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.
To get the Iris Data click here.
Loading Libraries
import numpy as npimport pandas as pdimport matplotlib.pyplot as plt |
Loading Data
data = pd.read_csv("Iris.csv") print (data.head(10)) |
Output:
Description
data.describe() |
Output:
Info
data.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") |
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() |
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() |
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() |
Output:
Code #5: Data preparation for Box Plot
# removing Id columnnew_data = data[["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"]]print(new_data.head()) |
Output :
Code #6: Box Plot for Iris Data
plt.figure(figsize = (10, 7))new_data.boxplot() |
Output :
