# NumPy¶

## The Scientific Python Trilogy¶

Why is Python so popular for research work?

MATLAB has typically been the most popular “language of technical computing”, with strong built-in support for efficient numerical analysis with matrices (the mat in MATLAB is for Matrix, not Maths), and plotting.

Other dynamic languages have cleaner, more logical syntax (Ruby, Haskell)

But Python users developed three critical libraries, matching the power of MATLAB for scientific work:

By combining a plotting library, a matrix maths library, and an easy-to-use interface allowing live plotting commands in a persistent environment, the powerful capabilities of MATLAB were matched by a free and open toolchain.

We’ve learned about Matplotlib and IPython in this course already. NumPy is the last part of the trilogy.

## Limitations of Python Lists¶

The normal Python List is just one dimensional. To make a matrix, we have to nest Python lists:

x = [list(range(5)) for N in range(5)]

x

[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]

x[2][2]

2


Applying an operation to every element is a pain:

x + 5

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [4], in <cell line: 1>()
----> 1 x + 5

TypeError: can only concatenate list (not "int") to list

[[elem + 5 for elem in row] for row in x]

[[5, 6, 7, 8, 9],
[5, 6, 7, 8, 9],
[5, 6, 7, 8, 9],
[5, 6, 7, 8, 9],
[5, 6, 7, 8, 9]]


Common useful operations like transposing a matrix or reshaping a 10 by 10 matrix into a 20 by 5 matrix are not easy to code in raw Python lists.

## The NumPy array¶

NumPy’s array type represents a multidimensional matrix $$M_{i,j,k...n}$$

The NumPy array seems at first to be just like a list:

import numpy as np

my_array = np.array(range(5))

my_array

array([0, 1, 2, 3, 4])

my_array[2]

2

for element in my_array:
print("Hello" * element)

Hello
HelloHello
HelloHelloHello
HelloHelloHelloHello


We can also see our first weakness of NumPy arrays versus Python lists:

my_array.append(4)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [10], in <cell line: 1>()
----> 1 my_array.append(4)

AttributeError: 'numpy.ndarray' object has no attribute 'append'


For NumPy arrays, you typically don’t change the data size once you’ve defined your array, whereas for Python lists, you can do this efficiently. However, you get back lots of goodies in return…

## Elementwise Operations¶

But most operations can be applied element-wise automatically!

my_array + 2

array([2, 3, 4, 5, 6])


These “vectorized” operations are very fast: (see here for more information on the %%timeit magic)

import numpy as np

big_list = range(10000)
big_array = np.arange(10000)

%%timeit
[x ** 2 for x in big_list]

3.22 ms ± 26.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%%timeit
big_array ** 2

5.66 µs ± 63.5 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)


## Arange and linspace¶

NumPy has two easy methods for defining floating-point evenly spaced arrays:

x = np.arange(0, 10, 0.1)  # Start, stop, step size


Note that using non-integer step size does not work with Python lists:

y = list(range(0, 10, 0.1))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [16], in <cell line: 1>()
----> 1 y = list(range(0, 10, 0.1))

TypeError: 'float' object cannot be interpreted as an integer


Similarly, we can quickly an evenly spaced range of a known size (eg. for graph plotting):

import math

values = np.linspace(0, math.pi, 100)  # Start, stop, number of steps

values

array([0.        , 0.03173326, 0.06346652, 0.09519978, 0.12693304,
0.1586663 , 0.19039955, 0.22213281, 0.25386607, 0.28559933,
0.31733259, 0.34906585, 0.38079911, 0.41253237, 0.44426563,
0.47599889, 0.50773215, 0.53946541, 0.57119866, 0.60293192,
0.63466518, 0.66639844, 0.6981317 , 0.72986496, 0.76159822,
0.79333148, 0.82506474, 0.856798  , 0.88853126, 0.92026451,
0.95199777, 0.98373103, 1.01546429, 1.04719755, 1.07893081,
1.11066407, 1.14239733, 1.17413059, 1.20586385, 1.23759711,
1.26933037, 1.30106362, 1.33279688, 1.36453014, 1.3962634 ,
1.42799666, 1.45972992, 1.49146318, 1.52319644, 1.5549297 ,
1.58666296, 1.61839622, 1.65012947, 1.68186273, 1.71359599,
1.74532925, 1.77706251, 1.80879577, 1.84052903, 1.87226229,
1.90399555, 1.93572881, 1.96746207, 1.99919533, 2.03092858,
2.06266184, 2.0943951 , 2.12612836, 2.15786162, 2.18959488,
2.22132814, 2.2530614 , 2.28479466, 2.31652792, 2.34826118,
2.37999443, 2.41172769, 2.44346095, 2.47519421, 2.50692747,
2.53866073, 2.57039399, 2.60212725, 2.63386051, 2.66559377,
2.69732703, 2.72906028, 2.76079354, 2.7925268 , 2.82426006,
2.85599332, 2.88772658, 2.91945984, 2.9511931 , 2.98292636,
3.01465962, 3.04639288, 3.07812614, 3.10985939, 3.14159265])


NumPy comes with ‘vectorised’ versions of common functions which work element-by-element when applied to arrays:

%matplotlib inline

from matplotlib import pyplot as plt

plt.plot(values, np.sin(values))

[<matplotlib.lines.Line2D at 0x7fe3f3c774c0>]


So we don’t have to use awkward list comprehensions when using these.

## Multi-Dimensional Arrays¶

NumPy’s true power comes from multi-dimensional arrays:

np.zeros([3, 4, 2])  # 3 arrays with 4 rows and 2 columns each

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.]]])


Unlike a list-of-lists in Python, we can reshape arrays:

x = np.array(range(40))
x

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, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39])

y = x.reshape([4, 5, 2])
y

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, 25],
[26, 27],
[28, 29]],

[[30, 31],
[32, 33],
[34, 35],
[36, 37],
[38, 39]]])


And index multiple columns at once:

y[3, 2, 1]

35


Including selecting on inner axes while taking all from the outermost:

y[:, 2, 1]

array([ 5, 15, 25, 35])


And subselecting ranges:

y[2:, :1, :]  # Last 2 axes, 1st row, all columns

array([[[20, 21]],

[[30, 31]]])


And transpose arrays:

y.transpose()

array([[[ 0, 10, 20, 30],
[ 2, 12, 22, 32],
[ 4, 14, 24, 34],
[ 6, 16, 26, 36],
[ 8, 18, 28, 38]],

[[ 1, 11, 21, 31],
[ 3, 13, 23, 33],
[ 5, 15, 25, 35],
[ 7, 17, 27, 37],
[ 9, 19, 29, 39]]])


You can get the dimensions of an array with shape

y.shape

(4, 5, 2)

y.transpose().shape

(2, 5, 4)


Some numpy functions apply by default to the whole array, but can be chosen to act only on certain axes:

x = np.arange(12).reshape(4, 3)
x

array([[ 0,  1,  2],
[ 3,  4,  5],
[ 6,  7,  8],
[ 9, 10, 11]])

x.mean(1)  # Mean along the second axis, leaving the first.

array([ 1.,  4.,  7., 10.])

x.mean(0)  # Mean along the first axis, leaving the second.

array([4.5, 5.5, 6.5])

x.mean()  # mean of all axes

5.5


## Array Datatypes¶

A Python list can contain data of mixed type:

x = ["hello", 2, 3.4]

type(x[2])

float

type(x[1])

int


A NumPy array always contains just one datatype:

np.array(x)

array(['hello', '2', '3.4'], dtype='<U32')


NumPy will choose the least-generic-possible datatype that can contain the data:

y = np.array([2, 3.4])

y

array([2. , 3.4])


You can access the array’s dtype, or check the type of individual elements:

y.dtype

dtype('float64')

type(y[0])

numpy.float64

z = np.array([3, 4, 5])
z

array([3, 4, 5])

type(z[0])

numpy.int64


The results are, when you get to know them, fairly obvious string codes for datatypes: NumPy supports all kinds of datatypes beyond the python basics.

NumPy will convert python type names to dtypes:

x = [2, 3.6, 7.2, 0]

int_array = np.array(x, dtype=int)

int_array

array([2, 3, 7, 0])

int_array.dtype

dtype('int64')

float_array = np.array(x, dtype=float)

float_array

array([2. , 3.6, 7.2, 0. ])

float_array.dtype

dtype('float64')


This is another really powerful feature of NumPy.

By default, array operations are element-by-element:

np.arange(5) * np.arange(5)

array([ 0,  1,  4,  9, 16])


If we multiply arrays with non-matching shapes we get an error:

np.arange(5) * np.arange(6)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [51], in <cell line: 1>()
----> 1 np.arange(5) * np.arange(6)

ValueError: operands could not be broadcast together with shapes (5,) (6,)

np.zeros([2, 3]) * np.zeros([2, 4])

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [52], in <cell line: 1>()
----> 1 np.zeros([2, 3]) * np.zeros([2, 4])

ValueError: operands could not be broadcast together with shapes (2,3) (2,4)

m1 = np.arange(100).reshape([10, 10])

m2 = np.arange(100).reshape([10, 5, 2])

m1 + m2

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [55], in <cell line: 1>()
----> 1 m1 + m2

ValueError: operands could not be broadcast together with shapes (10,10) (10,5,2)


Arrays must match in all dimensions in order to be compatible:

np.ones([3, 3]) * np.ones([3, 3])  # Note elementwise multiply, *not* matrix multiply.

array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])


Except, that if one array has any Dimension 1, then the data is REPEATED to match the other.

col = np.arange(10).reshape([10, 1])
col

array([[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9]])

row = col.transpose()
row

array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])

col.shape  # "Column Vector"

(10, 1)

row.shape  # "Row Vector"

(1, 10)

row + col

array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],
[ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],
[ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
[ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13],
[ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14],
[ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
[ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16],
[ 8,  9, 10, 11, 12, 13, 14, 15, 16, 17],
[ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]])

10 * row + col

array([[ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90],
[ 1, 11, 21, 31, 41, 51, 61, 71, 81, 91],
[ 2, 12, 22, 32, 42, 52, 62, 72, 82, 92],
[ 3, 13, 23, 33, 43, 53, 63, 73, 83, 93],
[ 4, 14, 24, 34, 44, 54, 64, 74, 84, 94],
[ 5, 15, 25, 35, 45, 55, 65, 75, 85, 95],
[ 6, 16, 26, 36, 46, 56, 66, 76, 86, 96],
[ 7, 17, 27, 37, 47, 57, 67, 77, 87, 97],
[ 8, 18, 28, 38, 48, 58, 68, 78, 88, 98],
[ 9, 19, 29, 39, 49, 59, 69, 79, 89, 99]])


This works for arrays with more than one unit dimension.

## Another example¶

x = np.array([1, 2]).reshape(1, 2)
x

array([[1, 2]])

y = np.array([3, 4, 5]).reshape(3, 1)
y

array([[3],
[4],
[5]])

result = x + y
result.shape

(3, 2)

result

array([[4, 5],
[5, 6],
[6, 7]])


What numpy is doing:

## Newaxis¶

Broadcasting is very powerful, and numpy allows indexing with np.newaxis to temporarily create new one-long dimensions on the fly.

import numpy as np

x = np.arange(10).reshape(2, 5)
y = np.arange(8).reshape(2, 2, 2)

x

array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])

y

array([[[0, 1],
[2, 3]],

[[4, 5],
[6, 7]]])

x_dash = x[:, :, np.newaxis, np.newaxis]
x_dash.shape

(2, 5, 1, 1)

y_dash = y[:, np.newaxis, :, :]
y_dash.shape

(2, 1, 2, 2)

res = x_dash * y_dash

res.shape

(2, 5, 2, 2)

np.sum(res)

830


Note that newaxis works because a $$3 \times 1 \times 3$$ array and a $$3 \times 3$$ array contain the same data, differently shaped:

threebythree = np.arange(9).reshape(3, 3)
threebythree

array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])

threebythree[:, np.newaxis, :]

array([[[0, 1, 2]],

[[3, 4, 5]],

[[6, 7, 8]]])


NumPy multiply is element-by-element, not a dot-product:

a = np.arange(9).reshape(3, 3)
a

array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])

b = np.arange(3, 12).reshape(3, 3)
b

array([[ 3,  4,  5],
[ 6,  7,  8],
[ 9, 10, 11]])

a * b

array([[ 0,  4, 10],
[18, 28, 40],
[54, 70, 88]])


We can what we’ve learned about the algebra of broadcasting and newaxis to get a dot-product, (matrix inner product).

First we add new axes to $$A$$ and $$B$$:

a[:, :, np.newaxis].shape

(3, 3, 1)

b[np.newaxis, :, :].shape

(1, 3, 3)


Now we use broadcasting to generate $$A_{ij}B_{jk}$$ as a 3-d matrix:

a[:, :, np.newaxis] * b[np.newaxis, :, :]

array([[[ 0,  0,  0],
[ 6,  7,  8],
[18, 20, 22]],

[[ 9, 12, 15],
[24, 28, 32],
[45, 50, 55]],

[[18, 24, 30],
[42, 49, 56],
[72, 80, 88]]])


Then we sum over the middle, $$j$$ axis, [which is the 1-axis of three axes numbered (0,1,2)] of this 3-d matrix. Thus we generate $$\Sigma_j A_{ij}B_{jk}$$.

(a[:, :, np.newaxis] * b[np.newaxis, :, :]).sum(1)

array([[ 24,  27,  30],
[ 78,  90, 102],
[132, 153, 174]])


Or if you prefer:

(a.reshape(3, 3, 1) * b.reshape(1, 3, 3)).sum(1)

array([[ 24,  27,  30],
[ 78,  90, 102],
[132, 153, 174]])


We can see that the broadcasting concept gives us a powerful and efficient way to express many linear algebra operations computationally.

## Dot Products using numpy functions¶

However, as the dot-product is a common operation, numpy has a built in function:

np.dot(a, b)

array([[ 24,  27,  30],
[ 78,  90, 102],
[132, 153, 174]])


This can also be written as:

a.dot(b)

array([[ 24,  27,  30],
[ 78,  90, 102],
[132, 153, 174]])


If you are using Python 3.5 or later, a dedicated matrix multiplication operator has been added, allowing you to do the following:

a @ b

array([[ 24,  27,  30],
[ 78,  90, 102],
[132, 153, 174]])


## Record Arrays¶

These are a special array structure designed to match the CSV “Record and Field” model. It’s a very different structure from the normal NumPy array, and different fields can contain different datatypes. We saw this when we looked at CSV files:

x = np.arange(50).reshape([10, 5])

record_x = x.view(
dtype={"names": ["col1", "col2", "another", "more", "last"], "formats": [int] * 5}
)

record_x

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)],
[(25, 26, 27, 28, 29)],
[(30, 31, 32, 33, 34)],
[(35, 36, 37, 38, 39)],
[(40, 41, 42, 43, 44)],
[(45, 46, 47, 48, 49)]],
dtype=[('col1', '<i8'), ('col2', '<i8'), ('another', '<i8'), ('more', '<i8'), ('last', '<i8')])


Record arrays can be addressed with field names like they were a dictionary:

record_x["col1"]

array([[ 0],
[ 5],
[10],
[15],
[20],
[25],
[30],
[35],
[40],
[45]])


We’ve seen these already when we used NumPy’s CSV parser.

## Logical arrays, masking, and selection¶

Numpy defines operators like == and < to apply to arrays element by element:

x = np.zeros([3, 4])
x

array([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])

y = np.arange(-1, 2)[:, np.newaxis] * np.arange(-2, 2)[np.newaxis, :]
y

array([[ 2,  1,  0, -1],
[ 0,  0,  0,  0],
[-2, -1,  0,  1]])

iszero = x == y
iszero

array([[False, False,  True, False],
[ True,  True,  True,  True],
[False, False,  True, False]])


A logical array can be used to select elements from an array:

y[np.logical_not(iszero)]

array([ 2,  1, -1, -2, -1,  1])


Although when printed, this comes out as a flat list, if assigned to, the selected elements of the array are changed!

y[iszero] = 5

y

array([[ 2,  1,  5, -1],
[ 5,  5,  5,  5],
[-2, -1,  5,  1]])


## Numpy memory¶

Numpy memory management can be tricksy:

x = np.arange(5)
y = x[:]

y[2] = 0
x

array([0, 1, 0, 3, 4])


It does not behave like lists!

x = list(range(5))
y = x[:]

y[2] = 0
x

[0, 1, 2, 3, 4]


We must use np.copy to force separate memory. Otherwise NumPy tries its hardest to make slices be views on data.

Now, this has all been very theoretical, but let’s go through a practical example, and see how powerful NumPy can be.