tp2-3 maths + BDD

BDD/tp/BDDs2/tp5-6-7/Partie2.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import psycopg2 as psy
+import getpass
+import matplotlib.pyplot as plt
+
+data = pd. read_csv (r'vgsales.csv')
+df = pd.DataFrame(data)
+df = df.drop_duplicates()
+
+co = None
+
+try:
+    co = psy. connect(host='londres',
+                        database ='dbanperederi',
+                        user='anperederi',
+                        password = getpass.getpass("Mot de passe:"))
+
+    datafr = pd.read_sql('''SELECT ROUND(AVG(global_sales),2) as vente_moyennes, year 
+                            FROM VGSales 
+                            WHERE genre = 'Adventure' and year != 'NaN'
+                            GROUP BY year
+                            ORDER by year;''', con=co)
+    
+    fig = datafr.plot(x='year', y='vente_moyennes', kind='line', title='Evolution des ventes globales des jeux daventure', figsize=(10, 5), legend=True, fontsize=12)
+    plt.show()
+
+    datafr = pd.read_sql('''SELECT SUM(global_sales) as total_vente, year 
+                            FROM VGSales 
+                            WHERE genre = 'Adventure' AND year != 'NaN'
+                            GROUP BY year
+                            ORDER by year;''', con=co)
+    
+    fig = datafr.plot(x='year', y='total_vente', kind='line', title='Evolution des ventes globales des jeux', figsize=(10, 5), legend=True, fontsize=12)
+    plt.show()
+
+    datafr = pd.read_sql('''SELECT SUM(global_sales) as total_vente, year 
+                            FROM VGSales 
+                            WHERE year != 'NaN'
+                            GROUP BY year
+                            ORDER by year;''', con=co)
+    
+    fig = datafr.plot(x='year', y='total_vente', kind='line', title='Evolution des ventes globales des jeux', figsize=(10, 5), legend=True, fontsize=12)
+    plt.show()
+
+except (Exception , psy.DatabaseError ) as error :
+    print ( error )
+finally :
+    if co is not None:
+        co.close ()

BDD/tp/BDDs2/tp5-6-7/Partie3.py

@@ -0,0 +1,82 @@
+import pandas as pd
+import psycopg2 as psy
+import getpass
+import matplotlib.pyplot as plt
+
+data = pd. read_csv (r'vgsales.csv')
+df = pd.DataFrame(data)
+df = df.drop_duplicates()
+
+co = None
+
+try:
+    co = psy. connect(host='londres',
+                        database ='dbanperederi',
+                        user='anperederi',
+                        password = getpass.getpass("Mot de passe:"))
+
+#Q2 Afficher sous forme de courbe les résultats de la requête précédente. 
+    datafr = pd.read_sql('''SELECT platform, SUM(eu_sales) as total_vente
+                            FROM VGSales 
+                            GROUP BY platform;''', con=co)
+    
+    fig = datafr.plot(x='platform', y='total_vente', kind='line', title='Ventes par plateforme en europe', figsize=(10, 5), legend=True, fontsize=12)
+    fig.set_xlabel('Platforme')
+    fig.set_ylabel('Ventes (en millions)')
+    fig.set_xticks(datafr.index)
+    fig.set_xticklabels(datafr['platform'], rotation=45)
+    plt.show()
+
+#Q4
+    datafr = pd.read_sql('''SELECT platform, SUM(eu_sales) as total_vente
+                            FROM VGSales 
+                            GROUP BY platform;''', con=co)
+    
+    fig = datafr.plot(x='platform', y='total_vente', kind='bar', title='Ventes par plateforme en europe', figsize=(10, 5), legend=True, fontsize=12)
+    fig.set_xlabel('Platforme')
+    fig.set_ylabel('Ventes (en millions)')
+    fig.set_xticks(datafr.index)
+    fig.set_xticklabels(datafr['platform'], rotation=45)
+    plt.show()
+
+
+#Q5
+    datafr = pd.read_sql('''SELECT platform, SUM(na_sales) as total_vente_na, SUM(eu_sales) as total_vente_eu, SUM(jp_sales) as total_vente_jp, SUM(other_sales) as total_vente_autre
+                            FROM VGSales
+                            GROUP BY platform;''', con=co)
+    
+    fig = datafr.plot(x='platform', y=['total_vente_na', 'total_vente_eu', 'total_vente_jp', 'total_vente_autre'], kind='line', title='Ventes par plateforme', figsize=(10, 5), legend=True, fontsize=12)
+    fig.set_xlabel('Platforme')
+    fig.set_ylabel('Ventes (en millions)')
+    fig.set_xticks(datafr.index)
+    fig.set_xticklabels(datafr['platform'], rotation=45)
+    plt.show()
+#Q6
+    datafr = pd.read_sql('''SELECT platform, SUM(na_sales) as total_vente_na, SUM(eu_sales) as total_vente_eu, SUM(jp_sales) as total_vente_jp, SUM(other_sales) as total_vente_autre
+                            FROM VGSales
+                            GROUP BY platform;''', con=co)
+    
+    fig = datafr.plot(x='platform', y=['total_vente_na', 'total_vente_eu', 'total_vente_jp', 'total_vente_autre'], kind='line', title='Ventes par plateforme', figsize=(10, 5), legend=True, fontsize=12, style=['-', '--', '-.', ':'])
+    fig.set_xlabel('Platforme')
+    fig.set_ylabel('Ventes (en millions)')
+    fig.set_xticks(datafr.index)
+    fig.set_xticklabels(datafr['platform'], rotation=45)
+    plt.show()
+
+#Q7
+    datafr = pd.read_sql('''SELECT platform, SUM(na_sales) as total_vente_na, SUM(eu_sales) as total_vente_eu, SUM(jp_sales) as total_vente_jp, SUM(other_sales) as total_vente_autre
+                            FROM VGSales
+                            GROUP BY platform;''', con=co)
+    
+    fig = datafr.plot(x='platform', y=['total_vente_na', 'total_vente_eu', 'total_vente_jp', 'total_vente_autre'], kind='bar', title='Ventes par plateforme', figsize=(10, 5), legend=True, fontsize=12, style=['b', 'g', 'r', 'c'])
+    fig.set_xlabel('Platforme')
+    fig.set_ylabel('Ventes (en millions)')
+    fig.set_xticks(datafr.index)
+    fig.set_xticklabels(datafr['platform'], rotation=45)
+    plt.show()
+
+except (Exception , psy.DatabaseError ) as error :
+    print ( error )
+finally :
+    if co is not None:
+        co.close ()

BDD/tp/BDDs2/tp5-6-7/Partie4.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import psycopg2 as psy
+import getpass
+import matplotlib.pyplot as plt
+
+data = pd. read_csv (r'vgsales.csv')
+df = pd.DataFrame(data)
+df = df.drop_duplicates()
+
+co = None
+
+try:
+    co = psy. connect(host='londres',
+                        database ='dbanperederi',
+                        user='anperederi',
+                        password = getpass.getpass("Mot de passe:"))
+
+#Q2 Afficher sous forme de courbe les résultats de la requête précédente. 
+    datafr = pd.read_sql('''SELECT na_sales as total_vente_na, eu_sales as total_vente_eu, jp_sales as total_vente_jp, other_sales as total_vente_autre
+                            FROM VGSales
+                            WHERE name = 'Mario Kart 64';''', con=co)
+    
+    fig = datafr.transpose().plot(kind='bar', title='Ventes par plateforme', figsize=(10, 5), legend=True, fontsize=12)
+    fig.set_xticklabels(['NA', 'EU', 'JP', 'OTHER'], rotation=45)
+    plt.show()
+
+#Q3 Même question sous forme de diagramme “camembert”. Les noms des zones devront être à côté
+#de la tranche concernée.
+    datafr = pd.read_sql('''SELECT na_sales as total_vente_na, eu_sales as total_vente_eu, jp_sales as total_vente_jp, other_sales as total_vente_autre
+                            FROM VGSales
+                            WHERE name = 'Mario Kart 64';''', con=co)
+    
+    fig = datafr.transpose().plot(kind='pie', legend=False ,title='Ventes par plateforme', figsize=(10, 5), fontsize=12, subplots=True)
+    plt.show()
+
+#Q4 Même question mais le pourcentage de ventes dans chaque zone doit apparaître à côté de la
+#tranche concernée et le nom de la zone doit apparaître dans la légende
+    datafr = pd.read_sql('''SELECT na_sales as total_vente_na, eu_sales as total_vente_eu, jp_sales as total_vente_jp, other_sales as total_vente_autre
+                            FROM VGSales
+                            WHERE name = 'Mario Kart 64';''', con=co)
+    
+    fig = datafr.transpose().plot(kind='pie',title='Ventes par plateforme', figsize=(10, 5), fontsize=12, subplots=True, autopct='%1.1f%%', legend=False)
+    plt.show()
+    
+
+except (Exception , psy.DatabaseError ) as error :
+    print ( error )
+finally :
+    if co is not None:
+        co.close ()

BDD/tp/BDDs2/tp5-6-7/Partie5.py

@@ -0,0 +1,95 @@
+import pandas as pd
+import psycopg2 as psy
+import getpass
+import matplotlib.pyplot as plt
+
+data = pd. read_csv (r'vgsales.csv')
+df = pd.DataFrame(data)
+df = df.drop_duplicates()
+
+co = None
+
+try:
+    co = psy. connect(host='londres',
+                        database ='dbanperederi',
+                        user='anperederi',
+                        password = getpass.getpass("Mot de passe:"))
+
+#Q1 Écrire la requête SQL permettant de calculer le total des ventes de jeux dans le monde par genre.
+    datafr = pd.read_sql('''SELECT round(sum(global_sales),2) as total_vente, genre 
+                            FROM VGSales
+                            GROUP BY genre
+                            ORDER BY total_vente DESC;''', con=co)
+    
+    fig = datafr .plot.pie(y='total_vente', labels=datafr['genre'], autopct='%1.1f%%', figsize=(5, 5), legend=False) # Generation du graphique
+    plt.show () # Affichage
+
+#Q2 Écrire la requête SQL permettant de calculer e pourcentage des ventes dans le monde par genre.
+    datafr = pd.read_sql('''SELECT (sum(global_sales)/(SELECT sum(global_sales) FROM VGSales))*100 as pourcentage
+                            FROM VGSales
+                            GROUP BY genre
+                            ORDER BY pourcentage DESC;''', con=co)
+    
+    fig = datafr.plot(kind='pie', legend=False ,title='Ventes par plateforme', figsize=(5, 5), fontsize=12, subplots=True)
+    plt.show()
+    
+    # fig = datafr .plot.pie(y='pourcentage', labels=datafr['genre'], autopct='%1.1f%%', figsize=(5, 5), legend=False) # Generation du graphique
+    # plt.show () # Affichage
+
+#Q3 Afficher les résultats sous forme de diagramme camembert.
+    datafr = pd.read_sql('''SELECT (sum(global_sales)/(SELECT sum(global_sales) FROM VGSales))*100 as pourcentage 
+                            FROM VGSales
+                            GROUP BY genre
+                            ORDER BY pourcentage DESC;''', con=co)
+    
+    fig = datafr.plot(kind='pie', legend=False ,title='Ventes par plateforme', figsize=(5, 5), fontsize=12, subplots=True)
+    plt.show()
+    
+    # fig = datafr .plot.pie(y='pourcentage', labels=datafr['genre'], autopct='%1.1f%%', figsize=(5, 5), legend=False) # Generation du graphique
+    # plt.show () # Affichage
+    
+#Q4 Afficher le total des ventes par genre sous forme de diagrammes camembert de façon à voir sur la même figure :
+# un camembert représentant les ventes avant l’année 1990 (exclue),
+# un camembert représentant les ventes entre 1990 et 1999,
+# un camembert représentant les ventes entre 2000 et 2009,
+# un camembert représentant les ventes à partir de 2010.
+    datafr1 = pd.read_sql('''SELECT (sum(global_sales)/(SELECT sum(global_sales) FROM VGSales WHERE year < 1990))*100 as pourcentage
+                            FROM VGSales
+                            WHERE year < 1990
+                            GROUP BY genre
+                            ORDER BY pourcentage DESC;''', con=co)
+    
+    datafr2 = pd.read_sql('''SELECT (sum(global_sales)/(SELECT sum(global_sales) FROM VGSales WHERE year >= 1990 AND year < 2000))*100 as pourcentage
+                            FROM VGSales
+                            WHERE year >= 1990 AND year <= 1999
+                            GROUP BY genre
+                            ORDER BY pourcentage DESC;''', con=co)
+    
+    datafr3 = pd.read_sql('''SELECT (sum(global_sales)/(SELECT sum(global_sales) FROM VGSales WHERE year >= 2000 AND year < 2010))*100 as pourcentage
+                            FROM VGSales
+                            WHERE year >= 2000 AND year <= 2009
+                            GROUP BY genre
+                            ORDER BY pourcentage DESC;''', con=co)
+    
+    datafr4 = pd.read_sql('''SELECT (sum(global_sales)/(SELECT sum(global_sales) FROM VGSales WHERE year >= 2010))*100 as pourcentage
+                            FROM VGSales
+                            WHERE year >= 2010
+                            GROUP BY genre
+                            ORDER BY pourcentage DESC;''', con=co)
+    
+    _, axes = plt.subplots(nrows=2, ncols=2)
+    
+    fig = datafr1.plot(y=0, kind='pie', legend=False ,title='Ventes par plateforme', ax=axes[0,0])
+    fig = datafr2.plot(y=0, kind='pie', legend=False ,title='Ventes par plateforme', ax=axes[0,1])
+    fig = datafr3.plot(y=0, kind='pie', legend=False ,title='Ventes par plateforme', ax=axes[1,0])
+    fig = datafr4.plot(y=0, kind='pie', legend=False ,title='Ventes par plateforme', ax=axes[1,1])
+    plt.show()
+    
+    # fig = datafr .plot.pie(y='pourcentage', labels=datafr['genre'], autopct='%1.1f%%', figsize=(5, 5), legend=False) # Generation du graphique
+    # plt.show () # Affichage
+
+except (Exception , psy.DatabaseError ) as error :
+    print ( error )
+finally :
+    if co is not None:
+        co.close ()

BDD/tp/BDDs2/tp5-6-7/TP.py

@@ -0,0 +1,56 @@
+import pandas as pd
+import psycopg2 as psy
+import getpass
+
+data = pd. read_csv (r'vgsales.csv')
+df = pd.DataFrame(data)
+df = df.drop_duplicates()
+
+co = None
+
+try:
+    co = psy. connect(host='londres',
+                        database ='dbanperederi',
+                        user='anperederi',
+                        password = getpass.getpass("Mot de passe:"))
+
+    curs = co.cursor()
+
+    curs. execute ('''DROP TABLE IF EXISTS VGSales ;''')
+
+    curs. execute ('''CREATE TABLE VGSales (
+                    name varchar (160) ,
+                    platform varchar(160) ,
+                    year numeric (4),
+                    genre varchar (160) ,
+                    publisher varchar (160) ,
+                    na_sales numeric (5,2) DEFAULT 0,
+                    eu_sales numeric (5,2) DEFAULT 0,
+                    jp_sales numeric (5,2) DEFAULT 0,
+                    other_sales numeric (5,2) DEFAULT 0,
+                    global_sales numeric (5,2) DEFAULT 0 ,
+                    PRIMARY KEY (name, platform, year)
+                    );''')
+
+    for row in df.itertuples ():
+        curs. execute ('''INSERT INTO VGSales VALUES (%s ,%s ,%s ,%s, %s ,%s ,%s ,%s, %s ,%s);''',
+                        (row.Name , row.Platform , row.Year , row.Genre , row.Publisher , row.NA_Sales , row.EU_Sales , row.JP_Sales , row.Other_Sales , row.Global_Sales ))
+
+    #curs.execute('''UPDATE''')
+
+    co.commit ()
+    curs.close ()
+except (Exception , psy.DatabaseError ) as error :
+    print ( error )
+finally :
+    if co is not None:
+        co.close ()
+
+
+"""
+Evolution du script:
+
+Ajout de la fonction drop_duplicates() pour supprimer les doublons dans le fichier csv.
+
+"""
+

BDD/tp/BDDs2/tp5-6-7/dataframe.py

@@ -0,0 +1,38 @@
+import pandas as pd
+import psycopg2 as psy
+import getpass
+
+co = None
+
+try:
+    co = psy.connect (host='londres',
+                      database ='dbanperederi',
+                      user='anperederi',
+                      password = getpass.getpass ("Mot de passe:"))
+    df = pd.read_sql('''SELECT *
+                    FROM VGSales ;''', con=co)
+    print(df)
+except (Exception , psy. DatabaseError ) as error :
+    print ( error )
+finally :
+    if co is not None:
+        co. close ()
+
+#======================RESULTAT======================#
+"""
+                                                   name platform    year       genre  ... eu_sales  jp_sales  other_sales  global_sales
+0                                            Wii Sports      Wii  2006.0      Sports  ...    29.02      3.77         8.46         82.74
+1                                     Super Mario Bros.      NES  1985.0    Platform  ...     3.58      6.81         0.77         40.24
+2                                     Wii Sports Resort      Wii  2009.0      Sports  ...    11.01      3.28         2.96         33.00
+3                                                Tetris       GB  1989.0      Puzzle  ...     2.26      4.22         0.58         30.26
+4                                 New Super Mario Bros.       DS  2006.0    Platform  ...     9.23      6.50         2.90         30.01
+..                                                 ...      ...     ...         ...  ...      ...       ...          ...           ...
+16591                                    K-1 Grand Prix       PS  1999.0    Fighting  ...     0.01      0.00         0.00          0.02
+16592                           Carmageddon: Max Damage     XOne  2016.0      Action  ...     0.01      0.00         0.00          0.02
+16593                  The Ultimate Battle of the Sexes      Wii  2010.0        Misc  ...     0.01      0.00         0.00          0.02
+16594             Help Wanted: 50 Wacky Jobs (jp sales)      Wii  2008.0  Simulation  ...     0.01      0.01         0.00          0.02
+16595  SCORE International Baja 1000: The Official Game      PS2  2008.0      Racing  ...     0.00      0.00         0.00          0.00
+
+[16596 rows x 10 columns]
+
+"""

BDD/tp/BDDs2/tp5-6-7/requete.sql

@@ -0,0 +1,179 @@
+import matplotlib.pyplot as plt
+
+--Q5 La colonne Global_Sales est redondante car elle peut être calculée grâce aux autres colonnes.
+--Cela pose d’ailleurs problème puisque certaines données sont incohérentes. Écrire une requête SQL
+--permettant de détecter les lignes incohérentes 
+SELECT COUNT(global_sales) FROM VGSales WHERE global_sales != (na_sales + eu_sales + jp_sales + other_sales);
+/*
+count
+-------
+   4548
+*/
+
+
+--Q6 Écrire une requête SQL permettant de recalculer la colonne Global_sales en fonction des
+--autres colonnes pour ces lignes problématiques
+Update VGSales SET global_sales=(na_sales + eu_sales + jp_sales + other_sales) WHERE global_sales != (na_sales + eu_sales + jp_sales + other_sales);
+/*
+UPDATE 4548
+*/
+
+--2
+--Q1 Écrire la requête SQL permettant de calculer les ventes moyennes par année de sortie des jeux, pour les jeux du genre ’Adventure’
+SELECT ROUND(AVG(global_sales),2), year 
+FROM VGSales 
+WHERE genre = 'Adventure' and year != 'NaN'
+GROUP BY year
+ORDER by year;
+/*
+=========================RESULTAT=========================
+ round | year 
+-------+------
+  0.40 | 1983
+  4.38 | 1987
+  1.12 | 1991
+  3.06 | 1992
+  0.07 | 1993
+  0.94 | 1994
+  0.05 | 1995
+  0.25 | 1996
+  0.36 | 1997
+  0.39 | 1998
+  0.40 | 1999
+  0.19 | 2000
+  0.44 | 2001
+  0.26 | 2002
+  0.18 | 2003
+  0.22 | 2004
+  0.20 | 2005
+  0.16 | 2006
+  0.29 | 2007
+  0.15 | 2008
+  0.15 | 2009
+  0.11 | 2010
+  0.15 | 2011
+  0.10 | 2012
+  0.10 | 2013
+  0.08 | 2014
+  0.15 | 2015
+  0.05 | 2016
+(28 lignes)
+*/
+
+--Q2 Afficher le résultat de la requête précédente sous forme de courbe.
+
+--Q3 Même question pour le total des ventes par année de sortie des jeux
+SELECT SUM(global_sales) as total_vente, year 
+FROM VGSales 
+WHERE genre = 'Adventure' and year != 'NaN'
+GROUP BY year
+ORDER by year;
+/*
+=========================RESULTAT=========================
+ total_vente | year 
+-------------+------
+        0.40 | 1983
+        4.38 | 1987
+        2.24 | 1991
+       12.24 | 1992
+        0.07 | 1993
+        3.74 | 1994
+        0.71 | 1995
+        4.18 | 1996
+        4.97 | 1997
+        9.04 | 1998
+        7.61 | 1999
+        2.96 | 2000
+*/
+
+--Q4 Même question pour l’ensemble des jeux.
+SELECT SUM(global_sales) as total_vente, year 
+FROM VGSales 
+WHERE year != 'NaN'
+GROUP BY year
+ORDER by year;
+/*
+=========================RESULTAT=========================
+ total_vente | year 
+-------------+------
+       11.38 | 1980
+       35.33 | 1981
+       28.88 | 1982
+       16.80 | 1983
+       50.35 | 1984
+       53.49 | 1985
+       37.08 | 1986
+       21.70 | 1987
+       47.21 | 1988
+       73.45 | 1989
+       49.37 | 1990
+       32.23 | 1991
+*/
+
+--PARTIE3
+--Q1 Écrire la requête SQL permettant de calculer le total des ventes en Europe par plateforme.
+SELECT platform, SUM(eu_sales) as total_vente
+FROM VGSales 
+GROUP BY platform;
+/*
+=========================RESULTAT=========================
+  platform | total_vente 
+----------+-------------
+ TG16     |        0.00
+ PSP      |       67.83
+ 2600     |        5.47
+ PS4      |      123.70
+ N64      |       41.06
+ GBA      |       74.60
+ GG       |        0.00
+ PSV      |       16.33
+ XB       |       60.38
+ DC       |        1.69
+ XOne     |       44.77
+ PS2      |      339.11
+*/
+
+--Q4 Écrire la requête SQL permettant de calculer le total des ventes aux Etats-Unis, en Europe, 
+--au Japon et ailleurs par plateforme.
+SELECT platform, SUM(na_sales) as total_vente_na, SUM(eu_sales) as total_vente_eu, SUM(jp_sales) as total_vente_jp, SUM(other_sales) as total_vente_autre
+FROM VGSales
+GROUP BY platform;
+/*
+=========================RESULTAT=========================
+ platform | total_vente_na | total_vente_eu | total_vente_jp | total_vente_autre
+----------+----------------+----------------+----------------+-------------------
+ TG16     |           0.00 |           0.00 |           0.16 |              0.00
+ PSP      |         108.53 |          67.83 |          76.79 |             42.19
+ 2600     |          90.25 |           5.47 |           0.00 |              0.91
+ PS4      |          96.80 |         123.70 |          14.30 |             43.36
+ N64      |         137.82 |          41.06 |          34.22 |              4.38
+ GBA      |         187.54 |          74.60 |          47.33 |              7.73
+ GG       |           0.00 |           0.00 |           0.04 |              0.00
+ PSV      |          16.20 |          16.33 |          20.96 |              8.45
+ XB       |         186.69 |          60.38 |           1.38 |              8.72
+ DC       |           5.43 |           1.69 |           8.56 |              0.27
+ XOne     |          83.19 |          44.77 |           0.34 |             11.92
+ PS2      |         581.97 |         339.11 |         139.64 |            193.44
+*/
+
+--Q5 Afficher sous forme de graphique les résultats de la requête précédente avec une courbe par
+--zone géographique. Cette fois-ci la légende devra apparaître.
+
+--PARTIE4
+--Q1 Écrire la requête SQL de récupérer les ventes (Etats-unis, Europe, Japon, Autre) du jeu Mario Kart 64.
+SELECT SUM(na_sales) as total_vente_na, SUM(eu_sales) as total_vente_eu, SUM(jp_sales) as total_vente_jp, SUM(other_sales) as total_vente_autre
+FROM VGSales
+WHERE name = 'Mario Kart 64';
+/*
+=========================RESULTAT=========================
+ total_vente_na | total_vente_eu | total_vente_jp | total_vente_autre 
+----------------+----------------+----------------+-------------------
+           5.55 |           1.94 |           2.23 |              0.15
+*/
+
+--PARTIE5
+--Q1 Écrire la requête SQL permettant de calculer le total des ventes de jeux dans le monde par genre.
+SELECT round(sum(global_sales),2) as total_vente, genre 
+FROM VGSales
+GROUP BY genre
+ORDER BY total_vente DESC;

Maths/tp/Stats/tp3/Prix_Appartements.csv

@@ -0,0 +1,415 @@
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