Box plot and Histogram exploration on Iris data
Attribute Information about data set:
Attribute Information:
-> sepal length in cm
-> sepal width in cm
-> petal length in cm
-> petal width in cm
-> class:
Iris Setosa
Iris Versicolour
Iris Virginica
Number of Instances: 150
Summary Statistics:
Min Max Mean SD Class Correlation
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
Class Distribution: 33.3% for each of 3 classes.
Loading Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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Loading Data
data = pd.read_csv( "Iris.csv" )
print (data.head( 10 ))
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Output:
Description
Output:
Info
Output:
Code #1: Histogram for Sepal Length
plt.figure(figsize = ( 10 , 7 ))
x = data[ "SepalLengthCm" ]
plt.hist(x, bins = 20 , color = "green" )
plt.title( "Sepal Length in cm" )
plt.xlabel( "Sepal_Length_cm" )
plt.ylabel( "Count" )
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Output:
Code #2: Histogram for Sepal Width
plt.figure(figsize = ( 10 , 7 ))
x = data.SepalWidthCm
plt.hist(x, bins = 20 , color = "green" )
plt.title( "Sepal Width in cm" )
plt.xlabel( "Sepal_Width_cm" )
plt.ylabel( "Count" )
plt.show()
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Output:
Code #3: Histogram for Petal Length
plt.figure(figsize = ( 10 , 7 ))
x = data.PetalLengthCm
plt.hist(x, bins = 20 , color = "green" )
plt.title( "Petal Length in cm" )
plt.xlabel( "Petal_Length_cm" )
plt.ylabel( "Count" )
plt.show()
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Output:
Code #4: Histogram for Petal Width
plt.figure(figsize = ( 10 , 7 ))
x = data.PetalWidthCm
plt.hist(x, bins = 20 , color = "green" )
plt.title( "Petal Width in cm" )
plt.xlabel( "Petal_Width_cm" )
plt.ylabel( "Count" )
plt.show()
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Output:
Code #5: Data preparation for Box Plot
new_data = data[[ "SepalLengthCm" , "SepalWidthCm" , "PetalLengthCm" , "PetalWidthCm" ]]
print (new_data.head())
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Output :
Code #6: Box Plot for Iris Data
plt.figure(figsize = ( 10 , 7 ))
new_data.boxplot()
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Output :
Last Updated :
21 Mar, 2024
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