Matplotlib基础
Matplotlib 是 Python 的绘图库。 它可与 NumPy 一起使用，提供了一种有效的 MatLab 开源替代方案。 它也可以和图形工具包一起使用，如 PyQt 和 wxPython。
首先创建一组数据：
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
array([ 0. , 0.1010101 , 0.2020202 , 0.3030303 , 0.4040404 ,
0.50505051, 0.60606061, 0.70707071, 0.80808081, 0.90909091,
1.01010101, 1.11111111, 1.21212121, 1.31313131, 1.41414141,
1.51515152, 1.61616162, 1.71717172, 1.81818182, 1.91919192,
2.02020202, 2.12121212, 2.22222222, 2.32323232, 2.42424242,
2.52525253, 2.62626263, 2.72727273, 2.82828283, 2.92929293,
3.03030303, 3.13131313, 3.23232323, 3.33333333, 3.43434343,
3.53535354, 3.63636364, 3.73737374, 3.83838384, 3.93939394,
4.04040404, 4.14141414, 4.24242424, 4.34343434, 4.44444444,
4.54545455, 4.64646465, 4.74747475, 4.84848485, 4.94949495,
5.05050505, 5.15151515, 5.25252525, 5.35353535, 5.45454545,
5.55555556, 5.65656566, 5.75757576, 5.85858586, 5.95959596,
6.06060606, 6.16161616, 6.26262626, 6.36363636, 6.46464646,
6.56565657, 6.66666667, 6.76767677, 6.86868687, 6.96969697,
7.07070707, 7.17171717, 7.27272727, 7.37373737, 7.47474747,
7.57575758, 7.67676768, 7.77777778, 7.87878788, 7.97979798,
8.08080808, 8.18181818, 8.28282828, 8.38383838, 8.48484848,
8.58585859, 8.68686869, 8.78787879, 8.88888889, 8.98989899,
9.09090909, 9.19191919, 9.29292929, 9.39393939, 9.49494949,
9.5959596 , 9.6969697 , 9.7979798 , 9.8989899 , 10. ])
array([ 0. , 0.10083842, 0.20064886, 0.2984138 , 0.39313661,
0.48385164, 0.56963411, 0.64960951, 0.72296256, 0.78894546,
0.84688556, 0.8961922 , 0.93636273, 0.96698762, 0.98775469,
0.99845223, 0.99897117, 0.98930624, 0.96955595, 0.93992165,
0.90070545, 0.85230712, 0.79522006, 0.73002623, 0.65739025,
0.57805259, 0.49282204, 0.40256749, 0.30820902, 0.21070855,
0.11106004, 0.01027934, -0.09060615, -0.19056796, -0.28858706,
-0.38366419, -0.47483011, -0.56115544, -0.64176014, -0.7158225 ,
-0.7825875 , -0.84137452, -0.89158426, -0.93270486, -0.96431712,
-0.98609877, -0.99782778, -0.99938456, -0.99075324, -0.97202182,
-0.94338126, -0.90512352, -0.85763861, -0.80141062, -0.73701276,
-0.66510151, -0.58640998, -0.50174037, -0.41195583, -0.31797166,
-0.22074597, -0.12126992, -0.0205576 , 0.0803643 , 0.18046693,
0.27872982, 0.37415123, 0.46575841, 0.55261747, 0.63384295,
0.7086068 , 0.77614685, 0.83577457, 0.8868821 , 0.92894843,
0.96154471, 0.98433866, 0.99709789, 0.99969234, 0.99209556,
0.97438499, 0.94674118, 0.90944594, 0.86287948, 0.8075165 ,
0.74392141, 0.6727425 , 0.59470541, 0.51060568, 0.42130064,
0.32770071, 0.23076008, 0.13146699, 0.03083368, -0.07011396,
-0.17034683, -0.26884313, -0.36459873, -0.45663749, -0.54402111])
接下来开始进行绘制，
plt.plot(x, y)
plt.show()
如果想要在一个图中绘制两条曲线：
cosy = np.cos(x)
siny = y.copy()
plt.plot(x, siny)
plt.plot(x, cosy)
plt.show()
一、线条颜色
matplotlib给我们提供了：
-
b:blue
-
g:greeen
-
r:red
-
c:cyan 青色
-
m:magenta 品红
-
y:yellow
-
k:black
-
w:white
除了这些还可以通过十六进制直接指定颜色，比如color='#eeefff'
二、线条样式
matplotlib给我们提供了：
- ':'
- '-.'
- '--'
- '-'
三、坐标调整
- plt.xlim()
- plt.ylim()
- plt.axis()
- plt.xlabel()
- plt.ylabel()
plt.plot(x, siny)
plt.plot(x, cosy, color='red', linestyle='--')
plt.xlim(-5, 15) # 对x轴坐标进行调整
plt.ylim(0, 1.5) # 对y轴坐标进行调整
plt.show()
plt.plot(x, siny)
plt.plot(x, cosy, color='red', linestyle='--')
plt.axis([-1, 11, -2, 2]) # 对坐标轴坐标进行调整
plt.show()
plt.plot(x, siny)
plt.plot(x, cosy, color='red', linestyle='--')
plt.xlabel("x")
plt.ylabel("y")
plt.show()
四、添加图例和标题
-
plt.legend()
-
plt.title
首先在plt.plot函数中先添加label，然后使用plt.legend()
plt.plot(x, siny, label='sin(x)')
plt.plot(x, cosy, color='red', linestyle='--', label='cos(x)')
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.title("Welcom to Beijing!")
plt.show()
五、散点图
- plt.scatter()
plt.scatter(x, siny)
plt.scatter(x, cosy, color='red')
plt.show()
换个数据试一下？
x = np.random.normal(0, 1, 10000)
y = np.random.normal(0, 1, 10000)
plt.scatter(x, y)
plt.show()
- plt.scatter(x, y, alpha=?)
x = np.random.normal(0, 1, 10000)
y = np.random.normal(0, 1, 10000)
plt.scatter(x, y, alpha=0.5)
plt.show()
六、简单的数据可视化
-
数据加载
在sklearn中已经封装了很多小型的数据集，方便我们对一些机器学习算法的学习和了解。
import numpy as np import matplotlib.pyplot as plt from sklearn import datasets # 加载鸢尾花数据集 iris = datasets.load_iris() -
数据集探索
iris.keys() print(iris.DESCR) iris.data iris.data.shape iris.feature_names iris.target iris.target.shape iris.target_names -
数据可视化
X = iris.data[:, :2] X.shape plt.scatter(X[:, 0], X[:, 1]) plt.show()y = iris.target plt.scatter(X[y==0,0], X[y==0,1], color='red') plt.scatter(X[y==1,0], X[y==1,1], color='blue') plt.scatter(X[y==2,0], X[y==2,1], color='green') plt.show()y = iris.target plt.scatter(X[y==0,0], X[y==0,1], color='red', marker='o') plt.scatter(X[y==1,0], X[y==1,1], color='blue', marker='+') plt.scatter(X[y==2,0], X[y==2,1], color='green', marker='x') plt.show() 散点图的marker：
- 'o'
- '^'
- 'v'
- '1'
- '2'
- '3'
- '4'
- '8'
- 's'
- 'p'
- '*'
- '+'
- 'x'
- 'h'
- 'H'
- 'D'
- 'd'
- '<'
- '>'
X = iris.data[:, 2:] y = iris.target plt.scatter(X[y==0,0], X[y==0,1], color='red', marker='o') plt.scatter(X[y==1,0], X[y==1,1], color='blue', marker='+') plt.scatter(X[y==2,0], X[y==2,1], color='green', marker='x') plt.show()
其实，关于matplot能做的远不止这些，九牛一毛。要学的还有很多，继续加油！后续在补充一些吧！