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TASK: To Perform Exploratory Data Analysis On Iris Dataset

# Importing all the libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")

from sklearn import metrics
sns.set()
iris_data = pd.read_csv("E:\Jupyter Notebook\EDA On Iris DataSet/Iris.csv")
print(iris_data.head())
print(iris_data.shape)
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species 0 1 5.1 3.5 1.4 0.2 Iris-setosa 1 2 4.9 3.0 1.4 0.2 Iris-setosa 2 3 4.7 3.2 1.3 0.2 Iris-setosa 3 4 4.6 3.1 1.5 0.2 Iris-setosa 4 5 5.0 3.6 1.4 0.2 Iris-setosa (153, 6)
iris_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 153 entries, 0 to 152 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 153 non-null int64 1 SepalLengthCm 153 non-null float64 2 SepalWidthCm 153 non-null float64 3 PetalLengthCm 153 non-null float64 4 PetalWidthCm 153 non-null float64 5 Species 153 non-null object dtypes: float64(4), int64(1), object(1) memory usage: 7.3+ KB

Result:
1 All columns are not having any Null Entries (Non-null)

2 Four columns are numerical type (float64 bit)

3 Only Single column categorical type (Object)