科学计算库Numpy基础操作
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pycharm,python3.7,numpy版本1.15.1
2018年9月11日04:23:06
""" 科学计算库Numpy基础操作 时间:2018911 0011 """ import numpy print(""" ------以矩阵的方式读取数据------ ------------genfromtxt函数(‘文件路径‘,delimiter = ‘分隔符‘,dtype = 读取方式)---------------------""") """ numpy.ndarray可以当做一个矩阵 """ np_test = numpy.genfromtxt(‘Numpy_test.txt‘, delimiter = ‘,‘, dtype = str) # 通常以str方式读取,如果有float,再进行转换 print(type(np_test)) print(np_test) # print(help(numpy.genfromtxt)) # 打印帮助文档 print(""" ------numpy.array------ ------------numpy中最核心的结构------------------------------------------""") # 传入list结构,转换为ndarray格式 vector = numpy.array([5, 10, 15, 20]) # 创造一维矩阵 matrix = numpy.array([ [5, 10, 15], [20, 25, 30], [35, 40, 45] ]) # 创造二维矩阵 print(vector) print(matrix) print(""" ------print(numpy对象.shape)------ ------------打印矩阵结构(例如2×3,3×3之类)------------------------------------------""") vector = numpy.array([1, 2, 3, 4]) print(vector.shape) matrix = numpy.array([ [1, 2, 3], [4, 5, 6] ]) print(matrix.shape) print(""" ------numpy.array结构------ ------------内部结构必须相同------------------------------------------""") numbers = numpy.array([1, 2, 3, 4]) print(numbers) print(numbers.dtype) numbers = numpy.array([1, 2, 3, 4.0]) print(numbers) print(numbers.dtype) numbers = numpy.array([1, 2, 3, ‘4‘]) print(numbers) print(numbers.dtype) print(""" ------数据选取------ ------------与python一样,通过索引读取数据------------------------------------------""") data_test = numpy.genfromtxt(‘Numpy_test.txt‘, delimiter = ‘,‘, dtype = str) print(data_test) # 读取hanmeimei的国家 hmm_contry = data_test[2, 3] print("hanmeimei的国家是:{0}".format(hmm_contry)) print(""" ------切片选取------ ------------与python一样,(起始值:终止值:步长)------------------------------------------""") vector = numpy.array([5, 10, 15, 20]) print(vector[0:3]) # 左闭右开的区间 matrix = numpy.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) print(matrix[:, 1]) # 取中间一列也就是每一行,第二个值 print(matrix[:, 0:2]) # 取头两列也就是每一行,第一到第二个值(0:1左开右闭,所以要写0:2) print(""" ------numpy判断------ ------------判断某个元素是否在矩阵当中------------------------------------------""") vector = numpy.array([5, 10, 15, 20]) matrix = numpy.array([ [5, 10, 15], [20, 25, 30], [35, 40, 45] ]) print(vector == 10) print(matrix == 10) equal_to_ten = vector == 10 # 返回bool类型的值 print(vector[equal_to_ten]) # 当做索引 second_column_25 = (matrix[:, 1] == 25) # 判断第二列是否有25的值 print(second_column_25) # 对应每一行False True False print(matrix[second_column_25]) equal_to_ten_and_five = (vector == 10) & (vector == 5) # 是同时否存在10和5,与操作 print(equal_to_ten_and_five) equal_to_ten_or_five = (vector == 10) | (vector == 5) # 是否存在10或5,或操作 print(equal_to_ten_or_five) print(vector[equal_to_ten_or_five]) print(""" ------numpy.array类型的改变------ ------------astype()函数,数据类型转换------------------------------------------""") vector = numpy.array([‘1‘, ‘2‘, ‘3‘]) print(vector.dtype) print(vector) vector = vector.astype(float) print(vector.dtype) print(vector) print(""" ------numpy.array求极值的操作------ ------------min(),max()函数------------------------------------------""") vector = numpy.array([5, 10, 15, 20]) print(vector.min()) print(vector.max()) print(""" ------numpy.array按行求和,按列求和------ ---------sum()方法---参数axis = 1按行,axis = 0按列------------------------------------------""") matrix = numpy.array([ [5, 10, 15], [20, 25, 30], [35, 40, 45] ]) print(matrix.sum(axis = 1)) # 按行求和 print(matrix.sum(axis = 0)) # 按列求和 print(""" ------矩阵变换------ ---------造矩阵:arange();矩阵变换:reshape()------------------------------------------""") print(numpy.arange(15)) a = numpy.arange(15).reshape(3, 5) # 3行5列 print(a) print(a.shape) # 矩阵a的行列 print(a.ndim) # 维度 print(a.dtype) # 数据类型 print(a.size) # 元素个数 print(""" ------初始化矩阵------ ---------造0矩阵:zeros((元组表示矩阵行列))方法,创造1矩阵ones()方法------------------------------------------""") zero_matrix = numpy.zeros((3, 5)) print(zero_matrix) one_matrix = numpy.ones((2, 3, 4), dtype = numpy.int32) print(one_matrix) print(""" ------序列矩阵------ ---------aragne()方法------------------------------------------""") sq_matrix = numpy.arange(10, 30, 5) # 从10开始,不包括30,步长为5 print(sq_matrix) print(""" ------随机模块------ ---------random((元组表示矩阵行列))方法------------------------------------------""") ran = 10 * numpy.random.random((2, 3)) ran = ran.astype(numpy.int32) print(ran) print(""" ------平均间隔矩阵------ ---------linspase(起始值,最大值,个数)方法------------------------------------------""") lins = numpy.linspace(0, 1.1, 20) print(lins) print(""" ------向量运算------ ---------加减乘方------------------------------------------""") a = numpy.array([20, 30, 40, 50]) b = numpy.arange(4) print(a) print(b) c = a - b print(c) c = c - 1 print(c) print(b ** 2) # 每一个值分别运算 print(a < 35) print(""" ------矩阵乘法------ ---------对应位置乘法和点乘------------------------------------------""") A = numpy.array([ [1, 1], [0, 1] ]) B = numpy.array([ [2, 0], [3, 4] ]) print(‘A矩阵: {0}‘.format(A)) print(‘B矩阵: {0}‘.format(B)) print(‘A*B对应位置相乘: {0}‘.format(A * B)) print(‘A·B点乘: {0}‘.format(A.dot(B))) print(‘A·B点乘: {0}‘.format(numpy.dot(A, B))) print(""" ------矩阵科学计算------ ---------e的幂,平方根------------------------------------------""") B = numpy.arange(3) print(B) print(numpy.exp(B)) # 每个元素的为指数,e为底的次方 print(numpy.sqrt(B)) # 每个元素的平方根 print(""" ------矩阵操作------ ---------向下取整floor(),矩阵转向量ravel(),向量转矩阵a.shape(),矩阵转置a.T-----------------------""") a = numpy.floor(10 * numpy.random.random((3, 4))) print(‘矩阵a: {0}‘.format(a), ‘ ‘) print(a.ravel(), ‘ ‘) # 向量转矩阵 a.shape = (6, 2) # 矩阵转向量 print(a, ‘ ‘) print(a.T, ‘ ‘) # 矩阵转置 print(a.reshape(3, -1), ‘ ‘) # 只管3行,列数多少自动计算 print(""" ------矩阵拼接------ ---------按行拼接hstack(元组),按列拼接vstack(元组)------------------------------------------""") a = numpy.floor(10 * numpy.random.random((2, 2))) b = numpy.floor(10 * numpy.random.random((2, 2))) print(a, ‘ ‘) print(b, ‘ ‘) print(numpy.hstack((a, b)), ‘ ‘) print(numpy.vstack((a, b)), ‘ ‘) print(""" ------矩阵切分------ ---------纵切横分hsplit(矩阵,份数/(位置元组)),---横切纵分vsplit(矩阵,份数/(位置元组)),---------------""") a = numpy.floor(10 * numpy.random.random((2, 12))) print(a, ‘ ‘) print(numpy.hsplit(a, 3), ‘ ‘) print(numpy.hsplit(a, (3, 4))) # 在第三个元素后切一刀,在第四个元素后切一刀 a = numpy.floor(10 * numpy.random.random((12, 2))) print(a, ‘ ‘) print(numpy.vsplit(a, 3)) print(""" ------复制问题------ -------------------------------------------指向操作----------------------------------------------------""") """ python中,一切皆对象,a与b指向同一个对象。当命令b去改变对象时,a由于也是指向该对象,所以都会改变。 """ a = numpy.arange(12) b = a print(b is a) b.shape = (3, 4) print(a.shape) print(id(a)) print(id(b)) print("""-----------------------------浅拷贝a.view()----------------------------------------------------""") """ 一切皆对象,所以矩阵中的元素也是对象。 类似于python中的copy.copy(),浅拷贝,只拷贝一层对象,也就是之拷贝矩阵,并不拷贝矩阵中的对象。 """ c = a.view() print(c is a) c.shape = (2, 6) print(a.shape) c[0, 4] = 1234 print(a) print(id(a)) print(id(c)) print("""-----------------------------深拷贝a.copy()----------------------------------------------------""") """ 一切皆对象,所以矩阵中的元素也是对象。 类似于python中的copy.deepcopy(),深拷贝,拷贝所有层次的对象,不止拷贝矩阵, 还将矩阵中元素所指向的对象也拷贝一份 """ d = a.copy() print(d is a) d[0, 0] = 9999 print(d) print(a) print(""" ------按行按列取最大值------ ---------------------a.argmax(按行axis = 1/按列axis = 0)---------------------------------------------------""") data = numpy.sin(numpy.arange(20)).reshape(5, 4) print(data) ind = data.argmax(axis = 0) # 每列的最大值,返回一个向量 print(ind) print(data.shape[0]) # 返回行数 print(data.shape[1]) # 返回列数 data_max = data[ind, range(data.shape[1])] print(data_max) print(""" ------矩阵扩展------ ---------------------a.tile(a,(行,列))---------------------------------------------------""") a = numpy.arange(0, 40, 10) print(a) b = numpy.tile(a, (3, 5)) print(b) print(""" ------元素排序------ -----------a.sort(按行axis = 1/按列axis = 0)---排序索引值numpy.argsort(a)---------------------""") a = numpy.array([ [4, 3, 5], [1, 2, 1] ]) print(a, ‘ ‘) b = numpy.sort(a, axis = 1) print(b, ‘ ‘) a.sort(axis = 1) print(a, ‘ ‘) a = numpy.array([4, 3, 1, 2]) j = numpy.argsort(a) # 返回排序索引从小到大2310,2号位最小,0号位最大 print(j, ‘ ‘) print(a[j])
运行结果:
D:Pythonpython.exe G:/编程/python/project/TYD/01/01/02/09-14.py
------以矩阵的方式读取数据------
------------genfromtxt函数(‘文件路径‘,delimiter = ‘分隔符‘,dtype = 读取方式)---------------------
<class ‘numpy.ndarray‘>
[[‘name‘ ‘gender‘ ‘age‘ ‘contry‘]
[‘lilei‘ ‘m‘ ‘18‘ ‘cn‘]
[‘hanmeimei‘ ‘f‘ ‘18‘ ‘cn‘]
[‘lucy‘ ‘f‘ ‘19‘ ‘uk‘]
[‘lili‘ ‘f‘ ‘17‘ ‘usa‘]
[‘tom‘ ‘m‘ ‘18‘ ‘uk‘]]
------numpy.array------
------------numpy中最核心的结构------------------------------------------
[ 5 10 15 20]
[[ 5 10 15]
[20 25 30]
[35 40 45]]
------print(numpy对象.shape)------
------------打印矩阵结构(例如2×3,3×3之类)------------------------------------------
(4,)
(2, 3)
------numpy.array结构------
------------内部结构必须相同------------------------------------------
[1 2 3 4]
int32
[1. 2. 3. 4.]
float64
[‘1‘ ‘2‘ ‘3‘ ‘4‘]
<U11
------数据选取------
------------与python一样,通过索引读取数据------------------------------------------
[[‘name‘ ‘gender‘ ‘age‘ ‘contry‘]
[‘lilei‘ ‘m‘ ‘18‘ ‘cn‘]
[‘hanmeimei‘ ‘f‘ ‘18‘ ‘cn‘]
[‘lucy‘ ‘f‘ ‘19‘ ‘uk‘]
[‘lili‘ ‘f‘ ‘17‘ ‘usa‘]
[‘tom‘ ‘m‘ ‘18‘ ‘uk‘]]
hanmeimei的国家是:cn
------切片选取------
------------与python一样,(起始值:终止值:步长)------------------------------------------
[ 5 10 15]
[2 5 8]
[[1 2]
[4 5]
[7 8]]
------numpy判断------
------------判断某个元素是否在矩阵当中------------------------------------------
[False True False False]
[[False True False]
[False False False]
[False False False]]
[10]
[False True False]
[[20 25 30]]
[False False False False]
[ True True False False]
[ 5 10]
------numpy.array类型的改变------
------------astype()函数,数据类型转换------------------------------------------
<U1
[‘1‘ ‘2‘ ‘3‘]
float64
[1. 2. 3.]
------numpy.array求极值的操作------
------------min(),max()函数------------------------------------------
5
20
------numpy.array按行求和,按列求和------
---------sum()方法---参数axis = 1按行,axis = 0按列------------------------------------------
[ 30 75 120]
[60 75 90]
------矩阵变换------
---------造矩阵:arange();矩阵变换:reshape()------------------------------------------
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]]
(3, 5)
2
int32
15
------初始化矩阵------
---------造0矩阵:zeros((元组表示矩阵行列))方法,创造1矩阵ones()方法------------------------------------------
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
[[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]
[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]]
------序列矩阵------
---------aragne()方法------------------------------------------
[10 15 20 25]
------随机模块------
---------random((元组表示矩阵行列))方法------------------------------------------
[[6 7 3]
[9 3 9]]
------平均间隔矩阵------
---------linspase(起始值,最大值,个数)方法------------------------------------------
[0. 0.05789474 0.11578947 0.17368421 0.23157895 0.28947368
0.34736842 0.40526316 0.46315789 0.52105263 0.57894737 0.63684211
0.69473684 0.75263158 0.81052632 0.86842105 0.92631579 0.98421053
1.04210526 1.1 ]
------向量运算------
---------加减乘方------------------------------------------
[20 30 40 50]
[0 1 2 3]
[20 29 38 47]
[19 28 37 46]
[0 1 4 9]
[ True True False False]
------矩阵乘法------
---------对应位置乘法和点乘------------------------------------------
A矩阵:
[[1 1]
[0 1]]
B矩阵:
[[2 0]
[3 4]]
A*B对应位置相乘:
[[2 0]
[0 4]]
A·B点乘:
[[5 4]
[3 4]]
A·B点乘:
[[5 4]
[3 4]]
------矩阵科学计算------
---------e的幂,平方根------------------------------------------
[0 1 2]
[1. 2.71828183 7.3890561 ]
[0. 1. 1.41421356]
------矩阵操作------
---------向下取整floor(),矩阵转向量ravel(),向量转矩阵a.shape(),矩阵转置a.T-----------------------
矩阵a:
[[8. 2. 5. 7.]
[6. 5. 1. 3.]
[4. 9. 7. 9.]]
[8. 2. 5. 7. 6. 5. 1. 3. 4. 9. 7. 9.]
[[8. 2.]
[5. 7.]
[6. 5.]
[1. 3.]
[4. 9.]
[7. 9.]]
[[8. 5. 6. 1. 4. 7.]
[2. 7. 5. 3. 9. 9.]]
[[8. 2. 5. 7.]
[6. 5. 1. 3.]
[4. 9. 7. 9.]]
------矩阵拼接------
---------按行拼接hstack(元组),按列拼接vstack(元组)------------------------------------------
[[1. 7.]
[3. 7.]]
[[7. 6.]
[3. 6.]]
[[1. 7. 7. 6.]
[3. 7. 3. 6.]]
[[1. 7.]
[3. 7.]
[7. 6.]
[3. 6.]]
------矩阵切分------
---------纵切横分hsplit(矩阵,份数/(位置元组)),---横切纵分vsplit(矩阵,份数/(位置元组)),---------------
[[4. 0. 0. 8. 6. 3. 3. 8. 0. 0. 7. 6.]
[2. 1. 4. 3. 7. 8. 1. 6. 1. 0. 2. 9.]]
[array([[4., 0., 0., 8.],
[2., 1., 4., 3.]]), array([[6., 3., 3., 8.],
[7., 8., 1., 6.]]), array([[0., 0., 7., 6.],
[1., 0., 2., 9.]])]
[array([[4., 0., 0.],
[2., 1., 4.]]), array([[8.],
[3.]]), array([[6., 3., 3., 8., 0., 0., 7., 6.],
[7., 8., 1., 6., 1., 0., 2., 9.]])]
[[2. 8.]
[1. 3.]
[6. 8.]
[5. 4.]
[0. 9.]
[4. 6.]
[3. 6.]
[4. 9.]
[7. 9.]
[7. 6.]
[0. 2.]
[2. 8.]]
[array([[2., 8.],
[1., 3.],
[6., 8.],
[5., 4.]]), array([[0., 9.],
[4., 6.],
[3., 6.],
[4., 9.]]), array([[7., 9.],
[7., 6.],
[0., 2.],
[2., 8.]])]
------复制问题------
-------------------------------------------指向操作----------------------------------------------------
True
(3, 4)
62145264
62145264
-----------------------------浅拷贝a.view()----------------------------------------------------
False
(3, 4)
[[ 0 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
62145264
62187344
-----------------------------深拷贝a.copy()----------------------------------------------------
False
[[9999 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
[[ 0 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
------按行按列取最大值------
---------------------a.argmax(按行axis = 1/按列axis = 0)---------------------------------------------------
[[ 0. 0.84147098 0.90929743 0.14112001]
[-0.7568025 -0.95892427 -0.2794155 0.6569866 ]
[ 0.98935825 0.41211849 -0.54402111 -0.99999021]
[-0.53657292 0.42016704 0.99060736 0.65028784]
[-0.28790332 -0.96139749 -0.75098725 0.14987721]]
[2 0 3 1]
5
4
[0.98935825 0.84147098 0.99060736 0.6569866 ]
------矩阵扩展------
---------------------a.tile(a,(行,列))---------------------------------------------------
[ 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]]
------元素排序------
-----------a.sort(按行axis = 1/按列axis = 0)---排序索引值numpy.argsort(a)---------------------
[[4 3 5]
[1 2 1]]
[[3 4 5]
[1 1 2]]
[[3 4 5]
[1 1 2]]
[2 3 1 0]
[1 2 3 4]
Process finished with exit code 0
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