python数据分析scipy和matplotlib（三）

# 引入matplotlib包
import matplotlib.pyplot as plt
# 创建figure
fig = plt.figure()

ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,3)
ax4 = fig.add_subplot(2,2,4)

# 在subplot上作图
import numpy as np

random_arr = np.random.randn(100)
#print random_arr

# 默认是在最后一次使用subplot的位置上作图
plt.plot(random_arr)
plt.show()

import scipy as sp
from scipy import stats
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-5, 15, 50)
# print x.shape

# 绘制高斯分布
plt.plot(x, sp.stats.norm.pdf(x=x, loc=5, scale=2))

# 叠加直方图
plt.hist(sp.stats.norm.rvs(loc=5, scale=2, size=200), bins=50, normed=True, color=‘red‘, alpha=0.5)
plt.show()

# 绘制直方图
import matplotlib.pyplot as plt
import numpy as np
plt.hist(np.random.randn(100), bins=10, color=‘b‘, alpha=0.3)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
# 绘制散点图
x = np.arange(50)
y = x + 5 * np.random.rand(50)
plt.scatter(x, y)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
# 柱状图
x = np.arange(5)
y1, y2 = np.random.randint(1, 25, size=(2, 5))
width = 0.25
ax = plt.subplot(1,1,1)
ax.bar(x, y1, width, color=‘r‘)
ax.bar(x+width, y2, width, color=‘g‘)
ax.set_xticks(x+width)
ax.set_xticklabels([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘])
plt.show()

import matplotlib.pyplot as plt
import numpy as np
m = np.random.rand(10,10)
plt.imshow(m, interpolation=‘nearest‘, cmap=plt.cm.ocean)
plt.colorbar()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
fig, subplot_arr = plt.subplots(2,2)
subplot_arr[0,0].hist(np.random.randn(100), bins=10, color=‘b‘, alpha=0.3)
plt.show()