Numpy tutorial(final) 20160303

2016.03.02

Numpy Tutorial

이남기 ([email protected])

Shape Manipulation (1)u Changing the shape of an array• An Array has a shape given by the number of elements along

each axis:>>> a = np.floor(10*np.random.random((3,4))) >>> a array([[ 2., 8., 0., 6.],

[ 4., 5., 1., 1.], [ 8., 9., 3., 6.]

]) >>> a.shape (3, 4)

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numpy.floor• 소수점 버리고반올림

Shape Manipulation (2)u The shape of an array can be changed with various

commands:>>> a.ravel() # flatten the arrayarray([ 2., 8., 0., 6., 4., 5., 1., 1., 8., 9., 3., 6.]) >>> a.shape = (6, 2) >>> a.Tarray([[ 2., 0., 4., 1., 8., 3.],

[ 8., 6., 5., 1., 9., 6.]])

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a.shape = (6, 2) a.T

Shape Manipulation (3)u Stacking together different arrays

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>>> a = np.array([1,2,3])>>> aarray([1,2,3])>>> b = np.array([4,5,6])>>> barray([4,5,6])>>> np.vstack((a,b))array([[1,2,3],

[4,5,6]])>>> np.hstack((a,b))array([1,2,3,4,5,6])


• The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to vstack only for 1D arrays:

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>>> from numpy import newaxis>>> np.column_stack((a,b)) # With 2D arraysarray([[ 8., 8., 1., 8.],

[ 0., 0., 0., 4.]])

123 456

a b 142536

np.column_stack((a,b))


• The function newaxis add an axis to N-dimensional array

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>>> a = np.array( [[1,2][3,4]])

>>> a[0,newaxis]array([[1,2]])>>> a[1,newaxis]array([[3,4]])>>> a[:,newaxis,:]array( [[[1,2]], #shape

[[3,4]]] #(2,1,2)>>> a[newaxis,:,:]array( [[[1,2], #shape

[3,4]]]) #(1,2,2)

a[:,newaxis,:] = a[range(0,1), newaxis, range(0,1)

a[[1,2],[3,4]]

a[0][1,2]

a[1][3,4]

Adding an axis

Adding an axis [[3,4]]

[[1,2]]

a[:,newaxis,:]array( [ [[1,2]],

[[3,4]] ])


• The function newaxis add an axis to N-dimensional array

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>>> a = np.array( [[1,2][3,4]])

>>> a[0,newaxis]array([[1,2]])>>> a[1,newaxis]array([[3,4]])>>> a[:,newaxis,:]array( [[[1,2]], #shape

[[3,4]]] #(2,1,2)>>> a[newaxis,:,:]array( [[[1,2], #shape

[3,4]]]) #(1,2,2)

a[[1,2],[3,4]]

shape(2,2)

a[[[1,2]],[[3,4]]]

a[:,newaxis,:]

shape(2,1,2)


• The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to vstack only for 1D arrays:

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>>> np.column_stack((a[:,newaxis],b[:,newaxis]))array([[ 4., 2.],

[ 2., 8.]])>>> np.vstack((a[:,newaxis],b[:,newaxis])) # The behavior of vstack is differentarray([[ 4.],

[ 2.],[ 2.],[ 8.]])

42

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a[:,newaxis]

b[:,newaxis]

4228

np.vstack

Shape Manipulation (8)u Splitting one array into several smaller ones

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>>> a = np.floor(10*np.random.random((2,12)))>>> aarray([[ 9., 5., 6., 3., 6., 8., 0., 7., 9., 7., 2., 7.],

[ 1., 4., 9., 2., 2., 1., 0., 6., 2., 2., 4., 0.]])>>> np.hsplit(a,3)[array([[ 9., 5., 6., 3.],

[ 1., 4., 9., 2.]]), array([[ 6., 8., 0., 7.],[ 2., 1., 0., 6.]]), array([[ 9., 7., 2., 7.],[ 2., 2., 4., 0.]])]

956368079727149221062240

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68072106

97272240

Split a into 3

Shape Manipulation (9)u Splitting one array into several smaller ones

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>>> a = np.floor(10*np.random.random((2,12)))>>> aarray([[ 9., 5., 6., 3., 6., 8., 0., 7., 9., 7., 2., 7.],

[ 1., 4., 9., 2., 2., 1., 0., 6., 2., 2., 4., 0.]])>>> np.hsplit(a,(3,4))[array([[ 9., 5., 6.],

[ 1., 4., 9.]]), array([[ 3.],

[ 2.]]), array([[ 6., 8., 0., 7., 9., 7., 2., 7.],

[ 2., 1., 0., 6., 2., 2., 4., 0.]])]

956368079727149221062240

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6807972721062240

# Split a after the third and the fourth column

Copies and views (1)u No copy at all

• Simple assignments make no copy of array objects or of their data.

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>>> a = np.arange(12)>>> b = a>>> b is aTrue>>> b.shape = 3,4>>> a.shape(3, 4) a

a.shape

b

b.shapeno new object is createda and b are two names for the same ndarray object

ndarrayobject

Copies and views (2)u No copy at all

• Python passes mutable objects as references, so function calls make no copy.

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>>> def f(x):... print(id(x))...>>> id(a) # id is a unique identifier of an object148293216>>> f(a)148293216

Copies and views (3)u View or Shallow Copy

• Different array objects can share the same data. The view method creates a new array object that looks at the same data.

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a

a.view

c

c.viewc.base

a.base

ndarrayobject1

ndarrayobject1’

>>> c = a.view()>>> c is aFalse>>> c.base is a True>>> c.flags.owndataFalse>>>>>> c.shape = 2,6>>> a.shape(3, 4)>>> c[0,4] = 1234 >>> aarray([[ 0, 1, 2, 3],

[1234, 5, 6, 7],[ 8, 9, 10, 11]])

data

01234567891011

01234567891011

a c



14

a

a.view

c

c.viewc.base

a.base

ndarrayobject1

ndarrayobject1’

ndarray.flags.owndata• The�array�owns�the�memory�it�uses�or�borrows�it�from�another�object.

data


[1234, 5, 6, 7],[ 8, 9, 10, 11]])



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[1234, 5, 6, 7],[ 8, 9, 10, 11]])

a

a.view

c

c.viewc.base

a.base

ndarrayobject1

ndarrayobject1’

data

01234567891011

01234567891011

a c


• Slicing an array returns a view of it:

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>>> aarray([[ 0, 1, 2, 3],

[1234, 5, 6, 7],[ 8, 9, 10, 11]])

>>> s = a[ : , 1:3] >>> s[:] = 10 >>> aarray([[ 0, 10, 10, 3],

[1234, 10, 10, 7],[ 8, 10, 10, 11]])

Copies and views (7)u Deep Copy

• The copy method makes a complete copy of the array and its data.

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>>> d = a.copy()>>> d is aFalse>>> d.base is aFalse>>> d[0,0] = 9999>>> aarray([[ 0, 10, 10, 3],

[1234, 10, 10, 7],[ 8, 10, 10, 11]])

a

a.shape

d

d.shape

ndarrayobject

ndarrayobject

# a new array object with new data is created# d doesn't share anything with a

Numpy tutorial(final) 20160303

Engineering

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