From c752b88a95fcf2d52afd3343c56b0f1dc95be530 Mon Sep 17 00:00:00 2001 From: Kirscher Date: Fri, 3 Mar 2023 10:41:25 +0000 Subject: [PATCH] solved regex issue for IR performances --- France2022_10KM_TCM.csv | 451 ++++++++++++++++++++++++++++++++++++++++ scraping.ipynb | 50 ++--- scraping.py | 8 +- 3 files changed, 478 insertions(+), 31 deletions(-) create mode 100644 France2022_10KM_TCM.csv diff --git a/France2022_10KM_TCM.csv b/France2022_10KM_TCM.csv new file mode 100644 index 0000000..8602ee4 --- /dev/null +++ b/France2022_10KM_TCM.csv @@ -0,0 +1,451 @@ +Athlète,Ligue,Performance,Naissance,Catégorie,hours,minutes,seconds,time_delta,time_gap,duration +GRESSIER Jimmy,H-F,IA,1997,SEM,0,27,41,0 days 00:27:41,0 days 00:00:00,1661.0 +AUGUSTO Bastien,CEN,N1,1999,SEM,0,28,29,0 days 00:28:29,0 days 00:00:48,1709.0 +MONTOYA Raphael,I-F,N1,1995,SEM,0,28,48,0 days 00:28:48,0 days 00:01:07,1728.0 +BRULET Mathieu,N-A,N1,1987,M0M,0,28,53,0 days 00:28:53,0 days 00:01:12,1733.0 +MEKDAFOU Youssef,I-F,N1,1992,SEM,0,28,53,0 days 00:28:53,0 days 00:01:12,1733.0 +BORDEAU Pierre,I-F,N1,2000,ESM,0,28,55,0 days 00:28:55,0 days 00:01:14,1735.0 +COUZINIER Pierre,BRE,N1,1996,SEM,0,29,1,0 days 00:29:01,0 days 00:01:20,1741.0 +GHEBRESLASIE Merhawi ,I-F,N2,1992,SEM,0,29,6,0 days 00:29:06,0 days 00:01:25,1746.0 +ANGLADA Clement,N-A,N2,1994,SEM,0,29,7,0 days 00:29:07,0 days 00:01:26,1747.0 +EL BOUAJAJI Mohamed-Amine,H-F,N2,1997,SEM,0,29,7,0 days 00:29:07,0 days 00:01:26,1747.0 +BARGETTO Maxime,N-A,N2,1990,SEM,0,29,10,0 days 00:29:10,0 days 00:01:29,1750.0 +BOUGNOT Igor,ARA,N2,1992,SEM,0,29,14,0 days 00:29:14,0 days 00:01:33,1754.0 +LE BARON Luc,BFC,N2,2002,ESM,0,29,17,0 days 00:29:17,0 days 00:01:36,1757.0 +MARQUANT Antonin,H-F,N2,2002,ESM,0,29,22,0 days 00:29:22,0 days 00:01:41,1762.0 +BOUKEBAL Aziz,OCC,N2,1995,SEM,0,29,23,0 days 00:29:23,0 days 00:01:42,1763.0 +BAZIN Mathieu,NOR,N2,1990,SEM,0,29,24,0 days 00:29:24,0 days 00:01:43,1764.0 +GODEFROY Alexis,OCC,N2,1994,SEM,0,29,24,0 days 00:29:24,0 days 00:01:43,1764.0 +BANINI Teo-Ruben,I-F,N2,2001,ESM,0,29,45,0 days 00:29:45,0 days 00:02:04,1785.0 +LE ROY Corentin,BFC,N3,1991,SEM,0,29,46,0 days 00:29:46,0 days 00:02:05,1786.0 +ALI Elias,G-E,N3,2001,ESM,0,29,48,0 days 00:29:48,0 days 00:02:07,1788.0 +LAURET Marc,NOR,N3,1998,SEM,0,29,49,0 days 00:29:49,0 days 00:02:08,1789.0 +LAURENT Thomas,ARA,N3,1992,SEM,0,29,51,0 days 00:29:51,0 days 00:02:10,1791.0 +SCHULZ Bastien,PCA,N3,1995,SEM,0,29,55,0 days 00:29:55,0 days 00:02:14,1795.0 +GIRARD Florian,G-E,N3,1992,SEM,0,29,56,0 days 00:29:56,0 days 00:02:15,1796.0 +DODE Sylvain,CEN,N3,1986,M0M,0,30,0,0 days 00:30:00,0 days 00:02:19,1800.0 +CHAUMOND Mickael,N-A,N3,1990,SEM,0,30,4,0 days 00:30:04,0 days 00:02:23,1804.0 +TISSIER Eliott,N-A,N3,2001,ESM,0,30,5,0 days 00:30:05,0 days 00:02:24,1805.0 +FONTANA Leo,NOR,N3,1995,SEM,0,30,5,0 days 00:30:05,0 days 00:02:24,1805.0 +PRUVOT Yohann,H-F,N3,1985,M0M,0,30,8,0 days 00:30:08,0 days 00:02:27,1808.0 +XOLIN Pierre,BRE,N3,1997,SEM,0,30,10,0 days 00:30:10,0 days 00:02:29,1810.0 +DENISE Auxence,H-F,N3,1993,SEM,0,30,12,0 days 00:30:12,0 days 00:02:31,1812.0 +LAURENT Martin,I-F,N3,1997,SEM,0,30,13,0 days 00:30:13,0 days 00:02:32,1813.0 +VILLECHENAUD Antoine,N-A,N3,1995,SEM,0,30,14,0 days 00:30:14,0 days 00:02:33,1814.0 +GERARD Axel,P-L,N3,1997,SEM,0,30,17,0 days 00:30:17,0 days 00:02:36,1817.0 +MAISONNEUVE Benjamin,I-F,N3,1990,SEM,0,30,18,0 days 00:30:18,0 days 00:02:37,1818.0 +DES BOSCS Arnaud,G-E,N3,1999,SEM,0,30,18,0 days 00:30:18,0 days 00:02:37,1818.0 +THUILLIER Maxime,H-F,N3,2000,ESM,0,30,19,0 days 00:30:19,0 days 00:02:38,1819.0 +FARES Hatem,I-F,N3,1993,SEM,0,30,19,0 days 00:30:19,0 days 00:02:38,1819.0 +ALLALI Nasser,BFC,N3,1988,SEM,0,30,20,0 days 00:30:20,0 days 00:02:39,1820.0 +CHENAIS Joan,I-F,N3,1997,SEM,0,30,24,0 days 00:30:24,0 days 00:02:43,1824.0 +FERNANDES Marc,H-F,N3,1995,SEM,0,30,27,0 days 00:30:27,0 days 00:02:46,1827.0 +LEMAIRE Kevin,H-F,N3,1997,SEM,0,30,28,0 days 00:30:28,0 days 00:02:47,1828.0 +LEBON Hugaux,NOR,N3,1995,SEM,0,30,29,0 days 00:30:29,0 days 00:02:48,1829.0 +VANDEWALLE Matthieu,NOR,N3,1988,SEM,0,30,30,0 days 00:30:30,0 days 00:02:49,1830.0 +DULIN Alexis,ARA,N3,1992,SEM,0,30,32,0 days 00:30:32,0 days 00:02:51,1832.0 +LABIED Mehdi,G-E,N4,2001,ESM,0,30,35,0 days 00:30:35,0 days 00:02:54,1835.0 +ROSE Louis,PCA,N4,2000,ESM,0,30,36,0 days 00:30:36,0 days 00:02:55,1836.0 +POULIN Clementin,G-E,N4,2000,ESM,0,30,40,0 days 00:30:40,0 days 00:02:59,1840.0 +MOGIN Dorian,G-E,N4,1997,SEM,0,30,41,0 days 00:30:41,0 days 00:03:00,1841.0 +DESCHAMPS Alexis,CEN,N4,1998,SEM,0,30,41,0 days 00:30:41,0 days 00:03:00,1841.0 +GIRARD Clement,I-F,,1998,SEM,0,30,44,0 days 00:30:44,0 days 00:03:03,1844.0 +WEBER Nicolas,I-F,N4,1991,SEM,0,30,44,0 days 00:30:44,0 days 00:03:03,1844.0 +LEBECQUE Antoine,H-F,N4,1997,SEM,0,30,44,0 days 00:30:44,0 days 00:03:03,1844.0 +PRIGENT Fabien,BRE,N4,1992,SEM,0,30,45,0 days 00:30:45,0 days 00:03:04,1845.0 +PEIXOTO Jeremy,ARA,N4,1990,SEM,0,30,45,0 days 00:30:45,0 days 00:03:04,1845.0 +RINGARD Clement,H-F,N4,1997,SEM,0,30,48,0 days 00:30:48,0 days 00:03:07,1848.0 +GABET Christopher,H-F,N4,1997,SEM,0,30,48,0 days 00:30:48,0 days 00:03:07,1848.0 +VALENTIN Nicolas,I-F,N4,1994,SEM,0,30,49,0 days 00:30:49,0 days 00:03:08,1849.0 +DELEU Thomas,H-F,N4,1993,SEM,0,30,49,0 days 00:30:49,0 days 00:03:08,1849.0 +BOUR Ludovic,G-E,N4,1994,SEM,0,30,50,0 days 00:30:50,0 days 00:03:09,1850.0 +JUMELIN Yoan,NOR,N4,1992,SEM,0,30,52,0 days 00:30:52,0 days 00:03:11,1852.0 +BARRET Ruddy,REU,N4,1993,SEM,0,30,53,0 days 00:30:53,0 days 00:03:12,1853.0 +ELIOT Clement,NOR,N4,1994,SEM,0,30,54,0 days 00:30:54,0 days 00:03:13,1854.0 +RIGAUT Jean-Baptiste,NOR,N4,1992,SEM,0,30,56,0 days 00:30:56,0 days 00:03:15,1856.0 +MAES Paul,H-F,N4,1987,M0M,0,30,59,0 days 00:30:59,0 days 00:03:18,1859.0 +ROLLET Antoine,H-F,N4,1990,SEM,0,31,1,0 days 00:31:01,0 days 00:03:20,1861.0 +MAROLLEAU Armand,N-A,N4,2001,ESM,0,31,2,0 days 00:31:02,0 days 00:03:21,1862.0 +BENHADDOU Julien,OCC,N4,2002,ESM,0,31,4,0 days 00:31:04,0 days 00:03:23,1864.0 +LALOUF Antoine,P-L,N4,1998,SEM,0,31,4,0 days 00:31:04,0 days 00:03:23,1864.0 +THELLIER Vincent,H-F,N4,2001,ESM,0,31,4,0 days 00:31:04,0 days 00:03:23,1864.0 +VERIEN Samy,I-F,N4,1986,M0M,0,31,6,0 days 00:31:06,0 days 00:03:25,1866.0 +THOMAS Etienne,P-L,N4,2002,ESM,0,31,9,0 days 00:31:09,0 days 00:03:28,1869.0 +LEGEAY Quentin,I-F,N4,1995,SEM,0,31,11,0 days 00:31:11,0 days 00:03:30,1871.0 +COCQUET Barnabe,ARA,N4,1999,SEM,0,31,11,0 days 00:31:11,0 days 00:03:30,1871.0 +MESLET Baptiste,P-L,N4,1991,SEM,0,31,12,0 days 00:31:12,0 days 00:03:31,1872.0 +DUBIEZ Leo,H-F,N4,2000,ESM,0,31,15,0 days 00:31:15,0 days 00:03:34,1875.0 +FORET Loic,NOR,N4,1999,SEM,0,31,15,0 days 00:31:15,0 days 00:03:34,1875.0 +ZATOUNI Hamza,I-F,N4,1996,SEM,0,31,15,0 days 00:31:15,0 days 00:03:34,1875.0 +BERREKAM Thomas,H-F,IR1,1994,SEM,0,31,17,0 days 00:31:17,0 days 00:03:36,1877.0 +LAMBLIN Simon,CEN,IR1,1993,SEM,0,31,18,0 days 00:31:18,0 days 00:03:37,1878.0 +LEONE Maxime,NOR,IR1,2001,ESM,0,31,19,0 days 00:31:19,0 days 00:03:38,1879.0 +CHANET Clement,G-E,IR1,2000,ESM,0,31,20,0 days 00:31:20,0 days 00:03:39,1880.0 +LE FERRAND Philippe,H-F,IR1,1984,M0M,0,31,21,0 days 00:31:21,0 days 00:03:40,1881.0 +TERRIEN Nicolas,G-E,IR1,2000,ESM,0,31,22,0 days 00:31:22,0 days 00:03:41,1882.0 +LE DOUSSAL Erwann,NOR,IR1,1999,SEM,0,31,22,0 days 00:31:22,0 days 00:03:41,1882.0 +DE VERNEJOUL Mathias,P-L,IR1,2001,ESM,0,31,22,0 days 00:31:22,0 days 00:03:41,1882.0 +KAKE MAKANERA Alkhassane ,CEN,IR1,2003,JUM,0,31,23,0 days 00:31:23,0 days 00:03:42,1883.0 +DAOUADJI Kamel,G-E,IR1,1985,M0M,0,31,23,0 days 00:31:23,0 days 00:03:42,1883.0 +APHERVE Olivier,H-F,IR1,2001,ESM,0,31,23,0 days 00:31:23,0 days 00:03:42,1883.0 +RIVET Benjamin,N-A,IR1,2000,ESM,0,31,23,0 days 00:31:23,0 days 00:03:42,1883.0 +ZATOUNI Amine,I-F,IR1,1992,SEM,0,31,25,0 days 00:31:25,0 days 00:03:44,1885.0 +ANCELLIN Baptiste,H-F,IR1,2000,ESM,0,31,25,0 days 00:31:25,0 days 00:03:44,1885.0 +MOULIN Loic,ARA,IR1,1992,SEM,0,31,26,0 days 00:31:26,0 days 00:03:45,1886.0 +LEGROS Louis,I-F,IR1,1998,SEM,0,31,28,0 days 00:31:28,0 days 00:03:47,1888.0 +FOUTREIN Pierre,H-F,IR1,1995,SEM,0,31,29,0 days 00:31:29,0 days 00:03:48,1889.0 +FRANCOIS Florent,H-F,IR1,1990,SEM,0,31,31,0 days 00:31:31,0 days 00:03:50,1891.0 +PLAS Guillaume,G-E,IR1,1989,SEM,0,31,32,0 days 00:31:32,0 days 00:03:51,1892.0 +PAUTRAT Alexis,BFC,IR1,1986,M0M,0,31,32,0 days 00:31:32,0 days 00:03:51,1892.0 +COUGNAUD Clement,I-F,IR1,2001,ESM,0,31,32,0 days 00:31:32,0 days 00:03:51,1892.0 +DESGEORGES Adrien,ARA,IR1,2001,ESM,0,31,32,0 days 00:31:32,0 days 00:03:51,1892.0 +BOISCO Maxime,I-F,IR1,1987,M0M,0,31,33,0 days 00:31:33,0 days 00:03:52,1893.0 +LAURIER Sebastien,G-E,IR1,1985,M0M,0,31,34,0 days 00:31:34,0 days 00:03:53,1894.0 +GUINOISEAU Lucas,P-L,IR1,1995,SEM,0,31,36,0 days 00:31:36,0 days 00:03:55,1896.0 +NKOA Gabriel,I-F,IR1,1993,SEM,0,31,37,0 days 00:31:37,0 days 00:03:56,1897.0 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Daniel,NOR,IR1,1993,SEM,0,31,46,0 days 00:31:46,0 days 00:04:05,1906.0 +DEMARCQ Raphael,H-F,IR1,1995,SEM,0,31,46,0 days 00:31:46,0 days 00:04:05,1906.0 +BRIAND Alexandre,P-L,IR1,2001,ESM,0,31,47,0 days 00:31:47,0 days 00:04:06,1907.0 +BATEL Martin,NOR,IR1,2001,ESM,0,31,48,0 days 00:31:48,0 days 00:04:07,1908.0 +SUZE Guillaume,NOR,IR1,1988,SEM,0,31,51,0 days 00:31:51,0 days 00:04:10,1911.0 +CANTET Jeremy,BRE,IR1,1990,SEM,0,31,52,0 days 00:31:52,0 days 00:04:11,1912.0 +MAUBIAN Jean-Baptiste,N-A,IR1,1992,SEM,0,31,53,0 days 00:31:53,0 days 00:04:12,1913.0 +MOINARD Wilfried,P-L,IR1,1993,SEM,0,31,53,0 days 00:31:53,0 days 00:04:12,1913.0 +JOLIET Jordan,BRE,IR1,1997,SEM,0,31,54,0 days 00:31:54,0 days 00:04:13,1914.0 +DANIEL Kevin,H-F,IR1,1997,SEM,0,31,56,0 days 00:31:56,0 days 00:04:15,1916.0 +GILBERT Maxime,G-E,IR1,1997,SEM,0,31,56,0 days 00:31:56,0 days 00:04:15,1916.0 +DRUEZ Gael,P-L,IR1,2001,ESM,0,31,57,0 days 00:31:57,0 days 00:04:16,1917.0 +MONTCHARMONT Ivan,ARA,IR1,2002,ESM,0,31,58,0 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Terrence,I-F,IR2,1996,SEM,0,32,24,0 days 00:32:24,0 days 00:04:43,1944.0 +CARROT Robin,ARA,IR2,1999,SEM,0,32,24,0 days 00:32:24,0 days 00:04:43,1944.0 +MERLAUD Romain,I-F,IR2,1988,SEM,0,32,26,0 days 00:32:26,0 days 00:04:45,1946.0 +LETELLIER Remi,H-F,IR2,1983,M0M,0,32,30,0 days 00:32:30,0 days 00:04:49,1950.0 +MONDESIR Wilfrid,MAR,IR2,1983,M0M,0,32,32,0 days 00:32:32,0 days 00:04:51,1952.0 +DE SAINT AUNA Antoine,NOR,IR2,2002,ESM,0,32,33,0 days 00:32:33,0 days 00:04:52,1953.0 +BEAUVAL Martin,NOR,IR2,2000,ESM,0,32,33,0 days 00:32:33,0 days 00:04:52,1953.0 +MONDAIN Axel,COR,IR2,1998,SEM,0,32,36,0 days 00:32:36,0 days 00:04:55,1956.0 +HAIE Pierre,P-L,IR2,1996,SEM,0,32,36,0 days 00:32:36,0 days 00:04:55,1956.0 +BLET Valerian,CEN,IR2,1995,SEM,0,32,37,0 days 00:32:37,0 days 00:04:56,1957.0 +JUMELIN Victorien,NOR,IR2,1990,SEM,0,32,38,0 days 00:32:38,0 days 00:04:57,1958.0 +DEGRAEVE Yann,H-F,IR2,1992,SEM,0,32,39,0 days 00:32:39,0 days 00:04:58,1959.0 +CHENY Alexandre,I-F,IR2,1992,SEM,0,32,39,0 days 00:32:39,0 days 00:04:58,1959.0 +VERBEKE Timothee,H-F,IR2,1986,M0M,0,32,40,0 days 00:32:40,0 days 00:04:59,1960.0 +SELLIN Alban,I-F,IR2,1996,SEM,0,32,40,0 days 00:32:40,0 days 00:04:59,1960.0 +EL IDRISSI Bilale,I-F,IR2,1998,SEM,0,32,40,0 days 00:32:40,0 days 00:04:59,1960.0 +CAUSSE Pierre,NOR,IR2,1995,SEM,0,32,41,0 days 00:32:41,0 days 00:05:00,1961.0 +HERIN Hugo,PCA,IR2,1988,SEM,0,32,41,0 days 00:32:41,0 days 00:05:00,1961.0 +BARREAU Charles-Henri,CEN,IR2,1987,M0M,0,32,42,0 days 00:32:42,0 days 00:05:01,1962.0 +LENOIR Valentin,ARA,IR2,2000,ESM,0,32,42,0 days 00:32:42,0 days 00:05:01,1962.0 +IUREA Armand,PCA,IR2,1988,SEM,0,32,42,0 days 00:32:42,0 days 00:05:01,1962.0 +PALUMBO Kevin,I-F,IR2,1990,SEM,0,32,42,0 days 00:32:42,0 days 00:05:01,1962.0 +FOUILLE Maxime,I-F,IR2,1999,SEM,0,32,43,0 days 00:32:43,0 days 00:05:02,1963.0 +COUTARD Jeremy,NOR,IR2,1997,SEM,0,32,43,0 days 00:32:43,0 days 00:05:02,1963.0 +LEPREST Aurelien,P-L,IR2,1997,SEM,0,32,43,0 days 00:32:43,0 days 00:05:02,1963.0 +KIRSCHER Tristan,G-E,IR2,2000,ESM,0,32,43,0 days 00:32:43,0 days 00:05:02,1963.0 +TURLURE Mathieu,G-E,IR2,1994,SEM,0,32,45,0 days 00:32:45,0 days 00:05:04,1965.0 +MATHIEU Anthony,H-F,IR2,1988,SEM,0,32,45,0 days 00:32:45,0 days 00:05:04,1965.0 +DAGNELIES Cyril,N-A,IR2,1988,SEM,0,32,46,0 days 00:32:46,0 days 00:05:05,1966.0 +FAURE Thibaud,NOR,IR2,1996,SEM,0,32,46,0 days 00:32:46,0 days 00:05:05,1966.0 +LAPORTE Valentin,G-E,IR2,1998,SEM,0,32,46,0 days 00:32:46,0 days 00:05:05,1966.0 +ABDELKARIM Abdelmoumaine ,H-F,IR2,2000,ESM,0,32,46,0 days 00:32:46,0 days 00:05:05,1966.0 +DOREZ Nicolas,H-F,IR3,1993,SEM,0,32,49,0 days 00:32:49,0 days 00:05:08,1969.0 +BARONNAT Christopher,G-E,IR3,1984,M0M,0,32,50,0 days 00:32:50,0 days 00:05:09,1970.0 +GILGERT Kevin,CEN,IR3,1990,SEM,0,32,50,0 days 00:32:50,0 days 00:05:09,1970.0 +HARISMENDY Alexis,P-L,IR3,1992,SEM,0,32,50,0 days 00:32:50,0 days 00:05:09,1970.0 +LECOMTE Sylvain,H-F,IR3,1986,M0M,0,32,51,0 days 00:32:51,0 days 00:05:10,1971.0 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Bastien,PCA,IR3,1994,SEM,0,33,2,0 days 00:33:02,0 days 00:05:21,1982.0 +BUCHTA Louis,H-F,IR3,1998,SEM,0,33,2,0 days 00:33:02,0 days 00:05:21,1982.0 +LEROY Ludovic,P-L,IR3,1989,SEM,0,33,2,0 days 00:33:02,0 days 00:05:21,1982.0 +DEBARROS Frederic,H-F,IR3,1986,M0M,0,33,3,0 days 00:33:03,0 days 00:05:22,1983.0 +BAILLIN Louis,H-F,IR3,1998,SEM,0,33,4,0 days 00:33:04,0 days 00:05:23,1984.0 +MARYNIAK William,I-F,IR3,1992,SEM,0,33,4,0 days 00:33:04,0 days 00:05:23,1984.0 +DECAE Remi,H-F,IR3,1984,M0M,0,33,4,0 days 00:33:04,0 days 00:05:23,1984.0 +PASSEROU Philippe,G-E,IR3,1991,SEM,0,33,4,0 days 00:33:04,0 days 00:05:23,1984.0 +BAC Anthony,NOR,IR3,1990,SEM,0,33,5,0 days 00:33:05,0 days 00:05:24,1985.0 +IBN ELHAG Mohamed ,I-F,IR3,2001,ESM,0,33,5,0 days 00:33:05,0 days 00:05:24,1985.0 +DIDIER Quentin,G-E,IR3,1991,SEM,0,33,5,0 days 00:33:05,0 days 00:05:24,1985.0 +LOUVEAU Vincent,BRE,IR3,1986,M0M,0,33,5,0 days 00:33:05,0 days 00:05:24,1985.0 +GILLET Renaud,G-E,IR3,1989,SEM,0,33,5,0 days 00:33:05,0 days 00:05:24,1985.0 +VERHAEGHE Paul,H-F,IR3,1995,SEM,0,33,6,0 days 00:33:06,0 days 00:05:25,1986.0 +BLONDEAU Remi,G-E,IR3,1984,M0M,0,33,6,0 days 00:33:06,0 days 00:05:25,1986.0 +MONTRAU Florent,CEN,IR3,1986,M0M,0,33,7,0 days 00:33:07,0 days 00:05:26,1987.0 +BUNEL Thibault,NOR,IR3,1991,SEM,0,33,8,0 days 00:33:08,0 days 00:05:27,1988.0 +VIDAL Lucas,ARA,IR3,1999,SEM,0,33,9,0 days 00:33:09,0 days 00:05:28,1989.0 +NEGRE Arnaud,I-F,IR3,1990,SEM,0,33,12,0 days 00:33:12,0 days 00:05:31,1992.0 +LEFEBVRE Jerome,NOR,IR3,1989,SEM,0,33,12,0 days 00:33:12,0 days 00:05:31,1992.0 +CAILLAULT Romain,I-F,IR3,2001,ESM,0,33,12,0 days 00:33:12,0 days 00:05:31,1992.0 +COCHIN Leo,ARA,IR3,1999,SEM,0,33,13,0 days 00:33:13,0 days 00:05:32,1993.0 +SOUKOUNA Issa ,P-L,IR3,1999,SEM,0,33,13,0 days 00:33:13,0 days 00:05:32,1993.0 +BOSSART Stephane,H-F,IR3,2001,ESM,0,33,13,0 days 00:33:13,0 days 00:05:32,1993.0 +LEPRINCE Paul,P-L,IR3,1998,SEM,0,33,15,0 days 00:33:15,0 days 00:05:34,1995.0 +CANO Valentin,ARA,IR3,2000,ESM,0,33,15,0 days 00:33:15,0 days 00:05:34,1995.0 +GUERIN Kevin,N-A,IR3,1997,SEM,0,33,15,0 days 00:33:15,0 days 00:05:34,1995.0 +MAGNY Sylvain,BFC,IR3,1993,SEM,0,33,15,0 days 00:33:15,0 days 00:05:34,1995.0 +RODRIGUES Jean-Francois,I-F,IR3,1995,SEM,0,33,16,0 days 00:33:16,0 days 00:05:35,1996.0 +MALHERBE Valentin,BFC,IR3,1997,SEM,0,33,16,0 days 00:33:16,0 days 00:05:35,1996.0 +BAERT Benoit,H-F,IR3,1987,M0M,0,33,16,0 days 00:33:16,0 days 00:05:35,1996.0 +AYMARD Nathan,CEN,IR3,2001,ESM,0,33,17,0 days 00:33:17,0 days 00:05:36,1997.0 +ROCANIERES Pierre,OCC,IR3,1993,SEM,0,33,17,0 days 00:33:17,0 days 00:05:36,1997.0 +MOREL Xavier,H-F,IR3,1987,M0M,0,33,18,0 days 00:33:18,0 days 00:05:37,1998.0 +VAN WERSCH Valentin,OCC,IR3,2002,ESM,0,33,18,0 days 00:33:18,0 days 00:05:37,1998.0 +JOLLY Gregory,0,,0,,0,33,19,0 days 00:33:19,0 days 00:05:38,1999.0 +LE NEDIC Michael,0,,0,,0,33,20,0 days 00:33:20,0 days 00:05:39,2000.0 +BRIAN Tom ,P-L,IR3,1994,SEM,0,33,20,0 days 00:33:20,0 days 00:05:39,2000.0 +FORTOT Nicolas,G-E,IR3,1989,SEM,0,33,21,0 days 00:33:21,0 days 00:05:40,2001.0 +PREGEANT Mathieu,ARA,IR3,1991,SEM,0,33,21,0 days 00:33:21,0 days 00:05:40,2001.0 +MADELON Anthony,G-E,IR3,1993,SEM,0,33,21,0 days 00:33:21,0 days 00:05:40,2001.0 +PHILBERT Benjamin,I-F,IR3,1985,M0M,0,33,21,0 days 00:33:21,0 days 00:05:40,2001.0 +LEMERE Wilfried,P-L,IR3,1997,SEM,0,33,22,0 days 00:33:22,0 days 00:05:41,2002.0 +DERYCKE-CHEMIN Gautier,I-F,IR3,1997,SEM,0,33,22,0 days 00:33:22,0 days 00:05:41,2002.0 +PENICAUD Pierre,NOR,IR3,1983,M0M,0,33,22,0 days 00:33:22,0 days 00:05:41,2002.0 +VILLARD Johann,H-F,IR3,1991,SEM,0,33,22,0 days 00:33:22,0 days 00:05:41,2002.0 +LACHAISE Matthieu,I-F,IR3,2002,ESM,0,33,24,0 days 00:33:24,0 days 00:05:43,2004.0 +BARBEAU Vincent,ARA,IR3,1991,SEM,0,33,24,0 days 00:33:24,0 days 00:05:43,2004.0 +CARRASCOSA Tom,G-E,IR3,1989,SEM,0,33,24,0 days 00:33:24,0 days 00:05:43,2004.0 +ROBILLOT Alfred,P-L,IR3,1992,SEM,0,33,25,0 days 00:33:25,0 days 00:05:44,2005.0 +NANCEL Edouard,I-F,IR3,1986,M0M,0,33,26,0 days 00:33:26,0 days 00:05:45,2006.0 +CHARYK Julien,N-A,IR3,2000,ESM,0,33,26,0 days 00:33:26,0 days 00:05:45,2006.0 +COQUARD-FONTAN Charly,I-F,IR3,1999,SEM,0,33,26,0 days 00:33:26,0 days 00:05:45,2006.0 +RUGGIERO-JULIO Vincent,ARA,IR3,1996,SEM,0,33,26,0 days 00:33:26,0 days 00:05:45,2006.0 +COCQUEMPOT Alexis,CEN,IR3,1986,M0M,0,33,27,0 days 00:33:27,0 days 00:05:46,2007.0 +DUPUY Vincent,H-F,IR3,2001,ESM,0,33,28,0 days 00:33:28,0 days 00:05:47,2008.0 +VAVON Nicolas,H-F,IR3,1989,SEM,0,33,28,0 days 00:33:28,0 days 00:05:47,2008.0 +BOVE Francois-Joseph,P-L,IR3,1996,SEM,0,33,29,0 days 00:33:29,0 days 00:05:48,2009.0 +CARDUNER Cedric,H-F,IR3,1992,SEM,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +BEAUFILS Henrique,H-F,IR3,2001,ESM,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +FROISSART Camille,H-F,IR3,1983,M0M,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +COURQUIN Theo,I-F,IR3,1986,M0M,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +DEMANY Nicolas,I-F,IR3,1999,SEM,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +GOLAIN Maxime,ARA,IR3,1989,SEM,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +DUBOIS Arnaud,NOR,IR3,1989,SEM,0,33,30,0 days 00:33:30,0 days 00:05:49,2010.0 +HERNOULD Franky ,H-F,IR3,1992,SEM,0,33,31,0 days 00:33:31,0 days 00:05:50,2011.0 +TREHOUT Nicolas,H-F,IR3,2000,ESM,0,33,32,0 days 00:33:32,0 days 00:05:51,2012.0 +BLYAU Martin,COR,IR3,1986,M0M,0,33,33,0 days 00:33:33,0 days 00:05:52,2013.0 +CLERC-DUBOIS Clement,CEN,IR3,1995,SEM,0,33,33,0 days 00:33:33,0 days 00:05:52,2013.0 +DETRES Adrien,H-F,IR3,1999,SEM,0,33,34,0 days 00:33:34,0 days 00:05:53,2014.0 +PILLARD Nicolas,H-F,IR3,2000,ESM,0,33,34,0 days 00:33:34,0 days 00:05:53,2014.0 +FUSILLER Simon,H-F,IR3,2000,ESM,0,33,34,0 days 00:33:34,0 days 00:05:53,2014.0 +DEVILLIERS Nicolas,I-F,IR4,1990,SEM,0,33,35,0 days 00:33:35,0 days 00:05:54,2015.0 +SURY Nathan,N-A,IR3,2000,ESM,0,33,35,0 days 00:33:35,0 days 00:05:54,2015.0 +BELLINO Corentin,BFC,IR3,2000,ESM,0,33,35,0 days 00:33:35,0 days 00:05:54,2015.0 +ALUZE Dimitri,NOR,IR4,2001,ESM,0,33,36,0 days 00:33:36,0 days 00:05:55,2016.0 +REMY Guillaume,P-L,IR4,2000,ESM,0,33,36,0 days 00:33:36,0 days 00:05:55,2016.0 +MORIZOT Antoine,I-F,IR4,2001,ESM,0,33,37,0 days 00:33:37,0 days 00:05:56,2017.0 +DROSSEAU Domice,H-F,IR4,1985,M0M,0,33,38,0 days 00:33:38,0 days 00:05:57,2018.0 +LAFOND Dimitri,CEN,IR4,2001,ESM,0,33,38,0 days 00:33:38,0 days 00:05:57,2018.0 +CHOQUET Yoann,I-F,IR3,1986,M0M,0,33,38,0 days 00:33:38,0 days 00:05:57,2018.0 +LECOMTE Matheo,I-F,IR3,1992,SEM,0,33,38,0 days 00:33:38,0 days 00:05:57,2018.0 +MOTTE Pierre-Andre,H-F,IR4,1985,M0M,0,33,39,0 days 00:33:39,0 days 00:05:58,2019.0 +COUPU Elie,CEN,IR4,1987,M0M,0,33,39,0 days 00:33:39,0 days 00:05:58,2019.0 +HINHANE Aldrick,NOR,IR4,1992,SEM,0,33,42,0 days 00:33:42,0 days 00:06:01,2022.0 +GAFFE Eddy,G-E,IR4,1992,SEM,0,33,42,0 days 00:33:42,0 days 00:06:01,2022.0 +HUBERT Gaetan,NOR,IR4,1988,SEM,0,33,42,0 days 00:33:42,0 days 00:06:01,2022.0 +GUILBOT Corentin,H-F,IR4,1988,SEM,0,33,43,0 days 00:33:43,0 days 00:06:02,2023.0 +LE GARREC Quentin,P-L,IR4,1992,SEM,0,33,43,0 days 00:33:43,0 days 00:06:02,2023.0 +LATIFI Yohan,N-A,IR4,2000,ESM,0,33,44,0 days 00:33:44,0 days 00:06:03,2024.0 +HUMEZ Alexis,I-F,IR4,1987,M0M,0,33,44,0 days 00:33:44,0 days 00:06:03,2024.0 +LEFEVRE Frederic,H-F,IR4,2002,ESM,0,33,45,0 days 00:33:45,0 days 00:06:04,2025.0 +BOUCHET Clement,H-F,IR4,1999,SEM,0,33,46,0 days 00:33:46,0 days 00:06:05,2026.0 +GUYARD Damien,H-F,IR4,1987,M0M,0,33,47,0 days 00:33:47,0 days 00:06:06,2027.0 +MACHIN Jean-Marc,H-F,IR4,1996,SEM,0,33,48,0 days 00:33:48,0 days 00:06:07,2028.0 +POCHET Maxime,G-E,IR4,1994,SEM,0,33,48,0 days 00:33:48,0 days 00:06:07,2028.0 +NZIE NZIE Egon,N-A,IR4,1997,SEM,0,33,48,0 days 00:33:48,0 days 00:06:07,2028.0 +GUIDEZ Vincent,H-F,IR4,2001,ESM,0,33,48,0 days 00:33:48,0 days 00:06:07,2028.0 +GERARD Christopher,H-F,IR4,2000,ESM,0,33,49,0 days 00:33:49,0 days 00:06:08,2029.0 +BIENAIME Louis,G-E,IR4,1986,M0M,0,33,49,0 days 00:33:49,0 days 00:06:08,2029.0 +BUSSIERE Nicolas,G-E,IR4,1987,M0M,0,33,49,0 days 00:33:49,0 days 00:06:08,2029.0 +LAMBERT Bastien,NOR,IR4,1991,SEM,0,33,49,0 days 00:33:49,0 days 00:06:08,2029.0 +ESSALKI Smain-Ismail,G-E,IR4,1990,SEM,0,33,49,0 days 00:33:49,0 days 00:06:08,2029.0 +CHOLLET Nicolas,H-F,IR4,2002,ESM,0,33,49,0 days 00:33:49,0 days 00:06:08,2029.0 +THENOT Benjamin,G-E,IR4,1996,SEM,0,33,50,0 days 00:33:50,0 days 00:06:09,2030.0 +BARTOLO Gino,P-L,IR4,1990,SEM,0,33,50,0 days 00:33:50,0 days 00:06:09,2030.0 +LE MORVAN Romain,G-E,IR4,1988,SEM,0,33,51,0 days 00:33:51,0 days 00:06:10,2031.0 +DECREUS Thomas,PCA,IR4,1985,M0M,0,33,53,0 days 00:33:53,0 days 00:06:12,2033.0 +DUFIEF Bastien,H-F,IR4,1997,SEM,0,33,55,0 days 00:33:55,0 days 00:06:14,2035.0 +NEEL Valentin,BRE,IR4,2002,ESM,0,33,55,0 days 00:33:55,0 days 00:06:14,2035.0 +LAROUSSE Thomas,I-F,IR4,1997,SEM,0,33,55,0 days 00:33:55,0 days 00:06:14,2035.0 +MEUNIER Florian,H-F,IR4,1995,SEM,0,33,56,0 days 00:33:56,0 days 00:06:15,2036.0 +MARTINOT Geoffrey,NOR,IR4,1998,SEM,0,33,56,0 days 00:33:56,0 days 00:06:15,2036.0 +GUILLERY Gildas,G-E,IR4,1985,M0M,0,33,57,0 days 00:33:57,0 days 00:06:16,2037.0 +FRERE Michel,H-F,IR4,1990,SEM,0,33,58,0 days 00:33:58,0 days 00:06:17,2038.0 +DHABIT Clement,BRE,IR4,1985,M0M,0,33,58,0 days 00:33:58,0 days 00:06:17,2038.0 +DUMONT Sebastien,G-E,IR4,1995,SEM,0,33,58,0 days 00:33:58,0 days 00:06:17,2038.0 +CRETON Mathieu,G-E,IR4,2001,ESM,0,33,59,0 days 00:33:59,0 days 00:06:18,2039.0 +DELLOUX David,BFC,IR4,1983,M0M,0,33,59,0 days 00:33:59,0 days 00:06:18,2039.0 +BREUT Fred,I-F,IR4,1995,SEM,0,33,59,0 days 00:33:59,0 days 00:06:18,2039.0 +BERRA Maxime,PCA,IR4,1989,SEM,0,34,0,0 days 00:34:00,0 days 00:06:19,2040.0 +BASSOT Etienne,BRE,IR4,1988,SEM,0,34,0,0 days 00:34:00,0 days 00:06:19,2040.0 +FERRAZ Guillaume,BFC,IR4,1997,SEM,0,34,1,0 days 00:34:01,0 days 00:06:20,2041.0 +FRICKER Geoffroy,I-F,IR4,1987,M0M,0,34,2,0 days 00:34:02,0 days 00:06:21,2042.0 +BAYET Paul,I-F,IR4,1998,SEM,0,34,2,0 days 00:34:02,0 days 00:06:21,2042.0 +PLANET Adrien,I-F,IR4,1991,SEM,0,34,3,0 days 00:34:03,0 days 00:06:22,2043.0 +VITTAZ Remi,H-F,IR4,2002,ESM,0,34,4,0 days 00:34:04,0 days 00:06:23,2044.0 +CHARTIER Valentin,N-A,IR4,2000,ESM,0,34,5,0 days 00:34:05,0 days 00:06:24,2045.0 +DUCHESNE Remi,H-F,IR4,1999,SEM,0,34,5,0 days 00:34:05,0 days 00:06:24,2045.0 +QUENEHERVE Pierre-Yves,BFC,IR4,1994,SEM,0,34,6,0 days 00:34:06,0 days 00:06:25,2046.0 +ROY Emmanuel,P-L,IR4,1998,SEM,0,34,8,0 days 00:34:08,0 days 00:06:27,2048.0 +DEMANY Yannis,N-A,IR4,2001,ESM,0,34,8,0 days 00:34:08,0 days 00:06:27,2048.0 +HOFMANN Julien,G-E,IR4,1989,SEM,0,34,9,0 days 00:34:09,0 days 00:06:28,2049.0 +HAIMART Jean-Baptiste,BFC,IR4,1989,SEM,0,34,9,0 days 00:34:09,0 days 00:06:28,2049.0 +PORET Nicolas,ARA,IR4,1996,SEM,0,34,10,0 days 00:34:10,0 days 00:06:29,2050.0 +SOULEYMAN HAMIT Moussa ,I-F,IR4,1986,M0M,0,34,11,0 days 00:34:11,0 days 00:06:30,2051.0 +DELANNOY Frederic,I-F,IR4,1997,SEM,0,34,11,0 days 00:34:11,0 days 00:06:30,2051.0 +BUFFE Julien,CEN,IR4,1984,M0M,0,34,11,0 days 00:34:11,0 days 00:06:30,2051.0 +BARRAL David,NOR,IR4,1989,SEM,0,34,11,0 days 00:34:11,0 days 00:06:30,2051.0 +SICARD Alexandre,I-F,IR4,1987,M0M,0,34,11,0 days 00:34:11,0 days 00:06:30,2051.0 +ROCANIERES Hadrien,H-F,IR4,1998,SEM,0,34,11,0 days 00:34:11,0 days 00:06:30,2051.0 +COULAUD Edouard,H-F,IR4,1985,M0M,0,34,12,0 days 00:34:12,0 days 00:06:31,2052.0 +BOULANDE Antoine,PCA,IR4,2001,ESM,0,34,12,0 days 00:34:12,0 days 00:06:31,2052.0 +BAZZO BORTOT Symon,N-A,IR4,1992,SEM,0,34,12,0 days 00:34:12,0 days 00:06:31,2052.0 +ROBIN Aurelien,H-F,IR4,1998,SEM,0,34,15,0 days 00:34:15,0 days 00:06:34,2055.0 +LEVECQUE Vincent,H-F,IR4,1996,SEM,0,34,15,0 days 00:34:15,0 days 00:06:34,2055.0 +CHERON David,I-F,IR4,1984,M0M,0,34,15,0 days 00:34:15,0 days 00:06:34,2055.0 +CHRISTI LOUIS Frederic,H-F,IR4,1986,M0M,0,34,16,0 days 00:34:16,0 days 00:06:35,2056.0 +PINDY Melvin,H-F,IR4,1983,M0M,0,34,17,0 days 00:34:17,0 days 00:06:36,2057.0 +DURAND Franck,CEN,IR4,1995,SEM,0,34,17,0 days 00:34:17,0 days 00:06:36,2057.0 +VERGNAUD Johnny,I-F,IR4,1991,SEM,0,34,17,0 days 00:34:17,0 days 00:06:36,2057.0 +MODER Lucas,I-F,IR4,1983,M0M,0,34,17,0 days 00:34:17,0 days 00:06:36,2057.0 +VANTARD Florent,G-E,IR4,1997,SEM,0,34,17,0 days 00:34:17,0 days 00:06:36,2057.0 +VANSUYT Guillaume,H-F,IR4,1985,M0M,0,34,18,0 days 00:34:18,0 days 00:06:37,2058.0 +CUGNY Jerome,BFC,IR4,1992,SEM,0,34,18,0 days 00:34:18,0 days 00:06:37,2058.0 +BAROUX Nicolas,N-A,IR4,1996,SEM,0,34,18,0 days 00:34:18,0 days 00:06:37,2058.0 +ROSIAUX Lucas,I-F,IR4,1987,M0M,0,34,19,0 days 00:34:19,0 days 00:06:38,2059.0 +MAUROUARD Thibaut,H-F,R1,1985,M0M,0,34,19,0 days 00:34:19,0 days 00:06:38,2059.0 +LEMONNIER Guillaume,H-F,IR4,1999,SEM,0,34,19,0 days 00:34:19,0 days 00:06:38,2059.0 +EVRARD Gael,NOR,IR4,1997,SEM,0,34,21,0 days 00:34:21,0 days 00:06:40,2061.0 +MASSE Lucas,NOR,IR4,2002,ESM,0,34,21,0 days 00:34:21,0 days 00:06:40,2061.0 +DUEZ Cyriaque,NOR,R1,1988,SEM,0,34,22,0 days 00:34:22,0 days 00:06:41,2062.0 +MALLET Dimitri,H-F,R1,1987,M0M,0,34,22,0 days 00:34:22,0 days 00:06:41,2062.0 +BLASSET Steeven,H-F,IR4,1996,SEM,0,34,24,0 days 00:34:24,0 days 00:06:43,2064.0 +GUINDO Yacouba ,NOR,R1,1991,SEM,0,34,24,0 days 00:34:24,0 days 00:06:43,2064.0 +TARDY Victor,CEN,R1,2002,ESM,0,34,25,0 days 00:34:25,0 days 00:06:44,2065.0 +PLANCHOT Pierre-Emmanuel,CEN,R1,1998,SEM,0,34,26,0 days 00:34:26,0 days 00:06:45,2066.0 +BAZIN Arnaud,NOR,R1,1993,SEM,0,34,26,0 days 00:34:26,0 days 00:06:45,2066.0 +BEAUDOIN Antoine,NOR,R1,1994,SEM,0,34,27,0 days 00:34:27,0 days 00:06:46,2067.0 +PROVINS Corentin,G-E,R1,1984,M0M,0,34,27,0 days 00:34:27,0 days 00:06:46,2067.0 +REYNAERT Benoit,I-F,R1,2002,ESM,0,34,28,0 days 00:34:28,0 days 00:06:47,2068.0 +ZAJAC Killian,NOR,R1,2000,ESM,0,34,28,0 days 00:34:28,0 days 00:06:47,2068.0 +CARLIER Guillaume,H-F,R1,1983,M0M,0,34,30,0 days 00:34:30,0 days 00:06:49,2070.0 +TOUMLILT Ilyas,I-F,R1,1997,SEM,0,34,32,0 days 00:34:32,0 days 00:06:51,2072.0 +BORDON Dario,G-E,R1,1983,M0M,0,34,32,0 days 00:34:32,0 days 00:06:51,2072.0 +MARCHE Thibault,I-F,R1,1994,SEM,0,34,37,0 days 00:34:37,0 days 00:06:56,2077.0 +JALIER Quentin,H-F,R1,2001,ESM,0,34,38,0 days 00:34:38,0 days 00:06:57,2078.0 +KERVELLA Thibault,BFC,R1,2001,ESM,0,34,38,0 days 00:34:38,0 days 00:06:57,2078.0 +FASQUEL Florian,G-E,R1,1994,SEM,0,34,38,0 days 00:34:38,0 days 00:06:57,2078.0 +BRULE Mathis,BRE,R1,1997,SEM,0,34,39,0 days 00:34:39,0 days 00:06:58,2079.0 +MILLET Quentin,NOR,R1,1989,SEM,0,34,40,0 days 00:34:40,0 days 00:06:59,2080.0 +HURE Jonathan,I-F,R1,1999,SEM,0,34,40,0 days 00:34:40,0 days 00:06:59,2080.0 +BUREAU Emeric,P-L,R1,2001,ESM,0,34,40,0 days 00:34:40,0 days 00:06:59,2080.0 +THUVIEN Francois,NOR,R1,1985,M0M,0,34,41,0 days 00:34:41,0 days 00:07:00,2081.0 +LE COUSTUMER Philippe,P-L,R1,1996,SEM,0,34,41,0 days 00:34:41,0 days 00:07:00,2081.0 +LEGRAND Baptiste,NOR,R1,1983,M0M,0,34,42,0 days 00:34:42,0 days 00:07:01,2082.0 +RICHARD Maxime,I-F,R1,1986,M0M,0,34,42,0 days 00:34:42,0 days 00:07:01,2082.0 +PANLOU Maxime,H-F,R1,2001,ESM,0,34,43,0 days 00:34:43,0 days 00:07:02,2083.0 +OBRY Florian,I-F,R1,1986,M0M,0,34,43,0 days 00:34:43,0 days 00:07:02,2083.0 +MIQUET Cedric,PCA,R1,1983,M0M,0,34,43,0 days 00:34:43,0 days 00:07:02,2083.0 +PUJOLAS Benjamin,G-E,R1,1994,SEM,0,34,48,0 days 00:34:48,0 days 00:07:07,2088.0 +BRUN Eliott,PCA,R1,1987,M0M,0,34,49,0 days 00:34:49,0 days 00:07:08,2089.0 +MOUALEK Mustapha,H-F,R1,1996,SEM,0,34,49,0 days 00:34:49,0 days 00:07:08,2089.0 +LEFEVRE Dylan,I-F,R1,2001,ESM,0,34,51,0 days 00:34:51,0 days 00:07:10,2091.0 +SIMON Thibault,I-F,R1,1987,M0M,0,34,51,0 days 00:34:51,0 days 00:07:10,2091.0 +AIGUADEL-JALEME Yohann,BFC,R1,2000,ESM,0,34,54,0 days 00:34:54,0 days 00:07:13,2094.0 +GUILLEMIN Jeremy,CEN,R1,2001,ESM,0,34,56,0 days 00:34:56,0 days 00:07:15,2096.0 +ALVAREZ Charles,NOR,R1,1989,SEM,0,34,57,0 days 00:34:57,0 days 00:07:16,2097.0 +BLANPAIN Leopold,I-F,R1,1986,M0M,0,34,59,0 days 00:34:59,0 days 00:07:18,2099.0 +GESSENT Philippe,NOR,R1,2001,ESM,0,35,0,0 days 00:35:00,0 days 00:07:19,2100.0 +DARSAU CARRE Rodolphe,H-F,R1,2000,ESM,0,35,0,0 days 00:35:00,0 days 00:07:19,2100.0 +WATTEBLED Hugo,I-F,R1,1988,SEM,0,35,2,0 days 00:35:02,0 days 00:07:21,2102.0 +AZI Nicolas,I-F,R1,1984,M0M,0,35,3,0 days 00:35:03,0 days 00:07:22,2103.0 +BERTHE Sebastien,I-F,R1,1988,SEM,0,35,4,0 days 00:35:04,0 days 00:07:23,2104.0 +BEDEAU Yoan,I-F,R1,2002,ESM,0,35,4,0 days 00:35:04,0 days 00:07:23,2104.0 +ENGUEHARD Kiko,I-F,R1,2002,ESM,0,35,4,0 days 00:35:04,0 days 00:07:23,2104.0 +TAILLEUR Bryan,H-F,R1,1993,SEM,0,35,5,0 days 00:35:05,0 days 00:07:24,2105.0 +HELLEC Florian,BFC,R1,1992,SEM,0,35,8,0 days 00:35:08,0 days 00:07:27,2108.0 +APPERT Thibault,G-E,R1,1985,M0M,0,35,8,0 days 00:35:08,0 days 00:07:27,2108.0 +BOUDEAU Marcelin,I-F,R2,1997,SEM,0,35,9,0 days 00:35:09,0 days 00:07:28,2109.0 +LAOUR Jeremy,NOR,R2,2002,ESM,0,35,12,0 days 00:35:12,0 days 00:07:31,2112.0 +RECZKOWICZ Julien,N-A,R2,1992,SEM,0,35,12,0 days 00:35:12,0 days 00:07:31,2112.0 +BERTRAND Romain,H-F,R2,1984,M0M,0,35,14,0 days 00:35:14,0 days 00:07:33,2114.0 +CIPRO Arthur,G-E,R2,2000,ESM,0,35,16,0 days 00:35:16,0 days 00:07:35,2116.0 +CAROULLE Nicolas,N-A,R2,2001,ESM,0,35,19,0 days 00:35:19,0 days 00:07:38,2119.0 +BRUNET Timothee,ARA,R2,2001,ESM,0,35,25,0 days 00:35:25,0 days 00:07:44,2125.0 +VALOT Charles,H-F,R2,2001,ESM,0,35,27,0 days 00:35:27,0 days 00:07:46,2127.0 +JOLY Clement,N-A,R2,1989,SEM,0,35,32,0 days 00:35:32,0 days 00:07:51,2132.0 +VICHERAT Wilfried,H-F,R2,2000,ESM,0,35,35,0 days 00:35:35,0 days 00:07:54,2135.0 +LADEN Christopher,I-F,R2,2000,ESM,0,35,40,0 days 00:35:40,0 days 00:07:59,2140.0 +LEMOINE Loic,I-F,R2,1987,M0M,0,35,44,0 days 00:35:44,0 days 00:08:03,2144.0 +STEPHAN Yannick,H-F,R2,1984,M0M,0,35,46,0 days 00:35:46,0 days 00:08:05,2146.0 +CAVARD Thibault,NOR,R2,2001,ESM,0,35,47,0 days 00:35:47,0 days 00:08:06,2147.0 +LEGIEMBLE Anthony,BRE,R2,1992,SEM,0,35,48,0 days 00:35:48,0 days 00:08:07,2148.0 +SCHMITT Nicolas,ARA,R2,2001,ESM,0,35,51,0 days 00:35:51,0 days 00:08:10,2151.0 +LELONG Vincent,OCC,R2,1984,M0M,0,36,1,0 days 00:36:01,0 days 00:08:20,2161.0 +DENJEAN Fabien,I-F,R2,1999,SEM,0,36,2,0 days 00:36:02,0 days 00:08:21,2162.0 +GOUGEON Tom,NOR,R3,2001,ESM,0,36,7,0 days 00:36:07,0 days 00:08:26,2167.0 +CHAHLAOUI Karim,P-L,R3,1985,M0M,0,36,14,0 days 00:36:14,0 days 00:08:33,2174.0 +GINDA Leo,P-L,R3,2000,ESM,0,36,25,0 days 00:36:25,0 days 00:08:44,2185.0 +HOGEDE Cedric,I-F,R3,1996,SEM,0,36,26,0 days 00:36:26,0 days 00:08:45,2186.0 +SION Bastien,BFC,R3,2001,ESM,0,36,28,0 days 00:36:28,0 days 00:08:47,2188.0 +GALIANA Alexandre,H-F,R3,2002,ESM,0,36,49,0 days 00:36:49,0 days 00:09:08,2209.0 +WARUGURU Benson ,H-F,R3,2000,ESM,0,37,27,0 days 00:37:27,0 days 00:09:46,2247.0 +RYCKELYNCK Benjamin,N-A,R4,1987,M0M,0,39,58,0 days 00:39:58,0 days 00:12:17,2398.0 diff --git a/scraping.ipynb b/scraping.ipynb index 217f9e3..fb00dd6 100644 --- a/scraping.ipynb +++ b/scraping.ipynb @@ -54,7 +54,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: lxml in c:\\users\\trist\\anaconda3\\lib\\site-packages (4.6.3)\n" + "Requirement already satisfied: lxml in /opt/mamba/lib/python3.10/site-packages (4.9.2)\n" ] } ], @@ -622,7 +622,9 @@ "cell_type": "code", "execution_count": 22, "id": "79be518f-607e-4888-986f-34f9b265d5c4", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "re_perf = re.compile(\"[A-Z]{1,2}[1-8](?=<)|I[A,B](?=<)\")" @@ -1311,7 +1313,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 40, @@ -1320,14 +1322,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1462,7 +1456,9 @@ "cell_type": "code", "execution_count": 44, "id": "6ab0b1fb-daee-4f65-8680-faf5fa919ee2", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "#data=data.drop(\"Club\", axis=1)\n", diff --git a/scraping.py b/scraping.py index af49139..ae0e99c 100644 --- a/scraping.py +++ b/scraping.py @@ -1,6 +1,8 @@ import sys import os -import lxml + +os.system('pip install lxml') + import urllib import bs4 import pandas as pd @@ -8,8 +10,6 @@ import re from urllib import request -os.system('pip install lxml') - def read_input(): if len(sys.argv[1:])<=2: url=sys.argv[1:][0] @@ -134,7 +134,7 @@ def read_header(page): else: ligue.append(match.group()) -re_perf = re.compile("[A-Z]{1}[1-8](?=<)|I[A,B](?=<)") +re_perf = re.compile("[A-Z]{1,2}[1-8](?=<)|I[A,B](?=<)") perfs=[] for i in L: match= str(re_perf.findall(str(i))).replace('[','').replace(']','').replace('\'','')