详细介绍鸢尾花iris数据集；

matplotlib.pyplot.scatter绘制散点图

matplotlib.axes.Axes.scatter绘制散点图

本文速览

目录1、鸢尾花(iris)数据集

数据集导入、查看特征

DESCR

data

feature_names

target

target_names

将鸢尾花数据集转为DataFrame数据集

2、matplotlib.pyplot.scatter法绘制散点图

3、matplotlib.axes.Axes.scatter法绘制散点图

1、鸢尾花(iris)数据集数据集导入、查看特征

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

from pandas import Series,DataFrame

from sklearn import datasets

iris=datasets.load_iris()

dir(iris)

['DESCR', 'data', 'feature_names', 'target', 'target_names']DESCR

#DESCR为数据集的描述信息，输出来看看：

print(iris.DESCR)Iris Plants Database

====================

Notes

-----

Data Set Characteristics:

:Number of Instances: 150 (50 in each of three classes)

:Number of Attributes: 4 numeric, predictive attributes and the class

:Attribute Information:#四列数据的四个特征

- sepal length in cm

- sepal width in cm

- petal length in cm

- petal width in cm

- class:#数据描述三类鸢尾花

- Iris-Setosa

- Iris-Versicolour

- Iris-Virginica

:Summary Statistics:#四列数据的简单统计信息

============== ==== ==== ======= ===== ====================

Min Max Mean SD Class Correlation

============== ==== ==== ======= ===== ====================

sepal length: 4.3 7.9 5.84 0.83 0.7826

sepal width: 2.0 4.4 3.05 0.43 -0.4194

petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)

petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)

============== ==== ==== ======= ===== ====================

:Missing Attribute Values: None

:Class Distribution: 33.3% for each of 3 classes.

:Creator: R.A. Fisher

:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)

:Date: July, 1988

This is a copy of UCI ML iris datasets.

http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the

pattern recognition literature. Fisher's paper is a classic in the field and

is referenced frequently to this day. (See Duda & Hart, for example.) The

data set contains 3 classes of 50 instances each, where each class refers to a

type of iris plant. One class is linearly separable from the other 2; the

latter are NOT linearly separable from each other.

References

----------

- Fisher,R.A. "The use of multiple measurements in taxonomic problems"

Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to

Mathematical Statistics" (John Wiley, NY, 1950).

- Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.

(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System

Structure and Classification Rule for Recognition in Partially Exposed

Environments". IEEE Transactions on Pattern Analysis and Machine

Intelligence, Vol. PAMI-2, No. 1, 67-71.

- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions

on Information Theory, May 1972, 431-433.

- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II

conceptual clustering system finds 3 classes in the data.

- Many, many more ...

data

鸢尾花四个特征的数据。

print(type(iris.data))

print(iris.data.shape)

iris.data[:10,:]

#数据格式为numpy.ndarray

(150, 4)#数据集大小为150行4列

array([[5.1, 3.5, 1.4, 0.2],#数据集前十行

[4.9, 3. , 1.4, 0.2],

[4.7, 3.2, 1.3, 0.2],

[4.6, 3.1, 1.5, 0.2],

[5. , 3.6, 1.4, 0.2],

[5.4, 3.9, 1.7, 0.4],

[4.6, 3.4, 1.4, 0.3],

[5. , 3.4, 1.5, 0.2],

[4.4, 2.9, 1.4, 0.2],

[4.9, 3.1, 1.5, 0.1]])feature_names

以上4列数据的名称，从左到右依次为花萼长度、花萼宽度、花瓣长度、花瓣宽度，单位都是cm。

print(iris.feature_names)

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']target

使用数字0. ,1. ,2.标识每行数据代表什么类的鸢尾花。

print(iris.target)#150个元素的list

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]target_names

鸢尾花的名称，Setosa(山鸢尾花)、Versicolour(杂色鸢尾花)、Virginica(维吉尼亚鸢尾花)。

print(iris.target_names)

['setosa' 'versicolor' 'virginica']将鸢尾花数据集转为DataFrame数据集

x, y = iris.data, iris.target

pd_iris = pd.DataFrame(np.hstack((x, y.reshape(150, 1))),columns=['sepal length(cm)','sepal width(cm)','petal length(cm)','petal width(cm)','class'] )

#np.hstack()类似linux中的paste

#np.vstack()类似linux中的cat

pd_iris.head()

2、matplotlib.pyplot.scatter法绘制散点图取数据集前两列绘制简单散点图

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

from pandas import Series,DataFrame

#数据准备

from sklearn import datasets

iris=datasets.load_iris()

x, y = iris.data, iris.target

pd_iris = pd.DataFrame(np.hstack((x, y.reshape(150, 1))),columns=['sepal length(cm)','sepal width(cm)','petal length(cm)','petal width(cm)','class'] )

plt.figure(dpi=100)

plt.scatter(pd_iris['sepal length(cm)'],pd_iris['sepal width(cm)'])

#根据sepal length(cm)和sepal width(cm)两列，每一行两个数值确定的点绘制到figure上即为散点三种不同鸢尾花的数据使用不同的图形(marker)和颜色表示

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

from pandas import Series,DataFrame

#数据准备

from sklearn import datasets

iris=datasets.load_iris()

x, y = iris.data, iris.target

pd_iris = pd.DataFrame(np.hstack((x, y.reshape(150, 1))),columns=['sepal length(cm)','sepal width(cm)','petal length(cm)','petal width(cm)','class'] )

plt.figure(dpi=150)#设置图的分辨率

plt.style.use('Solarize_Light2')#使用Solarize_Light2风格绘图

iris_type=pd_iris['class'].unique()#根据class列将点分为三类

iris_name=iris.target_names#获取每一类的名称

colors = ['#c72e29','#098154','#fb832d']#三种不同颜色

markers = ['$\clubsuit$','.','+']#三种不同图形

for i in range(len(iris_type)):

plt.scatter(pd_iris.loc[pd_iris['class'] == iris_type[i], 'sepal length(cm)'],#传入数据x

pd_iris.loc[pd_iris['class'] == iris_type[i], 'sepal width(cm)'],#传入数据y

s = 50,#散点图形(marker)的大小

c = colors[i],#marker颜色

marker = markers[i],#marker形状

#marker=matplotlib.markers.MarkerStyle(marker = markers[i],fillstyle='full'),#设置marker的填充

alpha=0.8,#marker透明度，范围为0-1

facecolors='r',#marker的填充颜色，当上面c参数设置了颜色，优先c

edgecolors='none',#marker的边缘线色

linewidths=1,#marker边缘线宽度，edgecolors不设置时，该参数不起作用

label = iris_name[i])#后面图例的名称取自label

plt.legend(loc = 'upper right')

3、matplotlib.axes.Axes.scatter法绘制散点图

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

from pandas import Series,DataFrame

#数据准备

from sklearn import datasets

iris=datasets.load_iris()

x, y = iris.data, iris.target

pd_iris = pd.DataFrame(np.hstack((x, y.reshape(150, 1))),columns=['sepal length(cm)','sepal width(cm)','petal length(cm)','petal width(cm)','class'] )

fig,ax = plt.subplots(dpi=150)

iris_type=pd_iris['class'].unique()#根据class列将点分为三类

iris_name=iris.target_names#获取每一类的名称

colors = ['#c72e29','#098154','#fb832d']#三种不同颜色

markers = ['$\clubsuit$','.','+']#三种不同图形

for i in range(len(iris_type)):

plt.scatter(pd_iris.loc[pd_iris['class'] == iris_type[i], 'sepal length(cm)'],#传入数据x

pd_iris.loc[pd_iris['class'] == iris_type[i], 'sepal width(cm)'],#传入数据y

s = 50,#散点图形(marker)的大小

c = colors[i],#marker颜色

marker = markers[i],#marker形状

#marker=matplotlib.markers.MarkerStyle(marker = markers[i],fillstyle='full'),#设置marker的填充

alpha=0.8,#marker透明度，范围为0-1

facecolors='r',#marker的填充颜色，当上面c参数设置了颜色，优先c

edgecolors='none',#marker的边缘线色

linewidths=1,#marker边缘线宽度，edgecolors不设置时，改参数不起作用

label = iris_name[i])#后面图例的名称取自label

plt.legend(loc = 'upper right')

4、参考资料