唐宇迪-python计算库numpy
python计算库numpy
- 导入numpy
- 读取文件
- 创建numpy数组
- 查看数组大小
- 查看数组类型
- numpy数组的访问
- 作比较
- 类型转换
- 矩阵的和
- numpy生成等差数列
- 访问数组的维度
- 数组总元素
- 生成全1、全0的数组
- 生成等差数列（np.arange）
- 生成随机数
- 生成等差数列 (np.linspace)
- 数组运算
- numpy的常用方法
- np.exp和np.sqrt
- np.floor和.resize和.ravel和np.random
- np.floor和np.hstack和np.random.random()
- np.vsplit
- id
- view方法
- copy方法
- np.tile
- np.argsort
导入numpy
import numpy
读取文件
xxx=numpy.genfromtxt("xxx.txt", delimiter=",")
# numpy.genfromtxt("xxx.txt", delimiter=",",dtype="U75", skip_header=1)
print(type(xxx))
# 创建numpy数组
#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]]
查看数组大小
#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)
查看数组类型
#Each value in a NumPy array has to have the same data type
#NumPy will automatically figure out an appropriate data type when reading in data or converting lists to arrays.
#You can check the data type of a NumPy array using the dtype property.
numbers = numpy.array([1, 2, 3, 4])
numbers.dtype
numpy数组的访问
与list访问方法相同
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
print(matrix[:,1])
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
print(matrix[:,0:2])
作比较
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]
类型转换
#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)
矩阵的和
# 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])
numpy生成等差数列
import numpy as np
a = np.arange(15).reshape(3, 5)
a
# array([[ 0, 1, 2, 3, 4],
# [ 5, 6, 7, 8, 9],
# [10, 11, 12, 13, 14]])
访问数组的维度
#the number of axes (dimensions) of the array
a.ndim
# 2
数组总元素
#the total number of elements of the array
a.size
生成全1、全0的数组
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]]])
生成等差数列（np.arange）
#To create sequences of numbers
np.arange( 10, 30, 5 )
# array([10, 15, 20, 25])
np.arange(12).reshape(4,3)
# array([[ 0, 1, 2],
# [ 3, 4, 5],
# [ 6, 7, 8],
# [ 9, 10, 11]])
生成随机数
np.random.random((2,3))
# array([[ 0.40130659, 0.45452825, 0.79776512],
# [ 0.63220592, 0.74591134, 0.64130737]])
生成等差数列 (np.linspace)
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])
数组运算
#the product operator * operates elementwise in NumPy arrays
a = np.array( [20,30,40,50] )
b = np.arange( 4 )
#print a
#print b
#b
c = a-b
#print c
b**2
#print b**2
print(a<35)
#The matrix product can be performed using the dot function or method
A = np.array( [[1,1],
[0,1]] )
B = np.array( [[2,0],
[3,4]] )
print(A)
print(B)
#print A*B
print(A.dot(B))
print(np.dot(A, B))
numpy的常用方法
np.exp和np.sqrt
import numpy as np
B = np.arange(3)
print(B)
#print np.exp(B)
print(np.sqrt(B))
np.floor和.resize和.ravel和np.random
#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)
np.floor和np.hstack和np.random.random()
np.random.random():Return random floats in the half-open interval [0.0, 1.0).
np.hstack:Stack arrays in sequence horizontally (column wise).
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))
np.vsplit
Split an array into multiple sub-arrays vertically (row-wise).
a = np.floor(10*np.random.random((2,12)))
#print a
#print np.hsplit(a,3)
#print np.hsplit(a,(3,4)) # Split a after the third and the fourth column
a = np.floor(10*np.random.random((12,2)))
print(a)
np.vsplit(a,3)
id
Return the identity of an object.
#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))
view方法
a.view(dtype=None, type=None)
New view of array with the same data.
#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
a
copy方法
a.copy(order=‘C’)
Return a copy of the array.
#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)
np.tile
Construct an array by repeating A the number of times given by reps.
a = np.arange(0, 40, 10)
b = np.tile(a, (3, 5))
print(a)
print(b)
#[ 0 10 20 30]
#[[ 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30]
# [ 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30]
# [ 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30]]
np.argsort
Returns the indices that would sort an array.
a = np.array([[4, 3, 5], [1, 2, 1]])
#print(a)
#b = np.sort(a, axis=1)
#print(b)
#b
#a.sort(axis=1)
#print(a)
a = np.array([4, 3, 1, 2])
j = np.argsort(a)
print(j)
print(a[j])
#[2 3 1 0]
#[1 2 3 4]