{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "1a88e988-3689-419b-a0fa-a84c8ba3d795", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "c435d244-d11b-47ee-92a7-0c16faf2fd2e", "metadata": {}, "source": [ "# TP2 : statistiques descriptives" ] }, { "cell_type": "markdown", "id": "b87b3d1e-af55-4725-8dd2-0ef923483d18", "metadata": {}, "source": [ "## I. Données du baccalauréat" ] }, { "cell_type": "markdown", "id": "87abf58e-746a-400c-8e1e-ab8d1a3c72e7", "metadata": {}, "source": [ "### 1. Introduction à pandas" ] }, { "cell_type": "markdown", "id": "ee711024-f001-4968-b48a-917e03fadd4a", "metadata": {}, "source": [ "Pour charger un fichier de données à l'aide de la librairie **pandas**, on peut utiliser la commande `data = pd.read_csv(\"nom_fichier.csv\")`.\n", "Par exemple, en ayant au placé le fichier `fr-en-reussite-au-baccalaureat-age.csv` dans le même dossier, on peut effectuer la commande suivante." ] }, { "cell_type": "code", "execution_count": 3, "id": "c09e9b82-10f7-4cb9-9c67-6b25a0bbfa63", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Année de sessionAgeNombre admis baccalauréat généralPourcentage admis baccalauréat généralNombre admis baccalauréat technologiquePourcentage admis baccalauréat technologiqueNombre admis baccalauréat professionnelPourcentage admis baccalauréat professionnelNombre admis baccalaureatPourcentage admis baccalauréat
0199725-29 ans9826.830645.9219169.8259562.2
1199816 ans ou moins22294.5240.00NaN22493.3
2199817 ans1395493.871692.700.01467093.7
3199819 ans6664070.95311682.21183787.513159376.4
4199820 ans2539365.83470875.72967181.38977274.2
.................................
343202230 ans ou plus6136.75757.0187689.8199484.7
344202422 ans12647.58948.4160676.0182171.0
345202430 ans ou plus5227.14156.2132688.7141980.6
346202521 ans51554.436657.7376177.5464272.2
347202523 ans6543.03850.774179.484472.8
\n", "

348 rows × 10 columns

\n", "
" ], "text/plain": [ " Année de session Age Nombre admis baccalauréat général \\\n", "0 1997 25-29 ans 98 \n", "1 1998 16 ans ou moins 222 \n", "2 1998 17 ans 13954 \n", "3 1998 19 ans 66640 \n", "4 1998 20 ans 25393 \n", ".. ... ... ... \n", "343 2022 30 ans ou plus 61 \n", "344 2024 22 ans 126 \n", "345 2024 30 ans ou plus 52 \n", "346 2025 21 ans 515 \n", "347 2025 23 ans 65 \n", "\n", " Pourcentage admis baccalauréat général \\\n", "0 26.8 \n", "1 94.5 \n", "2 93.8 \n", "3 70.9 \n", "4 65.8 \n", ".. ... \n", "343 36.7 \n", "344 47.5 \n", "345 27.1 \n", "346 54.4 \n", "347 43.0 \n", "\n", " Nombre admis baccalauréat technologique \\\n", "0 306 \n", "1 2 \n", "2 716 \n", "3 53116 \n", "4 34708 \n", ".. ... \n", "343 57 \n", "344 89 \n", "345 41 \n", "346 366 \n", "347 38 \n", "\n", " Pourcentage admis baccalauréat technologique \\\n", "0 45.9 \n", "1 40.0 \n", "2 92.7 \n", "3 82.2 \n", "4 75.7 \n", ".. ... \n", "343 57.0 \n", "344 48.4 \n", "345 56.2 \n", "346 57.7 \n", "347 50.7 \n", "\n", " Nombre admis baccalauréat professionnel \\\n", "0 2191 \n", "1 0 \n", "2 0 \n", "3 11837 \n", "4 29671 \n", ".. ... \n", "343 1876 \n", "344 1606 \n", "345 1326 \n", "346 3761 \n", "347 741 \n", "\n", " Pourcentage admis baccalauréat professionnel Nombre admis baccalaureat \\\n", "0 69.8 2595 \n", "1 NaN 224 \n", "2 0.0 14670 \n", "3 87.5 131593 \n", "4 81.3 89772 \n", ".. ... ... \n", "343 89.8 1994 \n", "344 76.0 1821 \n", "345 88.7 1419 \n", "346 77.5 4642 \n", "347 79.4 844 \n", "\n", " Pourcentage admis baccalauréat \n", "0 62.2 \n", "1 93.3 \n", "2 93.7 \n", "3 76.4 \n", "4 74.2 \n", ".. ... \n", "343 84.7 \n", "344 71.0 \n", "345 80.6 \n", "346 72.2 \n", "347 72.8 \n", "\n", "[348 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Bac = pd.read_csv(\"fr-en-reussite-au-baccalaureat-age.csv\", sep=';')\n", "Bac" ] }, { "cell_type": "markdown", "id": "6f961e16-631a-471c-9068-b12d0e6db071", "metadata": {}, "source": [ "On obtient une description des colonnes et des types de données à l'aide du suffixe `.info()`." ] }, { "cell_type": "code", "execution_count": 4, "id": "577a378c-fca8-4237-aeb7-127a7e83da64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 348 entries, 0 to 347\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Année de session 348 non-null int64 \n", " 1 Age 348 non-null object \n", " 2 Nombre admis baccalauréat général 348 non-null int64 \n", " 3 Pourcentage admis baccalauréat général 348 non-null float64\n", " 4 Nombre admis baccalauréat technologique 348 non-null int64 \n", " 5 Pourcentage admis baccalauréat technologique 348 non-null float64\n", " 6 Nombre admis baccalauréat professionnel 348 non-null int64 \n", " 7 Pourcentage admis baccalauréat professionnel 341 non-null float64\n", " 8 Nombre admis baccalaureat 348 non-null int64 \n", " 9 Pourcentage admis baccalauréat 348 non-null float64\n", "dtypes: float64(4), int64(5), object(1)\n", "memory usage: 27.3+ KB\n" ] } ], "source": [ "Bac.info()" ] }, { "cell_type": "markdown", "id": "a5d477f0-e1f4-4ba8-8f97-77cb98502738", "metadata": {}, "source": [ "On peut alors obtenir un sous-ensemble des données avec `data[\"nom_col\"]` ou `data[[\"nom_col_1\", \"nom_col_2\", ...]]` et obtenir des indicateurs statistiques en ajoutant le suffixe `.describe()`.\n", "\n", "Pour plus d'informations sur la bibliothèque **pandas**, voir https://pandas.pydata.org/docs/getting_started/index.html." ] }, { "cell_type": "code", "execution_count": 5, "id": "8f47347c-2740-414a-9293-f58d0852be64", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Année de sessionAge
0199725-29 ans
1199816 ans ou moins
2199817 ans
3199819 ans
4199820 ans
.........
343202230 ans ou plus
344202422 ans
345202430 ans ou plus
346202521 ans
347202523 ans
\n", "

348 rows × 2 columns

\n", "
" ], "text/plain": [ " Année de session Age\n", "0 1997 25-29 ans\n", "1 1998 16 ans ou moins\n", "2 1998 17 ans\n", "3 1998 19 ans\n", "4 1998 20 ans\n", ".. ... ...\n", "343 2022 30 ans ou plus\n", "344 2024 22 ans\n", "345 2024 30 ans ou plus\n", "346 2025 21 ans\n", "347 2025 23 ans\n", "\n", "[348 rows x 2 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Bac[[\"Année de session\", \"Age\"]]" ] }, { "cell_type": "code", "execution_count": 6, "id": "4795555a-82c1-4e89-a39c-1ab4a3c4c957", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Année de sessionNombre admis baccalauréat généralPourcentage admis baccalauréat généralNombre admis baccalauréat technologiquePourcentage admis baccalauréat technologiqueNombre admis baccalauréat professionnelPourcentage admis baccalauréat professionnelNombre admis baccalaureatPourcentage admis baccalauréat
count348.000000348.000000348.000000348.000000348.000000348.000000341.000000348.000000348.000000
mean2011.00000051329.87356363.34097722829.30459869.26589122973.28160978.89413597132.45977078.982184
std8.378647101354.03319526.40929341128.95218419.41465641994.86426912.418891178872.36318711.762553
min1997.00000026.00000016.3000002.00000028.6000000.0000000.000000178.00000051.800000
25%2004.000000102.75000038.20250090.75000052.5500001098.50000075.0000002069.50000071.875000
50%2011.000000771.00000064.450000991.50000071.1000002468.00000080.0000009748.00000078.350000
75%2018.00000023748.25000091.50000031327.75000087.22500022302.75000086.50000089934.00000088.675000
max2025.000000384158.00000099.300000152778.000000100.000000190899.000000100.000000722971.00000099.100000
\n", "
" ], "text/plain": [ " Année de session Nombre admis baccalauréat général \\\n", "count 348.000000 348.000000 \n", "mean 2011.000000 51329.873563 \n", "std 8.378647 101354.033195 \n", "min 1997.000000 26.000000 \n", "25% 2004.000000 102.750000 \n", "50% 2011.000000 771.000000 \n", "75% 2018.000000 23748.250000 \n", "max 2025.000000 384158.000000 \n", "\n", " Pourcentage admis baccalauréat général \\\n", "count 348.000000 \n", "mean 63.340977 \n", "std 26.409293 \n", "min 16.300000 \n", "25% 38.202500 \n", "50% 64.450000 \n", "75% 91.500000 \n", "max 99.300000 \n", "\n", " Nombre admis baccalauréat technologique \\\n", "count 348.000000 \n", "mean 22829.304598 \n", "std 41128.952184 \n", "min 2.000000 \n", "25% 90.750000 \n", "50% 991.500000 \n", "75% 31327.750000 \n", "max 152778.000000 \n", "\n", " Pourcentage admis baccalauréat technologique \\\n", "count 348.000000 \n", "mean 69.265891 \n", "std 19.414656 \n", "min 28.600000 \n", "25% 52.550000 \n", "50% 71.100000 \n", "75% 87.225000 \n", "max 100.000000 \n", "\n", " Nombre admis baccalauréat professionnel \\\n", "count 348.000000 \n", "mean 22973.281609 \n", "std 41994.864269 \n", "min 0.000000 \n", "25% 1098.500000 \n", "50% 2468.000000 \n", "75% 22302.750000 \n", "max 190899.000000 \n", "\n", " Pourcentage admis baccalauréat professionnel \\\n", "count 341.000000 \n", "mean 78.894135 \n", "std 12.418891 \n", "min 0.000000 \n", "25% 75.000000 \n", "50% 80.000000 \n", "75% 86.500000 \n", "max 100.000000 \n", "\n", " Nombre admis baccalaureat Pourcentage admis baccalauréat \n", "count 348.000000 348.000000 \n", "mean 97132.459770 78.982184 \n", "std 178872.363187 11.762553 \n", "min 178.000000 51.800000 \n", "25% 2069.500000 71.875000 \n", "50% 9748.000000 78.350000 \n", "75% 89934.000000 88.675000 \n", "max 722971.000000 99.100000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Bac.describe()" ] }, { "cell_type": "markdown", "id": "cf3263b1-c4f4-47f7-a953-f937cc9ea768", "metadata": {}, "source": [ "### 2. Représentrations grapiques" ] }, { "cell_type": "markdown", "id": "5108a3d6-8e88-46c5-8425-85f8234dbb94", "metadata": {}, "source": [ "Il est possible d'extraire les données correspondant à une valeur d'une colonne, et de trier les données en fonction d'une colonne. " ] }, { "cell_type": "code", "execution_count": 7, "id": "a86eb3ba-e8bc-43f3-8b99-0bdcc6c76a0a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Année de sessionAgeNombre admis baccalauréat généralPourcentage admis baccalauréat généralNombre admis baccalauréat technologiquePourcentage admis baccalauréat technologiqueNombre admis baccalauréat professionnelPourcentage admis baccalauréat professionnelNombre admis baccalaureatPourcentage admis baccalauréat
70202516 ans ou moins59497.54097.6990.064397.4
138202524 ans3846.31841.944883.750476.4
139202530 ans ou plus5127.34957.6113587.6123578.8
204202517 ans1807498.8189196.367890.62064398.3
205202519 ans2392085.52097882.24389080.68878882.2
206202520 ans258968.4242168.91183378.11684375.0
2072025Ensemble36853296.213586290.917760783.968200191.6
279202518 ans32247897.510992893.711256386.254496994.2
280202522 ans14746.510252.6150479.9175373.3
281202525-29 ans6132.83148.4104587.0113778.4
346202521 ans51554.436657.7376177.5464272.2
347202523 ans6543.03850.774179.484472.8
\n", "
" ], "text/plain": [ " Année de session Age Nombre admis baccalauréat général \\\n", "70 2025 16 ans ou moins 594 \n", "138 2025 24 ans 38 \n", "139 2025 30 ans ou plus 51 \n", "204 2025 17 ans 18074 \n", "205 2025 19 ans 23920 \n", "206 2025 20 ans 2589 \n", "207 2025 Ensemble 368532 \n", "279 2025 18 ans 322478 \n", "280 2025 22 ans 147 \n", "281 2025 25-29 ans 61 \n", "346 2025 21 ans 515 \n", "347 2025 23 ans 65 \n", "\n", " Pourcentage admis baccalauréat général \\\n", "70 97.5 \n", "138 46.3 \n", "139 27.3 \n", "204 98.8 \n", "205 85.5 \n", "206 68.4 \n", "207 96.2 \n", "279 97.5 \n", "280 46.5 \n", "281 32.8 \n", "346 54.4 \n", "347 43.0 \n", "\n", " Nombre admis baccalauréat technologique \\\n", "70 40 \n", "138 18 \n", "139 49 \n", "204 1891 \n", "205 20978 \n", "206 2421 \n", "207 135862 \n", "279 109928 \n", "280 102 \n", "281 31 \n", "346 366 \n", "347 38 \n", "\n", " Pourcentage admis baccalauréat technologique \\\n", "70 97.6 \n", "138 41.9 \n", "139 57.6 \n", "204 96.3 \n", "205 82.2 \n", "206 68.9 \n", "207 90.9 \n", "279 93.7 \n", "280 52.6 \n", "281 48.4 \n", "346 57.7 \n", "347 50.7 \n", "\n", " Nombre admis baccalauréat professionnel \\\n", "70 9 \n", "138 448 \n", "139 1135 \n", "204 678 \n", "205 43890 \n", "206 11833 \n", "207 177607 \n", "279 112563 \n", "280 1504 \n", "281 1045 \n", "346 3761 \n", "347 741 \n", "\n", " Pourcentage admis baccalauréat professionnel Nombre admis baccalaureat \\\n", "70 90.0 643 \n", "138 83.7 504 \n", "139 87.6 1235 \n", "204 90.6 20643 \n", "205 80.6 88788 \n", "206 78.1 16843 \n", "207 83.9 682001 \n", "279 86.2 544969 \n", "280 79.9 1753 \n", "281 87.0 1137 \n", "346 77.5 4642 \n", "347 79.4 844 \n", "\n", " Pourcentage admis baccalauréat \n", "70 97.4 \n", "138 76.4 \n", "139 78.8 \n", "204 98.3 \n", "205 82.2 \n", "206 75.0 \n", "207 91.6 \n", "279 94.2 \n", "280 73.3 \n", "281 78.4 \n", "346 72.2 \n", "347 72.8 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Bac_2025 = Bac[Bac[\"Année de session\"]==2025]\n", "Bac_2025" ] }, { "cell_type": "code", "execution_count": 8, "id": "e1c8060f-6523-463c-8a34-afbdae778d85", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Année de sessionAgeNombre admis baccalauréat généralPourcentage admis baccalauréat généralNombre admis baccalauréat technologiquePourcentage admis baccalauréat technologiqueNombre admis baccalauréat professionnelPourcentage admis baccalauréat professionnelNombre admis baccalaureatPourcentage admis baccalauréat
70202516 ans ou moins59497.54097.6990.064397.4
204202517 ans1807498.8189196.367890.62064398.3
279202518 ans32247897.510992893.711256386.254496994.2
205202519 ans2392085.52097882.24389080.68878882.2
206202520 ans258968.4242168.91183378.11684375.0
346202521 ans51554.436657.7376177.5464272.2
280202522 ans14746.510252.6150479.9175373.3
347202523 ans6543.03850.774179.484472.8
138202524 ans3846.31841.944883.750476.4
281202525-29 ans6132.83148.4104587.0113778.4
139202530 ans ou plus5127.34957.6113587.6123578.8
2072025Ensemble36853296.213586290.917760783.968200191.6
\n", "
" ], "text/plain": [ " Année de session Age Nombre admis baccalauréat général \\\n", "70 2025 16 ans ou moins 594 \n", "204 2025 17 ans 18074 \n", "279 2025 18 ans 322478 \n", "205 2025 19 ans 23920 \n", "206 2025 20 ans 2589 \n", "346 2025 21 ans 515 \n", "280 2025 22 ans 147 \n", "347 2025 23 ans 65 \n", "138 2025 24 ans 38 \n", "281 2025 25-29 ans 61 \n", "139 2025 30 ans ou plus 51 \n", "207 2025 Ensemble 368532 \n", "\n", " Pourcentage admis baccalauréat général \\\n", "70 97.5 \n", "204 98.8 \n", "279 97.5 \n", "205 85.5 \n", "206 68.4 \n", "346 54.4 \n", "280 46.5 \n", "347 43.0 \n", "138 46.3 \n", "281 32.8 \n", "139 27.3 \n", "207 96.2 \n", "\n", " Nombre admis baccalauréat technologique \\\n", "70 40 \n", "204 1891 \n", "279 109928 \n", "205 20978 \n", "206 2421 \n", "346 366 \n", "280 102 \n", "347 38 \n", "138 18 \n", "281 31 \n", "139 49 \n", "207 135862 \n", "\n", " Pourcentage admis baccalauréat technologique \\\n", "70 97.6 \n", "204 96.3 \n", "279 93.7 \n", "205 82.2 \n", "206 68.9 \n", "346 57.7 \n", "280 52.6 \n", "347 50.7 \n", "138 41.9 \n", "281 48.4 \n", "139 57.6 \n", "207 90.9 \n", "\n", " Nombre admis baccalauréat professionnel \\\n", "70 9 \n", "204 678 \n", "279 112563 \n", "205 43890 \n", "206 11833 \n", "346 3761 \n", "280 1504 \n", "347 741 \n", "138 448 \n", "281 1045 \n", "139 1135 \n", "207 177607 \n", "\n", " Pourcentage admis baccalauréat professionnel Nombre admis baccalaureat \\\n", "70 90.0 643 \n", "204 90.6 20643 \n", "279 86.2 544969 \n", "205 80.6 88788 \n", "206 78.1 16843 \n", "346 77.5 4642 \n", "280 79.9 1753 \n", "347 79.4 844 \n", "138 83.7 504 \n", "281 87.0 1137 \n", "139 87.6 1235 \n", "207 83.9 682001 \n", "\n", " Pourcentage admis baccalauréat \n", "70 97.4 \n", "204 98.3 \n", "279 94.2 \n", "205 82.2 \n", "206 75.0 \n", "346 72.2 \n", "280 73.3 \n", "347 72.8 \n", "138 76.4 \n", "281 78.4 \n", "139 78.8 \n", "207 91.6 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Bac_2025_tri = Bac_2025.sort_values(by=[\"Age\"])\n", "Bac_2025_tri" ] }, { "cell_type": "markdown", "id": "2b07d8df-cf9a-4eba-83f5-e8b2cb09de95", "metadata": {}, "source": [ "On peut tracer des graphiques avec les suffixes `.plt(x=..., y=....)`, `.plt.box()`, `.plot.bar(x=..., y=...)`..." ] }, { "cell_type": "code", "execution_count": 9, "id": "73293405-a049-4da8-9b4d-4384993092fd", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Bac_2025_tri.plot.bar(x=\"Age\", y=[\"Pourcentage admis baccalauréat technologique\",\"Pourcentage admis baccalauréat général\"])\n", "plt.title(\"Pourcentages de réussite au baccalauréat en 2025\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a18d88c3-d25b-45a1-8a90-a0b55df74a84", "metadata": {}, "source": [ "Tracer un diagramme en barres donnant les nombres d'admis au baccalauréat technologique et général pour l'année 2025 en fonction de la tranche d'âge." ] }, { "cell_type": "code", "execution_count": 1, "id": "14843607-50cd-4e11-bf0b-544c4f2ad547", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "72fac26b-2e61-4eac-a531-3b56a52e9251", "metadata": {}, "source": [ "Tracer un graphique donnant le pourcentage de réussite pour l'ensemble des candidats au baccalauréat technologique et général en fonction de l'année. Remarque : l'option `ylim=(0,100)` permet d'avoir un axe des ordonnées allant de $0$ à $100$." ] }, { "cell_type": "code", "execution_count": 2, "id": "06192fbe-1641-4689-9971-41a7f777f18f", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "6b749804-e050-4892-a354-7fd59800d3fe", "metadata": {}, "source": [ "Tracer un graphique donnant le nombre d'admis pour l'ensemble des candidats au baccalauréat technologique et général en fonction de l'année." ] }, { "cell_type": "code", "execution_count": 3, "id": "741beb51-bc3d-4a7c-88bc-7f9ea63f0bd9", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "e2f94ddc-4d63-49fd-aae4-498b7d7ae363", "metadata": {}, "source": [ "Tracer un graphique donnant le pourcentage d'admis pour les trente ans et plus au baccalauréat technologique et général en fonction de l'année." ] }, { "cell_type": "code", "execution_count": 4, "id": "bbf735f1-4fb6-4a03-a75c-7fd704c1db0c", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "c30ee50a-0492-45e7-aed3-082913c57bbe", "metadata": {}, "source": [ "Tracer un graphique donnant le nombre d'admis pour les trente ans et plus au baccalauréat technologique et général en fonction de l'année." ] }, { "cell_type": "code", "execution_count": 5, "id": "6dbad92c-cdcd-4b65-821f-9a4e98242ab3", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "b04e4248-025e-49fc-9b2a-4a8013aedfb1", "metadata": {}, "source": [ "## II. Ecrans" ] }, { "cell_type": "markdown", "id": "e0757d48-02f7-4f94-a618-db04c91f7de0", "metadata": {}, "source": [ "Charger le fichier `digital_diet_mental_health.csv` après l'avoir téléchargé dans le même dossier." ] }, { "cell_type": "code", "execution_count": 6, "id": "51ac4a38-2cb7-4af8-9d9e-68cf051016a8", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "39eccd11-692d-4e11-a975-23ba88b3ba37", "metadata": {}, "source": [ "Afficher les indicateurs statistiques de la colonne **daily_screen_time_hours**." ] }, { "cell_type": "code", "execution_count": 7, "id": "7838aaa1-38df-41e1-8d8a-2f61d24980af", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "825cb85d-1504-42dd-9f11-b4978cd71b85", "metadata": {}, "source": [ "Tracer un histogramme (en densité) de la colonne **daily_screen_time_hours**." ] }, { "cell_type": "code", "execution_count": 8, "id": "822c4989-34f4-4a67-b2c0-b2f25cd92a6c", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "1493904e-cfc7-4640-a406-14e1854977e9", "metadata": {}, "source": [ "Tracer la fonction de répartition empirique de la colonne **daily_screen_time_hours**. On pourra utiliser la fonction `plt.ecdf`." ] }, { "cell_type": "code", "execution_count": 9, "id": "5555036a-b815-4cd0-a2b2-340e12f89995", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "45d9619c-6192-4938-b38f-f6771d255f4a", "metadata": {}, "source": [ "Tracer les boxplots correspondant aux colonnes **phone_usage_hours**, **laptop_usage_hours** et **tablet_usage_hours**." ] }, { "cell_type": "code", "execution_count": 10, "id": "298f90eb-392d-410c-84a0-73d86f08eb0c", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "c9dc9a25-9cb1-45f4-a586-aea8c01f1acf", "metadata": {}, "source": [ "Donner les indicateurs statistiques et tracer l'histogramme et la fonction de répartition empirique de la colonne **gaming_hours**." ] }, { "cell_type": "code", "execution_count": 11, "id": "dde98be0-dbf9-4cee-99df-292c45367770", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "code", "execution_count": 12, "id": "948e2537-3807-4ef7-95b8-5fa54ff37e91", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "code", "execution_count": 13, "id": "aa2644ba-a04f-4175-afa9-2aad7f346a86", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "97f49e45-5507-49f8-a027-21865483db96", "metadata": {}, "source": [ "Tracer le nuage de points de **sleep_duration_hours** en fonction de **daily_screen_time_hours**." ] }, { "cell_type": "code", "execution_count": 14, "id": "27df1702-e03d-4158-b4a7-9431647dd607", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "b086419a-bd85-44ad-9b2a-345738647603", "metadata": {}, "source": [ "Tracer le nuage de points de **sleep_duration_hours** en fonction de **caffeine_intake_mg_per_day**." ] }, { "cell_type": "code", "execution_count": 15, "id": "cdfa913c-6a39-4b74-bf80-3aa663ddc08f", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "e5aae5ff-7481-4b13-976f-8d230be03cec", "metadata": {}, "source": [ "Tracer le nuage de points de **gaming_hours** en fonction de **daily_screen_time_hours**." ] }, { "cell_type": "code", "execution_count": 16, "id": "a8cf1411-8f6d-492e-8511-2d061d9232e3", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "1eb91d3a-f656-4f2a-b459-e7bc1b998e25", "metadata": {}, "source": [ "## III. Régression linéaire" ] }, { "cell_type": "markdown", "id": "5f67388f-0d87-4d09-8fa2-5d55313c8630", "metadata": {}, "source": [ "On considère les jeux de données suivants." ] }, { "cell_type": "code", "execution_count": 72, "id": "72a37c7a-2c7c-44cf-a503-1aa803bf95e8", "metadata": {}, "outputs": [], "source": [ "X1=np.array([ 2.1, 2.0, 4.8, 4.3, 1.4, 2.2, 3.9, 1.9, 1.2, 2.7, 2.8, 1.4, 4.6, 4.8, 3.5, \n", " 2.4, 3.2, 1.3, 0.4, 2.2, 3.1, 0.7, 2.8, 4.8, 3.3, 2.8, 0.2, 1.4, 4.9, 2.4, \n", " 3.7, 3.5, 3.2, 0.7, 1.5, 5.0, 0.3, 3.7, 0.2, 2.3, 1.7, 3.7, 4.0, 1.6, 1.1, \n", " 2.3, 3.5, 3.8, 3.8, 1.1])\n", "\n", "Y1=np.array([ 67, 68, 60, 61, 70, 66, 64, 67, 68, 67, 64, 69, 61, 60, 65, 67, 65, 70, 71, \n", " 69, 64, 69, 65, 61, 66, 64, 70, 71, 60, 67, 63, 63, 65, 72, 69, 60, 69, 60, \n", " 69, 65, 65, 62, 64, 66, 68, 65, 65, 61, 65, 72])\n", "\n", "X2=np.array([ 3.83, 0.68, 1.82, 1.17, 2.05, 0.01, 0.04, 4.87, 2.61, 4.32, 3.82, 2.70, 2.38, \n", " 1.75, 1.48, 0.28, 0.17, 0.28, 0.26, 4.59, 0.40, 0.11, 4.66, 4.65, 3.65, 2.01, \n", " 3.93, 0.89, 4.06, 3.18, 1.36, 3.49, 3.29, 2.18, 0.10, 2.24, 4.69, 0.75, 0.86, \n", " 2.23, 1.71, 2.09, 0.51, 4.16, 1.53, 4.44, 1.35, 0.50, 0.99, 1.65])\n", " \n", "Y2=np.array([ 3.6, 2.9, 3.0, 4.3, 7.2, 3.2, 1.4, 7.5, 8.1, 5.8, 3.5, 4.9, 2.2, 5.4, 3.5, 2.2, \n", " 2.8, 2.2, 3.2, 6.2, 6.8, 1.5, 5.0, 1.4, 3.5, 4.8, 3.6, 3.4, 5.6, 3.6, 3.2, 4.4, \n", " 8.3, 2.7, 5.6, 5.8, 6.9, 3.2, 3.2, 1.8, 6.1, 3.1, 3.8, 9.6, 3.7, 4.4, 2.9, 2.3, \n", " 5.1, 1.0])\n", "\n", "X3=np.array([ 0.56, 3.58, 1.06, 3.13, 3.57, 3.35, 2.98, 3.73, 0.25, 2.29, 3.98, 4.11, 1.97, \n", " 1.35, 3.28, 0.48, 3.06, 2.03, 1.68, 2.25, 3.67, 2.84, 2.94, 1.98, 1.93, 1.13, \n", " 2.77, 1.94, 0.70, 0.60, 2.68, 1.81, 0.81, 0.05, 1.04, 3.94, 3.27, 3.13, 2.06, \n", " 0.66, 1.67, 2.67, 4.28, 2.10, 0.70, 1.64, 1.78, 3.32, 4.40, 1.24])\n", "\n", "Y3=np.array([ 5.8, 4.7, 6.5, 7.0, 8.7, 7.1, 5.2, 3.1, 5.1, 6.9, 4.8, 11.1, 6.3, 5.7, 3.8, 4.3,\n", " 4.9, 8.4, 8.5, 6.5, 4.0, 6.3, 3.6, 5.5, 4.5, 3.1, 5.3, 6.5, 5.3, 4.6, 4.2, 4.0, \n", " 6.6, 7.9, 5.9, 4.1, 4.2, 5.3, 3.9, 10.0, 12.5, 6.3, 4.8, 7.6, 7.2, 6.2, 3.4, 8.0,\n", " 6.1, 6.8])\n", "\n", "X4=np.array([ 4.1, 1.1, 2.3, 3.2, 2.9, 5.2, 0.2, 4.7, 5.7, 3.5, 2.9, 1.9, 4.0, 3.8, 5.5, 3.3, \n", " 3.1, 5.7, 4.6, 3.8, 0.3, 4.9, 3.2, 3.9, 0.4, 1.6, 1.4, 0.3, 1.9, 1.3, 1.0, 1.4, \n", " 5.8, 1.8, 0.5, 2.6, 0.5, 0.4, 0.2, 0.3, 5.7, 2.6, 4.5, 3.9, 1.3, 4.2, 2.5, 2.6, \n", " 5.5, 4.6])\n", "\n", "Y4=np.array([ 4.77, 2.10, 5.70, 5.93, 5.73, 1.10, -2.39, 2.92, -1.31, 5.82, 5.89, 4.62, 4.80, \n", " 5.45, -0.22, 5.59, 5.91, -1.41, 3.32, 5.43, -1.01, 2.03, 5.52, 5.46, -0.90, 3.93, \n", " 3.59, -1.61, 4.34, 3.22, 2.07, 3.10, -1.79, 4.63, 0.08, 5.96, 0.10, -0.97, -1.72, \n", " -1.54, -1.65, 6.16, 3.84, 5.53, 2.93, 4.25, 5.60, 5.79, -0.06, 3.64])\n", "\n", "X5=np.array([ 7, 13, 15, 7, 6, 12, 7, 5, 11, 9, 11, 13, 14, 8, 13, 6, 10, 14, 5, 9, 10, 10, \n", " 9, 7, 6, 12, 7, 7, 8, 12])\n", "\n", "Y5=np.array([ 16, 21, 23, 15, 10, 19, 13, 12, 16, 13, 18, 21, 19, 12, 20, 13, 17, 22, 12, 16, \n", " 16, 16, 16, 14, 10, 20, 12, 14, 15, 7])" ] }, { "cell_type": "markdown", "id": "6ad45dfe-8094-4790-8a2b-4084b756e05d", "metadata": {}, "source": [ "La commande `stats.linregress` permet d'effectuer une regression linéaire au sens des moindres carrés du jeu de données $(X_1,Y_1)$ (la droite de régression est ici donnée par l'équation $y=ax+b$). " ] }, { "cell_type": "code", "execution_count": 81, "id": "30411692-f180-4f2c-9bcf-0fd6050e83e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coefficient de corrélation : -0.9089520149212604\n", "a = -2.2716639763830506\n", "b = 71.59541299450147\n" ] } ], "source": [ "res = stats.linregress(X1, Y1)\n", "print(\"coefficient de corrélation :\", res.rvalue)\n", "print(\"a =\", res.slope)\n", "print(\"b =\", res.intercept)" ] }, { "cell_type": "markdown", "id": "cc42396f-989a-4125-b2e3-8e91cea4e204", "metadata": {}, "source": [ "Tracer le nuage de points du jeu de données $(X_1,Y_1)$ et le superposer avec la droite de régression." ] }, { "cell_type": "code", "execution_count": 17, "id": "f0a28f0b-62b5-46fc-815a-55e2b213f027", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "markdown", "id": "597ea6e6-9a6b-4dff-bf3c-a6456524ba54", "metadata": {}, "source": [ "Faire de même avec les jeux de données $(X_2,Y_2)$, $(X_3,Y_3)$, $(X_4,Y_4)$ et $(X_5,Y_5)$, en affichant à chaque fois le coefficient de corrélation." ] }, { "cell_type": "code", "execution_count": 18, "id": "7304e528-1e7a-4cb8-b174-ff1adb727cd2", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "code", "execution_count": 19, "id": "08447f76-4d00-469c-a468-d39d27b3420d", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "code", "execution_count": 20, "id": "b6f77a08-07af-42f2-8698-c1f69b4e0349", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] }, { "cell_type": "code", "execution_count": 21, "id": "70db3bfa-bc3e-4ac7-8c79-7e95fa2f18a9", "metadata": {}, "outputs": [], "source": [ "#\n", "#\n", "#" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }