numpy 基本使用1

Numpy是一个非常强大的库，具有大量线性代数以及大规模科学计算的方法。

#-*- coding:utf-8 -*-
import numpy as np

#Numpy生成一维数组
a=np.array([1,2,3])
print type(a)
print a.shape
print a[0],a[1],a[2]
a[0]=5
print a
print '-'*100
# 输出
# <type 'numpy.ndarray'>
# (3L,)
# 1 2 3
# [5 2 3]

#Numpy生成二维数组
b=np.array([[1,2,3],[4,5,6]])
print b
print b.shape
print b[0,0],b[0,1],b[1,0]
print '-'*100
# 输出
# [[1 2 3]
#  [4 5 6]]
# (2L, 3L)
# 1 2 4

#numpy创建数组
a=np.zeros((2,2))#创建2x2的全0数组
print a
b=np.ones((1,2))#创建1x2的全1数组
print b
c=np.full((2,2),7)#创建2x2的全为7的数组
print c
d=np.eye(2)#创建单位数组
print d
e=np.random.random((2,2))#创建2x2的随机数组
print e
print '-'*100
# 输出
# [[ 0.  0.]
#  [ 0.  0.]]
# [[ 1.  1.]]
# [[7 7]
#  [7 7]]
# [[ 1.  0.]
#  [ 0.  1.]]
# [[ 0.22054647  0.57186555]
#  [ 0.79464255  0.90896572]]

#numpy的多种访问数组的方法
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
b = a[:2, 1:3]#0，1行 1，2列
print b
print a[0, 1]#第0行 第1列
b[0, 0] = 77
print a[0, 1]
print '-'*100
# 输出
# [[2 3]
#  [6 7]]
# 2
# 77

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
row_r1 = a[1, :]#取第二行，4列
row_r2 = a[1:2, :]#取第二行，1行X4列
print row_r1, row_r1.shape
print row_r2, row_r2.shape
print '-'*100
# 输出
# [5 6 7 8] (4L,)
# [[5 6 7 8]] (1L, 4L)

col_r1 = a[:, 1] #取第二列，3列
col_r2 = a[:, 1:2]#取第二列，3行X1列
print col_r1, col_r1.shape
print col_r2, col_r2.shape
print '-'*100
# 输出
# [ 2  6 10] (3L,)
# [[ 2]
#  [ 6]
#  [10]] (3L, 1L)

a = np.array([[1,2], [3, 4], [5, 6]])
print a[[0, 1, 2], [0, 1, 0]]  #输出a[0,0] a[1,1] a[2,0]
print np.array([a[0, 0], a[1, 1], a[2, 0]])
print '-'*100
# 输出
# [1 4 5]
# [1 4 5]

a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
print a
b = np.array([0, 2, 0, 1])
print a[np.arange(4), b]#输出a[0,0] a[1,2] a[2,0] a[3,1]
a[np.arange(4), b] += 10
print a
print '-'*100
# 输出
# [[ 1  2  3]
#  [ 4  5  6]
#  [ 7  8  9]
#  [10 11 12]]
# [ 1  6  7 11]
# [[11  2  3]
#  [ 4  5 16]
#  [17  8  9]
#  [10 21 12]]

a = np.array([[1,2], [3, 4], [5, 6]])
bool_idx = (a > 2)  #当a大于2时为True，否则为False
print bool_idx
print a[bool_idx] #true输出，false不输出
print a[a > 2] #符合a>2时输出
print '-'*100
# 输出
# [[False False]
#  [ True  True]
#  [ True  True]]
# [3 4 5 6]
# [3 4 5 6]

x = np.array([1, 2])
print x.dtype
x = np.array([1.0, 2.0])
print x.dtype
x = np.array([1, 2], dtype=np.int64)
print x.dtype
print '-'*100
# 输出
# int32
# float64
# int64

x = np.array([[1,2],[3,4]], dtype=np.float64)
y = np.array([[5,6],[7,8]], dtype=np.float64)
print x + y
print np.add(x, y)
print x - y
print np.subtract(x, y)
print x * y
print np.multiply(x, y)
print x / y
print np.divide(x, y)
print np.sqrt(x)
print '-'*100
# 输出
# [[  6.   8.]
#  [ 10.  12.]]
# [[  6.   8.]
#  [ 10.  12.]]
# [[-4. -4.]
#  [-4. -4.]]
# [[-4. -4.]
#  [-4. -4.]]
# [[  5.  12.]
#  [ 21.  32.]]
# [[  5.  12.]
#  [ 21.  32.]]
# [[ 0.2         0.33333333]
#  [ 0.42857143  0.5       ]]
# [[ 0.2         0.33333333]
#  [ 0.42857143  0.5       ]]
# [[ 1.          1.41421356]
#  [ 1.73205081  2.        ]]

x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])
v = np.array([9,10])
w = np.array([11, 12])
print v.dot(w)
print np.dot(v, w)#9x11+10x12
print x.dot(v)
print np.dot(x, v)
print x.dot(y)#矩阵X x 矩阵Y
print np.dot(x, y)
print '-'*100
# 输出
# 219
# 219
# [29 67]
# [29 67]
# [[19 22]
#  [43 50]]
# [[19 22]
#  [43 50]]

x = np.array([[1,2],[3,4]])
print np.sum(x)
print np.sum(x, axis=0)#行相加
print np.sum(x, axis=1)#列相加
print '-'*100
# 输出
# 10
# [4 6]
# [3 7]

#矩阵的逆
x = np.array([[1,2], [3,4]])
print x
print x.T
v = np.array([1,2,3])
print v
print v.T
print '-'*100
# 输出
# [[1 2]
#  [3 4]]
# [[1 3]
#  [2 4]]
# [1 2 3]
# [1 2 3]

#广播Broadcasting
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x)
for i in range(4):
    y[i, :] = x[i, :] + v#每行与v相加
print y

y = x + v
print y

vv = np.tile(v, (4, 1))
print vv
y = x + vv
print y
print '-'*100
# 输出
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]
# [[1 0 1]
#  [1 0 1]
#  [1 0 1]
#  [1 0 1]]
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]

v = np.array([1,2,3])
w = np.array([4,5])
print np.reshape(v, (3, 1))#将1行x3列的v转换成3行x1列矩阵
print np.reshape(v, (3, 1)) * w
x = np.array([[1,2,3], [4,5,6]])
print x + v
print (x.T + w).T
print x + np.reshape(w, (2, 1))
print x * 2
# 输出
# [[1]
#  [2]
#  [3]]
# [[ 4  5]
#  [ 8 10]
#  [12 15]]
# [[2 4 6]
#  [5 7 9]]
# [[ 5  6  7]
#  [ 9 10 11]]
# [[ 5  6  7]
#  [ 9 10 11]]
# [[ 2  4  6]
#  [ 8 10 12]]