NumPy Arrays Flashcards
my_list = [1,2,3]
np.array(my_list)
array([1, 2, 3])
my_matrix = [[1,2,3],[4,5,6],[7,8,9]]
np.array(my_matrix)
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
np.arange(0,10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
np.arange(0,11,2)
array([ 0, 2, 4, 6, 8, 10])
np.zeros(3)
array([ 0., 0., 0.])
np.zeros((5,5))
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.]])
np.ones(3)
array([ 1., 1., 1.])
np.ones((3,3))
array([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]])
np.linspace(0,10,3)
array([ 0., 5., 10.])
np.linspace(0,10,50)
array([ 0. , 0.20408163, 0.40816327, 0.6122449 ,
0.81632653, 1.02040816, 1.2244898 , 1.42857143,
1.63265306, 1.83673469, 2.04081633, 2.24489796,
2.44897959, 2.65306122, 2.85714286, 3.06122449,
3.26530612, 3.46938776, 3.67346939, 3.87755102,
4.08163265, 4.28571429, 4.48979592, 4.69387755,
4.89795918, 5.10204082, 5.30612245, 5.51020408,
5.71428571, 5.91836735, 6.12244898, 6.32653061,
6.53061224, 6.73469388, 6.93877551, 7.14285714,
7.34693878, 7.55102041, 7.75510204, 7.95918367,
8.16326531, 8.36734694, 8.57142857, 8.7755102 ,
8.97959184, 9.18367347, 9.3877551 , 9.59183673,
9.79591837, 10. ])
np.eye(4)
array([[ 1., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., 1., 0.],
[ 0., 0., 0., 1.]])
np.random.rand(2)
array([ 0.11570539, 0.35279769])
np.random.rand(5,5)
array([[ 0.66660768, 0.87589888, 0.12421056, 0.65074126, 0.60260888],
[ 0.70027668, 0.85572434, 0.8464595 , 0.2735416 , 0.10955384],
[ 0.0670566 , 0.83267738, 0.9082729 , 0.58249129, 0.12305748],
[ 0.27948423, 0.66422017, 0.95639833, 0.34238788, 0.9578872 ],
[ 0.72155386, 0.3035422 , 0.85249683, 0.30414307, 0.79718816]])
np.random.randn(2)
array([-0.27954018, 0.90078368])
np.random.randn(5,5)
array([[ 0.70154515, 0.22441999, 1.33563186, 0.82872577, -0.28247509],
[ 0.64489788, 0.61815094, -0.81693168, -0.30102424, -0.29030574],
[ 0.8695976 , 0.413755 , 2.20047208, 0.17955692, -0.82159344],
[ 0.59264235, 1.29869894, -1.18870241, 0.11590888, -0.09181687],
[-0.96924265, -1.62888685, -2.05787102, -0.29705576, 0.68915542]])
np.random.randint(1,100)
44
np.random.randint(1,100,10)
array([13, 64, 27, 63, 46, 68, 92, 10, 58, 24])
arr = np.arange(25)
arr
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, 24])
ranarr = np.random.randint(0,50,10)
ranarr
array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39])
where arr = np.arange(25)
arr.reshape(5,5)
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, 24]])
where ranarr is array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39]) :
ranarr.max()
49
where ranarr is array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39]) :
ranarr.argmax()
4
where ranarr is array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39]) :
randarr.min()
2
where ranarr is array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39]) :
ranarr.argmin()
5
where arr = np.arange(25) :
arr.shape
(25,)
where arr = np.arange(25) :
arr.reshape(1,25)
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, 24]])
where arr = np.arange(25) :
arr.reshape(1,25).shape
(1, 25)
where arr = np.arange(25) :
arr.reshape(25,1)
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],
[24]])
where arr = np.arange(25) :
arr.reshape(25,1).shape
(25, 1)
where arr = np.arange(25) :
arr.dytpe
dtype(‘int64’)
