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Exploring Data Distribution | Set 2

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Prerequisite: Exploring Data Distribution | Set 1
Terms related to Exploration of Data Distribution 

-> Boxplot
-> Frequency Table
-> Histogram 
-> Density Plot


To get the link to csv file used, click here.
Loading Libraries 

Python3

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

                    

Loading Data 

Python3

data = pd.read_csv("../data/state.csv")
 
# Adding a new column with derived data
data['PopulationInMillions'] = data['Population']/1000000
 
print (data.head(10))

                    

Output : 


 

  • Histogram: It is a way of visualizing data distribution through frequency table with bins on the x-axis and data count on the y-axis. 
    Code – Histogram

Python3

# Histogram Population In Millions
 
fig, ax2 = plt.subplots()
fig.set_size_inches(915)
 
ax2 = sns.distplot(data.PopulationInMillions, kde = False)
ax2.set_ylabel("Frequency", fontsize = 15)
ax2.set_xlabel("Population by State in Millions", fontsize = 15)
ax2.set_title("Population - Histogram", fontsize = 20)

                    
  • Output : 
  • Density Plot: It is related to histogram as it shows data-values being distributed as continuous line. It is a smoothed histogram version. The output below is the density plot superposed over histogram. 
    Code – Density Plot for the data

Python3

# Density Plot - Population
 
fig, ax3 = plt.subplots()
fig.set_size_inches(79)
 
ax3 = sns.distplot(data.Population, kde = True)
ax3.set_ylabel("Density", fontsize = 15)
ax3.set_xlabel("Murder Rate per Million", fontsize = 15)
ax3.set_title("Density Plot - Population", fontsize = 20)

                    
  • Output : 


Last Updated : 25 Jan, 2022
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