Exploring Data Distribution | Set 2

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

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

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(9,  15)

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(7,  9)

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 :