{"id":521,"date":"2019-07-01T23:28:00","date_gmt":"2019-07-01T15:28:00","guid":{"rendered":"http:\/\/note.systw.net\/note\/?p=521"},"modified":"2023-11-03T23:30:44","modified_gmt":"2023-11-03T15:30:44","slug":"python-numpy","status":"publish","type":"post","link":"https:\/\/systw.net\/note\/archives\/521","title":{"rendered":"Python Numpy"},"content":{"rendered":"\n

NumPy Ndarray
NumPy(Numerical Python)
\u5e38\u7528\u65bc\u6578\u64da\u5206\u6790\u7684\u5957\u4ef6<\/p>\n\n\n\n

ndarray
\u5132\u5b58\u6578\u64da\u7684\u4e00\u7a2e\u985e\u578b,\u6548\u80fd\u8f03\u597d<\/p>\n\n\n\n


\u5b89\u88dd:
#sudo apt-get install python-numpy<\/p>\n\n\n\n

\u8f09\u5165
# python
>>> import numpy as np<\/p>\n\n\n\n


ps:
\u4e0d\u8981\u4ee5 scientific notation for small numbers\u986f\u793andarray
np.set_printoptions(suppress=True)
ex:
print ndarraydata1
[ 1.500e-10 1.500e+00 1.500e+03]
np.set_printoptions(suppress=True)
print ndarraydata1
[ 0. 1.5 1500. ]<\/p>\n\n\n\n

…..<\/p>\n\n\n\n

\u5efa\u7acbndarray\u548c\u7522\u751f\u8cc7\u6599 <\/strong><\/h2>\n\n\n\n

\u5efa\u7acb\u7a7andarray<\/strong>
ndarraydata1 = np.array([])
print ndarraydata1<\/p>\n\n\n\n


\u7522\u751fndarray\u8cc7\u6599<\/strong>
import numpy as np
listdata=[1,2,3,4,5]
ndarraydata1 = np.array(listdata)
print ndarraydata1<\/p>\n\n\n\n

…<\/p>\n\n\n\n

\u7522\u751f\u4e00\u7d44\u6578\u5b57
.arange<\/strong>
ex:
>>> np.arange(5)
0 1 2 3 4

\u7522\u751f\u4e00\u7d44\u6578\u5b57
.linspace<\/strong>
ex:
>>> np.linspace(2.0, 3.0, 5)
array([ 2. , 2.25, 2.5 , 2.75, 3. ])<\/p>\n\n\n\n

0~1\u7684\u4e82\u6578
.random.rand<\/strong>
ex:
>>>np.random.rand(3)
array([ 0.6590211, 0.97780188, 0.7183219 ])<\/p>\n\n\n\n

-1~1\u7684\u4e82\u6578
.random.randn<\/strong>
ex:
>>>np.random.randn(3)
array([ 0.6590211, -0.7780188, 0.7183219 ])
>>>np.random.randn(2, 3)
array([[ 0.3590211, -0.6780188, 0.2183219 ],
[ 0.4590211, -0.8780188, 0.1183219 ] ])<\/p>\n\n\n\n

\u4e82\u6578
.random.randint<\/strong>
ex:
random.randint(3, size=10)
array([1, 0, 2, 0, 1, 1, 2, 0, 1, 0])<\/p>\n\n\n\n


……<\/p>\n\n\n\n

numpy \u8cc7\u6599\u64cd\u4f5c<\/strong><\/h2>\n\n\n\n

\u6392\u5e8f<\/strong>
ex:
\u7528\u7b2c2\u500b\u6b04\u4f4d\u6392\u5e8f
data = [ [5, 7, 3],
[4, 2, 2],
[0, 3, 5],
 ]
data = sorted(data, key=lambda x: x[1])
[[4, 2, 2],
[0, 3, 5],
[5, 7, 3]]<\/p>\n\n\n\n

\u79fb\u9664<\/strong>
ex:
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
index = [2, 3, 6]
new_a = np.delete(a, index)<\/p>\n\n\n\n


\u53d6\u4ee3<\/strong>
ex:
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
a[a>7]=10
a[a<3]=0
print a
[0, 0, 3, 4, 5, 6, 7, 10, 10]<\/p>\n\n\n\n


\u5c07\u8cc7\u6599\u6253\u4e82<\/strong>
ex:
indices = np.random.permutation(data_train.shape[0])
data_train = data_train[indices]
target = target[indices]<\/p>\n\n\n\n


……<\/p>\n\n\n\n

\u5408\u4f75ndarray<\/strong><\/h2>\n\n\n\n

\u7d2f\u7a4d
np.append<\/strong>
ex:
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
np.append(a,10)
array([1, 2, 3, 4, 5, 6, 7, 8, 9,10])<\/p>\n\n\n\n

np.column_stack()<\/strong>
ex:
listdata=[1,2,3,4,5]
ndarraydata1 = np.array(listdata)
listdata=[1,0,0,1,1]
ndarraydata2 = np.array(listdata)
ndarraydata=np.column_stack((ndarraydata1,ndarraydata2))
print ndarraydata
array([[1, 1],
[2, 0],
[3, 0],
[4, 1],
[5, 1]])<\/p>\n\n\n\n

np.row_stack()<\/strong>
ex:
ndarraydata=np.row_stack((ndarraydata1,ndarraydata2))
array([[1],
[2],
[3],
[4],
[5],
[1],
[0],
[0],
[0],
[1]])<\/p>\n\n\n\n

 …<\/p>\n\n\n\n

>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])<\/p>\n\n\n\n

np.vstack<\/strong>
ex: 
>>> np.vstack((a,b))
array([[1, 2, 3],
[4, 5, 6]])<\/p>\n\n\n\n

np.hstack<\/strong>
ex:
>>>np.hstack((a,b))
array([1, 2, 3, 4, 5, 6])

np.dstack<\/strong>
ex:
>>> np.dstack((a,b))
array([[[1, 4],
[2, 5],
[3, 6]]])<\/p>\n\n\n\n


\u5408\u4f75\u591a\u500barray
ex:
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> print(np.ravel(x))
[1 2 3 4 5 6]<\/p>\n\n\n\n

……………………………..<\/p>\n\n\n\n

ndarray\u7dad\u5ea6\u8f49\u63db<\/strong><\/h2>\n\n\n\n

\u4e00\u7dad\u8b8a2\u7dad
>>> a=[1 2 3 4 5 6]
>>> print a.reshape(6, 1)
[[1]
[2]
[3]
[4]
[5]
[6]]<\/p>\n\n\n\n

ps:\u900f\u904e-1\u53ef\u8b93Numpy\u81ea\u52d5\u8a08\u7b97\u6578\u91cf
>>> print a.reshape((-1,1)) #\u9019\u4e5f\u7b49\u540c(6,1)
[[1]
[2]
[3]
[4]
[5]
[6]]
>>> print a.reshape((1,-1)) #\u9019\u4e5f\u7b49\u540c(1,6)
[[1 2 3 4 5 6]]<\/p>\n\n\n\n


2\u7dad\u8b8a1\u7dad
z = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
z.shape (3, 4)
z.reshape(-1)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])<\/p>\n\n\n\n


\u6bd4\u8f03\u5169\u500bnumpy array\u5143\u7d20
for item in ndarraydata :
\u3000if item[0]==item[1]:
\u3000\u3000print ‘match’<\/p>\n\n\n\n


……………….<\/p>\n\n\n\n

\u8b80\u6a94\u548c\u5b58\u6a94<\/strong><\/h2>\n\n\n\n

refer
http:\/\/docs.scipy.org\/doc\/numpy\/reference\/generated\/numpy.loadtxt.html<\/p>\n\n\n\n


numpy loadtxt<\/strong>
\u8b80\u53d6\u6587\u5b57\u6a94
ex:
import numpy as np
data=np.loadtxt(‘file.txt’)<\/p>\n\n\n\n

delimiter<\/strong>
\u6307\u5b9a\u4f86\u6e90\u6a94\u5206\u9694\u7b26\u865f
default is any whitespace.
ex:
np.loadtxt(‘file.txt’, delimiter=’,’)<\/p>\n\n\n\n

skiprows \u7565\u904e\u884c\u6578<\/strong>
ex:
\u7565\u904e\u7b2c1\u884c
np.loadtxt(‘file.txt’, delimiter=’,’,skiprows=1)<\/p>\n\n\n\n

dtype \u6307\u5b9a\u8cc7\u6599\u985e\u578b<\/strong>
ex:
data=np.loadtxt(‘file.txt’,delimiter=’,’,dtype=[(‘f0’,str),(‘f1’,float),(‘f2’,float),(‘f3’,float)])
data=np.loadtxt(‘file.txt’,dtype=[(‘f0’,str),(‘f1’,float),(‘f2’,float),(‘f3’,float)])<\/p>\n\n\n\n


\u8cc7\u6599\u96c6\u900f\u904enploadtxt\u53d6\u5f97\u5f8c\uff0c\u8cc7\u6599\u96c6\u53ef\u900f\u904e[\u5217,\u6b04]\u65b9\u6cd5\u53d6\u5f97\u8cc7\u6599
ex:
\u53d6\u5217
dataset[0]\u3000 \u8868\u793a\u50c5\u53d6\u51fa\u7b2c0\u5217\u7684\u8cc7\u6599
dataset[1:] \u8868\u793a\u50c5\u51fa\u7b2c1\u5217\u5f8c\u7684\u8cc7\u6599
dataset[-1:]\u3000 \u8868\u793a\u50c5\u53d6\u51fa\u6700\u5f8c\u4e00\u5217\u7684\u8cc7\u6599
dataset[0:2]\u3000 \u8868\u793a\u50c5\u53d6\u51fa\u7b2c1\u5217~\u7b2c2\u5217\u7684\u8cc7\u6599
dataset[10:12]\u3000 \u8868\u793a\u50c5\u53d6\u51fa\u7b2c11\u5217~\u7b2c12\u5217\u7684\u8cc7\u6599
dataset[0:-1]\u3000 \u8868\u793a\u50c5\u53d6\u51fa\u7b2c1\u5217~\u5012\u6578\u7b2c\u4e8c\u5217\u7684\u8cc7\u6599
\u53d6\u6b04
dataset[:,0] \u8868\u793a\u50c5\u53d6\u51fa\u7b2c1\u6b04\u7684\u8cc7\u6599
dataset[:,1:] \u8868\u793a\u53d6\u51fa\u7b2c2\u6b04(\u5305\u542b\u7b2c2\u6b04)\u5f8c\u7684\u8cc7\u6599
dataset[:,2:4] \u8868\u793a\u53d6\u51fa\u7b2c2\u6b04\u5230\u7b2c3\u6b04\u7684\u8cc7\u6599<\/p>\n\n\n\n


ps:
\u8b80\u53d6\u5916\u90e8\u6578\u64da
#vi dataset.txt
0 1 1 1
0 2 2 2
0 1 2 3
0 2 1 4
1 9 9 7
1 8 8 8
1 9 8 9
#python
>>>import numpy as np
>>>f = open(“dataset.txt”)
>>>dataset =a np.loadtxt(f)
>>>print(dataset)
[[ 0. 1. 1. 1.]
[ 0. 2. 2. 2.]
[ 0. 1. 2. 3.]
[ 0. 2. 1. 4.]
[ 1. 9. 9. 7.]
[ 1. 8. 8. 8.]
[ 1. 9. 8. 9.]]
>>>data = dataset[:, 1:]
>>>target = dataset[:, 0] # select column 0, the stock price
>>> print(data)
[[ 1. 1. 1.]
[ 2. 2. 2.]
[ 1. 2. 1.]
[ 2. 1. 2.]
[ 9. 9. 9.]
[ 8. 8. 8.]
[ 9. 8. 9.]]
>>> print(target)
[ 0. 0. 0. 0. 1. 1. 1.]<\/p>\n\n\n\n


\u6aa2\u8996\u8cc7\u6599\u6b04\u5217
dataset.shape<\/p>\n\n\n\n

\u5132\u5b58\u6587\u5b57\u6a94
np.savetxt(‘test.out’, data_array, delimiter=’,’,fmt=’%10.5f’)<\/p>\n","protected":false},"excerpt":{"rendered":"

NumPy NdarrayNumPy(Numerical P …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[14],"tags":[],"class_list":["post-521","post","type-post","status-publish","format-standard","hentry","category-develop"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/posts\/521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/comments?post=521"}],"version-history":[{"count":0,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/posts\/521\/revisions"}],"wp:attachment":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/media?parent=521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/categories?post=521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/tags?post=521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}