moyen
Élève en POO
On souhaite dans cet exercice créer une classe Eleve
ayant quatre attributs :
un prénom prenom
de type str
;
un nom nom
de type str
;
une classe classe
de type str
;
des moyennes moyennes
de type dict
. Ce dictionnaire associe à des intitulés de matières (str
), les moyennes correspondantes (au format int
ou float
).
Cet exercice est en plusieurs parties et demande de compléter la classe Eleve
en ajoutant différentes méthodes.
ajout_methodes_precedentes
A partir de la deuxième question, la fonction ajout_methodes_precedentes
est appelée avant les tests afin de compléter votre travail en ajoutant les corrigés des questions précédentes à la classe Eleve
.
Méthode __init__
Lors de la création d'un objet de type Eleve
, on fournit les valeurs des attributs prenom
, nom
et classe
(dans cet ordre ).
L'attribut moyennes
est initialement vide.
Compléter la méthode __init__
(aussi appelée « constructeur » en Python) de la classe Eleve
.
Exemple
>>> albert = Eleve ( "Albert" , "Einstein" , "Te2" )
>>> albert . prenom
'Albert'
>>> albert . nom
'Einstein'
>>> albert . classe
'Te2'
>>> albert . moyennes
{}
Méthode modifie_moyenne
La méthode modifie_moyenne
prend deux paramètres, un intitulé de matière (str
) et une moyenne (au format int
ou float
) et ajoute ce couple (clé: valeur)
à l'attribut moyennes
d'un objet Eleve
.
Écrire la méthode modifie_moyenne
.
Exemple
>>> carl = Eleve ( "Carl Friedrich" , "Gauss" , "Te3" )
>>> carl . modifie_moyenne ( "arithmétique" , 20 )
>>> carl . modifie_moyenne ( "chimie" , 12 )
>>> carl . moyennes
{'arithmétique': 20, 'chimie': 12}
>>> carl . modifie_moyenne ( "chimie" , 13 )
>>> carl . moyennes
{'arithmétique': 20, 'chimie': 13}
Version vide Version à trous
.128013ben,49viE[3mo5_tPh}klpwf(: cg.a=ryS6]u/72){s18d050V0c0q0F0i0v0S0B0C0v0F0S0S0G010q0i0w010406050S0M0m0m0F0H0I040J0n0v0M0:0n0d050N0`0|0~100^0w04051g191j0N1g0^0V0i0h0(0*0,0.0s0i0D0s0v1x0s0q0?050Z0b0v0c1s0+0-011w1y1A1y0q1G1I1E0q0H1h0q0s0(130S0w0F0d0.0P011K1u010y0#0c0d0F0m0c1E1%1)1.1M1;1I1@1_0?0a0B0r0H0n0w0n0S0i160d0B0X1#0H0H0c0C2e191|0d1h0N1Z2r1W1Y1X1F0V1~0.1A0d1?2b1E1p1r0)1L2B0i2D0d0n2H1E0w2k1h2p2r2V0_1(2f2J1/2O0H0}0v0?0T2o2Z0@2Y1}2#1M2%2)0?0P2-1)2/2p2A012@0F2*040l2{2q0^2~2=0.31330f362}2Z2 3c0?0o3f383h3a300n2(320?0K3m2:2!1t2?3r2^040O3w393z3b3B3t040U3F3o3H3q3s330g3f1k2T192H2u0V1Y2z3p0C2P1`1h3Y1i3W2X1a2.053(0X2U3O2K010u0?2=3U3G3`0x0?0B3 3_2$0C0?0j1I0h0c452;3P0=040A3m0B4l44401/3|040X0y4e3y41434u2 0y0m0?0p0p2M2d4D4y3p4h0z4I3P0b4h0S0c0v4t3:2|3x2 4h0e3f4n462?0?2S2E0m4M3`4Y4!4W3p0d0?2O4+4U2q4:4g0?4Z4_044#4f3`4=042=4d4 4{4-0?0Q4j4 064m5g514v1/4O0?4Q4S4,1/0n0?0E5p4%044)4@4/4o1M5r040G5z4$3b4(2k5y5e5h4m595k4P4R4T2X5A0.5C5t585U304?0n4^2V5i2 5C5E4 5)4;5#5%2.5f5M5.4N5Q5o5Y5G015W5u5H553z575(5O5B0?5,655Z54564k5@660.5l045n5S3;5Z5 5|522$0?0m0n0I1?2D0S5F6q675D6z5j1M4h0R0t3m5?4l6g3{0?4s600142506Q0d0y6s2P0i1;0c0p6t6v0d2D6Q4K6Q6i6k6+4}6D3i6s0F0:0c2k6:044~6a5}546%6w646m5}4h5c6e5M6M6.5R6Q6o5T706X6(6x6|0k6U6@6_6{6p6E0.4h0L6=3p5+7w3P716u733w0N3?0c2r4)2r3,2s3!192v7O0F1H7H3X1q2/0N0X0Z0#0S04.
.128013ben,49viE[3mo5_tPh}klpwf(: cg.a=ryS6]u/72){s18d050V0c0q0F0i0v0S0B0C0v0F0S0S0G010q0i0w010406050S0M0m0m0F0H0I040J0n0v0M0:0n0d050N0`0|0~100^0w04051g191j0N1g0^0V0i0h0(0*0,0.0s0i0D0s0v1x0s0q0?050Z0b0v0c1s0+0-011w1y1A1y0q1G1I1E0q0H1h0q0s0(130S0w0F0d0.0P011K1u010y0#0c0d0F0m0c1E1%1)1.1M1;1I1@1_0?0a0B0r0H0n0w0n0S0i160d0B0X1#0H0H0c0C2e191|0d1h0N1Z2r1W1Y1X1F0V1~0.1A0d1?2b1E1p1r0)1L2B0i2D0d0n2H1E0w2k1h2p2r2V0_1(2f2J1/2O0H0}0v0?0T2o2Z0@2Y1}2#1M2%2)0?0P2-1)2/2p2A012@0F2*040l2{2q0^2~2=0.31330f362}2Z2 3c0?0o3f383h3a300n2(320?0K3m2:2!1t2?3r2^040O3w393z3b3B3t040U3F3o3H3q3s330g3f1k2T192H2u0V1Y2z3p0C2P1`1h3Y1i3W2X1a2.053(0X2U3O2K010u0?2=3U3G3`0x0?0B3 3_2$0C0?0j1I0h0c452;3P0=040A3m0B4l44401/3|040X0y4e3y41434u2 0y0m0?0p0p2M2d4D4y3p4h0z4I3P0b4h0S0c0v4t3:2|3x2 4h0e3f4n462?0?2S2E0m4M3`4Y4!4W3p0d0?2O4+4U2q4:4g0?4Z4_044#4f3`4=042=4d4 4{4-0?0Q4j4 064m5g514v1/4O0?4Q4S4,1/0n0?0E5p4%044)4@4/4o1M5r040G5z4$3b4(2k5y5e5h4m595k4P4R4T2X5A0.5C5t585U304?0n4^2V5i2 5C5E4 5)4;5#5%2.5f5M5.4N5Q5o5Y5G015W5u5H553z575(5O5B0?5,655Z54564k5@660.5l045n5S3;5Z5 5|522$0?0m0n0I1?2D0S5F6q675D6z5j1M4h0R0t3m5?4l6g3{0?4s600142506Q0d0y6s2P0i1;0c0p6t6v0d2D6Q4K6Q6i6k6+4}6D3i6s0F0:0c2k6:044~6a5}546%6w646m5}4h5c6e5M6M6.5R6Q6o5T706X6(6x6|0k6U6@6_6{6p6E0.4h0L6=3p5+7w3P716u733w0N3?0c2r4)2r3,2s3!192v7O0F1H7H3X1q2/0N0X0Z0#0S04.
Méthode moyenne_de
La méthode moyenne_de
prend en unique paramètre un intitulé de matière (str
) et renvoie la moyenne de cet élève dans cette matière.
Si l'élève ne possède pas de moyenne dans cette matière, la fonction renverra None
.
Écrire la méthode moyenne_de
.
Exemple
>>> donald = Eleve ( "Donald" , "Knuth" , "Te7" )
>>> donald . modifie_moyenne ( "informatique" , 20 )
>>> donald . modifie_moyenne ( "musique" , 13 )
>>> donald . moyenne_de ( "informatique" )
20
>>> donald . moyenne_de ( "musique" )
13
>>> donald . moyenne_de ( "lancer de javelot" )
>>>
Version vide Version à trous
.128013beqên,4é9viE[3mo5_tR;Ph}klpwNf(: cg.a=ry0S6]u/72){As18d050%0c0t0L0l0A0!0H0I0A0L0!0!0M010t0l0B010406050!0T0p0p0L0N0O040Q0q0A0T0{0q0f0H020L0p0B0v0H0u0c150N0d0T0c0!050U12141618100B04051D1w1G0U1D100%0l0k0:0=0@0_0x0l0J0x0A1U0x0t0~050+0b0A0c1P0?0^011T1V1X1V0t1%1)1#0t0N1E0t0x0:1b0!0B0L0f0_0W011+1R010E0-0c0f1j0c1#2123281-2b1)2e0p2g040a0H0w0N0q0B0q0!0l1e1g0)1 0N0N0c0I2B1w2i0f1E0U1}2N1`1|1{1$0%2k0_1X0f2d2y1#1M1O0;1,2X0l2Z0f0q2%1#0B2G1E2L2N2^11221g2)292.0N150A0~0H0#2K2|0 2{2j2~1-3032340W3723392L2W013e0L33040H0o3i2M103l3c0_3o3q0H0h3u3k2|3m3A340r3E3w3G3y3n0q313p340R3L3a2}1Q3d3Q3f3r0V3V3x3Y3z3!3S3r0$3(3N3*3P3R3B0j3:3b3=3I040#0P3`3X2*3?3#0#361x383M3{433}0#3h483j4a422 3,3q0#3t4g3v3W3H4l0~0#3D4p3F4b4k3@4u3K4x1H2?1w2%2Q0%1|2V3O0I2/2q0(1N1E2=0c2@383E054O0)4W4z1-0z0~3c4Y3)430C344,3;4c0I0~0m1)0k0c4;4%0_0}040G3L0H540H4r3O4)040)0E4}4j1-4/3r5d3m0E0p0~0s0s2,2A5n5i3O500F5s3=0b500!0c0A5c4E4-29500g3E565F3d0~4U2.0p5w435H5J573|0~5P5R5G0~5I4x5K4=2 4*3Y4|5E5)1-500X524x06555_5(4~015y0~5A5C5Z1-0q0~0K623z5N2G5Y5%5V4364040M5U5L68045O0q5Q5@5`556d295~04605D2`6j016f665.5|0f5X6n6i5/0_6f6h6c6z6F046b2^5^6q5{5e0_6u6w676A656!6P3c5-2^6V3m6L6I6E5+1,6*496U6,3O6Y5B6x4X6z6B6%0~0p0q0O2d2Z1v6N6J6#6g6/6W01500Y0y3L6T546s4(0~5b6!5g566D7e0f0E722/0l2b0c0s73750f2Z6!5u6!6{617t3m5T796:04150{0c2G7H5#7d3H7x7E7G7M5t0~5=536U7m6X5z6|6!707%5W7R74761u7W040n717R0L7T7V7?5S0~0S7Y3O6.7P7u7!7`7j5_7-01597p84297r6%7w7^7#7B0)7|5v8l1-7K6}3j8h7O6+8h6P7S0l7U6?8A6z5;5?6S6^8h597z887@8G8I8T6e5g2,8X6t7/7L6y7a7=8)7Q7D7`788,7e508N6@6^6r6z592G0t0T0N0f8#8x8%8z2M8h8+6~7a8F7_7F7{8w4 0~7~9f3n72818H838;7N867+6q8Q0~5B5A7|8@4h8_8`7a8|0*8 918b3m0z4@040D1f8J4q0U4!4V4F9T0U4I1w0t4K9Y2T2O0L1(9V4I1C4$7e2G0p0s0E0L0z7B0x0o0~1o1q1s1u0H9z961J391D9N0t1f0!0g0H1s002d2t0O1}1faa0T2Z0H0E1f2I0l1f0H0!230/4Z4P3m1/1W1Y1!9-9J0~8}9G4Y9R4P3r6a0k0q8H0H220N0H0%0i9=1d569Say1Y1;1Z2h9D9L9N7$2`aJ4#6C1K1F040Z2,2z0H1)1 2D2=0i0I0i0)0f0taQ0q8 0N0,b40e1`1*a 0N2Aasaua`1*aw2D3Oaza$aC9u049w9P2N9S3r1s0las7Ub90H0J0i0N0i0H0l1k1X0I2A1p2db45?a;2%a!1:aB2;4Ga-2`bwbr8k9p3O8n9j7v8d9d0s8t9j7I9j8y7|5$8D6O9l82bu8B7)a10 8g8{0~8S9I3O8F9m8Wc73=0q8Z9Hb`7ab@9j988K9ab-779y9s5`braG90927.5 7:ck6$b+co9eb(3=509icF4cb|9nb~8L9r5@1wb#1J4G9W4S109W0*0,0.04.
.128013beqên,4é9viE[3mo5_tR;Ph}klpwNf(: cg.a=ry0S6]u/72){As18d050%0c0t0L0l0A0!0H0I0A0L0!0!0M010t0l0B010406050!0T0p0p0L0N0O040Q0q0A0T0{0q0f0H020L0p0B0v0H0u0c150N0d0T0c0!050U12141618100B04051D1w1G0U1D100%0l0k0:0=0@0_0x0l0J0x0A1U0x0t0~050+0b0A0c1P0?0^011T1V1X1V0t1%1)1#0t0N1E0t0x0:1b0!0B0L0f0_0W011+1R010E0-0c0f1j0c1#2123281-2b1)2e0p2g040a0H0w0N0q0B0q0!0l1e1g0)1 0N0N0c0I2B1w2i0f1E0U1}2N1`1|1{1$0%2k0_1X0f2d2y1#1M1O0;1,2X0l2Z0f0q2%1#0B2G1E2L2N2^11221g2)292.0N150A0~0H0#2K2|0 2{2j2~1-3032340W3723392L2W013e0L33040H0o3i2M103l3c0_3o3q0H0h3u3k2|3m3A340r3E3w3G3y3n0q313p340R3L3a2}1Q3d3Q3f3r0V3V3x3Y3z3!3S3r0$3(3N3*3P3R3B0j3:3b3=3I040#0P3`3X2*3?3#0#361x383M3{433}0#3h483j4a422 3,3q0#3t4g3v3W3H4l0~0#3D4p3F4b4k3@4u3K4x1H2?1w2%2Q0%1|2V3O0I2/2q0(1N1E2=0c2@383E054O0)4W4z1-0z0~3c4Y3)430C344,3;4c0I0~0m1)0k0c4;4%0_0}040G3L0H540H4r3O4)040)0E4}4j1-4/3r5d3m0E0p0~0s0s2,2A5n5i3O500F5s3=0b500!0c0A5c4E4-29500g3E565F3d0~4U2.0p5w435H5J573|0~5P5R5G0~5I4x5K4=2 4*3Y4|5E5)1-500X524x06555_5(4~015y0~5A5C5Z1-0q0~0K623z5N2G5Y5%5V4364040M5U5L68045O0q5Q5@5`556d295~04605D2`6j016f665.5|0f5X6n6i5/0_6f6h6c6z6F046b2^5^6q5{5e0_6u6w676A656!6P3c5-2^6V3m6L6I6E5+1,6*496U6,3O6Y5B6x4X6z6B6%0~0p0q0O2d2Z1v6N6J6#6g6/6W01500Y0y3L6T546s4(0~5b6!5g566D7e0f0E722/0l2b0c0s73750f2Z6!5u6!6{617t3m5T796:04150{0c2G7H5#7d3H7x7E7G7M5t0~5=536U7m6X5z6|6!707%5W7R74761u7W040n717R0L7T7V7?5S0~0S7Y3O6.7P7u7!7`7j5_7-01597p84297r6%7w7^7#7B0)7|5v8l1-7K6}3j8h7O6+8h6P7S0l7U6?8A6z5;5?6S6^8h597z887@8G8I8T6e5g2,8X6t7/7L6y7a7=8)7Q7D7`788,7e508N6@6^6r6z592G0t0T0N0f8#8x8%8z2M8h8+6~7a8F7_7F7{8w4 0~7~9f3n72818H838;7N867+6q8Q0~5B5A7|8@4h8_8`7a8|0*8 918b3m0z4@040D1f8J4q0U4!4V4F9T0U4I1w0t4K9Y2T2O0L1(9V4I1C4$7e2G0p0s0E0L0z7B0x0o0~1o1q1s1u0H9z961J391D9N0t1f0!0g0H1s002d2t0O1}1faa0T2Z0H0E1f2I0l1f0H0!230/4Z4P3m1/1W1Y1!9-9J0~8}9G4Y9R4P3r6a0k0q8H0H220N0H0%0i9=1d569Say1Y1;1Z2h9D9L9N7$2`aJ4#6C1K1F040Z2,2z0H1)1 2D2=0i0I0i0)0f0taQ0q8 0N0,b40e1`1*a 0N2Aasaua`1*aw2D3Oaza$aC9u049w9P2N9S3r1s0las7Ub90H0J0i0N0i0H0l1k1X0I2A1p2db45?a;2%a!1:aB2;4Ga-2`bwbr8k9p3O8n9j7v8d9d0s8t9j7I9j8y7|5$8D6O9l82bu8B7)a10 8g8{0~8S9I3O8F9m8Wc73=0q8Z9Hb`7ab@9j988K9ab-779y9s5`braG90927.5 7:ck6$b+co9eb(3=509icF4cb|9nb~8L9r5@1wb#1J4G9W4S109W0*0,0.04.
Méthode moyenne_simple
La méthode moyenne_simple
calcule et renvoie la moyenne générale de l'élève. Celle-ci se calcule en effectuant la moyenne des moyennes.
Si l'élève n'a aucune moyenne, la fonction renverra None
.
Écrire la méthode moyenne_simple
.
Exemple
>>> jane = Eleve ( "Jane" , "Goodall" , "Te3" )
>>> jane . modifie_moyenne ( "éthologie" , 20 )
>>> jane . modifie_moyenne ( "théorie des groupes" , 14 )
>>> jane . moyenne_simple ()
17.0
Version vide Version à trous
.128013bqê,9vià3o_x;}lpwf( g0]6)2As1+8ené4E[m5tCRPhkN:c.a=rySu/7{d050+0G0O0Y0h0p0C0u0W0p0Y0C0C0Z010O0h0q010406050C0%0M0M0Y0!0#040$0k0p0%0 0k0H0u020Y0M0q0n0u0Q0G190!0c0%0G0C050(16181a1c140q04051H1A1K0(1H140+0h0g0@0_0{0}0S0h0v0S0p1Y0S0O12050/0b0p0G1T0`0|011X1Z1#1Z0O1+1-1)0O0!1I0O0S0@1f0C0q0Y0H0}0A011/1V010s0;0G0H1n0G1)25272c1;2f1-2i0M2k040a0u0R0!0k0q0k0C0h1i1k0-230!0!0G0W2F1A2m0H1I0(212R1~201 1*0+2o0}1#0H2h2C1)1Q1S0^1:2#0h2%0H0k2+1)0q2K1I2P2R2|15261k2-2d2=0!190p120u0D2O30132 2n321;3436380A3b273d2P2!013i0Y37040u0j3m2Q143p3g0}3s3u0u0J3y3o303q3E380N3I3A3K3C3r0k353t380y3P3e311U3h3U3j3v0)3Z3B3$3D3(3W3v0F3,3R3.3T3V3F0f3@3f3_3M040D0w3~3#2.3`3)0D3a1B3c3Q3 47410D3l4c3n4e46333:3u0D3x4k3z3!3L4p120D3H4t3J4f4o3{4y3O4B4m4w4F423Y4I4v3S4h3+4O3-4g4x423?4T3^4V4L0D3}4Z4D3%4L0A444)4n4+3)0A4b2|4J4Q4W0A4j2~1N2`1A2+2U0+202Z3S0W2?2u0,1R1I2_0G2{3c3I05580-5g4*0}0T123g5i4U2d0r385s4!330W120K1-0g0G5x5n0111040V3P0u5N0u4P3_5p040-0s5G4:0}5v3v5W3q0s0M120l0l2:2E5*5#3S5J0t5/3_0b5J0C0G0p5V4B5Q475J0e3I5P5t3h125e2=0M5?6012624B645y6604696b2d61635 335q3$5F5~650}5J0z5L4I5O6B6g5H5^125`5|6l1;0k120X6J3D672K6k6f6p6K120Z6o6v3r6Q2(6a6A6C5N6U0}6F046H5}2~6Z6L046N6u6h6P6j0k6%2|6D5X016?6X6T6Z0H126S4^6)6*6Z6-6/6O726M7g77043g6t6 6+7h04747o766r1:7n4d7b703q7e5{6:5h6=7i6_5H7k0M0k0#2h2%1z756`7q7s3c7A5:120*0o3P066B7p5S5U7g5Z5P7I710H0s127L1Q2f0G0l7L7N0H2%7g5;7g7C6I7.3q6n7R7J7=0Y0 0G2K7 6d6Y7S7K7M7O7x3n7p6x6z7a7b7p827E8m7G6@7j7=8j7}1y8e040L8y04198b8d847X040x8g5H738P7/8z7|7~4I7$5O7(127*8L3_7,7j7;8H8A2%0l0-8D5=8%478t8D6e7t8h898J8l2Q8n126y5M8r6Z5S0h8u2Q7W408}0h8c8 5!6=5Z2:8S7B5_7D7g6?6^6;8|8,8V8C8?6m128p7y7z8Z96122K0O0%0!0H9l3S8^9x6V8x9O6{7{8k7Q9s5H5J8F9R6!8H8a9e8K9W715J8O8X7%9E5T0G995m718)9!7:8U8k0l2D1o1-8;819n839*85929A4l7z8!04989L400b122ra29!9Na63S9q8G9T8B9V7F7S6xaf476?020v0O0nax2d0M0h124.an3_5Ja93z9C9D9t0O0k0/0paE9P7U3n9b47aGaI94aPac0s3UaW9S7Ma-729j9K8771amau8Q7HaK4g9|as9pa|a`8T040g3t1xat8vav120t936(aP9/aRaT3ta:6?0EaY9a7p8i0#a(9Cac9G9Ia?8{8804aSaUbl120(a:0Hah04aj9!80ala49?7pap9`a 7P8D0zbtbh5H5S5{5`8DaN13bga!2d5Sbw9Ja:0T5A040U1j9g140(5k5f1L500(521A0O54c32X2S0Y1,b~c15c1G9@3q2K0M0l0s0Y0T7_0S0j121s1u1w1y0ub)1L3d1H0P6}2t0u2:1Q1w0I0u0+270?0p000G0m8c0W0h0W1.2_0I0W0I0-0H0O0e0u1-5Pb}3q1?1!1$1(cf3Sb#0=9g915Kb;9F0.bxb;b?b^8W4~b}3v0q0G1h0u0d1~1.6}0h0C6^1O1J040B2:2Dc%1.c*cVcXcZ0O0u1yds0I1w1R3t2hds0icvcx1P1Rc+1$1^1%2^504~2~d3ac8$a}5u5w9`8+ar8.9 0qa1bMbca36G9od$04be8q7c7S979?b,3hbJbLdS1;bNd`6,bPb19Qd}9#dX9we2awa@3qazaBaDe83Sa$04aJb3a7c_bf6Cbq12bCbked3_8Rera#aHegbYaQb!12a+0!bH8Ubla=a:a_baa{e1ei4QbUe5eNesb2eKb4b61geQeUejbdb)8Ya)7ubBbjaVeu2dbmbo9h9t7{eyd/eA04b/by7Vene*bDe-9PbGf13Dd^2hake2eJ908w9reRa~9u9UbW3Zb|592R5e2R52b{0-0/0;0C04.
.128013bqê,9vià3o_x;}lpwf( g0]6)2As1+8ené4E[m5tCRPhkN:c.a=rySu/7{d050+0G0O0Y0h0p0C0u0W0p0Y0C0C0Z010O0h0q010406050C0%0M0M0Y0!0#040$0k0p0%0 0k0H0u020Y0M0q0n0u0Q0G190!0c0%0G0C050(16181a1c140q04051H1A1K0(1H140+0h0g0@0_0{0}0S0h0v0S0p1Y0S0O12050/0b0p0G1T0`0|011X1Z1#1Z0O1+1-1)0O0!1I0O0S0@1f0C0q0Y0H0}0A011/1V010s0;0G0H1n0G1)25272c1;2f1-2i0M2k040a0u0R0!0k0q0k0C0h1i1k0-230!0!0G0W2F1A2m0H1I0(212R1~201 1*0+2o0}1#0H2h2C1)1Q1S0^1:2#0h2%0H0k2+1)0q2K1I2P2R2|15261k2-2d2=0!190p120u0D2O30132 2n321;3436380A3b273d2P2!013i0Y37040u0j3m2Q143p3g0}3s3u0u0J3y3o303q3E380N3I3A3K3C3r0k353t380y3P3e311U3h3U3j3v0)3Z3B3$3D3(3W3v0F3,3R3.3T3V3F0f3@3f3_3M040D0w3~3#2.3`3)0D3a1B3c3Q3 47410D3l4c3n4e46333:3u0D3x4k3z3!3L4p120D3H4t3J4f4o3{4y3O4B4m4w4F423Y4I4v3S4h3+4O3-4g4x423?4T3^4V4L0D3}4Z4D3%4L0A444)4n4+3)0A4b2|4J4Q4W0A4j2~1N2`1A2+2U0+202Z3S0W2?2u0,1R1I2_0G2{3c3I05580-5g4*0}0T123g5i4U2d0r385s4!330W120K1-0g0G5x5n0111040V3P0u5N0u4P3_5p040-0s5G4:0}5v3v5W3q0s0M120l0l2:2E5*5#3S5J0t5/3_0b5J0C0G0p5V4B5Q475J0e3I5P5t3h125e2=0M5?6012624B645y6604696b2d61635 335q3$5F5~650}5J0z5L4I5O6B6g5H5^125`5|6l1;0k120X6J3D672K6k6f6p6K120Z6o6v3r6Q2(6a6A6C5N6U0}6F046H5}2~6Z6L046N6u6h6P6j0k6%2|6D5X016?6X6T6Z0H126S4^6)6*6Z6-6/6O726M7g77043g6t6 6+7h04747o766r1:7n4d7b703q7e5{6:5h6=7i6_5H7k0M0k0#2h2%1z756`7q7s3c7A5:120*0o3P066B7p5S5U7g5Z5P7I710H0s127L1Q2f0G0l7L7N0H2%7g5;7g7C6I7.3q6n7R7J7=0Y0 0G2K7 6d6Y7S7K7M7O7x3n7p6x6z7a7b7p827E8m7G6@7j7=8j7}1y8e040L8y04198b8d847X040x8g5H738P7/8z7|7~4I7$5O7(127*8L3_7,7j7;8H8A2%0l0-8D5=8%478t8D6e7t8h898J8l2Q8n126y5M8r6Z5S0h8u2Q7W408}0h8c8 5!6=5Z2:8S7B5_7D7g6?6^6;8|8,8V8C8?6m128p7y7z8Z96122K0O0%0!0H9l3S8^9x6V8x9O6{7{8k7Q9s5H5J8F9R6!8H8a9e8K9W715J8O8X7%9E5T0G995m718)9!7:8U8k0l2D1o1-8;819n839*85929A4l7z8!04989L400b122ra29!9Na63S9q8G9T8B9V7F7S6xaf476?020v0O0nax2d0M0h124.an3_5Ja93z9C9D9t0O0k0/0paE9P7U3n9b47aGaI94aPac0s3UaW9S7Ma-729j9K8771amau8Q7HaK4g9|as9pa|a`8T040g3t1xat8vav120t936(aP9/aRaT3ta:6?0EaY9a7p8i0#a(9Cac9G9Ia?8{8804aSaUbl120(a:0Hah04aj9!80ala49?7pap9`a 7P8D0zbtbh5H5S5{5`8DaN13bga!2d5Sbw9Ja:0T5A040U1j9g140(5k5f1L500(521A0O54c32X2S0Y1,b~c15c1G9@3q2K0M0l0s0Y0T7_0S0j121s1u1w1y0ub)1L3d1H0P6}2t0u2:1Q1w0I0u0+270?0p000G0m8c0W0h0W1.2_0I0W0I0-0H0O0e0u1-5Pb}3q1?1!1$1(cf3Sb#0=9g915Kb;9F0.bxb;b?b^8W4~b}3v0q0G1h0u0d1~1.6}0h0C6^1O1J040B2:2Dc%1.c*cVcXcZ0O0u1yds0I1w1R3t2hds0icvcx1P1Rc+1$1^1%2^504~2~d3ac8$a}5u5w9`8+ar8.9 0qa1bMbca36G9od$04be8q7c7S979?b,3hbJbLdS1;bNd`6,bPb19Qd}9#dX9we2awa@3qazaBaDe83Sa$04aJb3a7c_bf6Cbq12bCbked3_8Rera#aHegbYaQb!12a+0!bH8Ubla=a:a_baa{e1ei4QbUe5eNesb2eKb4b61geQeUejbdb)8Ya)7ubBbjaVeu2dbmbo9h9t7{eyd/eA04b/by7Vene*bDe-9PbGf13Dd^2hake2eJ908w9reRa~9u9UbW3Zb|592R5e2R52b{0-0/0;0C04.
Méthode moyenne_ponderee
La méthode moyenne_ponderee
prend comme unique paramètre un dictionnaire coeffs
associant des intitulés de matières (str
) à des coefficients (au format int
ou float
).
Cette fonction calcule la moyenne pondérée de l'élève en appliquant les coefficients fournis en paramètre.
Si l'élève n'a aucune moyenne, la fonction renverra None
.
Si l'élève possède une moyenne dans une matière à laquelle n'est associée aucun coefficient dans le dictionnaire coeffs
, la fonction générera une erreur de type ValueError
.
Écrire la méthode moyenne_ponderee
.
Générer une erreur
Il est possible de générer une erreur de type ValueError
en utilisant la structure suivante :
if condition_causant_une_erreur :
raise ValueError ( "Texte d'explication" )
Il existe de nombreux types d'erreurs différents selon le problème rencontré.
Exemple
>>> margaret = Eleve ( "Margaret" , "Hamilton" , "Te5" )
>>> margaret . modifie_moyenne ( "études spatiales" , 20 )
>>> margaret . modifie_moyenne ( "maths" , 14 )
>>> coeffs = { "études spatiales" : 1 , "maths" : 0.5 }
>>> margaret . moyenne_ponderee ( coeffs )
18.0
Version vide Version à trous
.128013bqê,9vià3o_x;}lpwf( g0]6)2As1+8Vené4E[m5tCRPhkN:c.a=rySI*u/7{d050.0H0P0Z0h0p0C0u0X0p0Z0C0C0!010P0h0q010406050C0*0N0N0Z0#0$040%0k0p0*120k0I0u020Z0N0q0n0u0R0H1c0#0c0*0H0C050+191b1d1f170q04051K1D1N0+1K170.0h0g0`0|0~100T0h0v0T0p1#0T0P15050=0b0p0H1W0}0 011!1$1(1$0P1.1:1,0P0#1L0P0T0`1i0C0q0Z0I100A011=1Y010s0@0H0I1q0H1,282a2f1@2i1:2l0N2n040a0u0S0#0k0q0k0C0h1l1n0:260#0#0H0X2I1D2p0I1L0+242U2123221-0.2r101(0I2k2F1,1T1V0{1?2(0h2*0I0k2.1,0q2N1L2S2U2 18291n2:2g2^0#1c0p150u0D2R3316322q351@37393b0A3e2a3g2S2%013l0Z3a040u0j3p2T173s3j103v3x0u0K3B3r333t3H3b0O3L3D3N3F3u0k383w3b0y3S3h341X3k3X3m3y0,3$3E3)3G3+3Z3y0F3/3U3;3W3Y3I0f3`3i3|3P040D0w413(2;3}3,0D3d1E3f3T424a440D3o4f3q4h49363?3x0D3A4n3C3%3O4s150D3K4w3M4i4r3~4B3R4E4p4z4I453#4L4y3V4k3.4R3:4j4A453_4W3{4Y4O0D404$4G3*4O0A474,4q4.3,0A4e2 4M4T4Z0A4m4{4S434~4v514X4H4^4D564%583@0A4K5b4-3=4/4Q5h4?5j4^4V5m4N4^4#5r4}4/4+5v534O0j4;5z4(3,0j4`4g525F3@0j505J574@5M553f1O2}1D2.2X0.232$3V0X2_2x0/1U1L2|0H2~5U4E055(0:5:5i010U153j3L5K2g0r3b5 5Q3G0X150L1:0g0H645c1@14040W3S0u6k0u601@5|040:0s6d5`623y6t5n2h0N150l0l2?2H6C6x3t6g0t6H3V0b6g0C0H0p6s5=65016g0e3L6m6U0I155.2^0N6L3|6W6Y6n3G156(6*4a6,4E6Z6e6/043j6c6T6`6V150z6i4L6l766_5`6N156P6R6=2g0k150Y7e3k6$2N6;6^6.017g040!6-6!7l2+6)75776k7p7a047c6S316U7r7i6 5`6#047n2 786y7r7t7o7v7O0k7y4{7A7B6U7D7F7j107J7*3u5}3)6~7Q7p7T7u707N6}6j7#7C6O6Q7G5U7I7h7-7N0N0k0$2k2*1C7V707@8d5`6g0-0o3S06767p6p6r7-6v6m7L6y0I0s15871T2i0H0l87890I2*7-6J7-7(808I156X8g8v8y0Z120H2N8N048P7=7W8E8a7;82706g737|7A7~7b8M8u3t7,8=4T8y888%8c7H8*150M858S8U8W8^6+150x7^5`8f8!7_8`8F8H4L8m6l8o158q964a8s858x048$8G8C0:8X6K9o2g8L7d9A6f8O9a8R9t8T0h8V8(3q7p8+747!7}6U6p0h813q7R3O939L959d9b6v2?9H3t9C9X2T7?849E6{9u8b8X9R4g7#779l042N0P0*0#0I9,6M7 9D8~9b9=aa9I9^1B8X919?7.9J949N9:6U6g999i8n9U9m0H9/5_6y9qaj8w9f8%0l2G1r1:9y8Ka8ax9P729{4o9}9 9Wa6430b152uaJaj9.7-8@ad9!9t8{9v8}8)8h72aU4a7r020v0P0na;2g0N0h155Da.6y6gaP3C9}at9e040P0k0=0pa{1@9c3f9Z3Va}a 8-b69 0s3Xbe9@88br7q9*a58Q9-aLa$acb1a)afa-9O83047Ka(8_040g3w1AbGao8 040t8,7zb69~7Wbabcbu7r0E7U9(aebtbXbnaua00;a3bxb+a)b#3wb%150+bu0IaW04aYaj8Ja!bAaja%bDbMbF8X0z8lb75`8paw8r63aB9saf0l2E0I0:2Nanay6I159zbL3|a#c49GbybM5(aw0sbRcv3V9Qbmcg6y9VaxbiaVaX2kaZcz4acBcX7fbCbHb8cccC04cecE3|a?a^a`c-4abk04b0c%a/6hcN9Tb8b`bdc=c#7sbuc@c_b5bYcS4j15d00lcG0scIb|d4d21@d6c}8.b:bp0#b 9#9Mb%bwbucZcac.c$bS7MaDa,9`dnb/70cQdsal9$cuda7f6v2^0Pdv159+dk6{dfdhc*b416d9dIch150#0?6Pb 2x0GbP0H0L2Mbqc*cydz4a0C2d04021zbb0n0(0p0u1c0I1z1;0Z0*e70k2?0_0*340kcH0h0X9L0I0Pe10*e3cddHaRb!bbb{dX7q15b)dxc7c!bfdBcK43dE9_c*aieG9@9Kduc*arb^3V7r0)dLdZcJaN04eOd|36dt9%e)9F04eU9|d(9kexbcdeekdgcJdPeH04eDeA7`e`d!eP71e%92dMeSf5aqeve?dJd+b=a4dLd0dib~f1dcey0pe_cHcJ9jdoff046Qd.d#fd7$fwa1b?bu0U67040V1mcu170+5@5/5VfQ0+5Y1D0P5!fV2!2V0Z1/fS5Y1JeJ4a2N0N0l0s0Z0U8C0T0j151v1xea0_d$1O3g1K0Q7Y2w0u2?1T1z0J0u0.2a0_0p000H0m8Ven0X1;2|0J0X0J0:ep0e0u1:6mfP3t1_1%1)1+f*2g6pfycue$d$e}106pfEfieAfH15fK9h31fO5)3y0q0H1k0u0d211;7Y0h0C7K1R1M040Beg0hgs1;gvglgngp0P0u1Bg`0J1z1U3w2kg`0i0u74g+2.gw1)1{1*2{5WgS31fPgB6oavaM6UaAf5aCa*9g8Ccqcs0HgFapcxaK8:a9e-106@eVeK6|f3e#hxc+d$fugI5{dVcR7pc0cUb@hCf6d{c`6ydyhZ8?eIhSeLagc*c,hFa=15a@a_d5a~c^dGb.cOb_fpdib*bh7pdmh`fedDb9fpfre{h~h?bli3fCd*04dqdL1camdU04dWh.9BeFhWc9h$cba+eMfb15hMd(aShR8#eRe,9Y7?dRbbilini07We!h_9Se=hOgKd-dOhSd:d=d@2Cdrd`7-d~h:e2a_e5e72aea0ueceeeg0uei26elenh2ereth,fBhO7NfkeAb(h iG7%iqitdAbJf8c)ixf7aBe+hwbTe:j78e15eYfnhHfsahjdiEjjc{jld8bYh)i6e^iOj4eCj62Tj15}hIjtjhf9iFdCb298dH9 gLhVjmi5j3ioe~fmj!6{ddjE51gT5^fR2Uf(5X0;0?0^04.
.128013bqê,9vià3o_x;}lpwf( g0]6)2As1+8Vené4E[m5tCRPhkN:c.a=rySI*u/7{d050.0H0P0Z0h0p0C0u0X0p0Z0C0C0!010P0h0q010406050C0*0N0N0Z0#0$040%0k0p0*120k0I0u020Z0N0q0n0u0R0H1c0#0c0*0H0C050+191b1d1f170q04051K1D1N0+1K170.0h0g0`0|0~100T0h0v0T0p1#0T0P15050=0b0p0H1W0}0 011!1$1(1$0P1.1:1,0P0#1L0P0T0`1i0C0q0Z0I100A011=1Y010s0@0H0I1q0H1,282a2f1@2i1:2l0N2n040a0u0S0#0k0q0k0C0h1l1n0:260#0#0H0X2I1D2p0I1L0+242U2123221-0.2r101(0I2k2F1,1T1V0{1?2(0h2*0I0k2.1,0q2N1L2S2U2 18291n2:2g2^0#1c0p150u0D2R3316322q351@37393b0A3e2a3g2S2%013l0Z3a040u0j3p2T173s3j103v3x0u0K3B3r333t3H3b0O3L3D3N3F3u0k383w3b0y3S3h341X3k3X3m3y0,3$3E3)3G3+3Z3y0F3/3U3;3W3Y3I0f3`3i3|3P040D0w413(2;3}3,0D3d1E3f3T424a440D3o4f3q4h49363?3x0D3A4n3C3%3O4s150D3K4w3M4i4r3~4B3R4E4p4z4I453#4L4y3V4k3.4R3:4j4A453_4W3{4Y4O0D404$4G3*4O0A474,4q4.3,0A4e2 4M4T4Z0A4m4{4S434~4v514X4H4^4D564%583@0A4K5b4-3=4/4Q5h4?5j4^4V5m4N4^4#5r4}4/4+5v534O0j4;5z4(3,0j4`4g525F3@0j505J574@5M553f1O2}1D2.2X0.232$3V0X2_2x0/1U1L2|0H2~5U4E055(0:5:5i010U153j3L5K2g0r3b5 5Q3G0X150L1:0g0H645c1@14040W3S0u6k0u601@5|040:0s6d5`623y6t5n2h0N150l0l2?2H6C6x3t6g0t6H3V0b6g0C0H0p6s5=65016g0e3L6m6U0I155.2^0N6L3|6W6Y6n3G156(6*4a6,4E6Z6e6/043j6c6T6`6V150z6i4L6l766_5`6N156P6R6=2g0k150Y7e3k6$2N6;6^6.017g040!6-6!7l2+6)75776k7p7a047c6S316U7r7i6 5`6#047n2 786y7r7t7o7v7O0k7y4{7A7B6U7D7F7j107J7*3u5}3)6~7Q7p7T7u707N6}6j7#7C6O6Q7G5U7I7h7-7N0N0k0$2k2*1C7V707@8d5`6g0-0o3S06767p6p6r7-6v6m7L6y0I0s15871T2i0H0l87890I2*7-6J7-7(808I156X8g8v8y0Z120H2N8N048P7=7W8E8a7;82706g737|7A7~7b8M8u3t7,8=4T8y888%8c7H8*150M858S8U8W8^6+150x7^5`8f8!7_8`8F8H4L8m6l8o158q964a8s858x048$8G8C0:8X6K9o2g8L7d9A6f8O9a8R9t8T0h8V8(3q7p8+747!7}6U6p0h813q7R3O939L959d9b6v2?9H3t9C9X2T7?849E6{9u8b8X9R4g7#779l042N0P0*0#0I9,6M7 9D8~9b9=aa9I9^1B8X919?7.9J949N9:6U6g999i8n9U9m0H9/5_6y9qaj8w9f8%0l2G1r1:9y8Ka8ax9P729{4o9}9 9Wa6430b152uaJaj9.7-8@ad9!9t8{9v8}8)8h72aU4a7r020v0P0na;2g0N0h155Da.6y6gaP3C9}at9e040P0k0=0pa{1@9c3f9Z3Va}a 8-b69 0s3Xbe9@88br7q9*a58Q9-aLa$acb1a)afa-9O83047Ka(8_040g3w1AbGao8 040t8,7zb69~7Wbabcbu7r0E7U9(aebtbXbnaua00;a3bxb+a)b#3wb%150+bu0IaW04aYaj8Ja!bAaja%bDbMbF8X0z8lb75`8paw8r63aB9saf0l2E0I0:2Nanay6I159zbL3|a#c49GbybM5(aw0sbRcv3V9Qbmcg6y9VaxbiaVaX2kaZcz4acBcX7fbCbHb8cccC04cecE3|a?a^a`c-4abk04b0c%a/6hcN9Tb8b`bdc=c#7sbuc@c_b5bYcS4j15d00lcG0scIb|d4d21@d6c}8.b:bp0#b 9#9Mb%bwbucZcac.c$bS7MaDa,9`dnb/70cQdsal9$cuda7f6v2^0Pdv159+dk6{dfdhc*b416d9dIch150#0?6Pb 2x0GbP0H0L2Mbqc*cydz4a0C2d04021zbb0n0(0p0u1c0I1z1;0Z0*e70k2?0_0*340kcH0h0X9L0I0Pe10*e3cddHaRb!bbb{dX7q15b)dxc7c!bfdBcK43dE9_c*aieG9@9Kduc*arb^3V7r0)dLdZcJaN04eOd|36dt9%e)9F04eU9|d(9kexbcdeekdgcJdPeH04eDeA7`e`d!eP71e%92dMeSf5aqeve?dJd+b=a4dLd0dib~f1dcey0pe_cHcJ9jdoff046Qd.d#fd7$fwa1b?bu0U67040V1mcu170+5@5/5VfQ0+5Y1D0P5!fV2!2V0Z1/fS5Y1JeJ4a2N0N0l0s0Z0U8C0T0j151v1xea0_d$1O3g1K0Q7Y2w0u2?1T1z0J0u0.2a0_0p000H0m8Ven0X1;2|0J0X0J0:ep0e0u1:6mfP3t1_1%1)1+f*2g6pfycue$d$e}106pfEfieAfH15fK9h31fO5)3y0q0H1k0u0d211;7Y0h0C7K1R1M040Beg0hgs1;gvglgngp0P0u1Bg`0J1z1U3w2kg`0i0u74g+2.gw1)1{1*2{5WgS31fPgB6oavaM6UaAf5aCa*9g8Ccqcs0HgFapcxaK8:a9e-106@eVeK6|f3e#hxc+d$fugI5{dVcR7pc0cUb@hCf6d{c`6ydyhZ8?eIhSeLagc*c,hFa=15a@a_d5a~c^dGb.cOb_fpdib*bh7pdmh`fedDb9fpfre{h~h?bli3fCd*04dqdL1camdU04dWh.9BeFhWc9h$cba+eMfb15hMd(aShR8#eRe,9Y7?dRbbilini07We!h_9Se=hOgKd-dOhSd:d=d@2Cdrd`7-d~h:e2a_e5e72aea0ueceeeg0uei26elenh2ereth,fBhO7NfkeAb(h iG7%iqitdAbJf8c)ixf7aBe+hwbTe:j78e15eYfnhHfsahjdiEjjc{jld8bYh)i6e^iOj4eCj62Tj15}hIjtjhf9iFdCb298dH9 gLhVjmi5j3ioe~fmj!6{ddjE51gT5^fR2Uf(5X0;0?0^04.
# Tests
(insensible à la casse)(Ctrl+I)
(Alt+: ; Ctrl pour inverser les colonnes)
(Esc)