Exploratory Data Analysis On Iris Dataset

Hi Everyone, We are Back with a New Project. In this Project, We Use Iris Dataset for Exploratory Data Analysis.

Necessary Library for this project,

1. Pandas
2. Numpy
3. Seaborn
4. Matplotlib

These Libraries are Enough for EDA. Now Let's Start

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
#Load Datasets
iris = pd.read_csv('iris.csv')
#check Shape of this project
print(iris.shape)
#Check Columns in this project
print(iris.columns)

#Check Species of Iris Datasets
print("iris['species'].value_counts()")

#Check Scatter Plot Between Sepal_length Vs Sepal_width
iris.plot(kind='scatter', x='sepal_length', y='sepal_width')
plt.show()

Create a Whitegrid Scatter Plot Between sepal_leangth Vs Sepal_width , Hue= species

sns.set_style('whitegrid')
sns.FacetGrid(iris, hue='species',size=4) \
    .map(plt.scatter, 'sepal_length', 'sepal_width') \
    .add_legend()
plt.show()

Now We Show a Pairplot

plt.close()
sns.set_style('whitegrid')
sns.pairplot(iris,hue='species',size=3)
plt.show()

sns.FacetGrid(iris, hue='species', size=5) \
    .map(sns.distplot, 'sepal_width') \
    .add_legend()
plt.show()

Output:-

PDF and CDF

counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=10, density=True)
pdf = counts/(sum(counts))
print(pdf)
print(bin_edges)

cdf = np.cumsum(pdf)
plt.plot(bin_edges[1:],pdf)
plt.plot(bin_edges[1:], cdf)

counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=20, density=True)
pdf = counts/(sum(counts))
plt.plot(bin_edges[1:], pdf)
plt.show()

Box Plot

sns.boxplot(x='species',y='petal_length',data=iris)
plt.showow()

sns.violinplot(x='species',y='petal_length',data=iris,size=8)
plt.show()

Jointplot

sns.jointplot(x='petal_length',y='petal_width',data=iris_setosa,kind='kde')
plt.show()

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