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Created 3 years ago
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)