Python 利用循环画散点图

 import pandas as pd
 data = pd.read_csv('D:/suning/iris.csv')
 data = data.iloc[:,1:]

 ###2维散点图
 import matplotlib.pyplot as plt

 k=[
 'Sepal.Length',
 'Sepal.Width',
 'Petal.Length',
 'Petal.Width',]
 for i in k:
     for m in k:
         if i != m:
             plt.figure(figsize=(10,10))
             result =data.Species.unique()
             plt.scatter(data.loc[data.Species == result[2], i], data.loc[data.Species == result[2],m], s = 35, marker='*', c ='g')
             plt.scatter(data.loc[data.Species == result[1], i], data.loc[data.Species == result[1],m], s = 35, marker='+', c ='r')
             plt.scatter(data.loc[data.Species == result[0], i], data.loc[data.Species == result[0],m], s = 35, marker='o',c = 'y')
             # 添加轴标签和标题
             plt.title( '')
             plt.xlabel(i)
             plt.ylabel(m)
             # 去除图边框的顶部刻度和右边刻度
             #lt.tick_params(top = 'off', right = 'off')
             # 添加图例plt.legend(loc = 'upper left')
             plt.show()

 ####三维散点图
 import numpy as np
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D

 k=[
 'Sepal.Length',
 'Sepal.Width',
 'Petal.Length',
 'Petal.Width',]
 for i in k:
     for m in k:
         for z in k:
             if i != m and m!=z and 1!=z:
                 plt.figure(figsize=(10,10))
                 result = data.Species.unique()
                 ax = plt.subplot(111, projection='3d')  # 创建一个三维的绘图工程
                 ax.scatter(data.loc[data.Species == result[2], i], data.loc[data.Species == result[2], m], data.loc[data.Species == result[2], z], c='g',marker='*')  # 绘制数据点
                 ax.scatter(data.loc[data.Species == result[1], i], data.loc[data.Species == result[1], m], data.loc[data.Species == result[1], z], c='r',marker='+')  # 绘制数据点
                 ax.scatter(data.loc[data.Species == result[0], i], data.loc[data.Species == result[0], m], data.loc[data.Species == result[0], z], c='y',marker='o')  # 绘制数据点
                 ax.set_zlabel(z)  # 坐标轴
                 ax.set_ylabel(m)
                 ax.set_xlabel(i)
                 plt.show()