numpy get started
Import numpy library and view numpy version
import numpy as np
np.__version__'1.13.0'import matplotlib.pyplot as pltcat = plt.imread('cat.jpg')print(cat)[[[231 186 131]
[232 187 132]
[233 188 133]
...,
[100 54 54]
[ 92 48 47]
[ 85 43 44]]
[[232 187 132]
[232 187 132]
[233 188 133]
...,
[100 54 54]
[ 92 48 47]
[ 84 42 43]]
[[232 187 132]
[233 188 133]
[233 188 133]
...,
[ 99 53 53]
[ 91 47 46]
[ 83 41 42]]
...,
[[199 119 82]
[199 119 82]
[200 120 83]
...,
[189 99 65]
[187 97 63]
[187 97 63]]
[[199 119 82]
[199 119 82]
[199 119 82]
...,
[188 98 64]
[186 96 62]
[188 95 62]]
[[199 119 82]
[199 119 82]
[199 119 82]
...,
[188 98 64]
[188 95 62]
[188 95 62]]]type(cat)numpy.ndarraycat.shape(456, 730, 3)plt.imshow(cat)
plt.show()
#请问电影是什么，nd.array 四维
#(x,456,760,3)First, create ndarray
1. Use np.array () created by the python list
List of parameters:
[1, 4, 2, 5, 3]
note:
- Type numpy default ndarray all the elements are the same
- If passed in the list contains different types, the unity of the same type, priority: str> float> int
l = [3,1,4,5,9,6]
n = np.array(l)display(n,l)array([3, 1, 4, 5, 9, 6])[3, 1, 4, 5, 9, 6]display(n.shape,l.shape)--------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-5eeacc6c47ae> in <module>()
----> 1 display(n.shape,l.shape)AttributeError: 'list' object has no attribute 'shape'n2 = np.array([[3,4,7,1],[3,0,1,8],[2,4,6,8]])
display(n2.shape)(3, 4)n3 = np.array(['0',9.18,20])
n3array(['0', '9.18', '20'],
dtype='<U4')n4 = np.array([1,2,3.14])
n4array([ 1. , 2. , 3.14])2. Create routines using the function np
It contains the following common method of creating:
1) np.ones(shape, dtype=None, order='C')
n = np.ones((4,5))
n
array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]])n2 = np.ones((4,5,6), dtype=int)
n2array([[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]]])2) np.zeros(shape, dtype=float, order='C')
n3 = np.zeros((4,5))
n3array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]])3) np.full(shape, fill_value, dtype=None, order='C')
n = np.full((4,5), dtype=int, fill_value=8)
narray([[8, 8, 8, 8, 8],
[8, 8, 8, 8, 8],
[8, 8, 8, 8, 8],
[8, 8, 8, 8, 8]])4) np.eye (N, M = None, k = 0, dtype = float)
diagonal and the other position 0
n = np.eye(4,5)
n
# 满秩矩阵
# x + y = 10
# x - y = 5
# 1 1
# 1 -1
# 第二行减去第一行
# 1 1
# 0 -2
# 1/2乘于第二行
# 1 1
# 0 -1
# 第二行加上第一行
# 1 0
# 0 -1
# 第二行乘与-1
# 1 0
# 0 1
# x + y
# 2x + 2Y
# 无解
# 1 1
# 2 2array([[1., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0.]])5) np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
n = np.linspace(0, 100, num=50, dtype=int,retstep=True, endpoint=False)
n(array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,
34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,
68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98]), 2.0)n = np.linspace(0, 150, num=50, dtype=np.int8)
n
# line
# 2^(n-1) -1
# lin = linear algebraarray([ 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30,
33, 36, 39, 42, 45, 48, 52, 55, 58, 61, 64,
67, 70, 73, 76, 79, 82, 85, 88, 91, 94, 97,
101, 104, 107, 110, 113, 116, 119, 122, 125, -128, -125,
-122, -119, -116, -113, -110, -106], dtype=int8)6) np.arange([start, ]stop, [step, ]dtype=None)
n = np.arange(10)
narray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])n = np.arange(1, 11, step=2)
narray([1, 3, 5, 7, 9])7) np.random.randint(low, high=None, size=None, dtype='l')
n = np.random.randint(10)
n 8n = np.random.randint(0, 255, size=(3,4,5))
narray([[[ 89, 68, 18, 202, 49],
[118, 159, 48, 190, 227],
[177, 104, 232, 158, 64],
[112, 125, 0, 7, 216]],
[[ 2, 180, 33, 152, 244],
[ 46, 66, 185, 155, 253],
[180, 135, 80, 135, 86],
[ 64, 218, 69, 128, 90]],
[[163, 7, 55, 60, 12],
[ 15, 14, 181, 87, 62],
[218, 7, 166, 100, 217],
[137, 0, 42, 49, 194]]])image = np.random.randint(0,255, size=(456,730,3))
image.shape(456, 730, 3)plt.imshow(image)
plt.show(image)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-97-a28aaec0347e> in <module>()
1 plt.imshow(image)
----> 2 plt.show(image)C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\pyplot.py in show(*args, **kw)
251 """
252 global _show
--> 253 return _show(*args, **kw)
254
255 C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\pylab\backend_inline.py in show(close, block)
39 # only call close('all') if any to close
40 # close triggers gc.collect, which can be slow
---> 41 if close and Gcf.get_all_fig_managers():
42 matplotlib.pyplot.close('all')
43 ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
8) np.random.randn(d0, d1, ..., dn)
The standard normal distribution too
n = np.random.randn(10)
narray([-0.4173303 , -0.41736696, -0.11888109, -0.51925789, 1.24985884,
1.52967696, 0.05327912, 0.84738899, 1.03118302, -0.64532473])9)np.random.normal(loc=0.0, scale=1.0, size=None)
n = np.random.normal(175, scale=5.0, size=50)
n array([177.62703208, 176.50746247, 173.26956915, 162.29355083,
172.05271936, 177.61948035, 172.52243162, 175.43294252,
181.14225673, 175.21450574, 179.56055092, 170.883815 ,
170.91435313, 176.25008762, 176.3347509 , 183.90347049,
178.91856559, 168.84725605, 176.32881783, 172.77973728,
173.12257339, 174.75054378, 166.60349541, 171.68263799,
168.83419713, 174.25085091, 175.66113435, 174.12039025,
177.22772738, 169.01523024, 175.57587527, 172.89083838,
179.52153939, 173.70318334, 179.06473552, 176.50099117,
175.83008746, 174.78059027, 175.58909128, 178.11274357,
183.45771692, 172.43399789, 179.56800892, 182.14239994,
176.43701867, 177.37866513, 179.55215095, 174.5389049 ,
175.48698667, 168.73145269])10) np.random.random(size=None)
Generating a random number between 0 and 1, the left and right to open and close
n = np.random.random(10)
narray([0.22608606, 0.62764532, 0.62219649, 0.05348086, 0.94994404,
0.29048963, 0.49340728, 0.04651386, 0.59005488, 0.59901244])Two, ndarray property
4 will be referred parameters:
ndim: Dimension
shape: shape (the length of each dimension)
size: Total length
dtype: Element Type
cat.ndim3cat.shape
(456, 730, 3)cat.size998640cat.dtypedtype('uint8')Third, the basic operation of ndarray
1. Index
Fully consistent with the one-dimensional list
Multidimensional empathy
l = [1,2,3,4,5]
l[2:4][3, 4]n = np.array(l)
n[2]3# 找一个二维ndarray中的某个数
n2 = np.random.randint(0,255, size=(4,4))
n2array([[ 8, 117, 209, 86],
[156, 192, 117, 180],
[ 33, 70, 53, 179],
[ 56, 236, 72, 45]])# 查找53
n2[2][2]53n2[2,2]53n3 = np.random.randint(0,255, size=(4,5,6))
n3array([[[128, 60, 108, 12, 112, 60],
[234, 111, 237, 54, 22, 95],
[127, 226, 30, 181, 20, 85],
[239, 233, 210, 165, 186, 57],
[ 27, 17, 72, 237, 208, 120]],
[[199, 169, 190, 153, 181, 75],
[179, 205, 116, 33, 239, 228],
[154, 204, 138, 5, 231, 97],
[ 55, 193, 245, 105, 78, 210],
[157, 227, 239, 230, 242, 185]],
[[ 67, 232, 113, 189, 245, 206],
[220, 56, 241, 141, 146, 59],
[ 46, 206, 152, 240, 105, 105],
[176, 252, 185, 212, 180, 127],
[165, 130, 206, 77, 11, 56]],
[[194, 82, 72, 80, 94, 237],
[179, 143, 191, 56, 37, 236],
[194, 65, 223, 45, 223, 125],
[ 92, 162, 94, 93, 69, 3],
[ 39, 179, 213, 180, 23, 141]]])n3[1,2,3]5np.random.seed(100)np.random.seed(100)
np.random.randn(10)array([-1.74976547, 0.3426804 , 1.1530358 , -0.25243604, 0.98132079,
0.51421884, 0.22117967, -1.07004333, -0.18949583, 0.25500144])n = np.array([1,2,3,np.nan])
np.sum(n)
np.nansum(n)6.0According to the index modify data
n3[1,2,3] = 8
n3array([[[128, 60, 108, 12, 112, 60],
[234, 111, 237, 54, 22, 95],
[127, 226, 30, 181, 20, 85],
[239, 233, 210, 165, 186, 57],
[ 27, 17, 72, 237, 208, 120]],
[[199, 169, 190, 153, 181, 75],
[179, 205, 116, 33, 239, 228],
[154, 204, 138, 8, 231, 97],
[ 55, 193, 245, 105, 78, 210],
[157, 227, 239, 230, 242, 185]],
[[ 67, 232, 113, 189, 245, 206],
[220, 56, 241, 141, 146, 59],
[ 46, 206, 152, 240, 105, 105],
[176, 252, 185, 212, 180, 127],
[165, 130, 206, 77, 11, 56]],
[[194, 82, 72, 80, 94, 237],
[179, 143, 191, 56, 37, 236],
[194, 65, 223, 45, 223, 125],
[ 92, 162, 94, 93, 69, 3],
[ 39, 179, 213, 180, 23, 141]]])2. Slice
Fully consistent with the one-dimensional list
Multidimensional empathy
l = [1,2,3,4,5]
l[::-1][5, 4, 3, 2, 1]l[::-2]
l[1, 2, 3, 4, 5]The data inversion, e.g. [1,2,3] ----> [3,2,1]
n = np.random.randint(0, 255, size=(4,5))
narray([[211, 112, 94, 165, 6],
[ 86, 15, 241, 38, 139],
[185, 247, 99, 91, 31],
[221, 33, 40, 137, 162]]):: two sliced
n[::-1]
narray([[211, 112, 94, 165, 6],
[ 86, 15, 241, 38, 139],
[185, 247, 99, 91, 31],
[221, 33, 40, 137, 162]])3. Modification
Use reshape function, note that the argument is a tuple!
n = np.arange(6)
n
array([0, 1, 2, 3, 4, 5])n2 = np.reshape(n,(3,2))
n2array([[0, 1],
[2, 3],
[4, 5]])cat.shape(456, 730, 3)n = np.reshape(cat, (8322, 120))
narray([[231, 186, 131, ..., 235, 190, 135],
[237, 192, 137, ..., 237, 192, 137],
[237, 192, 137, ..., 239, 192, 138],
...,
[203, 125, 89, ..., 201, 121, 86],
[200, 120, 85, ..., 197, 117, 82],
[197, 117, 82, ..., 188, 95, 62]], dtype=uint8)4. Cascade
- np.concatenate ()
cascade points to note:
- Cascade is a list of parameters: Be sure to add parentheses or brackets
- Dimensions must be the same
- Conforms to the shape
- [Emphasis] direction of the cascade of defaults shape the direction of the first dimension value represented by the tuple
- Can be varied by the cascade axis direction parameter
n1 = np.random.randint(0,255, size=(5,6))
n2 = np.random.randint(0,255, size=(5,6))
display(n1,n2)array([[ 67, 115, 248, 66, 212, 248],
[ 66, 156, 231, 250, 39, 195],
[248, 172, 19, 21, 200, 206],
[139, 25, 3, 18, 3, 49],
[ 55, 21, 12, 6, 218, 116]])array([[182, 251, 137, 33, 60, 6],
[169, 117, 245, 218, 96, 168],
[231, 59, 117, 179, 76, 84],
[ 6, 24, 25, 51, 136, 89],
[ 67, 156, 135, 101, 147, 90]])np.concatenate((n1,n2),axis=1)array([[ 67, 115, 248, 66, 212, 248, 182, 251, 137, 33, 60, 6],
[ 66, 156, 231, 250, 39, 195, 169, 117, 245, 218, 96, 168],
[248, 172, 19, 21, 200, 206, 231, 59, 117, 179, 76, 84],
[139, 25, 3, 18, 3, 49, 6, 24, 25, 51, 136, 89],
[ 55, 21, 12, 6, 218, 116, 67, 156, 135, 101, 147, 90]])- np.hstack and np.vstack
horizontal and vertical cascade cascade, to handle their own, a dimension of change
# hstack h
new_image = np.hstack((cat, image))
plt.imshow(new_image)
plt.show()
# vstack vertical
new_image = np.vstack((cat, image))
plt.imshow(new_image)
plt.show()
5. Segmentation
And cascade-like, three complete segmentation work function:
- np.split
- np.vsplit
- np.hsplit
n = np.random.randint(0,100,size = (4,6))
narray([[92, 7, 55, 5, 20, 53],
[42, 61, 91, 64, 95, 18],
[25, 93, 48, 35, 39, 13],
[42, 97, 73, 57, 14, 59]])np.vsplit(n,(1,2))[array([[92, 7, 55, 5, 20, 53]]),
array([[42, 61, 91, 64, 95, 18]]),
array([[25, 93, 48, 35, 39, 13],
[42, 97, 73, 57, 14, 59]])]n = np.random.randint(0,100, size=(6,6))
narray([[48, 77, 69, 24, 83, 20],
[80, 92, 21, 97, 16, 37],
[52, 99, 2, 33, 28, 3],
[ 5, 53, 34, 3, 0, 95],
[27, 73, 95, 85, 8, 48],
[30, 54, 49, 75, 44, 90]])np.vsplit(n, (2,5))[array([[48, 77, 69, 24, 83, 20],
[80, 92, 21, 97, 16, 37]]), array([[52, 99, 2, 33, 28, 3],
[ 5, 53, 34, 3, 0, 95],
[27, 73, 95, 85, 8, 48]]), array([[30, 54, 49, 75, 44, 90]])]np.split(n, 3, axis=1)[array([[48, 77],
[80, 92],
[52, 99],
[ 5, 53],
[27, 73],
[30, 54]]), array([[69, 24],
[21, 97],
[ 2, 33],
[34, 3],
[95, 85],
[49, 75]]), array([[83, 20],
[16, 37],
[28, 3],
[ 0, 95],
[ 8, 48],
[44, 90]])]np.vsplit(n, 3)[array([[48, 77, 69, 24, 83, 20],
[80, 92, 21, 97, 16, 37]]), array([[52, 99, 2, 33, 28, 3],
[ 5, 53, 34, 3, 0, 95]]), array([[27, 73, 95, 85, 8, 48],
[30, 54, 49, 75, 44, 90]])]np.hsplit(n, 3)[array([[48, 77],
[80, 92],
[52, 99],
[ 5, 53],
[27, 73],
[30, 54]]), array([[69, 24],
[21, 97],
[ 2, 33],
[34, 3],
[95, 85],
[49, 75]]), array([[83, 20],
[16, 37],
[28, 3],
[ 0, 95],
[ 8, 48],
[44, 90]])]np.hsplit(n,(2,4))[array([[33, 46],
[98, 40],
[47, 53],
[34, 91]]), array([[53, 7],
[12, 55],
[69, 50],
[32, 52]]), array([[56, 43],
[18, 64],
[69, 7],
[83, 38]])]cat.shape(456, 730, 3)456
730result = np.split(cat, 2, axis = 0)plt.imshow(result[0])
plt.show()
s_result = np.split(cat,2,axis = 1)len(s_result)2plt.imshow(s_result[0])
plt.show()
6. copy
All assignment operators will not create a copy of any of the elements ndarray. Action on the object after the assignment has entered into force for the original object.
l = [1,2,3,4]
l2 = l
l2[2] = 5
l[1, 2, 5, 4]n1 = np.arange(10)
n2 = n1
n2[3] = 100
n1array([ 0, 1, 2, 100, 4, 5, 6, 7, 8, 9])n3 = n1.copy()
n3[5] = 200
n1array([ 0, 1, 2, 100, 4, 5, 6, 7, 8, 9])Use copy () function to create a copy of the
Four, ndarray aggregation operations
1. summation np.sum
n = np.arange(11)
narray([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])np.sum(n)55n = np.random.randint(0,100, size=(5,6))
narray([[80, 20, 30, 66, 48, 50],
[52, 33, 3, 76, 35, 9],
[70, 99, 69, 50, 44, 31],
[40, 13, 52, 50, 33, 45],
[69, 42, 55, 30, 61, 22]])np.sum(n, axis=1)array([294, 208, 363, 233, 279])2. The maximum and minimum: np.max / np.min
Similarly
n = np.arange(11)
narray([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])np.median(n)5.0np.mean(n)5.0n = np.random.randint(0,100,size=10)
narray([42, 64, 40, 7, 0, 79, 32, 95, 95, 59])np.mean(n)51.3np.median(n)50.5np.max(n)10np.min(n)0n = np.random.randint(0,100, size=(5,6))
narray([[82, 44, 0, 33, 72, 99],
[66, 25, 36, 88, 74, 78],
[ 3, 53, 33, 76, 96, 69],
[62, 10, 16, 22, 12, 31],
[41, 57, 43, 79, 34, 7]])np.max(n, axis=0)array([82, 57, 43, 88, 96, 99])3. Other polymerization operation
Function Name NaN-safe Version Description
np.sum np.nansum Compute sum of elements
np.prod np.nanprod Compute product of elements
np.mean np.nanmean Compute mean of elements
np.std np.nanstd Compute standard deviation
np.var np.nanvar Compute variance
np.min np.nanmin Find minimum value
np.max np.nanmax Find maximum value
np.argmin np.nanargmin Find index of minimum value
np.argmax np.nanargmax Find index of maximum value
np.median np.nanmedian Compute median of elements
np.percentile np.nanpercentile Compute rank-based statistics of elements
np.any N/A Evaluate whether any elements are true
np.all N/A Evaluate whether all elements are true
np.power 幂运算np.argmin(n, axis=0)array([2, 3, 0, 3, 3, 4], dtype=int64)cat.shape(456, 730, 3)cat2 = cat.reshape((-1,3))
cat2.shape(332880, 3)n = np.random.randint(0,10, size=(4,5))
narray([[8, 8, 9, 1, 5],
[7, 9, 9, 5, 9],
[4, 1, 0, 0, 1],
[6, 5, 4, 2, 9]])np.reshape(n,(-1,))array([8, 8, 9, 1, 5, 7, 9, 9, 5, 9, 4, 1, 0, 0, 1, 6, 5, 4, 2, 9])cat3 = cat.reshape((456*730,3))
cat3.shape(332880, 3)cat3.max(axis = 0)array([255, 242, 219], dtype=uint8)max_cat = cat.max(axis = (0,1))
max_cat
array([255, 242, 219], dtype=uint8)
max_cat.shape
(3,)
cat.min()
0
The difference np.sum and np.nansum
nan not a number
a = np.array([1,2,np.nan])
a
array([ 1., 2., nan])
np.nansum(a)
3.0
Action File
Open the file using pandas president_heights.csv
get the data file
import pandas as pd
data = pd.read_csv('president_heights.csv')
type(data)
data
| order | name | height(cm) | |
|---|---|---|---|
| 0 | 1 | George Washington | 189 |
| 1 | 2 | John Adams | 170 |
| 2 | 3 | Thomas Jefferson | 189 |
| 3 | 4 | James Madison | 163 |
| 4 | 5 | James Monroe | 183 |
| 5 | 6 | John Quincy Adams | 171 |
| 6 | 7 | Andrew Jackson | 185 |
| 7 | 8 | Martin Van Buren | 168 |
| 8 | 9 | William Henry Harrison | 173 |
| 9 | 10 | John Tyler | 183 |
| 10 | 11 | James K. Polk | 173 |
| 11 | 12 | Zachary Taylor | 173 |
| 12 | 13 | Millard Fillmore | 175 |
| 13 | 14 | Franklin Pierce | 178 |
| 14 | 15 | James Buchanan | 183 |
| 15 | 16 | Abraham Lincoln | 193 |
| 16 | 17 | Andrew Johnson | 178 |
| 17 | 18 | Ulysses S. Grant | 173 |
| 18 | 19 | Rutherford B. Hayes | 174 |
| 19 | 20 | James A. Garfield | 183 |
| 20 | 21 | Chester A. Arthur | 183 |
| 21 | 23 | Benjamin Harrison | 168 |
| 22 | 25 | William McKinley | 170 |
| 23 | 26 | Theodore Roosevelt | 178 |
| 24 | 27 | William Howard Taft | 182 |
| 25 | 28 | Woodrow Wilson | 180 |
| 26 | 29 | Warren G. Harding | 183 |
| 27 | 30 | Calvin Coolidge | 178 |
| 28 | 31 | Herbert Hoover | 182 |
| 29 | 32 | Franklin D. Roosevelt | 188 |
| 30 | 33 | Harry S. Truman | 175 |
| 31 | 34 | Dwight D. Eisenhower | 179 |
| 32 | 35 | John F. Kennedy | 183 |
| 33 | 36 | Lyndon B. Johnson | 193 |
| 34 | 37 | Richard Nixon | 182 |
| 35 | 38 | Gerald Ford | 183 |
| 36 | 39 | Jimmy Carter | 177 |
| 37 | 40 | Ronald Reagan | 185 |
| 38 | 41 | George H. W. Bush | 188 |
| 39 | 42 | Bill Clinton | 188 |
| 40 | 43 | George W. Bush | 182 |
| 41 | 44 | Barack Obama | 185 |
heights = data['height(cm)']
heights
type(heights)
pandas.core.series.Series
np.max(heights)
193
np.mean(heights)
179.73809523809524
np.std(heights)
6.931843442745893
np.min(heights)
163
Five, ndarray matrix operations
1. Basic matrix operations
1) arithmetic operators:
- Math
n = np.random.randint(0,10, size=(4,5))
n
array([[2, 5, 0, 4, 6],
[0, 0, 7, 5, 0],
[6, 3, 2, 9, 2],
[5, 7, 0, 4, 5]])
# 加
n + 1
array([[ 3, 6, 1, 5, 7],
[ 1, 1, 8, 6, 1],
[ 7, 4, 3, 10, 3],
[ 6, 8, 1, 5, 6]])
# 减
n - 1
array([[ 1, 4, -1, 3, 5],
[-1, -1, 6, 4, -1],
[ 5, 2, 1, 8, 1],
[ 4, 6, -1, 3, 4]])
# 两个矩阵相加
n2 = np.random.randint(0,10,size=(4,5))
n2
array([[2, 4, 2, 5, 9],
[6, 6, 9, 6, 2],
[9, 7, 5, 6, 1],
[4, 6, 7, 2, 9]])
n + n2
array([[ 4, 9, 2, 9, 15],
[ 6, 6, 16, 11, 2],
[15, 10, 7, 15, 3],
[ 9, 13, 7, 6, 14]])
n3 = np.random.randint(0,10,size=(2,5))
n3
array([[8, 0, 0, 5, 8],
[4, 0, 3, 6, 7]])
n2 + n3
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-97-5f0861827bc6> in <module>()
----> 1 n2 + n3
ValueError: operands could not be broadcast together with shapes (4,5) (2,5)
2) matrix product np.dot ()
n1 = np.random.randint(0,10,size=(2,3))
n1
array([[8, 9, 4],
[1, 7, 5]])
n2 = np.random.randint(0,10, size=(3,4))
n2
array([[4, 4, 2, 7],
[4, 4, 7, 3],
[1, 3, 2, 7]])
np.dot(n1,n2)
array([[ 72, 80, 87, 111],
[ 37, 47, 61, 63]])
2. broadcast mechanism
Two rules [important] ndarray broadcast mechanism
- Rule number one: make up for the missing dimension 1
- Rule number two: assuming that the missing element is filled with the existing values
例1：
m = np.ones((2, 3))
a = np.arange(3)
求M+a
m = np.ones((2,3),dtype=int)
m
array([[1, 1, 1],
[1, 1, 1]])
n = np.arange(3)
n
array([0, 1, 2])
m + n
array([[1, 2, 3],
[1, 2, 3]])
例2：
a = np.arange(3).reshape((3, 1))
b = np.arange(3)
求a+b
a = np.arange(3).reshape((3,1))
a
array([[0],
[1],
[2]])
b = np.arange(3)
b
array([0, 1, 2])
a + b
array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4]])
Problem
A np.ones = ((. 4,. 1))
B = np.arange (. 4)
find a + b
Six, ndarray sort
Quiz:
knowledge numpy use of the above study, for a ndarray object selection sort.
def Sortn(x):
The code as short as possible
n = [5,2,3,6,9]
def bubble(n):
for i in range(len(n) -1):
for j in range(i+1, len(n)):
if n[i] > n[j]:
n[i], n[j] = n[j], n[i]
bubble(n)
n
[2, 3, 5, 6, 9]
# 选择排序
def select(n):
for i in range(len(n)):
# 选出最小值的索引
index = np.argmin(n[i:]) + i
# 把最小值和当前值的位置换一下
n[i], n[index] = n[index], n[i]
n = [4, 6,1,0,3]
select(n)
n
[0, 1, 3, 4, 6]
1. Quick Sort
np.sort () and ndarray.sort () can be, but there are differences:
- np.sort () does not change the input
- ndarray.sort () local processing, does not occupy space, but changing the input
n = np.random.randint(0,10,size=6)
n
array([6, 7, 1, 1, 8, 3])
np.sort(n)
array([1, 1, 3, 6, 7, 8])
np.sort(n)
n
array([6, 7, 1, 1, 8, 3])
n.sort()
n
array([1, 1, 3, 6, 7, 8])