numpy（矩陣操作）
number = numpy.array([1,2,3,4]) -----其中參數都必須爲相同的結構{關於array切片操作}
number.shape -------------查看矩陣的形狀
number.dtype(A)-----------查看參數A的類型
number == 10 -------判斷當前矩陣當中有沒有值等於10
numpy.array操作
import numpy
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
print(type(world_alcohol))
<type 'numpy.ndarray'>
#The numpy.array() function can take a list or list of lists as input. When we input a list, we get a one-dimensional array as a result:
vector = numpy.array([5, 10, 15, 20])
#When we input a list of lists, we get a matrix as a result:
matrix = numpy.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
print (vector)
print (matrix)
[ 5 10 15 20] [[ 5 10 15] [20 25 30] [35 40 45]]
#We can use the ndarray.shape property to figure out how many elements are in the array
vector = numpy.array([1, 2, 3, 4])
print(vector.shape)
#For matrices, the shape property contains a tuple with 2 elements.
matrix = numpy.array([[5, 10, 15], [20, 25, 30]])
print(matrix.shape)
(4,) (2, 3)
import numpy
#it will compare the second value to each element in the vector
# If the values are equal, the Python interpreter returns True; otherwise, it returns False
vector = numpy.array([5, 10, 15, 20])
vector == 10
array([False, True, False, False], dtype=bool)
#Compares vector to the value 10, which generates a new Boolean vector [False, True, False, False]. It assigns this result to equal_to_ten
vector = numpy.array([5, 10, 15, 20])
equal_to_ten = (vector == 10)
print (equal_to_ten)
print(vector[equal_to_ten])
[False True False False]
[10]
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
second_column_25 = matrix[:,1] == 25
print second_column_25
matrix[second_column_25, 1] = 10
print matrix
[False True False]
[[ 5 10 15]
[20 10 30]
[35 40 45]]
#We can convert the data type of an array with the ndarray.astype() method.
vector = numpy.array(["1", "2", "3"])
print vector.dtype
print vector
vector = vector.astype(float)
print vector.dtype
print vector
|S1
['1' '2' '3']
float64
[ 1. 2. 3.]
# The axis dictates which dimension we perform the operation on
#1 means that we want to perform the operation on each row, and 0 means on each column
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
matrix.sum(axis=1)
array([ 30, 75, 120])
矩陣的屬性
import numpy as np
a = np.arange(15).reshape(3, 5)
print(a)
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
a.shape
(3, 5)
#the number of axes (dimensions) of the array
a.ndim
2
a.dtype.name
'int32'
#the total number of elements of the array
a.size
15
矩陣初始化操作
np.zeros ((3,4))
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
np.ones( (2,3,4), dtype=np.int32 )
array([[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]]])
#To create sequences of numbers
np.arange( 10, 30, 5 )
array([10, 15, 20, 25])
np.random.random((2,3))
array([[ 0.40130659, 0.45452825, 0.79776512],
[ 0.63220592, 0.74591134, 0.64130737]])
from numpy import pi
np.linspace( 0, 2*pi, 100 )
array([ 0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,
0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,
0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,
0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,
1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,
1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,
1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,
2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,
2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,
2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,
3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,
3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,
3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,
4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,
4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,
4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,
5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,
5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,
5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,
6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531])
np.sin(np.linspace( 0, 2*pi, 100 ))
array([ 0.00000000e+00, 6.34239197e-02, 1.26592454e-01,
1.89251244e-01, 2.51147987e-01, 3.12033446e-01,
3.71662456e-01, 4.29794912e-01, 4.86196736e-01,
5.40640817e-01, 5.92907929e-01, 6.42787610e-01,
6.90079011e-01, 7.34591709e-01, 7.76146464e-01,
8.14575952e-01, 8.49725430e-01, 8.81453363e-01,
9.09631995e-01, 9.34147860e-01, 9.54902241e-01,
9.71811568e-01, 9.84807753e-01, 9.93838464e-01,
9.98867339e-01, 9.99874128e-01, 9.96854776e-01,
9.89821442e-01, 9.78802446e-01, 9.63842159e-01,
9.45000819e-01, 9.22354294e-01, 8.95993774e-01,
8.66025404e-01, 8.32569855e-01, 7.95761841e-01,
7.55749574e-01, 7.12694171e-01, 6.66769001e-01,
6.18158986e-01, 5.67059864e-01, 5.13677392e-01,
4.58226522e-01, 4.00930535e-01, 3.42020143e-01,
2.81732557e-01, 2.20310533e-01, 1.58001396e-01,
9.50560433e-02, 3.17279335e-02, -3.17279335e-02,
-9.50560433e-02, -1.58001396e-01, -2.20310533e-01,
-2.81732557e-01, -3.42020143e-01, -4.00930535e-01,
-4.58226522e-01, -5.13677392e-01, -5.67059864e-01,
-6.18158986e-01, -6.66769001e-01, -7.12694171e-01,
-7.55749574e-01, -7.95761841e-01, -8.32569855e-01,
-8.66025404e-01, -8.95993774e-01, -9.22354294e-01,
-9.45000819e-01, -9.63842159e-01, -9.78802446e-01,
-9.89821442e-01, -9.96854776e-01, -9.99874128e-01,
-9.98867339e-01, -9.93838464e-01, -9.84807753e-01,
-9.71811568e-01, -9.54902241e-01, -9.34147860e-01,
-9.09631995e-01, -8.81453363e-01, -8.49725430e-01,
-8.14575952e-01, -7.76146464e-01, -7.34591709e-01,
-6.90079011e-01, -6.42787610e-01, -5.92907929e-01,
-5.40640817e-01, -4.86196736e-01, -4.29794912e-01,
-3.71662456e-01, -3.12033446e-01, -2.51147987e-01,
-1.89251244e-01, -1.26592454e-01, -6.34239197e-02,
-2.44929360e-16])
不同複製結果對比（三種不同的複製方式）
import numpy as np
#Simple assignments make no copy of array objects or of their data.
a = np.arange(12)
b = a
# a and b are two names for the same ndarray object
b is a
b.shape = 3,4
print (a.shape)
print (id(a))
print (id(b))
(3, 4)
2664171249296
2664171249296
#-----------------------------------------------
#The view method creates a new array object that looks at the same data.
c = a.view()
c is a
c.shape = 2,6
print (a.shape)
c[0,4] = 1234
print(a)
print(c)
(3, 4)
[[ 0 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
[[ 0 1 2 3 1234 5]
[ 6 7 8 9 10 11]]
#---------------------------------------------
#The copy method makes a complete copy of the array and its data.
d = a.copy()
d is a
d[0,0] = 9999
print (d)
print (a)
[[9999 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
[[ 0 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
矩陣常用操作
import numpy as np
B = np.arange(3)
print (B)
print (np.exp(B))
print (np.sqrt(B))
[0 1 2]
[1. 2.71828183 7.3890561 ]
[0. 1. 1.41421356]
#Return the floor of the input
a = np.floor(10*np.random.random((3,4)))
#print a
#a.shape
## flatten the array
#print a.ravel()
#a.shape = (6, 2)
#print a
#print a.T
print a.resize((2,6))
print a
#If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated:
#a.reshape(3,-1)
a = np.floor(10*np.random.random((2,2)))
b = np.floor(10*np.random.random((2,2)))
print a
print '---'
print b
print '---'
print np.hstack((a,b))
#np.hstack((a,b))