numpy

In [12]: import numpy as np
# Generate some random data
In [13]: data = np.random.randn(2, 3)
In [14]: data
Out[14]:
array([[-0.2047, 0.4789, -0.5194],
[-0.5557, 1.9658, 1.3934]])

In [15]: data * 10
Out[15]:
array([[ -2.0471, 4.7894, -5.1944],
[ -5.5573, 19.6578, 13.9341]])

In [17]: data.shape
Out[17]: (2, 3)
In [18]: data.dtype
Out[18]: dtype('float64')

Creating ndarrays

In [19]: data1 = [6, 7.5, 8, 0, 1]
In [20]: arr1 = np.array(data1)
In [21]: arr1
Out[21]: array([ 6. , 7.5, 8. , 0. , 1. ])

In [22]: data2 = [[1, 2, 3, 4], [5, 6, 7, 8]]
In [23]: arr2 = np.array(data2)
In [24]: arr2
Out[24]:
array([[1, 2, 3, 4],
[5, 6, 7, 8]])

In [29]: np.zeros(10)
Out[29]: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
In [30]: np.zeros((3, 6))
Out[30]:
array([[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.]])

np.zeros((2,3,6))
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., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0.]]])

In [33]: arr1 = np.array([1, 2, 3], dtype=np.float64)
In [34]: arr2 = np.array([1, 2, 3], dtype=np.int32)

In [51]: arr = np.array([[1., 2., 3.], [4., 5., 6.]])
In [52]: arr
Out[52]:
array([[ 1., 2., 3.],
[ 4., 5., 6.]])
In [53]: arr * arr
Out[53]:
array([[ 1., 4., 9.],
[ 16., 25., 36.]])

In [55]: 1 / arr
Out[55]:
array([[ 1. , 0.5 , 0.3333],
[ 0.25 , 0.2 , 0.1667]])
In [56]: arr ** 0.5
Out[56]:
array([[ 1. , 1.4142, 1.7321],
[ 2. , 2.2361, 2.4495]])

In [57]: arr2 = np.array([[0., 4., 1.], [7., 2., 12.]])
In [58]: arr2
Out[58]:
array([[ 0., 4., 1.],
[ 7., 2., 12.]])
In [59]: arr2 > arr
Out[59]:
array([[False, True, False],
[ True, False, True]], dtype=bool)

In [72]: arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
In [73]: arr2d[2]
Out[73]: array([7, 8, 9])

In [74]: arr2d[0][2]
Out[74]: 3
In [75]: arr2d[0, 2]
Out[75]: 3

In [90]: arr2d
Out[90]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In [91]: arr2d[:2]
Out[91]:
array([[1, 2, 3],
[4, 5, 6]])

In [92]: arr2d[:2, 1:]
Out[92]:
array([[2, 3],
[5, 6]])

In [195]: arr = np.random.randn(6)
In [196]: arr
Out[196]: array([ 0.6095, -0.4938, 1.24 , -0.1357, 1.43 , -0.8469])
In [197]: arr.sort()
In [198]: arr
Out[198]: array([-0.8469, -0.4938, -0.1357, 0.6095, 1.24 , 1.43 ])

In [199]: arr = np.random.randn(5, 3)
In [200]: arr
Out[200]:
array([[ 0.6033, 1.2636, -0.2555],
[-0.4457, 0.4684, -0.9616],
[-1.8245, 0.6254, 1.0229],
[ 1.1074, 0.0909, -0.3501],
[ 0.218 , -0.8948, -1.7415]])

In [201]: arr.sort(1)
In [202]: arr
Out[202]:
array([[-0.2555, 0.6033, 1.2636],
[-0.9616, -0.4457, 0.4684],
[-1.8245, 0.6254, 1.0229],
[-0.3501, 0.0909, 1.1074],
[-1.7415, -0.8948, 0.218 ]])

In [206]: names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
In [207]: np.unique(names)
Out[207]:
array(['Bob', 'Joe', 'Will'],
dtype='<U4')
In [208]: ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4])
In [209]: np.unique(ints)
Out[209]: array([1, 2, 3, 4])