数据科学-Numpy数组基本操作_np数组取后10行-优快云博客

numpy索引和切片

numpy索引和切片

In [24]: t2
Out[24]:
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23]])

In [9]: t2[1]   #取第2行
Out[9]: array([ 6,  7,  8,  9, 10, 11])

In [10]: t2[2]   #取第3行
Out[10]: array([12, 13, 14, 15, 16, 17])

In [11]: t2[2:]  #取第3行到最后
Out[11]:
array([[12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23]])

In [12]: t2[1:]   #取第2行到最后
Out[12]:
array([[ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23]])

In [13]: t2[:1]  #取1行
Out[13]: array([[0, 1, 2, 3, 4, 5]])

In [14]: t2[1]
Out[14]: array([ 6,  7,  8,  9, 10, 11])

In [15]: t2[:2] #取2行
Out[15]:
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11]])

In [16]: t2[1,:]  #行列以逗号隔开，取第2行
Out[16]: array([ 6,  7,  8,  9, 10, 11])

In [25]: type(t2[2,3])
Out[25]: numpy.int32

In [26]: t2[2:3,3:4]  #取2行和3列的交集
Out[26]: array([[15]])

In [27]: t2[2:4,3:5]   #取3.4行和4.5列的交集
Out[27]:
array([[15, 16],
       [21, 22]])

In [29]: t2[[1,2],[3,4]]  #取1行3列和2行4列的数值
Out[29]: array([ 9, 16])

In [30]: t2[[0,1,2],[0,3,4]]  #取0行0列、1行3列和2行4列的数值
Out[30]: array([ 0,  9, 16])

numpy三元运算符

In [42]: t3=np.arange(24).reshape(4,6)

In [43]: t3
Out[43]:
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23]])

In [44]: np.where(t3<=10,100,300)   #条件运算
Out[44]:
array([[100, 100, 100, 100, 100, 100],
       [100, 100, 100, 100, 100, 300],
       [300, 300, 300, 300, 300, 300],
       [300, 300, 300, 300, 300, 300]])

numpy中的clip(裁剪)

In [45]: t4=np.arange(24).reshape(4,6)

In [46]: t4.clip(10,18)  #小于10的替换成10，大于18的替换成18
Out[46]:
array([[10, 10, 10, 10, 10, 10],
       [10, 10, 10, 10, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 18, 18, 18, 18, 18]])

numpy中的nan和inf

In [51]: t5=np.arange(24).reshape(4,6)

In [52]: t5=t5.astype(float)   #修改成浮点型才能修改nan

In [53]: t5[3,3]=np.nan

In [54]: t5
Out[54]:
array([[ 0.,  1.,  2.,  3.,  4.,  5.],
       [ 6.,  7.,  8.,  9., 10., 11.],
       [12., 13., 14., 15., 16., 17.],
       [18., 19., 20., nan, 22., 23.]])

In [55]: t5[3,3]
Out[55]: nan