鸢尾花数据集的探索性数据分析

介绍

在机器学习和数据科学中，探索性数据分析是检查数据集并总结其主要特征的过程。它可能包括可视化方法来更好地表示这些特征或对数据集有一个总体的了解。它是数据科学生命周期中非常重要的一步，通常会消耗一定的时间。

在本文中，我们将通过探索性数据分析了解鸢尾花数据集的一些特征。

鸢尾花数据集

鸢尾花数据集非常简单，通常被称为“Hello World”。数据集包含三种不同花卉（山鸢尾、维吉尼亚鸢尾和变色鸢尾）的 4 个特征。这些特征是萼片长度、萼片宽度、花瓣长度和花瓣宽度。数据集包含 150 个数据点，每个物种 50 个数据点。

鸢尾花数据集上的 EDA

首先，让我们使用 pandas 从 CSV 文件“iris_csv.csv”加载数据集，并对其进行总体概述。

数据集可以从以下链接下载。

https://datahub.io/machine-learning/iris/r/iris.csv

代码实现

示例 1

import pandas as pd 
import numpy as np 
import seaborn as sns 
import matplotlib.pyplot as plt 
%matplotlib inline 

df = pd.read_csv("/content/iris_csv.csv") 
df.head()

示例 2

df.info()

RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   sepallength  150 non-null    float64
 1   sepalwidth   150 non-null    float64
 2   petallength  150 non-null    float64
 3   petalwidth   150 non-null    float64
 4   class        150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB
df.shape

(150, 5)


## Statistics about dataset
df.describe()

示例 3

## checking for null values

df.isnull().sum()

sepallength    0
sepalwidth     0
petallength    0
petalwidth     0
class          0
dtype: int64

## Univariate analysis
df.groupby('class').agg(['mean', 'median'])  # passing a list of recognized strings
df.groupby('class').agg([np.mean, np.median])

示例 4

## Box plot 
plt.figure(figsize=(8,4)) 
sns.boxplot(x='class',y='sepalwidth',data=df ,palette='YlGnBu')

示例 5

## Distribution of particular species
sns.distplot(a=df['petalwidth'], bins=40, color='b')
plt.title('petal width distribution plot')

示例 6

## count of number of observation of each species

sns.countplot(x='class',data=df)

示例 7

## Correlation map using a heatmap matrix

sns.heatmap(df.corr(), linecolor='white', linewidths=1)

示例 8

## Multivariate analysis – analyis between two or more variable or features
## Scatter plot to see the relation between two or more features like sepal length, petal length,etc
axis = plt.axes()

axis.scatter(df.sepallength, df.sepalwidth)

axis.set(xlabel='Sepal_Length (cm)',
   ylabel='Sepal_Width (cm)',
   title='Sepal-Length vs Width');

示例 9

sns.scatterplot(x='sepallength', y='sepalwidth', hue='class', data=df,
plt.show()

示例 10

## From the above graph we can see that
# Iris-virginica has a longer sepal length while Iris-setosa has larger sepal width
# For setosa sepal width is more than sepal length
## Below is the Frequency histogram plot of all features
axis = df.plot.hist(bins=30, alpha=0.5)
axis.set_xlabel('Size in cm');

示例 11

# From the above graph we can see that sepalwidth is longer than any other feature followed by petalwidth
## examining correlation
sns.pairplot(df, hue='class')

示例 12

figure, ax = plt.subplots(2, 2, figsize=(8,8))

ax[0,0].set_title("sepallength")
ax[0,0].hist(df['sepallength'], bins=8)

ax[0,1].set_title("sepalwidth")
ax[0,1].hist(df['sepalwidth'], bins=6);

ax[1,0].set_title("petallength")
ax[1,0].hist(df['petallength'], bins=5);

ax[1,1].set_title("petalwidth")
ax[1,1].hist(df['petalwidth'], bins=5);

示例 13

# From the above plot we can see that –
# - Sepal length highest freq lies between 5.5 cm to 6 cm which is 30-35 cm
# - Petal length highest freq lies between 1 cm to 2 cm which is 50 cm
# - Sepal width highest freq lies between 3 cm to 3.5 cm which is 70 cm
# - Petal width highest freq lies between 0 cm to 0.5 cm which is 40-45 cm

结论

探索性数据分析被数据科学家和分析师广泛使用。它揭示了给定数据的许多特征、其分布以及它如何有用。

Mithilesh Pradhan

更新于： 2022-12-30

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