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Student IDUsernameFinal GradeQuiz 1: 1.1. La cellule, atome du vivant - Question 1.1.1 (Earned)Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.1 (Possible)Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.2 (Earned)Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.2 (Possible)Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.3 (Earned)Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.3 (Possible)Quiz 2: 1.2. Au cœur de la cellule, la molécule d’ADN - Question 1.2.1 (Earned)...Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.4 (Earned)Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.4 (Possible)Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.1 (Earned)Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.1 (Possible)Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.2 (Earned)Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.2 (Possible)Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.1 (Earned)Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.1 (Possible)Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.2 (Earned)Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.2 (Possible)
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9418 rows × 181 columns

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" ], "text/plain": [ " Student ID Username Final Grade \\\n", "0 9854021 Deewen 0.0 \n", "1 2491543 JeanMarcHa 0.0 \n", "2 935346 ChristelleMariais 0.01 \n", "3 839341 lfarhi 0.02 \n", "4 935202 ThierryParmentelat 0.0 \n", "5 1590534 FRechenmann 0.05 \n", "6 906027 apprenantatfun 0.0 \n", "7 1669686 Osiatis_FUN 0.0 \n", "8 5753203 Erufen 0.0 \n", "9 5581199 rguettaf 0.0 \n", "10 155198 St2phane 0.68 \n", "11 2952539 glmrenard2 0.89 \n", "12 21469 FredGenaux 0.0 \n", "13 10372546 ABOUO 0.0 \n", "14 10795818 badreddine18 0.98 \n", "15 6546721 Belmont888 0.0 \n", "16 7740746 Hmamlouk 0.0 \n", "17 40630 EmmanuelleAyache 0.0 \n", "18 5857517 Carole-Anne92 0.14 \n", "19 8764498 Shannon-Codeur 0.0 \n", "20 8831438 Datura-Stramonium 0.0 \n", "21 3915948 evamoris 0.0 \n", "22 13206496 Nessan 0.0 \n", "23 12705537 lenaicparisot 0.0 \n", "24 1139562 sebastianroussel 0.0 \n", "25 7956626 mraharivelo 0.0 \n", "26 40387 elghaziibrahim 0.0 \n", "27 5507194 LOUAPIVOTREMY 0.0 \n", "28 2315525 pp39 0.33 \n", "29 5474892 JMR1998 0.0 \n", "... ... ... ... \n", "9388 13621234 anita_kl 0.04 \n", "9389 17552729 ykondi 0.0 \n", "9390 16702570 sauger 0.0 \n", "9391 17553531 Amnabio 0.0 \n", "9392 6835006 yeshoua 0.0 \n", "9393 16258428 gabrielle241 0.0 \n", "9394 3811476 meirasim 0.0 \n", "9395 17555116 dembdamo 0.0 \n", "9396 16558592 Augustine2705 0.0 \n", "9397 4746951 pougetisa 0.0 \n", "9398 13385629 Coco9_ 0.0 \n", "9399 17555618 Akmoussi 0.0 \n", "9400 17040349 Asmaesh 0.0 \n", "9401 17556871 Rihabsaal 0.0 \n", "9402 17556810 ALVAREZA 0.0 \n", "9403 13877392 Angie_B 0.0 \n", "9404 17556899 helloworld2 0.04 \n", "9405 17140840 M-Nadjet 0.0 \n", "9406 16491813 sz40 0.0 \n", "9407 17516851 Missats 0.0 \n", "9408 17557462 GabrielaRezende 0.23 \n", "9409 17552639 Tpl97 0.0 \n", "9410 17557583 AlexandreIac 0.33 \n", "9411 13393564 tsamaille 0.0 \n", "9412 17558571 Armeloic 0.0 \n", "9413 17558804 Inoore 0.0 \n", "9414 17442088 BELLAJmed 0.0 \n", "9415 17559279 Melprt 0.04 \n", "9416 17414427 hudakhalid 0.0 \n", "9417 4525692 RiadS25 0.0 \n", "\n", " Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.1 (Earned) \\\n", "0 0.0 \n", "1 NaN \n", "2 1.0 \n", "3 1.0 \n", "4 0.0 \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 0.0 \n", "20 NaN \n", "21 0.0 \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 0.0 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 0.0 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 0.0 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 0.0 \n", "9408 1.0 \n", "9409 NaN \n", "9410 1.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.1 (Possible) \\\n", "0 1 \n", "1 NaN \n", "2 1.0 \n", "3 1.0 \n", "4 1 \n", "5 1 \n", "6 NaN \n", "7 NaN \n", "8 1 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 1.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 1 \n", "20 NaN \n", "21 1 \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 1 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 1 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 1 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 1 \n", "9408 1.0 \n", "9409 NaN \n", "9410 1.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.2 (Earned) \\\n", "0 0.0 \n", "1 NaN \n", "2 0.0 \n", "3 1.0 \n", "4 0.0 \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 0.0 \n", "20 NaN \n", "21 0.0 \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 0.0 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 0.0 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 0.0 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 0.0 \n", "9408 1.0 \n", "9409 NaN \n", "9410 0.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.2 (Possible) \\\n", "0 1 \n", "1 NaN \n", "2 1 \n", "3 1.0 \n", "4 1 \n", "5 1 \n", "6 NaN \n", "7 NaN \n", "8 1 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 1.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 1 \n", "20 NaN \n", "21 1 \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 1 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 1 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 1 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 1 \n", "9408 1.0 \n", "9409 NaN \n", "9410 1.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.3 (Earned) \\\n", "0 0.0 \n", "1 NaN \n", "2 0.0 \n", "3 1.0 \n", "4 0.0 \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 0.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 0.0 \n", "20 NaN \n", "21 0.0 \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 0.0 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 0.0 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 0.0 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 0.0 \n", "9408 1.0 \n", "9409 NaN \n", "9410 1.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.3 (Possible) \\\n", "0 1 \n", "1 NaN \n", "2 1 \n", "3 1.0 \n", "4 1 \n", "5 1 \n", "6 NaN \n", "7 NaN \n", "8 1 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 1.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 1 \n", "20 NaN \n", "21 1 \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 1 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 1 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 1 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 1 \n", "9408 1.0 \n", "9409 NaN \n", "9410 1.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 2: 1.2. Au cœur de la cellule, la molécule d’ADN - Question 1.2.1 (Earned) \\\n", "0 NaN \n", "1 NaN \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 1.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 1.0 \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 1.0 \n", "19 0.0 \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 1.0 \n", "29 NaN \n", "... ... \n", "9388 1.0 \n", "9389 NaN \n", "9390 NaN \n", "9391 0.0 \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 0.0 \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 0.0 \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 1.0 \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 1.0 \n", "9409 NaN \n", "9410 1.0 \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 1.0 \n", "9416 NaN \n", "9417 NaN \n", "\n", " ... \\\n", "0 ... \n", "1 ... \n", "2 ... \n", "3 ... \n", "4 ... \n", "5 ... \n", "6 ... \n", "7 ... \n", "8 ... \n", "9 ... \n", "10 ... \n", "11 ... \n", "12 ... \n", "13 ... \n", "14 ... \n", "15 ... \n", "16 ... \n", "17 ... \n", "18 ... \n", "19 ... \n", "20 ... \n", "21 ... \n", "22 ... \n", "23 ... \n", "24 ... \n", "25 ... \n", "26 ... \n", "27 ... \n", "28 ... \n", "29 ... \n", "... ... \n", "9388 ... \n", "9389 ... \n", "9390 ... \n", "9391 ... \n", "9392 ... \n", "9393 ... \n", "9394 ... \n", "9395 ... \n", "9396 ... \n", "9397 ... \n", "9398 ... \n", "9399 ... \n", "9400 ... \n", "9401 ... \n", "9402 ... \n", "9403 ... \n", "9404 ... \n", "9405 ... \n", "9406 ... \n", "9407 ... \n", "9408 ... \n", "9409 ... \n", "9410 ... \n", "9411 ... \n", "9412 ... \n", "9413 ... \n", "9414 ... \n", "9415 ... \n", "9416 ... \n", "9417 ... \n", "\n", " Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.4 (Earned) \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 0.0 \n", "4 NaN \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 2.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 0.0 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.4 (Possible) \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 2.0 \n", "4 NaN \n", "5 2.0 \n", "6 NaN \n", "7 NaN \n", "8 2.0 \n", "9 NaN \n", "10 NaN \n", "11 2.0 \n", "12 NaN \n", "13 2.0 \n", "14 2.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 2.0 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.1 (Earned) \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 0.0 \n", "4 NaN \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 0.0 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.1 (Possible) \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 1 \n", "4 NaN \n", "5 1 \n", "6 NaN \n", "7 NaN \n", "8 1 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 1 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 1 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.2 (Earned) \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 0.0 \n", "4 NaN \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 0.0 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.2 (Possible) \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 1 \n", "4 NaN \n", "5 1 \n", "6 NaN \n", "7 NaN \n", "8 1 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 1 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 1 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.1 (Earned) \\\n", "0 NaN \n", "1 NaN \n", "2 0.0 \n", "3 0.0 \n", "4 NaN \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 NaN \n", "11 0.0 \n", "12 NaN \n", "13 0.0 \n", "14 2.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 0.0 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.1 (Possible) \\\n", "0 NaN \n", "1 NaN \n", "2 2 \n", "3 2 \n", "4 NaN \n", "5 2 \n", "6 NaN \n", "7 NaN \n", "8 2 \n", "9 NaN \n", "10 NaN \n", "11 2.0 \n", "12 NaN \n", "13 2 \n", "14 2.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 2 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.2 (Earned) \\\n", "0 NaN \n", "1 NaN \n", "2 0.0 \n", "3 0.0 \n", "4 NaN \n", "5 0.0 \n", "6 NaN \n", "7 NaN \n", "8 0.0 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 0.0 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 0.0 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", " Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.2 (Possible) \n", "0 NaN \n", "1 NaN \n", "2 1 \n", "3 1 \n", "4 NaN \n", "5 1 \n", "6 NaN \n", "7 NaN \n", "8 1 \n", "9 NaN \n", "10 NaN \n", "11 1.0 \n", "12 NaN \n", "13 1 \n", "14 1.0 \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 1 \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "... ... \n", "9388 NaN \n", "9389 NaN \n", "9390 NaN \n", "9391 NaN \n", "9392 NaN \n", "9393 NaN \n", "9394 NaN \n", "9395 NaN \n", "9396 NaN \n", "9397 NaN \n", "9398 NaN \n", "9399 NaN \n", "9400 NaN \n", "9401 NaN \n", "9402 NaN \n", "9403 NaN \n", "9404 NaN \n", "9405 NaN \n", "9406 NaN \n", "9407 NaN \n", "9408 NaN \n", "9409 NaN \n", "9410 NaN \n", "9411 NaN \n", "9412 NaN \n", "9413 NaN \n", "9414 NaN \n", "9415 NaN \n", "9416 NaN \n", "9417 NaN \n", "\n", "[9418 rows x 181 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "donnees = pd.read_csv('inria_41003_selfpaced_problem_grade_report_2020-06-02-0932.csv',dtype=str)\n", "donnees" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Nombre d'élèvesPourcentage participationSemaineId
Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.1252027%Semaine 11
Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.2250927%Semaine 12
Quiz 1: 1.1. La cellule, atome du vivant - Question 1.1.3247926%Semaine 13
Quiz 2: 1.2. Au cœur de la cellule, la molécule d’ADN - Question 1.2.1239625%Semaine 14
Quiz 2: 1.2. Au cœur de la cellule, la molécule d’ADN - Question 1.2.2239025%Semaine 15
Quiz 3: 1.3. L’ADN code l’information génétique - Question 1.3.1233525%Semaine 16
Quiz 3: 1.3. L’ADN code l’information génétique - Question 1.3.2232625%Semaine 17
Quiz 4: 1.4. Qu’est-ce qu’un algorithme ? - Question 1.4.1226424%Semaine 18
Quiz 5: 1.5. Compter les nucléotides - Question 1.5.1197921%Semaine 19
Quiz 5: 1.5. Compter les nucléotides - Question 1.5.2198321%Semaine 110
Quiz 5: 1.5. Compter les nucléotides - Question 1.5.3197021%Semaine 111
Quiz 6: 1.6. Contenu en G-C et A-T des séquences - Question 1.6.1188420%Semaine 112
Quiz 7: 1.7. Promenade sur l’ADN - Question 1.7.1183219%Semaine 113
Quiz 8: 1.8. Changer l’échelle du chemin - Question 1.8.1169918%Semaine 114
Quiz 9: 1.9. Prédire l’origine de réplication ? - Question 1.9.1165218%Semaine 115
Quiz 10: 1.10. Des fenêtres glissantes et recouvrantes - Question 1.10.1157617%Semaine 116
Quiz 10: 1.10. Des fenêtres glissantes et recouvrantes - Question 1.10.2152816%Semaine 117
Quiz 10: 1.10. Des fenêtres glissantes et recouvrantes - Question 1.10.3152816%Semaine 118
Quiz 10: 1.10. Des fenêtres glissantes et recouvrantes - Question 1.10.4155617%Semaine 119
Quiz 11: 2.1. La séquence est-elle un bon modèle de l’ADN ? - Question 2.1.1145215%Semaine 220
Quiz 12: 2.2. Les gènes, de Mendel à la biologie moléculaire - Question 2.2.1140115%Semaine 221
Quiz 12: 2.2. Les gènes, de Mendel à la biologie moléculaire - Question 2.2.2139415%Semaine 222
Quiz 12: 2.2. Les gènes, de Mendel à la biologie moléculaire - Question 2.2.3137715%Semaine 223
Quiz 12: 2.2. Les gènes, de Mendel à la biologie moléculaire - Question 2.2.4139115%Semaine 224
Quiz 13: 2.3. Le code génétique - Question 2.3.1134514%Semaine 225
Quiz 13: 2.3. Le code génétique - Question 2.3.2134214%Semaine 226
Quiz 13: 2.3. Le code génétique - Question 2.3.3135414%Semaine 227
Quiz 13: 2.3. Le code génétique - Question 2.3.4134314%Semaine 228
Quiz 14: 2.4. Un algorithme de traduction - Question 2.4.1129014%Semaine 229
Quiz 14: 2.4. Un algorithme de traduction - Question 2.4.2126413%Semaine 230
...............
Quiz 32: 4.2. Évolution et similarité de séquences - Question 4.2.199211%Semaine 460
Quiz 32: 4.2. Évolution et similarité de séquences - Question 4.2.299611%Semaine 461
Quiz 33: 4.3. Quantifier la similarité de deux séquences - Question 4.3.193010%Semaine 462
Quiz 33: 4.3. Quantifier la similarité de deux séquences - Question 4.3.292110%Semaine 463
Quiz 34: 4.4. L’alignement de séquences devient un problème d’optimisation - Question 4.4.191610%Semaine 464
Quiz 35: 4.5. Un alignement de séquences vu comme un chemin dans une grille - Question 4.5.191210%Semaine 465
Quiz 36: 4.6. Si un chemin est optimal, tous ses chemins partiels sont optimaux - Question 4.6.190110%Semaine 466
Quiz 37: 4.7. Coûts et alignement - Question 4.7.18789%Semaine 467
Quiz 37: 4.7. Coûts et alignement - Question 4.7.28939%Semaine 468
Quiz 38: 4.8. Un algorithme récursif - Question 4.8.18229%Semaine 469
Quiz 38: 4.8. Un algorithme récursif - Question 4.8.2141515%Semaine 470
Quiz 39: 4.9. Eviter la récursivité : une version itérative - Question 4.9.1137815%Semaine 471
Quiz 40: 4.10. Cet algorithme est-il efficace ? - Question 4.10.18399%Semaine 472
Quiz 40: 4.10. Cet algorithme est-il efficace ? - Question 4.10.28469%Semaine 473
Quiz 41: 5.1. L’arbre des espèces - Question 5.1.18699%Semaine 574
Quiz 41: 5.1. L’arbre des espèces - Question 5.1.28769%Semaine 575
Quiz 42: 5.2. L’arbre, objet abstrait - Question 5.2.18639%Semaine 576
Quiz 42: 5.2. L’arbre, objet abstrait - Question 5.2.28679%Semaine 577
Quiz 43: 5.3. Remplir un tableau de distances - Question 5.3.1140415%Semaine 578
Quiz 43: 5.3. Remplir un tableau de distances - Question 5.3.28379%Semaine 579
Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.18449%Semaine 580
Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.28429%Semaine 581
Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.1135914%Semaine 582
Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.28229%Semaine 583
Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.38119%Semaine 584
Quiz 45: 5.5. Quand les différences sont trompeuses - Question 5.5.4135914%Semaine 585
Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.18299%Semaine 586
Quiz 46: 5.6. La diversité des algorithmes informatiques - Question 5.6.28289%Semaine 587
Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.18029%Semaine 588
Quiz 47: 5.7. Les applications en microbiologie - Question 5.7.28279%Semaine 589
\n", "

89 rows × 4 columns

\n", "
" ], "text/plain": [ " Nombre d'élèves \\\n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... 2520 \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... 2509 \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... 2479 \n", "Quiz 2: 1.2. Au cœur de la cellule, la molécule... 2396 \n", "Quiz 2: 1.2. Au cœur de la cellule, la molécule... 2390 \n", "Quiz 3: 1.3. L’ADN code l’information génétique... 2335 \n", "Quiz 3: 1.3. L’ADN code l’information génétique... 2326 \n", "Quiz 4: 1.4. Qu’est-ce qu’un algorithme ? - Que... 2264 \n", "Quiz 5: 1.5. Compter les nucléotides - Question... 1979 \n", "Quiz 5: 1.5. Compter les nucléotides - Question... 1983 \n", "Quiz 5: 1.5. Compter les nucléotides - Question... 1970 \n", "Quiz 6: 1.6. Contenu en G-C et A-T des séquence... 1884 \n", "Quiz 7: 1.7. Promenade sur l’ADN - Question 1.7.1 1832 \n", "Quiz 8: 1.8. Changer l’échelle du chemin - Ques... 1699 \n", "Quiz 9: 1.9. Prédire l’origine de réplication ?... 1652 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 1576 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 1528 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 1528 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 1556 \n", "Quiz 11: 2.1. La séquence est-elle un bon modèl... 1452 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 1401 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 1394 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 1377 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 1391 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.1 1345 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.2 1342 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.3 1354 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.4 1343 \n", "Quiz 14: 2.4. Un algorithme de traduction - Que... 1290 \n", "Quiz 14: 2.4. Un algorithme de traduction - Que... 1264 \n", "... ... \n", "Quiz 32: 4.2. Évolution et similarité de séquen... 992 \n", "Quiz 32: 4.2. Évolution et similarité de séquen... 996 \n", "Quiz 33: 4.3. Quantifier la similarité de deux ... 930 \n", "Quiz 33: 4.3. Quantifier la similarité de deux ... 921 \n", "Quiz 34: 4.4. L’alignement de séquences devient... 916 \n", "Quiz 35: 4.5. Un alignement de séquences vu com... 912 \n", "Quiz 36: 4.6. Si un chemin est optimal, tous se... 901 \n", "Quiz 37: 4.7. Coûts et alignement - Question 4.7.1 878 \n", "Quiz 37: 4.7. Coûts et alignement - Question 4.7.2 893 \n", "Quiz 38: 4.8. Un algorithme récursif - Question... 822 \n", "Quiz 38: 4.8. Un algorithme récursif - Question... 1415 \n", "Quiz 39: 4.9. Eviter la récursivité : une versi... 1378 \n", "Quiz 40: 4.10. Cet algorithme est-il efficace ?... 839 \n", "Quiz 40: 4.10. Cet algorithme est-il efficace ?... 846 \n", "Quiz 41: 5.1. L’arbre des espèces - Question 5.1.1 869 \n", "Quiz 41: 5.1. L’arbre des espèces - Question 5.1.2 876 \n", "Quiz 42: 5.2. L’arbre, objet abstrait - Questio... 863 \n", "Quiz 42: 5.2. L’arbre, objet abstrait - Questio... 867 \n", "Quiz 43: 5.3. Remplir un tableau de distances -... 1404 \n", "Quiz 43: 5.3. Remplir un tableau de distances -... 837 \n", "Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.1 844 \n", "Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.2 842 \n", "Quiz 45: 5.5. Quand les différences sont trompe... 1359 \n", "Quiz 45: 5.5. Quand les différences sont trompe... 822 \n", "Quiz 45: 5.5. Quand les différences sont trompe... 811 \n", "Quiz 45: 5.5. Quand les différences sont trompe... 1359 \n", "Quiz 46: 5.6. La diversité des algorithmes info... 829 \n", "Quiz 46: 5.6. La diversité des algorithmes info... 828 \n", "Quiz 47: 5.7. Les applications en microbiologie... 802 \n", "Quiz 47: 5.7. Les applications en microbiologie... 827 \n", "\n", " Pourcentage participation \\\n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... 27% \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... 27% \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... 26% \n", "Quiz 2: 1.2. Au cœur de la cellule, la molécule... 25% \n", "Quiz 2: 1.2. Au cœur de la cellule, la molécule... 25% \n", "Quiz 3: 1.3. L’ADN code l’information génétique... 25% \n", "Quiz 3: 1.3. L’ADN code l’information génétique... 25% \n", "Quiz 4: 1.4. Qu’est-ce qu’un algorithme ? - Que... 24% \n", "Quiz 5: 1.5. Compter les nucléotides - Question... 21% \n", "Quiz 5: 1.5. Compter les nucléotides - Question... 21% \n", "Quiz 5: 1.5. Compter les nucléotides - Question... 21% \n", "Quiz 6: 1.6. Contenu en G-C et A-T des séquence... 20% \n", "Quiz 7: 1.7. Promenade sur l’ADN - Question 1.7.1 19% \n", "Quiz 8: 1.8. Changer l’échelle du chemin - Ques... 18% \n", "Quiz 9: 1.9. Prédire l’origine de réplication ?... 18% \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 17% \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 16% \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 16% \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... 17% \n", "Quiz 11: 2.1. La séquence est-elle un bon modèl... 15% \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 15% \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 15% \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 15% \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... 15% \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.1 14% \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.2 14% \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.3 14% \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.4 14% \n", "Quiz 14: 2.4. Un algorithme de traduction - Que... 14% \n", "Quiz 14: 2.4. Un algorithme de traduction - Que... 13% \n", "... ... \n", "Quiz 32: 4.2. Évolution et similarité de séquen... 11% \n", "Quiz 32: 4.2. Évolution et similarité de séquen... 11% \n", "Quiz 33: 4.3. Quantifier la similarité de deux ... 10% \n", "Quiz 33: 4.3. Quantifier la similarité de deux ... 10% \n", "Quiz 34: 4.4. L’alignement de séquences devient... 10% \n", "Quiz 35: 4.5. Un alignement de séquences vu com... 10% \n", "Quiz 36: 4.6. Si un chemin est optimal, tous se... 10% \n", "Quiz 37: 4.7. Coûts et alignement - Question 4.7.1 9% \n", "Quiz 37: 4.7. Coûts et alignement - Question 4.7.2 9% \n", "Quiz 38: 4.8. Un algorithme récursif - Question... 9% \n", "Quiz 38: 4.8. Un algorithme récursif - Question... 15% \n", "Quiz 39: 4.9. Eviter la récursivité : une versi... 15% \n", "Quiz 40: 4.10. Cet algorithme est-il efficace ?... 9% \n", "Quiz 40: 4.10. Cet algorithme est-il efficace ?... 9% \n", "Quiz 41: 5.1. L’arbre des espèces - Question 5.1.1 9% \n", "Quiz 41: 5.1. L’arbre des espèces - Question 5.1.2 9% \n", "Quiz 42: 5.2. L’arbre, objet abstrait - Questio... 9% \n", "Quiz 42: 5.2. L’arbre, objet abstrait - Questio... 9% \n", "Quiz 43: 5.3. Remplir un tableau de distances -... 15% \n", "Quiz 43: 5.3. Remplir un tableau de distances -... 9% \n", "Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.1 9% \n", "Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.2 9% \n", "Quiz 45: 5.5. Quand les différences sont trompe... 14% \n", "Quiz 45: 5.5. Quand les différences sont trompe... 9% \n", "Quiz 45: 5.5. Quand les différences sont trompe... 9% \n", "Quiz 45: 5.5. Quand les différences sont trompe... 14% \n", "Quiz 46: 5.6. La diversité des algorithmes info... 9% \n", "Quiz 46: 5.6. La diversité des algorithmes info... 9% \n", "Quiz 47: 5.7. Les applications en microbiologie... 9% \n", "Quiz 47: 5.7. Les applications en microbiologie... 9% \n", "\n", " Semaine Id \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... Semaine 1 1 \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... Semaine 1 2 \n", "Quiz 1: 1.1. La cellule, atome du vivant - Ques... Semaine 1 3 \n", "Quiz 2: 1.2. Au cœur de la cellule, la molécule... Semaine 1 4 \n", "Quiz 2: 1.2. Au cœur de la cellule, la molécule... Semaine 1 5 \n", "Quiz 3: 1.3. L’ADN code l’information génétique... Semaine 1 6 \n", "Quiz 3: 1.3. L’ADN code l’information génétique... Semaine 1 7 \n", "Quiz 4: 1.4. Qu’est-ce qu’un algorithme ? - Que... Semaine 1 8 \n", "Quiz 5: 1.5. Compter les nucléotides - Question... Semaine 1 9 \n", "Quiz 5: 1.5. Compter les nucléotides - Question... Semaine 1 10 \n", "Quiz 5: 1.5. Compter les nucléotides - Question... Semaine 1 11 \n", "Quiz 6: 1.6. Contenu en G-C et A-T des séquence... Semaine 1 12 \n", "Quiz 7: 1.7. Promenade sur l’ADN - Question 1.7.1 Semaine 1 13 \n", "Quiz 8: 1.8. Changer l’échelle du chemin - Ques... Semaine 1 14 \n", "Quiz 9: 1.9. Prédire l’origine de réplication ?... Semaine 1 15 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... Semaine 1 16 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... Semaine 1 17 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... Semaine 1 18 \n", "Quiz 10: 1.10. Des fenêtres glissantes et recou... Semaine 1 19 \n", "Quiz 11: 2.1. La séquence est-elle un bon modèl... Semaine 2 20 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... Semaine 2 21 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... Semaine 2 22 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... Semaine 2 23 \n", "Quiz 12: 2.2. Les gènes, de Mendel à la biologi... Semaine 2 24 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.1 Semaine 2 25 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.2 Semaine 2 26 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.3 Semaine 2 27 \n", "Quiz 13: 2.3. Le code génétique - Question 2.3.4 Semaine 2 28 \n", "Quiz 14: 2.4. Un algorithme de traduction - Que... Semaine 2 29 \n", "Quiz 14: 2.4. Un algorithme de traduction - Que... Semaine 2 30 \n", "... ... .. \n", "Quiz 32: 4.2. Évolution et similarité de séquen... Semaine 4 60 \n", "Quiz 32: 4.2. Évolution et similarité de séquen... Semaine 4 61 \n", "Quiz 33: 4.3. Quantifier la similarité de deux ... Semaine 4 62 \n", "Quiz 33: 4.3. Quantifier la similarité de deux ... Semaine 4 63 \n", "Quiz 34: 4.4. L’alignement de séquences devient... Semaine 4 64 \n", "Quiz 35: 4.5. Un alignement de séquences vu com... Semaine 4 65 \n", "Quiz 36: 4.6. Si un chemin est optimal, tous se... Semaine 4 66 \n", "Quiz 37: 4.7. Coûts et alignement - Question 4.7.1 Semaine 4 67 \n", "Quiz 37: 4.7. Coûts et alignement - Question 4.7.2 Semaine 4 68 \n", "Quiz 38: 4.8. Un algorithme récursif - Question... Semaine 4 69 \n", "Quiz 38: 4.8. Un algorithme récursif - Question... Semaine 4 70 \n", "Quiz 39: 4.9. Eviter la récursivité : une versi... Semaine 4 71 \n", "Quiz 40: 4.10. Cet algorithme est-il efficace ?... Semaine 4 72 \n", "Quiz 40: 4.10. Cet algorithme est-il efficace ?... Semaine 4 73 \n", "Quiz 41: 5.1. L’arbre des espèces - Question 5.1.1 Semaine 5 74 \n", "Quiz 41: 5.1. L’arbre des espèces - Question 5.1.2 Semaine 5 75 \n", "Quiz 42: 5.2. L’arbre, objet abstrait - Questio... Semaine 5 76 \n", "Quiz 42: 5.2. L’arbre, objet abstrait - Questio... Semaine 5 77 \n", "Quiz 43: 5.3. Remplir un tableau de distances -... Semaine 5 78 \n", "Quiz 43: 5.3. Remplir un tableau de distances -... Semaine 5 79 \n", "Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.1 Semaine 5 80 \n", "Quiz 44: 5.4. L’algorithme UPGMA - Question 5.4.2 Semaine 5 81 \n", "Quiz 45: 5.5. Quand les différences sont trompe... Semaine 5 82 \n", "Quiz 45: 5.5. Quand les différences sont trompe... Semaine 5 83 \n", "Quiz 45: 5.5. Quand les différences sont trompe... Semaine 5 84 \n", "Quiz 45: 5.5. Quand les différences sont trompe... Semaine 5 85 \n", "Quiz 46: 5.6. La diversité des algorithmes info... Semaine 5 86 \n", "Quiz 46: 5.6. La diversité des algorithmes info... Semaine 5 87 \n", "Quiz 47: 5.7. Les applications en microbiologie... Semaine 5 88 \n", "Quiz 47: 5.7. Les applications en microbiologie... Semaine 5 89 \n", "\n", "[89 rows x 4 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liste_colonne=list(donnees.columns)[3::2]\n", "liste_colonne_fausse=list(donnees.columns)[4::2]\n", "compteur=0\n", "liste=[]\n", "nbr_eleves=donnees.loc[:,\"Username\"].count()\n", "## Nombre max de points posible pour le Quiz = 20\n", "NbMaxPointQuiz=20\n", "tabl=np.zeros(NbMaxPointQuiz*len(liste_colonne)).reshape(len(liste_colonne),NbMaxPointQuiz)\n", "for col in liste_colonne:\n", " addition=0\n", " liste_valeurs=list(donnees.loc[:,col].dropna())\n", " liste_valeurs_fausse=list(donnees.loc[:,liste_colonne_fausse[compteur]].dropna())\n", " for i in range(1,NbMaxPointQuiz):\n", " if liste_valeurs_fausse.count(f\"{i}.0\")!=0:\n", " total=liste_valeurs_fausse.count(f\"{i}.0\")\n", " liste.append(total)\n", " tabl[compteur][i]=liste_valeurs.count(f\"{i}.0\")\n", " addition+=liste_valeurs.count(f\"{i}.0\")\n", " tabl[compteur][0]=total-addition\n", " compteur=compteur+1\n", "pd.DataFrame(tabl,index=liste_colonne)\n", "semaine=[]\n", "for i in range(19):\n", " semaine.append(\"Semaine 1\")\n", "for i in range(22):\n", " semaine.append(\"Semaine 2\")\n", "for i in range(16):\n", " semaine.append(\"Semaine 3\")\n", "for i in range(16):\n", " semaine.append(\"Semaine 4\")\n", "for i in range(16):\n", " semaine.append(\"Semaine 5\")\n", "pourcentage=[f\"{int(round((liste[i]/nbr_eleves)*100,0))}%\" for i in range(len(liste))]\n", "Id=[i for i in range(1,len(pourcentage)+1)]\n", "pd.DataFrame(np.array([liste,pourcentage,semaine,Id]),\n", " index=[\"Nombre d'élèves\",\"Pourcentage participation\",\"Semaine\",\"Id\"],\n", " columns=[' '.join(liste_colonne_fausse[i].split()[:-1]) for i in range(len(liste_colonne_fausse))]).T" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Participation aux quiz avec le rapport le plus récent\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "##\n", "tableau=pd.DataFrame(np.array([liste,pourcentage,semaine,Id]),\n", " index=[\"Nombre d'élèves\",\"Pourcentage participation\",\"Semaine\",\"Id\"],\n", " columns=[' '.join(liste_colonne_fausse[i].split()[:-1]) for i in range(len(liste_colonne_fausse))]).T\n", "\n", "## Quiz avec parcours\n", "a=list(tableau.loc[tableau.loc[:,\"Semaine\"]==\"Semaine 1\",\"Nombre d'élèves\"])\n", "b=list(tableau.loc[tableau.loc[:,\"Semaine\"]==\"Semaine 2\",\"Nombre d'élèves\"])\n", "c=list(tableau.loc[tableau.loc[:,\"Semaine\"]==\"Semaine 3\",\"Nombre d'élèves\"])\n", "d=list(tableau.loc[tableau.loc[:,\"Semaine\"]==\"Semaine 4\",\"Nombre d'élèves\"])\n", "e=list(tableau.loc[tableau.loc[:,\"Semaine\"]==\"Semaine 5\",\"Nombre d'élèves\"])\n", "\n", "print(\"Participation aux quiz avec le rapport le plus récent\")\n", "fig,ax=plt.subplots(1,1,figsize=(20,7),sharey='all')\n", "ax.plot(tableau.loc[(tableau.loc[:,\"Semaine\"]==\"Semaine 1\") ,\"Id\"],[float(a[i]) for i in range(len(a))],\"ro-\")\n", "ax.plot(tableau.loc[(tableau.loc[:,\"Semaine\"]==\"Semaine 2\") ,\"Id\"],[float(b[i]) for i in range(len(b))],\"bo-\")\n", "ax.plot(tableau.loc[(tableau.loc[:,\"Semaine\"]==\"Semaine 3\") ,\"Id\"],[float(c[i]) for i in range(len(c))],\"go-\")\n", "ax.plot(tableau.loc[(tableau.loc[:,\"Semaine\"]==\"Semaine 4\") ,\"Id\"],[float(d[i]) for i in range(len(d))],\"mo-\")\n", "ax.plot(tableau.loc[(tableau.loc[:,\"Semaine\"]==\"Semaine 5\") ,\"Id\"],[float(e[i]) for i in range(len(e))],\"yo-\")\n", "ax.legend([\"Semaine 1\",\"Semaine 2\",\"Semaine 3\",\"Semaine 4\",\"Semaine 5\"])\n", "ax.xaxis.set_ticks(range(0,max(Id)))\n", "ax.yaxis.set_ticks(range(750,2600,100))\n", "ax.grid()\n", "plt.xlabel(\"Id\")\n", "plt.ylabel(\"Nombre de participants\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 4 }