import numpy as np
np.set_printoptions(precision=3, threshold=0, edgeitems=5)
import numpy_html

Q1

np.array([4, 5])

4

5

np.array([4, 10, 5])

4

10

5

np.array((4, 5))

4

5

Q2/3/4/5

np.zeros((4, 5))

0.	0.	0.	0.	0.
0.	0.	0.	0.	0.
0.	0.	0.	0.	0.
0.	0.	0.	0.	0.

np.zeros(    (4, 5)   )

0.	0.	0.	0.	0.
0.	0.	0.	0.	0.
0.	0.	0.	0.	0.
0.	0.	0.	0.	0.

sh = (4, 5)
np.zeros(sh)

0.	0.	0.	0.	0.
0.	0.	0.	0.	0.
0.	0.	0.	0.	0.
0.	0.	0.	0.	0.

np.ones((5, 4))

1.	1.	1.	1.
1.	1.	1.	1.
1.	1.	1.	1.
1.	1.	1.	1.
1.	1.	1.	1.

np.random.uniform(2, 3, (4, 5))

2.346	2.487	2.204	2.082	2.392
2.376	2.049	2.212	2.846	2.546
2.751	2.922	2.357	2.525	2.363
2.738	2.872	2.028	2.347	2.486

np.random.normal(2, 3, (4, 5))

1.161	-1.971	0.989	-0.747	7.269
1.082	5.655	3.215	4.794	-0.659
-1.933	2.413	8.227	-2.299	1.217
2.752	5.823	-0.362	5.556	-1.151

tab1000 = np.random.normal(1000, 0.01, (4, 5))
tab1000

999.989	999.998	999.992	1000.003	1000.003
1000.009	999.996	999.996	1000.011	999.981
1000.014	999.994	1000.003	1000.004	1000.003
1000.022	999.994	999.997	1000.011	999.997

print(np.mean(tab1000))

1000.1037314934902

print(np.std(tab1000))

0.8370454052254138

Q6/7

np.linspace(3, 4, 5)

3.

3.25

3.5

3.75

4.

np.arange(100, 1000, 10)

100
110
120
130
140
⋮
950
960
970
980
990

# Q9
b = np.arange(100, 1000, 10)
print(b.shape)
print(np.shape(b))

(90,)
(90,)

Q8

np.linspace(100, 200, 5)

100.

125.

150.

175.

200.

np.arange(100, 201, 25)

100

125

150

175

200

l = [100, 125, 150, 175, 200]
np.array(l)
# ou directement np.array([100, 125, 150, 175, 200])

100

125

150

175

200

np.arange(5) * 25 + 100

100

125

150

175

200

Q10....

print(np.shape(  np.array([4,5])  ))

(2,)

# comment creer tableau avec 1 ligne, 2 colonnes
#np.zeros((1,2))
np.array( [ [4, 5]  ]  )

4

5

# np.zeros etc accèptent des entiers à la place de shape
np.testing.assert_allclose( 
    np.zeros(100),
    np.zeros((100,))
)
print("OK")

OK

print(np.zeros(5))
print("----")
print(np.zeros((1,5)))
print("----")
print(np.zeros((2,5)))

[0. 0. 0. 0. 0.]
----
[[0. 0. 0. 0. 0.]]
----
[[0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]]

print(np.shape(   np.zeros((4, 5))   ))

(4, 5)

print(np.shape(  np.ones((5, 4))   ))

(5, 4)

print(np.shape(  np.random.uniform(2, 3, (4, 5))   ))

(4, 5)

print(np.shape(  np.random.normal(2, 3, (4, 5))   ))

(4, 5)

print(np.shape(  np.linspace(10, 20, 30)   ))

(30,)

print(np.shape(  np.arange(100, 1000, 10)   ))

(90,)

print(np.shape(  np.ones((100,        )) ))

(100,)

Q18

a = np.zeros((5,))
print(a)
print(np.shape(a))
a

[0. 0. 0. 0. 0.]
(5,)

0.

0.

0.

0.

0.

a = np.zeros((5, 1))
print(a)
print(np.shape(a))
a

[[0.]
 [0.]
 [0.]
 [0.]
 [0.]]
(5, 1)

0.

0.

0.

0.

0.

a = np.zeros((1, 5))
print(a)
print(np.shape(a))
a

[[0. 0. 0. 0. 0.]]
(1, 5)

0.

0.

0.

0.

0.

Q19

print(np.shape(   np.ones((100)).reshape(( 5, 20))           ))

(5, 20)

print(np.shape(   np.ones((100)).reshape(( -1, 20))           ))

(5, 20)

print(np.shape(   np.ones((100)).reshape(( 5, -1))           ))

(5, 20)

print(np.shape(   np.ones((100)).reshape(( -1, 5))           ))

(20, 5)

np.linspace(2, 5, 16)

2.
2.2
2.4
2.6
2.8
⋮
4.2
4.4
4.6
4.8
5.

np.linspace(2, 5, 16).reshape((2, 8))

2.	2.2	2.4	2.6	2.8	3.	3.2	3.4
3.6	3.8	4.	4.2	4.4	4.6	4.8	5.

np.linspace(2, 5, 16).reshape((-1, 4))

2.	2.2	2.4	2.6
2.8	3.	3.2	3.4
3.6	3.8	4.	4.2
4.4	4.6	4.8	5.