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,
- Pandas
- Numpy
- Seaborn
- 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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