np.newaxis

 

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
 
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
 
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]

#原始数据集维度
data = diabetes.data
print(data.shape)

#np.newaxis放在第一个参数位置
data_1 = data[np.newaxis,:]
print(data_1.shape)
#np.newaxis放在第二个参数位置
data_2 = data[:,np.newaxis,:]#等价于data_2 = data[:,np.newaxis]和data_2 = data[:,np.newaxis,...]
print(data_2.shape)
#np.newaxis放在第三个参数位置
data_3 = data[:,:,np.newaxis]
print(data_3.shape)
#此处相当于把data数据集转换成442*1*10后，在第三个维度中的10个系列中选取第3个系列(index为2)。
diabetes_X =data[:, np.newaxis, 2]
print(diabetes_X.shape)
print(data,diabetes_X,sep='\n')
print(data[:,2])#此处data[:,2]和data[:, np.newaxis, 2]的内容一样，只是显示的排列方式不一样。都是原data的第三列data[:2]的内容。

 

(442, 10)
(1, 442, 10)
(442, 1, 10)
(442, 10, 1)
(442, 1)
[[ 0.03807591  0.05068012  0.06169621 ... -0.00259226  0.01990842
  -0.01764613]
 [-0.00188202 -0.04464164 -0.05147406 ... -0.03949338 -0.06832974
  -0.09220405]
 [ 0.08529891  0.05068012  0.04445121 ... -0.00259226  0.00286377
  -0.02593034]
 ...
 [ 0.04170844  0.05068012 -0.01590626 ... -0.01107952 -0.04687948
   0.01549073]
 [-0.04547248 -0.04464164  0.03906215 ...  0.02655962  0.04452837
  -0.02593034]
 [-0.04547248 -0.04464164 -0.0730303  ... -0.03949338 -0.00421986
   0.00306441]]
[[ 0.06169621]
 [-0.05147406]
 [ 0.04445121]
 [-0.01159501]
 [-0.03638469]
 [-0.04069594]
 [-0.04716281]
 [-0.00189471]
 [ 0.06169621]
 [ 0.03906215]
 [-0.08380842]
 [ 0.01750591]
 [-0.02884001]
 [-0.00189471]
 [-0.02560657]
 [-0.01806189]
 [ 0.04229559]
 [ 0.01211685]
 [-0.0105172 ]
 [-0.01806189]
 [-0.05686312]
 [-0.02237314]
 [-0.00405033]
 [ 0.06061839]
 [ 0.03582872]
 [-0.01267283]
 [-0.07734155]
 [ 0.05954058]
 [-0.02129532]
 [-0.00620595]
 [ 0.04445121]
 [-0.06548562]
 [ 0.12528712]
 [-0.05039625]
 [-0.06332999]
 [-0.03099563]
 [ 0.02289497]
 [ 0.01103904]
 [ 0.07139652]
 [ 0.01427248]
 [-0.00836158]
 [-0.06764124]
 [-0.0105172 ]
 [-0.02345095]
 [ 0.06816308]
 [-0.03530688]
 [-0.01159501]
 [-0.0730303 ]
 [-0.04177375]
 [ 0.01427248]
 [-0.00728377]
 [ 0.0164281 ]
 [-0.00943939]
 [-0.01590626]
 [ 0.0250506 ]
 [-0.04931844]
 [ 0.04121778]
 [-0.06332999]
 [-0.06440781]
 [-0.02560657]
 [-0.00405033]
 [ 0.00457217]
 [-0.00728377]
 [-0.0374625 ]
 [-0.02560657]
 [-0.02452876]
 [-0.01806189]
 [-0.01482845]
 [-0.02991782]
 [-0.046085  ]
 [-0.06979687]
 [ 0.03367309]
 [-0.00405033]
 [-0.02021751]
 [ 0.00241654]
 [-0.03099563]
 [ 0.02828403]
 [-0.03638469]
 [-0.05794093]
 [-0.0374625 ]
 [ 0.01211685]
 [-0.02237314]
 [-0.03530688]
 [ 0.00996123]
 [-0.03961813]
 [ 0.07139652]
 [-0.07518593]
 [-0.00620595]
 [-0.04069594]
 [-0.04824063]
 [-0.02560657]
 [ 0.0519959 ]
 [ 0.00457217]
 [-0.06440781]
 [-0.01698407]
 [-0.05794093]
 [ 0.00996123]
 [ 0.08864151]
 [-0.00512814]
 [-0.06440781]
 [ 0.01750591]
 [-0.04500719]
 [ 0.02828403]
 [ 0.04121778]
 [ 0.06492964]
 [-0.03207344]
 [-0.07626374]
 [ 0.04984027]
 [ 0.04552903]
 [-0.00943939]
 [-0.03207344]
 [ 0.00457217]
 [ 0.02073935]
 [ 0.01427248]
 [ 0.11019775]
 [ 0.00133873]
 [ 0.05846277]
 [-0.02129532]
 [-0.0105172 ]
 [-0.04716281]
 [ 0.00457217]
 [ 0.01750591]
 [ 0.08109682]
 [ 0.0347509 ]
 [ 0.02397278]
 [-0.00836158]
 [-0.06117437]
 [-0.00189471]
 [-0.06225218]
 [ 0.0164281 ]
 [ 0.09618619]
 [-0.06979687]
 [-0.02129532]
 [-0.05362969]
 [ 0.0433734 ]
 [ 0.05630715]
 [-0.0816528 ]
 [ 0.04984027]
 [ 0.11127556]
 [ 0.06169621]
 [ 0.01427248]
 [ 0.04768465]
 [ 0.01211685]
 [ 0.00564998]
 [ 0.04660684]
 [ 0.12852056]
 [ 0.05954058]
 [ 0.09295276]
 [ 0.01535029]
 [-0.00512814]
 [ 0.0703187 ]
 [-0.00405033]
 [-0.00081689]
 [-0.04392938]
 [ 0.02073935]
 [ 0.06061839]
 [-0.0105172 ]
 [-0.03315126]
 [-0.06548562]
 [ 0.0433734 ]
 [-0.06225218]
 [ 0.06385183]
 [ 0.03043966]
 [ 0.07247433]
 [-0.0191397 ]
 [-0.06656343]
 [-0.06009656]
 [ 0.06924089]
 [ 0.05954058]
 [-0.02668438]
 [-0.02021751]
 [-0.046085  ]
 [ 0.07139652]
 [-0.07949718]
 [ 0.00996123]
 [-0.03854032]
 [ 0.01966154]
 [ 0.02720622]
 [-0.00836158]
 [-0.01590626]
 [ 0.00457217]
 [-0.04285156]
 [ 0.00564998]
 [-0.03530688]
 [ 0.02397278]
 [-0.01806189]
 [ 0.04229559]
 [-0.0547075 ]
 [-0.00297252]
 [-0.06656343]
 [-0.01267283]
 [-0.04177375]
 [-0.03099563]
 [-0.00512814]
 [-0.05901875]
 [ 0.0250506 ]
 [-0.046085  ]
 [ 0.00349435]
 [ 0.05415152]
 [-0.04500719]
 [-0.05794093]
 [-0.05578531]
 [ 0.00133873]
 [ 0.03043966]
 [ 0.00672779]
 [ 0.04660684]
 [ 0.02612841]
 [ 0.04552903]
 [ 0.04013997]
 [-0.01806189]
 [ 0.01427248]
 [ 0.03690653]
 [ 0.00349435]
 [-0.07087468]
 [-0.03315126]
 [ 0.09403057]
 [ 0.03582872]
 [ 0.03151747]
 [-0.06548562]
 [-0.04177375]
 [-0.03961813]
 [-0.03854032]
 [-0.02560657]
 [-0.02345095]
 [-0.06656343]
 [ 0.03259528]
 [-0.046085  ]
 [-0.02991782]
 [-0.01267283]
 [-0.01590626]
 [ 0.07139652]
 [-0.03099563]
 [ 0.00026092]
 [ 0.03690653]
 [ 0.03906215]
 [-0.01482845]
 [ 0.00672779]
 [-0.06871905]
 [-0.00943939]
 [ 0.01966154]
 [ 0.07462995]
 [-0.00836158]
 [-0.02345095]
 [-0.046085  ]
 [ 0.05415152]
 [-0.03530688]
 [-0.03207344]
 [-0.0816528 ]
 [ 0.04768465]
 [ 0.06061839]
 [ 0.05630715]
 [ 0.09834182]
 [ 0.05954058]
 [ 0.03367309]
 [ 0.05630715]
 [-0.06548562]
 [ 0.16085492]
 [-0.05578531]
 [-0.02452876]
 [-0.03638469]
 [-0.00836158]
 [-0.04177375]
 [ 0.12744274]
 [-0.07734155]
 [ 0.02828403]
 [-0.02560657]
 [-0.06225218]
 [-0.00081689]
 [ 0.08864151]
 [-0.03207344]
 [ 0.03043966]
 [ 0.00888341]
 [ 0.00672779]
 [-0.02021751]
 [-0.02452876]
 [-0.01159501]
 [ 0.02612841]
 [-0.05901875]
 [-0.03638469]
 [-0.02452876]
 [ 0.01858372]
 [-0.0902753 ]
 [-0.00512814]
 [-0.05255187]
 [-0.02237314]
 [-0.02021751]
 [-0.0547075 ]
 [-0.00620595]
 [-0.01698407]
 [ 0.05522933]
 [ 0.07678558]
 [ 0.01858372]
 [-0.02237314]
 [ 0.09295276]
 [-0.03099563]
 [ 0.03906215]
 [-0.06117437]
 [-0.00836158]
 [-0.0374625 ]
 [-0.01375064]
 [ 0.07355214]
 [-0.02452876]
 [ 0.03367309]
 [ 0.0347509 ]
 [-0.03854032]
 [-0.03961813]
 [-0.00189471]
 [-0.03099563]
 [-0.046085  ]
 [ 0.00133873]
 [ 0.06492964]
 [ 0.04013997]
 [-0.02345095]
 [ 0.05307371]
 [ 0.04013997]
 [-0.02021751]
 [ 0.01427248]
 [-0.03422907]
 [ 0.00672779]
 [ 0.00457217]
 [ 0.03043966]
 [ 0.0519959 ]
 [ 0.06169621]
 [-0.00728377]
 [ 0.00564998]
 [ 0.05415152]
 [-0.00836158]
 [ 0.114509  ]
 [ 0.06708527]
 [-0.05578531]
 [ 0.03043966]
 [-0.02560657]
 [ 0.10480869]
 [-0.00620595]
 [-0.04716281]
 [-0.04824063]
 [ 0.08540807]
 [-0.01267283]
 [-0.03315126]
 [-0.00728377]
 [-0.01375064]
 [ 0.05954058]
 [ 0.02181716]
 [ 0.01858372]
 [-0.01159501]
 [-0.00297252]
 [ 0.01750591]
 [-0.02991782]
 [-0.02021751]
 [-0.05794093]
 [ 0.06061839]
 [-0.04069594]
 [-0.07195249]
 [-0.05578531]
 [ 0.04552903]
 [-0.00943939]
 [-0.03315126]
 [ 0.04984027]
 [-0.08488624]
 [ 0.00564998]
 [ 0.02073935]
 [-0.00728377]
 [ 0.10480869]
 [-0.02452876]
 [-0.00620595]
 [-0.03854032]
 [ 0.13714305]
 [ 0.17055523]
 [ 0.00241654]
 [ 0.03798434]
 [-0.05794093]
 [-0.00943939]
 [-0.02345095]
 [-0.0105172 ]
 [-0.03422907]
 [-0.00297252]
 [ 0.06816308]
 [ 0.00996123]
 [ 0.00241654]
 [-0.03854032]
 [ 0.02612841]
 [-0.08919748]
 [ 0.06061839]
 [-0.02884001]
 [-0.02991782]
 [-0.0191397 ]
 [-0.04069594]
 [ 0.01535029]
 [-0.02452876]
 [ 0.00133873]
 [ 0.06924089]
 [-0.06979687]
 [-0.02991782]
 [-0.046085  ]
 [ 0.01858372]
 [ 0.00133873]
 [-0.03099563]
 [-0.00405033]
 [ 0.01535029]
 [ 0.02289497]
 [ 0.04552903]
 [-0.04500719]
 [-0.03315126]
 [ 0.097264  ]
 [ 0.05415152]
 [ 0.12313149]
 [-0.08057499]
 [ 0.09295276]
 [-0.05039625]
 [-0.01159501]
 [-0.0277622 ]
 [ 0.05846277]
 [ 0.08540807]
 [-0.00081689]
 [ 0.00672779]
 [ 0.00888341]
 [ 0.08001901]
 [ 0.07139652]
 [-0.02452876]
 [-0.0547075 ]
 [-0.03638469]
 [ 0.0164281 ]
 [ 0.07786339]
 [-0.03961813]
 [ 0.01103904]
 [-0.04069594]
 [-0.03422907]
 [ 0.00564998]
 [ 0.08864151]
 [-0.03315126]
 [-0.05686312]
 [-0.03099563]
 [ 0.05522933]
 [-0.06009656]
 [ 0.00133873]
 [-0.02345095]
 [-0.07410811]
 [ 0.01966154]
 [-0.01590626]
 [-0.01590626]
 [ 0.03906215]
 [-0.0730303 ]]
[ 0.06169621 -0.05147406  0.04445121 -0.01159501 -0.03638469 -0.04069594
 -0.04716281 -0.00189471  0.06169621  0.03906215 -0.08380842  0.01750591
 -0.02884001 -0.00189471 -0.02560657 -0.01806189  0.04229559  0.01211685
 -0.0105172  -0.01806189 -0.05686312 -0.02237314 -0.00405033  0.06061839
  0.03582872 -0.01267283 -0.07734155  0.05954058 -0.02129532 -0.00620595
  0.04445121 -0.06548562  0.12528712 -0.05039625 -0.06332999 -0.03099563
  0.02289497  0.01103904  0.07139652  0.01427248 -0.00836158 -0.06764124
 -0.0105172  -0.02345095  0.06816308 -0.03530688 -0.01159501 -0.0730303
 -0.04177375  0.01427248 -0.00728377  0.0164281  -0.00943939 -0.01590626
  0.0250506  -0.04931844  0.04121778 -0.06332999 -0.06440781 -0.02560657
 -0.00405033  0.00457217 -0.00728377 -0.0374625  -0.02560657 -0.02452876
 -0.01806189 -0.01482845 -0.02991782 -0.046085   -0.06979687  0.03367309
 -0.00405033 -0.02021751  0.00241654 -0.03099563  0.02828403 -0.03638469
 -0.05794093 -0.0374625   0.01211685 -0.02237314 -0.03530688  0.00996123
 -0.03961813  0.07139652 -0.07518593 -0.00620595 -0.04069594 -0.04824063
 -0.02560657  0.0519959   0.00457217 -0.06440781 -0.01698407 -0.05794093
  0.00996123  0.08864151 -0.00512814 -0.06440781  0.01750591 -0.04500719
  0.02828403  0.04121778  0.06492964 -0.03207344 -0.07626374  0.04984027
  0.04552903 -0.00943939 -0.03207344  0.00457217  0.02073935  0.01427248
  0.11019775  0.00133873  0.05846277 -0.02129532 -0.0105172  -0.04716281
  0.00457217  0.01750591  0.08109682  0.0347509   0.02397278 -0.00836158
 -0.06117437 -0.00189471 -0.06225218  0.0164281   0.09618619 -0.06979687
 -0.02129532 -0.05362969  0.0433734   0.05630715 -0.0816528   0.04984027
  0.11127556  0.06169621  0.01427248  0.04768465  0.01211685  0.00564998
  0.04660684  0.12852056  0.05954058  0.09295276  0.01535029 -0.00512814
  0.0703187  -0.00405033 -0.00081689 -0.04392938  0.02073935  0.06061839
 -0.0105172  -0.03315126 -0.06548562  0.0433734  -0.06225218  0.06385183
  0.03043966  0.07247433 -0.0191397  -0.06656343 -0.06009656  0.06924089
  0.05954058 -0.02668438 -0.02021751 -0.046085    0.07139652 -0.07949718
  0.00996123 -0.03854032  0.01966154  0.02720622 -0.00836158 -0.01590626
  0.00457217 -0.04285156  0.00564998 -0.03530688  0.02397278 -0.01806189
  0.04229559 -0.0547075  -0.00297252 -0.06656343 -0.01267283 -0.04177375
 -0.03099563 -0.00512814 -0.05901875  0.0250506  -0.046085    0.00349435
  0.05415152 -0.04500719 -0.05794093 -0.05578531  0.00133873  0.03043966
  0.00672779  0.04660684  0.02612841  0.04552903  0.04013997 -0.01806189
  0.01427248  0.03690653  0.00349435 -0.07087468 -0.03315126  0.09403057
  0.03582872  0.03151747 -0.06548562 -0.04177375 -0.03961813 -0.03854032
 -0.02560657 -0.02345095 -0.06656343  0.03259528 -0.046085   -0.02991782
 -0.01267283 -0.01590626  0.07139652 -0.03099563  0.00026092  0.03690653
  0.03906215 -0.01482845  0.00672779 -0.06871905 -0.00943939  0.01966154
  0.07462995 -0.00836158 -0.02345095 -0.046085    0.05415152 -0.03530688
 -0.03207344 -0.0816528   0.04768465  0.06061839  0.05630715  0.09834182
  0.05954058  0.03367309  0.05630715 -0.06548562  0.16085492 -0.05578531
 -0.02452876 -0.03638469 -0.00836158 -0.04177375  0.12744274 -0.07734155
  0.02828403 -0.02560657 -0.06225218 -0.00081689  0.08864151 -0.03207344
  0.03043966  0.00888341  0.00672779 -0.02021751 -0.02452876 -0.01159501
  0.02612841 -0.05901875 -0.03638469 -0.02452876  0.01858372 -0.0902753
 -0.00512814 -0.05255187 -0.02237314 -0.02021751 -0.0547075  -0.00620595
 -0.01698407  0.05522933  0.07678558  0.01858372 -0.02237314  0.09295276
 -0.03099563  0.03906215 -0.06117437 -0.00836158 -0.0374625  -0.01375064
  0.07355214 -0.02452876  0.03367309  0.0347509  -0.03854032 -0.03961813
 -0.00189471 -0.03099563 -0.046085    0.00133873  0.06492964  0.04013997
 -0.02345095  0.05307371  0.04013997 -0.02021751  0.01427248 -0.03422907
  0.00672779  0.00457217  0.03043966  0.0519959   0.06169621 -0.00728377
  0.00564998  0.05415152 -0.00836158  0.114509    0.06708527 -0.05578531
  0.03043966 -0.02560657  0.10480869 -0.00620595 -0.04716281 -0.04824063
  0.08540807 -0.01267283 -0.03315126 -0.00728377 -0.01375064  0.05954058
  0.02181716  0.01858372 -0.01159501 -0.00297252  0.01750591 -0.02991782
 -0.02021751 -0.05794093  0.06061839 -0.04069594 -0.07195249 -0.05578531
  0.04552903 -0.00943939 -0.03315126  0.04984027 -0.08488624  0.00564998
  0.02073935 -0.00728377  0.10480869 -0.02452876 -0.00620595 -0.03854032
  0.13714305  0.17055523  0.00241654  0.03798434 -0.05794093 -0.00943939
 -0.02345095 -0.0105172  -0.03422907 -0.00297252  0.06816308  0.00996123
  0.00241654 -0.03854032  0.02612841 -0.08919748  0.06061839 -0.02884001
 -0.02991782 -0.0191397  -0.04069594  0.01535029 -0.02452876  0.00133873
  0.06924089 -0.06979687 -0.02991782 -0.046085    0.01858372  0.00133873
 -0.03099563 -0.00405033  0.01535029  0.02289497  0.04552903 -0.04500719
 -0.03315126  0.097264    0.05415152  0.12313149 -0.08057499  0.09295276
 -0.05039625 -0.01159501 -0.0277622   0.05846277  0.08540807 -0.00081689
  0.00672779  0.00888341  0.08001901  0.07139652 -0.02452876 -0.0547075
 -0.03638469  0.0164281   0.07786339 -0.03961813  0.01103904 -0.04069594
 -0.03422907  0.00564998  0.08864151 -0.03315126 -0.05686312 -0.03099563
  0.05522933 -0.06009656  0.00133873 -0.02345095 -0.07410811  0.01966154
 -0.01590626 -0.01590626  0.03906215 -0.0730303 ]

 

#再举一个例子，机器学习中大名鼎鼎的iris数据集

iris = datasets.load_iris()
iris_data = iris.data
iris_target = iris.target
print(iris_data.shape)
print(iris_target.shape)
# 如果想把特征数据和标签数据放在同一个array中，运行iris_ = np.hstack((iris_data, iris_target))，结果报错;

# iris_ = np.hstack((iris_data, iris_target))#报错
# 报错信息：ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has
# 2 dimension(s) and the array at index 1 has 1 dimension(s)；
#说我们输入的arrays的维度数不一样，这时我们的np.newaxis就可以派上用场了
print(iris_target[:, np.newaxis].shape)
iris = np.hstack((iris_data, iris_target[:,np.newaxis]))
print(iris.shape)# 成功了

 

#输出：
(150, 4)
(150,)
(150, 1)
(150, 5)