. 

2. distplot 

Distplot is a histogram with a line on it. Displot is used for single variable distribution. 

sns.distplot(df[“sepal length”],color=‘darkorange’

Univariate and Bivariate Analysis

To visualize distplot alone, we can give hist=False  

sns.distplot(df[“sepal length”],hist=False,color=‘darkorange’

Univariate and Bivariate Analysis

3.  Boxplot 

Box plot is used to visualize the descriptive statistics of a variable. It is used to detect outliers. It represents the five-point summary. 

Five Point Summary 

min,max,median,lower quartile(Q1),upper quartile(Q3) 

sns.boxplot(df[“sepal length”],color=’darkorange’) 

Univariate and Bivariate Analysis

Interpreting boxplot 

Boxplot is the pictorial representation of descriptive statistics.  

Univariate and Bivariate Analysis

4. countplot 

Count plot is used for the distribution of categorical variables. It shows the count of each categorical bin. 

sns.countplot(df[‘iris’]) 

Univariate and Bivariate Analysis

Bivariate Analysis on Categorical Variables 

  1. Barplot 

Barplot is used to aggregate the categorical data based on some aggregate methods. By default it is mean. 

sns.barplot(x=‘iris’,y=‘sepal length’,data=df) 

Univariate and Bivariate Analysis

2. pointplot 

Pointplot helps to visualize the distribution of values at each level of the categorical variable. 

sns.pointplot(x=‘iris’,y=‘petal width’,data=df,color=‘darkorange’

Univariate and Bivariate Analysis

Bivariate Analysis on Continuous Variables 

  1. scatterplot

The scatterplot shows the correlation between two numerical variables.  

sns.scatterplot(df[“petal length”],df[‘petal width’],color=‘darkorange’

Univariate and Bivariate Analysis

2. lineplot 

The relationship between two numerical variables is shown in a line. 

sns.lineplot(df[‘petal length’],df[‘petal width’],color=‘darkorange’

Univariate and Bivariate Analysis

3. regplot 

Regplot is a scatterplot with a regression line to it.  

sns.regplot(df[‘petal length’],df[‘petal width’],color=‘darkorange’

Univariate and Bivariate Analysis

Conclusion 

In this article, I have covered the different types of univariate and bivariate analysis using the iris data set. Thanks for reading! 

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