├── CAFEP ├── CAFEP.xlsx ├── Figure_1.png ├── Figure_2.png ├── Figure_3.png └── Figure_4.png ├── CAPES ├── CAPES.xlsx ├── Figure_0.png ├── Figure_1.png ├── Figure_2.png ├── Figure_3.png ├── Figure_4.png └── Figure_5.png ├── RAPPORTS DE JURY ├── 2003.pdf ├── 2004.pdf ├── 2005.pdf ├── 2006.pdf ├── 2007.pdf ├── 2008.pdf ├── 2009.pdf ├── 2010.pdf ├── 2011.pdf ├── 2012.pdf ├── 2013.pdf ├── 2014.pdf ├── 2015.pdf ├── 2016.pdf ├── 2017.pdf ├── 2018.pdf ├── 2019.pdf ├── 2020.pdf ├── 2021.pdf ├── 2022.pdf └── 2023.pdf ├── README.md └── a.py /CAFEP/CAFEP.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XenocodeRCE/Philo-Capes-stats/main/CAFEP/CAFEP.xlsx -------------------------------------------------------------------------------- /CAPES/CAPES.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XenocodeRCE/Philo-Capes-stats/main/CAPES/CAPES.xlsx 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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Stats du CAPES / CAFEP externe de PHILO 2 | 3 | → Directement inspiré de : https://megamathsblog.blogspot.com/2021/12/statistiques-du-capes-externe-de.html 4 | 5 | ## Résultats 6 | ![Figure_1](https://github.com/XenocodeRCE/Philo-Capes-stats/assets/4918142/6fa9616f-fbda-4115-a1f1-9d551495e2f2) 7 | ![Figure_2](https://github.com/XenocodeRCE/Philo-Capes-stats/assets/4918142/ecd9b774-22e1-47ec-bb92-dcbedd8d7c37) 8 | ![Figure_3](https://github.com/XenocodeRCE/Philo-Capes-stats/assets/4918142/7fe6267e-5de3-40ad-91a4-64eee1fa0e80) 9 | ![Figure_4](https://github.com/XenocodeRCE/Philo-Capes-stats/assets/4918142/60983766-d9f4-4567-ae22-1ede6a87aacb) 10 | ![Figure_5](https://github.com/XenocodeRCE/Philo-Capes-stats/assets/4918142/66fa814e-f0c7-4c0f-9a36-09f3e4c5a784) 11 | -------------------------------------------------------------------------------- /a.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import matplotlib.pyplot as plt 3 | 4 | data = { 5 | "Année": [2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003], 6 | "Postes_cafep": [20, 20, 20, 22, 20, 20, 25, 25, 20, 20, 10, 20, 20, 20, 5, 9, 10, 15, 20, 20, 40], 7 | "Postes_capes": [121, 129, 129, 130, 120, 80, 100, 110, 103, 80, 80, 50, 32, 32, 26, 26, 30, 30, 48, 38, 60], 8 | "Inscrits_cafep": [275, 276, 317, None, 318, 326, 299, 273, 218, 208, 208, 218, 210, 229, 22, 239, 244, 287, 299, 213, 171], 9 | "Inscrits_capes": [1380, 1412, 1866, None, 1758, 792, 1756, 1630, 1497, 1505, 1082, 927, 918, 1130, 1096, 1375, 1440, 1594, 1603, 1783, 1929], 10 | "Présents_cafep": [164, 150, 159, None, 188, 184, 171, 157, 122, 118, 106, 101, 115, 144, 143, 170, 152, 179, 197, 149, 115], 11 | "Présents_capes": [846, 833, 1085, 1212, 1043, 1026, 1038, 1115, 1012, 1031, 606, 488, 448, 667, 672, 841, 934, 1020, 1082, 1216, 1363], 12 | "Admis_cafep" : [20, 20, 20, 22, 20, 20, 25, 25, 19, 19, 9, 9, 10, 8, 5, 7, 5, 4, 7, 10, 5], 13 | "Admis_capes" : [121, 129, 129, 130, 120, 80, 104, 110, 103, 80, 79, 50, 32, 32, 26, 26, 30, 30, 48, 38, 60] 14 | } 15 | 16 | 17 | 18 | df = pd.DataFrame(data) 19 | 20 | 21 | 22 | # Replace 'None' with NaN 23 | df['Inscrits_cafep'] = df['Inscrits_cafep'].astype('float') 24 | df['Inscrits_capes'] = df['Inscrits_capes'].astype('float') 25 | df['Présents_cafep'] = df['Présents_cafep'].astype('float') 26 | 27 | 28 | plt.plot(df['Année'], df['Postes_capes'], marker='o', color='red', label='Postes CAPES') 29 | plt.plot(df['Année'], df['Postes_cafep'], marker='o', color='blue', label='Postes CAFEP') 30 | 31 | plt.plot(df['Année'], df['Inscrits_capes'], marker='o', color='brown', label='Inscrits CAPES') 32 | plt.plot(df['Année'], df['Inscrits_cafep'], marker='o', color='dodgerblue', label='Inscrits CAFEP') 33 | 34 | plt.xlabel('Année') 35 | plt.ylabel('Nombre') 36 | plt.title('Évolution du nombre de postes, inscrits et présents au CAPES') 37 | plt.legend() 38 | plt.grid(True) 39 | plt.xticks(df['Année'], rotation=45) 40 | plt.tight_layout() 41 | 42 | plt.show() 43 | --------------------------------------------------------------------------------