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
