Python常见的数据分析和挖掘的库
Numpy:数组计算
Scipy:矩阵运算
Pandas:统计模块
Matplotlib:数据可视化
StatasModels:统计建模
Scikit-Learn:机器学习库
Keras:神经网络深度学习
TensorFlow:深度学习库
Gensim:文本挖掘库
Numpy：
import numpy as np
a = np.array([0,2,5,6,8])
print(a)
a1 = np.zeros(10)#默认情况下创建的是浮点型
print(a1)
a2 = np.zeros((3,4))
print(a2)
a3 = np.ones((3,4,2))
print(a3)
a4 = np.empty((2,3))
print(a4)
a5 = np.ones_like(a4)
print(a5)
a6 = np.eye(5)
print(a6)
a7 = np.arange(3,15)
print(a7)
b = np.array([[1,2,4],[4,5,6]])
print(b.dtype)
print(b.shape)
print(b + 3)
print(b + b)
print(b * 10)
print(2/b)
c1 = np.arange(12)
print(c1)
print(c1[2:9])
c1[2:7] = 7
print(c1)#给一个切片赋值，该值会自动广播到整个区间
L1 = range(12)
#L1[2:9] = 7这句是错误的，因为列表不支持对切片广播赋值
c2 = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(c2)
print(c2[2][1])
print(c2[0:2][1])
print(c2[:,0:2])
print(c2[0:2,1])
import numpy as np
a = np.array([0,2,5,6,8])
print(a)
a1 = np.zeros(10)#默认情况下创建的是浮点型
print(a1)
a2 = np.zeros((3,4))
print(a2)
a3 = np.ones((3,4,2))
print(a3)
a4 = np.empty((2,3))
print(a4)
a5 = np.ones_like(a4)
print(a5)
a6 = np.eye(5)
print(a6)
a7 = np.arange(3,15)
print(a7)
b = np.array([[1,2,4],[4,5,6]])
print(b.dtype)
print(b.shape)
print(b + 3)
print(b + b)
print(b * 10)
print(2/b)
c1 = np.arange(12)
print(c1)
print(c1[2:9])
c1[2:7] = 7
print(c1)#给一个切片赋值，该值会自动广播到整个区间
L1 = range(12)
#L1[2:9] = 7这句是错误的，因为列表不支持对切片广播赋值
c2 = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(c2)
print(c2[2][1])#8
print(c2[0:2][1])#[4,5,6]
print(c2[:,0:2])#[[1 2]
# [4 5]
# [7 8]]
print(c2[0:2,1])#[2,5]
name = np.array(['zhang','li','wang','zhang'])
print(name == 'zhang')#[ True False False True]
score = np.array([95,95,49,30,80,34,34,87,93,78,88,100])
mark = score >= 60
print(score[mark])
score[score <= 60] = 60
print(score)
#合并
aa = np.array([1,1,1])
bb = np.array([2,2,2])
print(np.vstack((aa,bb)))
print(np.hstack((aa,bb)))
print(np.exp(300))
print(np.sign(-11))
print(np.isnan(np.inf))
print(np.tan(1.2))
print(np.arcsin(1/np.pi))
print(np.add(4,5))
print(np.maximum(np.random.randn(10),np.random.randn(10)))
绘图：
import matplotlib.pyplot as plt
t = np.linspace(0,2*np.pi,100)
xs,ys = np.meshgrid(t,t)
x = np.linspace(-1,1,50)
y1 = 2*x +1
y2 = x**2
x0 = 1
y0 = 2**x0
plt.figure()
plt.xlim((-2,1))
plt.ylim((2,3))
plt.xlabel('daluzi')
plt.ylabel('daluzi')
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_position(('data', 0))
ax.spines['left'].set_position(('data',0))
#set line styles
l1, = plt.plot(x,y1,label="linear line")
l2, = plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--',label='square line')
#legend为我们添加图例
plt.legend(loc='upper right')#loc那句话的意思是将图例添加在右上角
plt.plot([x0, x0,], [0, y0,], 'k--', linewidth=2.5)
plt.scatter([x0, ], [y0, ], s=50, color='b')
plt.yticks([-2, -1.8, -1, 1.22, 3],[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
plt.plot(x,y1)
plt.plot(x,y2,color = 'red',linewidth = 1.0,linestyle = '--')
plt.show()
#散点图
import matplotlib.pyplot as plt
import numpy as np
n = 2014
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
T = np.arctan2(Y,X)
plt.scatter(X,Y,s=75,c = T,alpha = .5)
plt.xlim(-1.5, 1.5)
plt.xticks(()) # ignore xticks
plt.ylim(-1.5, 1.5)
plt.yticks(()) # ignore yticks
plt.show()
#3D图
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
# X, Y value
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y) # x-y 平面的网格
R = np.sqrt(X ** 2 + Y ** 2)
# height value
Z = np.sin(R)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
ax.contourf(X, Y, Z, zdir='z', offset=-1, cmap=plt.get_cmap('rainbow'))
plt.show()