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
{}
Version vide Version à trous
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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
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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
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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
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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
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.128013nmwgPoC,}*6]uRa8bsr[vce1S9_d(xiyk02; V5à+lI4t/E.3)Afh{:=êé7qNp050C0x0T0p0F0Q0s0L0w0Q0p0s0s0(010T0F0.010406050s0n0c0c0p0t0G040z0g0Q0n120g0b0L020p0c0.0K0L0o0x1c0t0,0n0x0s050U191b1d1f170.041D1K051N0U1N1P1K170C0F0v0`0|0~100#0F0e0#0Q1%0#0T15050=0r0Q0x1Y0}0 011$1(1*1(0T1:1=1.0T0r0g0C1f1/0t1L0T0#0`1i0s0.0p0b100J011@1!010!0@0x0b1q0x1.2f2h2m1_2p1=2s0c2u040a0L0f0t0g0.0g0s0F1l1n0:2d0t0t0x0w2P1D2w0b1L0U2b2#0T29282a0C2y101*0b2r2M1.1V1X0{1^2/0F2;0b251W1.0.2U1L2Z2#36182g1n2`2n2 0t1c0Q150L0y2Y3a16392x3c1_3e3g3i0J3l2h3n2Z2.013s0p3h040L0X3w2!173z3q103C3E0L0S3I3y3a3A3O3i0N3S3K3U3M3B0g3f3D3i0l3Z3o3b1Z3r3(3t3F0+3-3L3:3N3=3*3F0q3_3#3{3%3)3P0A413p433W040y0I483/2{443?0y3k1E3m3!494h4b0y3v4m3x4o4g3d3}3E0y3H4u3J3.3V4z150y3R4D3T4p4y454I3Y4L4w4G4P4c3,4S4F3$4r3^4Y3`4q4H4c404%424)4V0y474-4N3;4V0J4e4?4x4^3?0J4l364T4!4*0J4t524Z4a554C584(4O4 4K5d4.5f3~0J4R5i4@3|4_4X5o4}5q4 4$5t4U4 4,5y544_4=5C5a4V0X4{5G4/3?0X514n595M3~0X575Q5e4~5T5c3m1M341D2^2(0C2,3A0w252E0/1W1L330x355#4L055.0:5_5p010H153q3S5R2n0d3i655X3N0w150V1=0v0x6a5j1_14040%3Z0L6q0L661_62040:0!6j60683F6z5u2o0c150B0B2}2O6I6D3A6m0D6N3$0r6m0s0x0Q6y5{6b016m0i3S6s6!0b155@2 0c6R436$6(6t3N156.6:4h6=4L6)6k6^043q6i6Z706#150Y6o4S6r7c6 606T156V6X6{2n0g150W7k3r6,2U6`6~6@017m040(6?6*7r2=6/7b7d6q7v7g047i6Y386!7x7o75606+047t367e6E7x7z7u7B7U0g7E527G7H6!7J7L7p107P7:3B633:747W7v7Z7A767T736p7+7I6U6W7M5#7O7n7?7T0c0g0G2r2;1C7#767}8j606m0$0j3Z067c7v6v6x7?6B6s7R6E0b0!158d1V2p0x0B8d8f0b2;7?6P7?7.868O156%8m8B8E0p120x2U8T048V7{7$8K8g7`88766m79827G847h8S8A3A7=8{4!8E8e8-8i7N8:150u8b8Y8!8$8~6;150m7~608l8*7 908L8N4S8s6r8u158w9c4h8y8b8D048,8M8I0:8%6Q9u2n8R7j9G6l8U9g8X9z8Z0F8#8.3x7v8;7a7*836!6v0F873x7X3V999R9b9j9h6B2}9N3A9I9%2!7|8a9K719A8h8%9X4n7+7d9r042U0T0n0t0b9=6S859J949h9{ag9O9~1B8%979|7@9P9a9T9_6!6m9f9o8t9!9s0x9^5 6E9wap8C9l8-0B2N1r1=9E8QaeaD9V78a14va3a59$ac4a0r152BaPap9@7?8}aj9*9z919B938/8n78a!4h7x020e0T0Ka`2n0c0F155Ka@6E6maV3Ja3az9k040T0g0=0Qb11_9i3m9)3$b3b58?bca50!3(bk9}8ebx7w9:ab8W9?aRa,aib7a/ala?9U89047Qa.8 040v3D1AbMau95040D8=7Fbca47$bgbibA7x0P7!9.akbzb%btaAa60;a9bDb;a/b+3Db-150UbA0ba$04a(ap8Pa*bGapa-bJbSbL8%0Y8rbd608vaC8x69aH9yal0B2L0b0:2UataE6O159FbR43a+ca9MbEbS5.aC0!bXcB3$9Wbscm6E9#aDboa#a%2ra)cF4hcHc%7lbIbNbecicI04ckcK43a|a~b0c?4hbq04b6c-a^6ncT9Zbec0bjc{c+7ybAc}c bbb(cY4q15d60BcM0!cOc2dad81_dcd38@b_bv0tc59+9Sb-bCbAc)cgc@c,bY7SaJa=a0dtb^76cWdyar9,cAdg7l6B2 0TdB159;dq71dldnc:ba16dfdOcn150t0?6Vc52E0MbV0x0V2Tbwc:cEdF4h0s2k04010R0Q0L1c0b1z1?0p0nea0g2}0_0n3b0gcN0F0w9R0b1-cjdNaXb*bhc1d%7w15b/dDcdc*bldHcQ4adK9 c:aoeG9}9QdAc:axb~3$7x0kdRd)cPaT04eOe23ddz9-e)9L04eUa2d.9qexbidkendmcPdVeH04eDeA80e`d*eP77e%98dSeSf5aweve?dPd;b{aadRd6doc4f1diey0Qe_cNcP9pduff046Wd@d+fd7,fwa7b|bA0H6d040-1mcA170U5}5^5$fQ0U5)1D0T5+fV2*2$24262(0p1;fS5)1JeJ4h2U0c0B0!0p0H8I0#0X151v1xed0_d,1M3n1K0h7(2D0L2}1V1z0*0L0C2h0_0Q000x0E8#eq0w1?330*0w0*0:es0i0L1=6sfP3A1{1)1+1-f-2n6vfycAe$d,e}106vfEfieAfH15fK9n38fO5/3F0.0x1k0L0)2)1?7(0F0s7Q1T1O040Zej0Fgv1?gygogqgs0T0L1Bg}0*1z1W3D2rg}0O0L7ag.2^gz1+1}1,325%gV38fPgE6uaBaS6!aGf5aIa:9m8Icwcy0xgIavcDaQ8_afe-106}eVeK72f3e#hAc;d,fugL61d#cX7vc6c!b}hFf6e1d06EdEh$8|eIhVeLamc:c=hIa{15a}a dbb4c~dMb@cUb fpdob:bn7vdsh}fedJbffpfre{i1h_bri6fCd:04dwdR1casd!04d$h;9HeFhZcfh)cha;eMfb15hPd.aYhU8+eRe,9(7|dXbhioiqi37$e!h|9Ye=hRgNd?dUhVd_d{d}2Jdxe07?e415e7e9ebed0Lefehej0Lel2deoeqh501euigb)d5i0eAb.i2iJ7-itiwdGbPf8c/iAf7aHe+hzbZe:j78k15eYfnhKfsanjdiHjjd1jldeb(h,i9e^iRj4eCj62!hRf2jseNjuineTdNa5gOhYjmi8fkjF04fmir7qjC3Dibf45QgW5~fR2#f+5(0;0?0^04.
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