Python可视化|matplotlib10-绘制散点图scatter

"pythonic生物人"的第36篇分享。

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摘要

学习鸢尾花（iris）数据集；
matplotlib.pyplot.scatter和matplotlib.axes.Axes.scatter两种方法绘制散点图 scatter。

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  

4、参考资料

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正文开始啦

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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 ([e-mail])

   :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,:]

<class 'numpy.ndarray'>#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 inrange(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 inrange(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

https://scikit-learn.org/stable/datasets/index.html#iris-datasethttps://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html?highlight=scatter#matplotlib.pyplot.scatter

https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.scatter.html?highlight=scatter#matplotlib.axes.Axes.scatter

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