Initialization:
Ex1. import numpy as np
a = np.zeros((3,3,3))
a
OutPut:
OutPut:
array([[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]])
Ex 2:
Ex 2:
np.ones((2,2,4), dtype=int)
Output:
array([[[1, 1, 1, 1], [1, 1, 1, 1]],
Output:
array([[[1, 1, 1, 1], [1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1]]])
Ex3:
np.full((2,2),"hellottwyyw", dtype='<U7')
Out[12]:
array([['hellott', 'hellott'], ['hellott', 'hellott']], dtype='<U7')
In [13]:
np.random.rand(10)
Out[13]:
array([0.12520064, 0.07562049, 0.12900815, 0.55747886, 0.11532656, 0.53774122, 0.06926235, 0.29796341, 0.09515193, 0.05531125])
In [4]:
data = np.random.randint(5,10,(10,10))
Access of NumPy
In [5]:
data[4:8]
Out[5]:
array([[6, 8, 7, 5, 7, 6, 8, 9, 7, 5], [6, 6, 7, 6, 8, 8, 7, 9, 7, 8], [9, 6, 5, 7, 8, 9, 8, 9, 5, 5], [6, 7, 7, 7, 7, 6, 5, 5, 8, 8]])
In [22]:
data[4:8,4:6]
Out[22]:
array([[5, 6], [7, 8], [5, 9], [8, 9]])
In [28]:
data[4:6,4:6]
Out[28]:
array([[5, 6], [7, 8]])
Combine Data
In [29]:
data1 = np.random.randint(5,15,size=(5,3))
In [31]:
data2 = np.random.randint(5,15,size=(5,3))
In [32]:
np.hstack([data1,data2])
Out[32]:
array([[ 8, 10, 14, 6, 13, 6], [ 6, 10, 14, 10, 14, 13], [12, 5, 13, 5, 8, 13], [ 5, 14, 10, 12, 9, 6], [ 9, 7, 14, 13, 9, 8]])
In [33]:
np.vstack([data1, data2])
Out[33]:
array([[ 8, 10, 14], [ 6, 10, 14], [12, 5, 13], [ 5, 14, 10], [ 9, 7, 14], [ 6, 13, 6], [10, 14, 13], [ 5, 8, 13], [12, 9, 6], [13, 9, 8]])
In [34]:
np.concatenate([data1, data2], axis=1)
Out[34]:
array([[ 8, 10, 14, 6, 13, 6], [ 6, 10, 14, 10, 14, 13], [12, 5, 13, 5, 8, 13], [ 5, 14, 10, 12, 9, 6], [ 9, 7, 14, 13, 9, 8]])
In [35]:
data1 = np.random.randint(5,15,size=(5,3,2))
In [38]:
data2 = np.random.randint(5,15,size=(5,3,2))
In [41]:
np.concatenate([data1, data2], axis=2)
Out[41]:
array([[[ 5, 14, 14, 14], [ 8, 9, 10, 8], [12, 9, 14, 5]], [[ 6, 11, 6, 12], [ 9, 6, 5, 10], [10, 6, 9, 8]], [[11, 13, 8, 5], [13, 14, 12, 6], [ 7, 9, 14, 6]], [[13, 9, 5, 11], [ 5, 10, 5, 7], [ 6, 5, 14, 6]], [[ 5, 14, 11, 8], [ 5, 5, 6, 9], [ 7, 11, 11, 11]]])
Splitting Data
In [44]:
a1,a2 = np.hsplit(data,[5])
In [45]:
a1
Out[45]:
array([[9, 6, 5, 7, 9], [8, 9, 9, 6, 9], [9, 9, 8, 6, 7], [5, 5, 5, 6, 5], [6, 6, 7, 7, 5], [8, 5, 9, 5, 7], [7, 7, 6, 6, 5], [5, 6, 9, 6, 8], [6, 9, 6, 6, 7], [5, 7, 5, 5, 6]])
In [47]:
b1,b2,b3 = np.vsplit(data,[5,7])
In [48]:
b1
Out[48]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6], [8, 9, 9, 6, 9, 7, 5, 6, 7, 8], [9, 9, 8, 6, 7, 7, 6, 6, 9, 5], [5, 5, 5, 6, 5, 9, 9, 9, 8, 9], [6, 6, 7, 7, 5, 6, 9, 9, 8, 7]])
In [49]:
b2
Out[49]:
array([[8, 5, 9, 5, 7, 8, 5, 9, 6, 8], [7, 7, 6, 6, 5, 9, 6, 6, 5, 9]])
In [50]:
b3
Out[50]:
array([[5, 6, 9, 6, 8, 9, 9, 6, 9, 8], [6, 9, 6, 6, 7, 7, 5, 8, 5, 8], [5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
Shape, Dimension
In [52]:
data.shape
Out[52]:
(10, 10)
In [53]:
data.ndim
Out[53]:
2
In [54]:
data.size
Out[54]:
100
In [56]:
data.reshape(2,50)
Out[56]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6, 8, 9, 9, 6, 9, 7, 5, 6, 7, 8, 9, 9, 8, 6, 7, 7, 6, 6, 9, 5, 5, 5, 5, 6, 5, 9, 9, 9, 8, 9, 6, 6, 7, 7, 5, 6, 9, 9, 8, 7], [8, 5, 9, 5, 7, 8, 5, 9, 6, 8, 7, 7, 6, 6, 5, 9, 6, 6, 5, 9, 5, 6, 9, 6, 8, 9, 9, 6, 9, 8, 6, 9, 6, 6, 7, 7, 5, 8, 5, 8, 5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
In [57]:
d = np.array([1,2,3,4,5,6,7,9])
In [59]:
d.ndim
Out[59]:
1
In [60]:
d.shape
Out[60]:
(8,)
In [61]:
d.size
Out[61]:
8
In [62]:
d.reshape(8,1)
Out[62]:
array([[1], [2], [3], [4], [5], [6], [7], [9]])
In [65]:
data.reshape(-1,20)
Out[65]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6, 8, 9, 9, 6, 9, 7, 5, 6, 7, 8], [9, 9, 8, 6, 7, 7, 6, 6, 9, 5, 5, 5, 5, 6, 5, 9, 9, 9, 8, 9], [6, 6, 7, 7, 5, 6, 9, 9, 8, 7, 8, 5, 9, 5, 7, 8, 5, 9, 6, 8], [7, 7, 6, 6, 5, 9, 6, 6, 5, 9, 5, 6, 9, 6, 8, 9, 9, 6, 9, 8], [6, 9, 6, 6, 7, 7, 5, 8, 5, 8, 5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
Utility Function
In [68]:
np.sum(data)
Out[68]:
683
In [72]:
np.sum(data, axis=1)
Out[72]:
array([63, 74, 72, 70, 70, 70, 66, 75, 67, 56])
In [71]:
data
Out[71]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6], [8, 9, 9, 6, 9, 7, 5, 6, 7, 8], [9, 9, 8, 6, 7, 7, 6, 6, 9, 5], [5, 5, 5, 6, 5, 9, 9, 9, 8, 9], [6, 6, 7, 7, 5, 6, 9, 9, 8, 7], [8, 5, 9, 5, 7, 8, 5, 9, 6, 8], [7, 7, 6, 6, 5, 9, 6, 6, 5, 9], [5, 6, 9, 6, 8, 9, 9, 6, 9, 8], [6, 9, 6, 6, 7, 7, 5, 8, 5, 8], [5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
In [73]:
np.sin(data)
Out[73]:
array([[ 0.41211849, -0.2794155 , -0.95892427, 0.6569866 , 0.41211849, -0.2794155 , -0.95892427, -0.95892427, -0.95892427, -0.2794155 ], [ 0.98935825, 0.41211849, 0.41211849, -0.2794155 , 0.41211849, 0.6569866 , -0.95892427, -0.2794155 , 0.6569866 , 0.98935825], [ 0.41211849, 0.41211849, 0.98935825, -0.2794155 , 0.6569866 , 0.6569866 , -0.2794155 , -0.2794155 , 0.41211849, -0.95892427], [-0.95892427, -0.95892427, -0.95892427, -0.2794155 , -0.95892427, 0.41211849, 0.41211849, 0.41211849, 0.98935825, 0.41211849], [-0.2794155 , -0.2794155 , 0.6569866 , 0.6569866 , -0.95892427, -0.2794155 , 0.41211849, 0.41211849, 0.98935825, 0.6569866 ], [ 0.98935825, -0.95892427, 0.41211849, -0.95892427, 0.6569866 , 0.98935825, -0.95892427, 0.41211849, -0.2794155 , 0.98935825], [ 0.6569866 , 0.6569866 , -0.2794155 , -0.2794155 , -0.95892427, 0.41211849, -0.2794155 , -0.2794155 , -0.95892427, 0.41211849], [-0.95892427, -0.2794155 , 0.41211849, -0.2794155 , 0.98935825, 0.41211849, 0.41211849, -0.2794155 , 0.41211849, 0.98935825], [-0.2794155 , 0.41211849, -0.2794155 , -0.2794155 , 0.6569866 , 0.6569866 , -0.95892427, 0.98935825, -0.95892427, 0.98935825], [-0.95892427, 0.6569866 , -0.95892427, -0.95892427, -0.2794155 , -0.95892427, -0.2794155 , -0.2794155 , -0.95892427, -0.2794155 ]])
In [75]:
data.T
Out[75]:
array([[9, 8, 9, 5, 6, 8, 7, 5, 6, 5], [6, 9, 9, 5, 6, 5, 7, 6, 9, 7], [5, 9, 8, 5, 7, 9, 6, 9, 6, 5], [7, 6, 6, 6, 7, 5, 6, 6, 6, 5], [9, 9, 7, 5, 5, 7, 5, 8, 7, 6], [6, 7, 7, 9, 6, 8, 9, 9, 7, 5], [5, 5, 6, 9, 9, 5, 6, 9, 5, 6], [5, 6, 6, 9, 9, 9, 6, 6, 8, 6], [5, 7, 9, 8, 8, 6, 5, 9, 5, 5], [6, 8, 5, 9, 7, 8, 9, 8, 8, 6]])
In [76]:
data.mean(axis=1)
Out[76]:
array([6.3, 7.4, 7.2, 7. , 7. , 7. , 6.6, 7.5, 6.7, 5.6])
In [77]:
data.std(axis=1)
Out[77]:
array([1.48660687, 1.356466 , 1.4 , 1.84390889, 1.26491106, 1.54919334, 1.356466 , 1.5 , 1.26885775, 0.66332496])
Broadcasting
In [78]:
v = np.array([1,2,3,4,5])
In [79]:
s = 10
In [80]:
v + s
Out[80]:
array([11, 12, 13, 14, 15])
In [82]:
t = [10,11]
In [84]:
#v + t
In [85]:
a = np.array([[1],[2],[3],[4],[5]])
In [86]:
a.shape
Out[86]:
(5, 1)
In [87]:
a + v
Out[87]:
array([[ 2, 3, 4, 5, 6], [ 3, 4, 5, 6, 7], [ 4, 5, 6, 7, 8], [ 5, 6, 7, 8, 9], [ 6, 7, 8, 9, 10]])
In [88]:
a = np.random.randint(1,5,size=(10,2))
In [89]:
b = np.random.randint(1,5,size=(5,2))
In [94]:
a.reshape(1,10,2)
Out[94]:
array([[[3, 2], [4, 2], [3, 4], [3, 3], [3, 1], [1, 3], [2, 2], [1, 1], [4, 3], [2, 2]]])
In [95]:
b.reshape(5,1,2) - a.reshape(1,10,2)
Out[95]:
array([[[-1, 2], [-2, 2], [-1, 0], [-1, 1], [-1, 3], [ 1, 1], [ 0, 2], [ 1, 3], [-2, 1], [ 0, 2]], [[-2, 2], [-3, 2], [-2, 0], [-2, 1], [-2, 3], [ 0, 1], [-1, 2], [ 0, 3], [-3, 1], [-1, 2]], [[-1, 2], [-2, 2], [-1, 0], [-1, 1], [-1, 3], [ 1, 1], [ 0, 2], [ 1, 3], [-2, 1], [ 0, 2]], [[ 1, 0], [ 0, 0], [ 1, -2], [ 1, -1], [ 1, 1], [ 3, -1], [ 2, 0], [ 3, 1], [ 0, -1], [ 2, 0]], [[ 0, 2], [-1, 2], [ 0, 0], [ 0, 1], [ 0, 3], [ 2, 1], [ 1, 2], [ 2, 3], [-1, 1], [ 1, 2]]])
In [96]:
a = np.random.randint(1,5,size=(10,2))
b = np.random.randint(1,5,size=(5,3))
In [97]:
a.shape
Out[97]:
(10, 2)
In [98]:
b.shape
Out[98]:
(5, 3)
In [99]:
a.reshape(1,10,1,2) - b.reshape(5,1,3,1)
Out[99]:
array([[[[-3, -1], [ 0, 2], [-1, 1]], [[-2, 0], [ 1, 3], [ 0, 2]], [[ 0, -2], [ 3, 1], [ 2, 0]], [[ 0, 0], [ 3, 3], [ 2, 2]], [[-1, -3], [ 2, 0], [ 1, -1]], [[-2, -2], [ 1, 1], [ 0, 0]], [[ 0, 0], [ 3, 3], [ 2, 2]], [[-3, 0], [ 0, 3], [-1, 2]], [[-1, -3], [ 2, 0], [ 1, -1]], [[-2, -3], [ 1, 0], [ 0, -1]]], [[[ 0, 2], [-2, 0], [ 0, 2]], [[ 1, 3], [-1, 1], [ 1, 3]], [[ 3, 1], [ 1, -1], [ 3, 1]], [[ 3, 3], [ 1, 1], [ 3, 3]], [[ 2, 0], [ 0, -2], [ 2, 0]], [[ 1, 1], [-1, -1], [ 1, 1]], [[ 3, 3], [ 1, 1], [ 3, 3]], [[ 0, 3], [-2, 1], [ 0, 3]], [[ 2, 0], [ 0, -2], [ 2, 0]], [[ 1, 0], [-1, -2], [ 1, 0]]], [[[-3, -1], [-2, 0], [-2, 0]], [[-2, 0], [-1, 1], [-1, 1]], [[ 0, -2], [ 1, -1], [ 1, -1]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[-1, -3], [ 0, -2], [ 0, -2]], [[-2, -2], [-1, -1], [-1, -1]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[-3, 0], [-2, 1], [-2, 1]], [[-1, -3], [ 0, -2], [ 0, -2]], [[-2, -3], [-1, -2], [-1, -2]]], [[[-1, 1], [ 0, 2], [ 0, 2]], [[ 0, 2], [ 1, 3], [ 1, 3]], [[ 2, 0], [ 3, 1], [ 3, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[-1, 2], [ 0, 3], [ 0, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, -1], [ 1, 0], [ 1, 0]]], [[[-1, 1], [ 0, 2], [ 0, 2]], [[ 0, 2], [ 1, 3], [ 1, 3]], [[ 2, 0], [ 3, 1], [ 3, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[-1, 2], [ 0, 3], [ 0, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, -1], [ 1, 0], [ 1, 0]]]])
In [ ]:
Ex3:
np.full((2,2),"hellottwyyw", dtype='<U7')
Out[12]:
array([['hellott', 'hellott'], ['hellott', 'hellott']], dtype='<U7')
In [13]:
np.random.rand(10)
Out[13]:
array([0.12520064, 0.07562049, 0.12900815, 0.55747886, 0.11532656, 0.53774122, 0.06926235, 0.29796341, 0.09515193, 0.05531125])
In [4]:
data = np.random.randint(5,10,(10,10))
Access of NumPy
In [5]:
data[4:8]
Out[5]:
array([[6, 8, 7, 5, 7, 6, 8, 9, 7, 5], [6, 6, 7, 6, 8, 8, 7, 9, 7, 8], [9, 6, 5, 7, 8, 9, 8, 9, 5, 5], [6, 7, 7, 7, 7, 6, 5, 5, 8, 8]])
In [22]:
data[4:8,4:6]
Out[22]:
array([[5, 6], [7, 8], [5, 9], [8, 9]])
In [28]:
data[4:6,4:6]
Out[28]:
array([[5, 6], [7, 8]])
Combine Data
In [29]:
data1 = np.random.randint(5,15,size=(5,3))
In [31]:
data2 = np.random.randint(5,15,size=(5,3))
In [32]:
np.hstack([data1,data2])
Out[32]:
array([[ 8, 10, 14, 6, 13, 6], [ 6, 10, 14, 10, 14, 13], [12, 5, 13, 5, 8, 13], [ 5, 14, 10, 12, 9, 6], [ 9, 7, 14, 13, 9, 8]])
In [33]:
np.vstack([data1, data2])
Out[33]:
array([[ 8, 10, 14], [ 6, 10, 14], [12, 5, 13], [ 5, 14, 10], [ 9, 7, 14], [ 6, 13, 6], [10, 14, 13], [ 5, 8, 13], [12, 9, 6], [13, 9, 8]])
In [34]:
np.concatenate([data1, data2], axis=1)
Out[34]:
array([[ 8, 10, 14, 6, 13, 6], [ 6, 10, 14, 10, 14, 13], [12, 5, 13, 5, 8, 13], [ 5, 14, 10, 12, 9, 6], [ 9, 7, 14, 13, 9, 8]])
In [35]:
data1 = np.random.randint(5,15,size=(5,3,2))
In [38]:
data2 = np.random.randint(5,15,size=(5,3,2))
In [41]:
np.concatenate([data1, data2], axis=2)
Out[41]:
array([[[ 5, 14, 14, 14], [ 8, 9, 10, 8], [12, 9, 14, 5]], [[ 6, 11, 6, 12], [ 9, 6, 5, 10], [10, 6, 9, 8]], [[11, 13, 8, 5], [13, 14, 12, 6], [ 7, 9, 14, 6]], [[13, 9, 5, 11], [ 5, 10, 5, 7], [ 6, 5, 14, 6]], [[ 5, 14, 11, 8], [ 5, 5, 6, 9], [ 7, 11, 11, 11]]])
Splitting Data
In [44]:
a1,a2 = np.hsplit(data,[5])
In [45]:
a1
Out[45]:
array([[9, 6, 5, 7, 9], [8, 9, 9, 6, 9], [9, 9, 8, 6, 7], [5, 5, 5, 6, 5], [6, 6, 7, 7, 5], [8, 5, 9, 5, 7], [7, 7, 6, 6, 5], [5, 6, 9, 6, 8], [6, 9, 6, 6, 7], [5, 7, 5, 5, 6]])
In [47]:
b1,b2,b3 = np.vsplit(data,[5,7])
In [48]:
b1
Out[48]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6], [8, 9, 9, 6, 9, 7, 5, 6, 7, 8], [9, 9, 8, 6, 7, 7, 6, 6, 9, 5], [5, 5, 5, 6, 5, 9, 9, 9, 8, 9], [6, 6, 7, 7, 5, 6, 9, 9, 8, 7]])
In [49]:
b2
Out[49]:
array([[8, 5, 9, 5, 7, 8, 5, 9, 6, 8], [7, 7, 6, 6, 5, 9, 6, 6, 5, 9]])
In [50]:
b3
Out[50]:
array([[5, 6, 9, 6, 8, 9, 9, 6, 9, 8], [6, 9, 6, 6, 7, 7, 5, 8, 5, 8], [5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
Shape, Dimension
In [52]:
data.shape
Out[52]:
(10, 10)
In [53]:
data.ndim
Out[53]:
2
In [54]:
data.size
Out[54]:
100
In [56]:
data.reshape(2,50)
Out[56]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6, 8, 9, 9, 6, 9, 7, 5, 6, 7, 8, 9, 9, 8, 6, 7, 7, 6, 6, 9, 5, 5, 5, 5, 6, 5, 9, 9, 9, 8, 9, 6, 6, 7, 7, 5, 6, 9, 9, 8, 7], [8, 5, 9, 5, 7, 8, 5, 9, 6, 8, 7, 7, 6, 6, 5, 9, 6, 6, 5, 9, 5, 6, 9, 6, 8, 9, 9, 6, 9, 8, 6, 9, 6, 6, 7, 7, 5, 8, 5, 8, 5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
In [57]:
d = np.array([1,2,3,4,5,6,7,9])
In [59]:
d.ndim
Out[59]:
1
In [60]:
d.shape
Out[60]:
(8,)
In [61]:
d.size
Out[61]:
8
In [62]:
d.reshape(8,1)
Out[62]:
array([[1], [2], [3], [4], [5], [6], [7], [9]])
In [65]:
data.reshape(-1,20)
Out[65]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6, 8, 9, 9, 6, 9, 7, 5, 6, 7, 8], [9, 9, 8, 6, 7, 7, 6, 6, 9, 5, 5, 5, 5, 6, 5, 9, 9, 9, 8, 9], [6, 6, 7, 7, 5, 6, 9, 9, 8, 7, 8, 5, 9, 5, 7, 8, 5, 9, 6, 8], [7, 7, 6, 6, 5, 9, 6, 6, 5, 9, 5, 6, 9, 6, 8, 9, 9, 6, 9, 8], [6, 9, 6, 6, 7, 7, 5, 8, 5, 8, 5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
Utility Function
In [68]:
np.sum(data)
Out[68]:
683
In [72]:
np.sum(data, axis=1)
Out[72]:
array([63, 74, 72, 70, 70, 70, 66, 75, 67, 56])
In [71]:
data
Out[71]:
array([[9, 6, 5, 7, 9, 6, 5, 5, 5, 6], [8, 9, 9, 6, 9, 7, 5, 6, 7, 8], [9, 9, 8, 6, 7, 7, 6, 6, 9, 5], [5, 5, 5, 6, 5, 9, 9, 9, 8, 9], [6, 6, 7, 7, 5, 6, 9, 9, 8, 7], [8, 5, 9, 5, 7, 8, 5, 9, 6, 8], [7, 7, 6, 6, 5, 9, 6, 6, 5, 9], [5, 6, 9, 6, 8, 9, 9, 6, 9, 8], [6, 9, 6, 6, 7, 7, 5, 8, 5, 8], [5, 7, 5, 5, 6, 5, 6, 6, 5, 6]])
In [73]:
np.sin(data)
Out[73]:
array([[ 0.41211849, -0.2794155 , -0.95892427, 0.6569866 , 0.41211849, -0.2794155 , -0.95892427, -0.95892427, -0.95892427, -0.2794155 ], [ 0.98935825, 0.41211849, 0.41211849, -0.2794155 , 0.41211849, 0.6569866 , -0.95892427, -0.2794155 , 0.6569866 , 0.98935825], [ 0.41211849, 0.41211849, 0.98935825, -0.2794155 , 0.6569866 , 0.6569866 , -0.2794155 , -0.2794155 , 0.41211849, -0.95892427], [-0.95892427, -0.95892427, -0.95892427, -0.2794155 , -0.95892427, 0.41211849, 0.41211849, 0.41211849, 0.98935825, 0.41211849], [-0.2794155 , -0.2794155 , 0.6569866 , 0.6569866 , -0.95892427, -0.2794155 , 0.41211849, 0.41211849, 0.98935825, 0.6569866 ], [ 0.98935825, -0.95892427, 0.41211849, -0.95892427, 0.6569866 , 0.98935825, -0.95892427, 0.41211849, -0.2794155 , 0.98935825], [ 0.6569866 , 0.6569866 , -0.2794155 , -0.2794155 , -0.95892427, 0.41211849, -0.2794155 , -0.2794155 , -0.95892427, 0.41211849], [-0.95892427, -0.2794155 , 0.41211849, -0.2794155 , 0.98935825, 0.41211849, 0.41211849, -0.2794155 , 0.41211849, 0.98935825], [-0.2794155 , 0.41211849, -0.2794155 , -0.2794155 , 0.6569866 , 0.6569866 , -0.95892427, 0.98935825, -0.95892427, 0.98935825], [-0.95892427, 0.6569866 , -0.95892427, -0.95892427, -0.2794155 , -0.95892427, -0.2794155 , -0.2794155 , -0.95892427, -0.2794155 ]])
In [75]:
data.T
Out[75]:
array([[9, 8, 9, 5, 6, 8, 7, 5, 6, 5], [6, 9, 9, 5, 6, 5, 7, 6, 9, 7], [5, 9, 8, 5, 7, 9, 6, 9, 6, 5], [7, 6, 6, 6, 7, 5, 6, 6, 6, 5], [9, 9, 7, 5, 5, 7, 5, 8, 7, 6], [6, 7, 7, 9, 6, 8, 9, 9, 7, 5], [5, 5, 6, 9, 9, 5, 6, 9, 5, 6], [5, 6, 6, 9, 9, 9, 6, 6, 8, 6], [5, 7, 9, 8, 8, 6, 5, 9, 5, 5], [6, 8, 5, 9, 7, 8, 9, 8, 8, 6]])
In [76]:
data.mean(axis=1)
Out[76]:
array([6.3, 7.4, 7.2, 7. , 7. , 7. , 6.6, 7.5, 6.7, 5.6])
In [77]:
data.std(axis=1)
Out[77]:
array([1.48660687, 1.356466 , 1.4 , 1.84390889, 1.26491106, 1.54919334, 1.356466 , 1.5 , 1.26885775, 0.66332496])
Broadcasting
In [78]:
v = np.array([1,2,3,4,5])
In [79]:
s = 10
In [80]:
v + s
Out[80]:
array([11, 12, 13, 14, 15])
In [82]:
t = [10,11]
In [84]:
#v + t
In [85]:
a = np.array([[1],[2],[3],[4],[5]])
In [86]:
a.shape
Out[86]:
(5, 1)
In [87]:
a + v
Out[87]:
array([[ 2, 3, 4, 5, 6], [ 3, 4, 5, 6, 7], [ 4, 5, 6, 7, 8], [ 5, 6, 7, 8, 9], [ 6, 7, 8, 9, 10]])
In [88]:
a = np.random.randint(1,5,size=(10,2))
In [89]:
b = np.random.randint(1,5,size=(5,2))
In [94]:
a.reshape(1,10,2)
Out[94]:
array([[[3, 2], [4, 2], [3, 4], [3, 3], [3, 1], [1, 3], [2, 2], [1, 1], [4, 3], [2, 2]]])
In [95]:
b.reshape(5,1,2) - a.reshape(1,10,2)
Out[95]:
array([[[-1, 2], [-2, 2], [-1, 0], [-1, 1], [-1, 3], [ 1, 1], [ 0, 2], [ 1, 3], [-2, 1], [ 0, 2]], [[-2, 2], [-3, 2], [-2, 0], [-2, 1], [-2, 3], [ 0, 1], [-1, 2], [ 0, 3], [-3, 1], [-1, 2]], [[-1, 2], [-2, 2], [-1, 0], [-1, 1], [-1, 3], [ 1, 1], [ 0, 2], [ 1, 3], [-2, 1], [ 0, 2]], [[ 1, 0], [ 0, 0], [ 1, -2], [ 1, -1], [ 1, 1], [ 3, -1], [ 2, 0], [ 3, 1], [ 0, -1], [ 2, 0]], [[ 0, 2], [-1, 2], [ 0, 0], [ 0, 1], [ 0, 3], [ 2, 1], [ 1, 2], [ 2, 3], [-1, 1], [ 1, 2]]])
In [96]:
a = np.random.randint(1,5,size=(10,2))
b = np.random.randint(1,5,size=(5,3))
In [97]:
a.shape
Out[97]:
(10, 2)
In [98]:
b.shape
Out[98]:
(5, 3)
In [99]:
a.reshape(1,10,1,2) - b.reshape(5,1,3,1)
Out[99]:
array([[[[-3, -1], [ 0, 2], [-1, 1]], [[-2, 0], [ 1, 3], [ 0, 2]], [[ 0, -2], [ 3, 1], [ 2, 0]], [[ 0, 0], [ 3, 3], [ 2, 2]], [[-1, -3], [ 2, 0], [ 1, -1]], [[-2, -2], [ 1, 1], [ 0, 0]], [[ 0, 0], [ 3, 3], [ 2, 2]], [[-3, 0], [ 0, 3], [-1, 2]], [[-1, -3], [ 2, 0], [ 1, -1]], [[-2, -3], [ 1, 0], [ 0, -1]]], [[[ 0, 2], [-2, 0], [ 0, 2]], [[ 1, 3], [-1, 1], [ 1, 3]], [[ 3, 1], [ 1, -1], [ 3, 1]], [[ 3, 3], [ 1, 1], [ 3, 3]], [[ 2, 0], [ 0, -2], [ 2, 0]], [[ 1, 1], [-1, -1], [ 1, 1]], [[ 3, 3], [ 1, 1], [ 3, 3]], [[ 0, 3], [-2, 1], [ 0, 3]], [[ 2, 0], [ 0, -2], [ 2, 0]], [[ 1, 0], [-1, -2], [ 1, 0]]], [[[-3, -1], [-2, 0], [-2, 0]], [[-2, 0], [-1, 1], [-1, 1]], [[ 0, -2], [ 1, -1], [ 1, -1]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[-1, -3], [ 0, -2], [ 0, -2]], [[-2, -2], [-1, -1], [-1, -1]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[-3, 0], [-2, 1], [-2, 1]], [[-1, -3], [ 0, -2], [ 0, -2]], [[-2, -3], [-1, -2], [-1, -2]]], [[[-1, 1], [ 0, 2], [ 0, 2]], [[ 0, 2], [ 1, 3], [ 1, 3]], [[ 2, 0], [ 3, 1], [ 3, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[-1, 2], [ 0, 3], [ 0, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, -1], [ 1, 0], [ 1, 0]]], [[[-1, 1], [ 0, 2], [ 0, 2]], [[ 0, 2], [ 1, 3], [ 1, 3]], [[ 2, 0], [ 3, 1], [ 3, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, 0], [ 1, 1], [ 1, 1]], [[ 2, 2], [ 3, 3], [ 3, 3]], [[-1, 2], [ 0, 3], [ 0, 3]], [[ 1, -1], [ 2, 0], [ 2, 0]], [[ 0, -1], [ 1, 0], [ 1, 0]]]])
In [ ]:
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