import numpy as np
#Q25
a = np.arange(100, 1000, 10)
print(a)
[100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530 540 550 560 570 580 590 600 610 620 630 640 650 660 670 680 690 700 710 720 730 740 750 760 770 780 790 800 810 820 830 840 850 860 870 880 890 900 910 920 930 940 950 960 970 980 990]
#Q26
b = a.reshape( (9, -1) )
b = a.reshape( (9, 10) )
b = a.reshape( (-1, 10) )
print(b)
[[100 110 120 130 140 150 160 170 180 190] [200 210 220 230 240 250 260 270 280 290] [300 310 320 330 340 350 360 370 380 390] [400 410 420 430 440 450 460 470 480 490] [500 510 520 530 540 550 560 570 580 590] [600 610 620 630 640 650 660 670 680 690] [700 710 720 730 740 750 760 770 780 790] [800 810 820 830 840 850 860 870 880 890] [900 910 920 930 940 950 960 970 980 990]]
#Q27
c = np.ones((9, 10))
c = np.ones(b.shape)
print(c)
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
#Q28
#d = np.full(c.shape, 1000.)
d = c * 1000
d = c + 999
print(d)
[[1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.]]
#Q29
e = b + d
print(e)
[[1100. 1110. 1120. 1130. 1140. 1150. 1160. 1170. 1180. 1190.] [1200. 1210. 1220. 1230. 1240. 1250. 1260. 1270. 1280. 1290.] [1300. 1310. 1320. 1330. 1340. 1350. 1360. 1370. 1380. 1390.] [1400. 1410. 1420. 1430. 1440. 1450. 1460. 1470. 1480. 1490.] [1500. 1510. 1520. 1530. 1540. 1550. 1560. 1570. 1580. 1590.] [1600. 1610. 1620. 1630. 1640. 1650. 1660. 1670. 1680. 1690.] [1700. 1710. 1720. 1730. 1740. 1750. 1760. 1770. 1780. 1790.] [1800. 1810. 1820. 1830. 1840. 1850. 1860. 1870. 1880. 1890.] [1900. 1910. 1920. 1930. 1940. 1950. 1960. 1970. 1980. 1990.]]
#Q30
e = np.arange(1100, 2000, 10).reshape((9, 10))
e = np.arange(1100, 2000, 10).reshape((9, -1))
e = np.arange(1100, 2000, 10).reshape((-1, 10))
e = np.linspace(1100, 1990, 90).reshape((9, 10))
e = np.linspace(1100, 1990, 90).reshape((9, -1))
e = np.linspace(1100, 1990, 90).reshape((-1, 10))
e = (np.arange(90)*10. + 1100).reshape((9, 10))
e = (np.arange(90)*10. + 1100).reshape((9, -1))
e = (np.arange(90)*10. + 1100).reshape((-1, 10))
print(e)
[[1100. 1110. 1120. 1130. 1140. 1150. 1160. 1170. 1180. 1190.] [1200. 1210. 1220. 1230. 1240. 1250. 1260. 1270. 1280. 1290.] [1300. 1310. 1320. 1330. 1340. 1350. 1360. 1370. 1380. 1390.] [1400. 1410. 1420. 1430. 1440. 1450. 1460. 1470. 1480. 1490.] [1500. 1510. 1520. 1530. 1540. 1550. 1560. 1570. 1580. 1590.] [1600. 1610. 1620. 1630. 1640. 1650. 1660. 1670. 1680. 1690.] [1700. 1710. 1720. 1730. 1740. 1750. 1760. 1770. 1780. 1790.] [1800. 1810. 1820. 1830. 1840. 1850. 1860. 1870. 1880. 1890.] [1900. 1910. 1920. 1930. 1940. 1950. 1960. 1970. 1980. 1990.]]
#Q31
(10 * np.arange(1, 11)**2).reshape((2, -1))
array([[ 10, 40, 90, 160, 250], [ 360, 490, 640, 810, 1000]])
e[0,0]
1100.0
e[0,0] = 999
print(e)
[[ 999. 1110. 1120. 1130. 1140. 1150. 1160. 1170. 1180. 1190.] [1200. 1210. 1220. 1230. 1240. 1250. 1260. 1270. 1280. 1290.] [1300. 1310. 1320. 1330. 1340. 1350. 1360. 1370. 1380. 1390.] [1400. 1410. 1420. 1430. 1440. 1450. 1460. 1470. 1480. 1490.] [1500. 1510. 1520. 1530. 1540. 1550. 1560. 1570. 1580. 1590.] [1600. 1610. 1620. 1630. 1640. 1650. 1660. 1670. 1680. 1690.] [1700. 1710. 1720. 1730. 1740. 1750. 1760. 1770. 1780. 1790.] [1800. 1810. 1820. 1830. 1840. 1850. 1860. 1870. 1880. 1890.] [1900. 1910. 1920. 1930. 1940. 1950. 1960. 1970. 1980. 1990.]]
e[1,0]
1200.0
e[1,0] = 999
#q36
e[2,0]
1300.0
e[6,0]
1700.0
e[3,-1]
1490.0
e[3,-1] = 999
#q40
e[3,-3]
1470.0
e[3,-3] = 999
e[-1,-2]
1980.0
e[-1,-2] = 999
print(e)
[[ 999. 1110. 1120. 1130. 1140. 1150. 1160. 1170. 1180. 1190.] [ 999. 1210. 1220. 1230. 1240. 1250. 1260. 1270. 1280. 1290.] [1300. 1310. 1320. 1330. 1340. 1350. 1360. 1370. 1380. 1390.] [1400. 1410. 1420. 1430. 1440. 1450. 1460. 999. 1480. 999.] [1500. 1510. 1520. 1530. 1540. 1550. 1560. 1570. 1580. 1590.] [1600. 1610. 1620. 1630. 1640. 1650. 1660. 1670. 1680. 1690.] [1700. 1710. 1720. 1730. 1740. 1750. 1760. 1770. 1780. 1790.] [1800. 1810. 1820. 1830. 1840. 1850. 1860. 1870. 1880. 1890.] [1900. 1910. 1920. 1930. 1940. 1950. 1960. 1970. 999. 1990.]]
e[:,0]
array([ 999., 999., 1300., 1400., 1500., 1600., 1700., 1800., 1900.])
e[:,0] = 888
print(e)
[[ 888. 1110. 1120. 1130. 1140. 1150. 1160. 1170. 1180. 1190.] [ 888. 1210. 1220. 1230. 1240. 1250. 1260. 1270. 1280. 1290.] [ 888. 1310. 1320. 1330. 1340. 1350. 1360. 1370. 1380. 1390.] [ 888. 1410. 1420. 1430. 1440. 1450. 1460. 999. 1480. 999.] [ 888. 1510. 1520. 1530. 1540. 1550. 1560. 1570. 1580. 1590.] [ 888. 1610. 1620. 1630. 1640. 1650. 1660. 1670. 1680. 1690.] [ 888. 1710. 1720. 1730. 1740. 1750. 1760. 1770. 1780. 1790.] [ 888. 1810. 1820. 1830. 1840. 1850. 1860. 1870. 1880. 1890.] [ 888. 1910. 1920. 1930. 1940. 1950. 1960. 1970. 999. 1990.]]
#q46
e[:,1]
array([1110., 1210., 1310., 1410., 1510., 1610., 1710., 1810., 1910.])
e[:,1] = 777
print(e)
[[ 888. 777. 1120. 1130. 1140. 1150. 1160. 1170. 1180. 1190.] [ 888. 777. 1220. 1230. 1240. 1250. 1260. 1270. 1280. 1290.] [ 888. 777. 1320. 1330. 1340. 1350. 1360. 1370. 1380. 1390.] [ 888. 777. 1420. 1430. 1440. 1450. 1460. 999. 1480. 999.] [ 888. 777. 1520. 1530. 1540. 1550. 1560. 1570. 1580. 1590.] [ 888. 777. 1620. 1630. 1640. 1650. 1660. 1670. 1680. 1690.] [ 888. 777. 1720. 1730. 1740. 1750. 1760. 1770. 1780. 1790.] [ 888. 777. 1820. 1830. 1840. 1850. 1860. 1870. 1880. 1890.] [ 888. 777. 1920. 1930. 1940. 1950. 1960. 1970. 999. 1990.]]
e[:,1] = np.arange(1, 10)
print(e[:, :-2]) # afficher une partie du tableau pour éviter les retours à la ligne
[[8.88e+02 1.00e+00 1.12e+03 1.13e+03 1.14e+03 1.15e+03 1.16e+03 1.17e+03] [8.88e+02 2.00e+00 1.22e+03 1.23e+03 1.24e+03 1.25e+03 1.26e+03 1.27e+03] [8.88e+02 3.00e+00 1.32e+03 1.33e+03 1.34e+03 1.35e+03 1.36e+03 1.37e+03] [8.88e+02 4.00e+00 1.42e+03 1.43e+03 1.44e+03 1.45e+03 1.46e+03 9.99e+02] [8.88e+02 5.00e+00 1.52e+03 1.53e+03 1.54e+03 1.55e+03 1.56e+03 1.57e+03] [8.88e+02 6.00e+00 1.62e+03 1.63e+03 1.64e+03 1.65e+03 1.66e+03 1.67e+03] [8.88e+02 7.00e+00 1.72e+03 1.73e+03 1.74e+03 1.75e+03 1.76e+03 1.77e+03] [8.88e+02 8.00e+00 1.82e+03 1.83e+03 1.84e+03 1.85e+03 1.86e+03 1.87e+03] [8.88e+02 9.00e+00 1.92e+03 1.93e+03 1.94e+03 1.95e+03 1.96e+03 1.97e+03]]
#q49
e[:,6]
array([1160., 1260., 1360., 1460., 1560., 1660., 1760., 1860., 1960.])
e[6,:]
array([ 888., 7., 1720., 1730., 1740., 1750., 1760., 1770., 1780., 1790.])
e[1,4:8]
array([1240., 1250., 1260., 1270.])
#q52
e[1,4:8] = 999
e[1,4:8] = np.arange(24, 28)
#q54
e[1,4:8] = [10, 100, 1000, 1000]
e[1,4:8] = np.array([10, 100, 1000, 1000])
# si on voulait 10 100 1000 10000 (10k à la fin)
e[1,4:8] = 10 ** np.arange(1, 5)
print(e[1,4:8])
[ 10. 100. 1000. 10000.]
e[1,4:8] = e[2,4:8]
print(e[:, :-2]) # afficher une partie du tableau pour éviter les retours à la ligne
[[8.88e+02 1.00e+00 1.12e+03 1.13e+03 1.14e+03 1.15e+03 1.16e+03 1.17e+03] [8.88e+02 2.00e+00 1.22e+03 1.23e+03 1.34e+03 1.35e+03 1.36e+03 1.37e+03] [8.88e+02 3.00e+00 1.32e+03 1.33e+03 1.34e+03 1.35e+03 1.36e+03 1.37e+03] [8.88e+02 4.00e+00 1.42e+03 1.43e+03 1.44e+03 1.45e+03 1.46e+03 9.99e+02] [8.88e+02 5.00e+00 1.52e+03 1.53e+03 1.54e+03 1.55e+03 1.56e+03 1.57e+03] [8.88e+02 6.00e+00 1.62e+03 1.63e+03 1.64e+03 1.65e+03 1.66e+03 1.67e+03] [8.88e+02 7.00e+00 1.72e+03 1.73e+03 1.74e+03 1.75e+03 1.76e+03 1.77e+03] [8.88e+02 8.00e+00 1.82e+03 1.83e+03 1.84e+03 1.85e+03 1.86e+03 1.87e+03] [8.88e+02 9.00e+00 1.92e+03 1.93e+03 1.94e+03 1.95e+03 1.96e+03 1.97e+03]]
#q56
e[1,4:8] = e[1,4:8]**2
#q57
e[1,0:4]
e[1,:4]
array([ 888., 2., 1220., 1230.])
e[1, -4: ]
e[1,-4:]
array([1.8496e+06, 1.8769e+06, 1.2800e+03, 1.2900e+03])
e[-1,-4:]
array([1960., 1970., 999., 1990.])
#q60
e[-1,-4:] = e[-1,-4:]**2
#q61
e[ : : 2 , 3 ]
e[::2,3]
array([1130., 1330., 1530., 1730., 1930.])
e[::2,3] = 999
#q63
e[::2,3] = np.arange(5)
#
nb_lignes = e.shape[0]
N = (nb_lignes + 1) // 2
e[::2,3] = np.arange(N)
#
N = e[::2, 3].shape[0]
e[::2,3] = np.arange(N)
e[ ::-2 , 3 ]
array([4., 3., 2., 1., 0.])
# pas ok, on commence à l'indice 0 puis on s'arrête car on va dans le négatif
# ... e[0::-2,3]
#q65
e[ ::2 , 3:-2 ]
e[::2,3:-2]
array([[0.0000e+00, 1.1400e+03, 1.1500e+03, 1.1600e+03, 1.1700e+03], [1.0000e+00, 1.3400e+03, 1.3500e+03, 1.3600e+03, 1.3700e+03], [2.0000e+00, 1.5400e+03, 1.5500e+03, 1.5600e+03, 1.5700e+03], [3.0000e+00, 1.7400e+03, 1.7500e+03, 1.7600e+03, 1.7700e+03], [4.0000e+00, 1.9400e+03, 1.9500e+03, 3.8416e+06, 3.8809e+06]])
e[::2,3:-2] = 999
e[::2,3:-2] = np.arange(e[::2,3:-2].shape[0])
#
N = e[::2,3:-2].shape[0]
e[::2,3:-2] = np.arange(N)
#q68
e[ ::2 , 3:6 ]
e[::2,3:6]
array([[0., 1., 2.], [0., 1., 2.], [0., 1., 2.], [0., 1., 2.], [0., 1., 2.]])
e[::2,3:]
array([[0.00000e+00, 1.00000e+00, 2.00000e+00, 3.00000e+00, 4.00000e+00, 1.18000e+03, 1.19000e+03], [0.00000e+00, 1.00000e+00, 2.00000e+00, 3.00000e+00, 4.00000e+00, 1.38000e+03, 1.39000e+03], [0.00000e+00, 1.00000e+00, 2.00000e+00, 3.00000e+00, 4.00000e+00, 1.58000e+03, 1.59000e+03], [0.00000e+00, 1.00000e+00, 2.00000e+00, 3.00000e+00, 4.00000e+00, 1.78000e+03, 1.79000e+03], [0.00000e+00, 1.00000e+00, 2.00000e+00, 3.00000e+00, 4.00000e+00, 9.98001e+05, 3.96010e+06]])
e[::2,3:-2]
array([[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.]])
#q71
e[ 3::2 , 3: ]
e[3::2,3:]
array([[1430., 1440., 1450., 1460., 999., 1480., 999.], [1630., 1640., 1650., 1660., 1670., 1680., 1690.], [1830., 1840., 1850., 1860., 1870., 1880., 1890.]])