Lecture 26: Differences between Python and Matlab¶
Below, I have copied our Matlab session with details.
Most important differences in one table.
| Concept | Python | Matlab |
|---|---|---|
| Vector | xs = [1,2,3] (or np.array([1,2,3])) |
v = [1 2 3] |
| Matrix | list of lists or numpy array of shape (m,n) |
same as vector, use ; to separate rows |
| default type of number | float or int based on syntax e.g. 1.0 vs 1 |
default is float (double precision) |
| accessing element in vector/list | xs[i] |
v(i) |
| indexing | starts from 0 |
starts from 1. e.g. v(1) = 1 for v = [1 2 3] |
| last element | xs[-1] |
v(end) |
| slicing | xs = [1,2,3,4], xs[1:3] is [2,3] last element not included |
v = [1 2 3 4], v(2:4) is [2 3 4] |
| range | list(range(10)) |
0:9 |
| steppy range | list(range(0,10,2)) |
0:2:8 |
| strings | "asdf" or 'asdf' |
only 'asdf' |
| Price | Free and open source | Costs money, not open source (this also has some advantages, e.g. certain specialized libraries are better) |
Other important points:
Variables are always copied by value (not by reference, so none of the mutable/immutable distinction). E.g.:
v = [1 2 3]
w = v
w(1) = 999
Will change the first element of w but not of v. (unlike in Python where w=v would make w point to the same list as v and any change in w's elements would change the same element in v)
Discussed in demo at the bottom:
- vectors, matrices etc.
- series with specified increment
- accessing index in vector or matrix, slicing. (last element included)
- size
- transpose
- matrix multiplication is built-in
* - element-wise multiplication
.* - for, while, if-else formatting
eps,realmax,realmin,Inf,pi- you can make scripts with
.mfile format - basic plotting (surprise: it's exactly the same)
# for loops
x = ones(1,10);
for n = 2:6
x(n) = 2 * x(n - 1);
end
# if-else:
if r == c
A(r,c) = 2;
elseif abs(r-c) == 1
A(r,c) = -1;
else
A(r,c) = 0;
end
# Function syntax:
# y is the thing that will be returned
function [y z]= average(x)
if ~isvector(x)
error('Input must be a vector')
end
y = sum(x)/length(x);
z = 1
endXKCD Plots¶
In [1]:
from matplotlib import pyplot as plt
import numpy as np
plt.xkcd()
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.xticks([])
plt.yticks([])
ax.set_ylim([0, 200])
ax.set_xlim([-30, 200])
data = np.concatenate((np.arange(100),100 + 50*np.arange(100)))
plt.annotate(
'THE DAY I REALIZED\nI CAN MAKE\nXKCD PLOTS',
xy=(100, 100), arrowprops=dict(arrowstyle='->'), xytext=(120, 50))
plt.plot(data)
plt.xlabel('time')
plt.ylabel('My love for Python')
plt.show()
Our Matlab Session:¶
Trial>> v = [1 2 3]
v =
1 2 3
Trial>> v = [1 2 3]'
v =
1
2
3
Trial>> M = [1 2 3; 4 5 6; 7 8 9]
M =
1 2 3
4 5 6
7 8 9
Trial>> M'
ans =
1 4 7
2 5 8
3 6 9
Trial>> M''
ans =
1 2 3
4 5 6
7 8 9
Trial>> M'';
Trial>> M = M'';
Trial>> M = M''
M =
1 2 3
4 5 6
7 8 9
Trial>> M * M
ans =
30 36 42
66 81 96
102 126 150
Trial>>
Trial>> eye(n)
Undefined function or variable 'n'.
Trial>> eye(3)
ans =
1 0 0
0 1 0
0 0 1
Trial>> M
M =
1 2 3
4 5 6
7 8 9
Trial>> M .* M
ans =
1 4 9
16 25 36
49 64 81
Trial>> M(1,1)
ans =
1
Trial>> M(2,2)
ans =
5
Trial>> M
M =
1 2 3
4 5 6
7 8 9
Trial>> M(1:2,1:2)
ans =
1 2
4 5
Trial>> M(2:3,2:3)
ans =
5 6
8 9
Trial>> 1:10
ans =
1 2 3 4 5 6 7 8 9 10
Trial>> 1:2:10
ans =
1 3 5 7 9
Trial>> 1:2:10
ans =
1 3 5 7 9
Trial>> 1:2:11
ans =
1 3 5 7 9 11
Trial>> v = 1:15
v =
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Trial>> v(1:10:2)
ans =
1
Trial>> v(1:2:10)
ans =
1 3 5 7 9
Trial>> v
v =
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Trial>> v(-1)
Subscript indices must either be real positive integers or logicals.
Trial>> v(end)
ans =
15
Trial>> M
M =
1 2 3
4 5 6
7 8 9
Trial>> size(M)
ans =
3 3
Trial>> eps
ans =
2.2204e-16
Trial>> Inf
ans =
Inf
Trial>> realmax
ans =
1.7977e+308
Trial>> realmin
ans =
2.2251e-308
Trial>> figure
Trial>> v = 1:100
v =
Columns 1 through 16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Columns 17 through 32
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Columns 33 through 48
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
Columns 49 through 64
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
Columns 65 through 80
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
Columns 81 through 96
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
Columns 97 through 100
97 98 99 100
Trial>> v = 0:0.01:1
v =
Columns 1 through 9
0 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 0.0700 0.0800
Columns 10 through 18
0.0900 0.1000 0.1100 0.1200 0.1300 0.1400 0.1500 0.1600 0.1700
Columns 19 through 27
0.1800 0.1900 0.2000 0.2100 0.2200 0.2300 0.2400 0.2500 0.2600
Columns 28 through 36
0.2700 0.2800 0.2900 0.3000 0.3100 0.3200 0.3300 0.3400 0.3500
Columns 37 through 45
0.3600 0.3700 0.3800 0.3900 0.4000 0.4100 0.4200 0.4300 0.4400
Columns 46 through 54
0.4500 0.4600 0.4700 0.4800 0.4900 0.5000 0.5100 0.5200 0.5300
Columns 55 through 63
0.5400 0.5500 0.5600 0.5700 0.5800 0.5900 0.6000 0.6100 0.6200
Columns 64 through 72
0.6300 0.6400 0.6500 0.6600 0.6700 0.6800 0.6900 0.7000 0.7100
Columns 73 through 81
0.7200 0.7300 0.7400 0.7500 0.7600 0.7700 0.7800 0.7900 0.8000
Columns 82 through 90
0.8100 0.8200 0.8300 0.8400 0.8500 0.8600 0.8700 0.8800 0.8900
Columns 91 through 99
0.9000 0.9100 0.9200 0.9300 0.9400 0.9500 0.9600 0.9700 0.9800
Columns 100 through 101
0.9900 1.0000
Trial>> plot(v .* v)
Trial>> title("awesome plot")
Trial>> random(10)
Error using random (line 64)
The NAME argument must be a distribution name.
Trial>> rand(4)
ans =
0.1067 0.8173 0.2599 0.1818
0.9619 0.8687 0.8001 0.2638
0.0046 0.0844 0.4314 0.1455
0.7749 0.3998 0.9106 0.1361
Trial>> rand(2,50)
ans =
Columns 1 through 9
0.8693 0.5499 0.8530 0.3510 0.4018 0.2399 0.1839 0.4173 0.9027
0.5797 0.1450 0.6221 0.5132 0.0760 0.1233 0.2400 0.0497 0.9448
Columns 10 through 18
0.4909 0.3377 0.3692 0.7803 0.2417 0.0965 0.9421 0.5752 0.2348
0.4893 0.9001 0.1112 0.3897 0.4039 0.1320 0.9561 0.0598 0.3532
Columns 19 through 27
0.8212 0.0430 0.6491 0.6477 0.5470 0.7447 0.6868 0.3685 0.7802
0.0154 0.1690 0.7317 0.4509 0.2963 0.1890 0.1835 0.6256 0.0811
Columns 28 through 36
0.9294 0.4868 0.4468 0.5085 0.8176 0.6443 0.8116 0.3507 0.8759
0.7757 0.4359 0.3063 0.5108 0.7948 0.3786 0.5328 0.9390 0.5502
Columns 37 through 45
0.6225 0.2077 0.4709 0.8443 0.2259 0.2277 0.3111 0.4302 0.9049
0.5870 0.3012 0.2305 0.1948 0.1707 0.4357 0.9234 0.1848 0.9797
Columns 46 through 50
0.4389 0.2581 0.5949 0.6028 0.2217
0.1111 0.4087 0.2622 0.7112 0.1174
Trial>> A = rand(2, 50)
A =
Columns 1 through 9
0.2967 0.4242 0.0855 0.8010 0.9289 0.4886 0.2373 0.9631 0.5211
0.3188 0.5079 0.2625 0.0292 0.7303 0.5785 0.4588 0.5468 0.2316
Columns 10 through 18
0.4889 0.6791 0.3674 0.0377 0.9133 0.0987 0.3354 0.1366 0.1068
0.6241 0.3955 0.9880 0.8852 0.7962 0.2619 0.6797 0.7212 0.6538
Columns 19 through 27
0.4942 0.7150 0.8909 0.6987 0.0305 0.5000 0.9047 0.6177 0.8055
0.7791 0.9037 0.3342 0.1978 0.7441 0.4799 0.6099 0.8594 0.5767
Columns 28 through 36
0.1829 0.8865 0.4899 0.9787 0.5005 0.0596 0.0424 0.5216 0.8181
0.2399 0.0287 0.1679 0.7127 0.4711 0.6820 0.0714 0.0967 0.8175
Columns 37 through 45
0.7224 0.6596 0.9730 0.8003 0.4324 0.0835 0.1734 0.8314 0.0605
0.1499 0.5186 0.6490 0.4538 0.8253 0.1332 0.3909 0.8034 0.3993
Columns 46 through 50
0.5269 0.6569 0.2920 0.0155 0.1672
0.4168 0.6280 0.4317 0.9841 0.1062
Trial>> scatter(A(1), A(2))
Trial>> scatter(A(:,1), A(:,2))
Trial>> A = rand(100,1)
A =
0.3724
0.1981
0.4897
0.3395
0.9516
0.9203
0.0527
0.7379
0.2691
0.4228
0.5479
0.9427
0.4177
0.9831
0.3015
0.7011
0.6663
0.5391
0.6981
0.6665
0.1781
0.1280
0.9991
0.1711
0.0326
0.5612
0.8819
0.6692
0.1904
0.3689
0.4607
0.9816
0.1564
0.8555
0.6448
0.3763
0.1909
0.4283
0.4820
0.1206
0.5895
0.2262
0.3846
0.5830
0.2518
0.2904
0.6171
0.2653
0.8244
0.9827
0.7302
0.3439
0.5841
0.1078
0.9063
0.8797
0.8178
0.2607
0.5944
0.0225
0.4253
0.3127
0.1615
0.1788
0.4229
0.0942
0.5985
0.4709
0.6959
0.6999
0.6385
0.0336
0.0688
0.3196
0.5309
0.6544
0.4076
0.8200
0.7184
0.9686
0.5313
0.3251
0.1056
0.6110
0.7788
0.4235
0.0908
0.2665
0.1537
0.2810
0.4401
0.5271
0.4574
0.8754
0.5181
0.9436
0.6377
0.9577
0.2407
0.6761
Trial>> B = rand(100,1)
B =
0.2891
0.6718
0.6951
0.0680
0.2548
0.2240
0.6678
0.8444
0.3445
0.7805
0.6753
0.0067
0.6022
0.3868
0.9160
0.0012
0.4624
0.4243
0.4609
0.7702
0.3225
0.7847
0.4714
0.0358
0.1759
0.7218
0.4735
0.1527
0.3411
0.6074
0.1917
0.7384
0.2428
0.9174
0.2691
0.7655
0.1887
0.2875
0.0911
0.5762
0.6834
0.5466
0.4257
0.6444
0.6476
0.6790
0.6358
0.9452
0.2089
0.7093
0.2362
0.1194
0.6073
0.4501
0.4587
0.6619
0.7703
0.3502
0.6620
0.4162
0.8419
0.8329
0.2564
0.6135
0.5822
0.5407
0.8699
0.2648
0.3181
0.1192
0.9398
0.6456
0.4795
0.6393
0.5447
0.6473
0.5439
0.7210
0.5225
0.9937
0.2187
0.1058
0.1097
0.0636
0.4046
0.4484
0.3658
0.7635
0.6279
0.7720
0.9329
0.9727
0.1920
0.1389
0.6963
0.0938
0.5254
0.5303
0.8611
0.4849
Trial>> scatter(A, B)