print('Muestra de datos') print(df_articles.head()) # head() 5 Filas por defecto print('\nFormato del dataframe') print(df_articles.shape) print('\nBúsqueda de valores nulls por columna') print(df_articles.isnull().sum()) print('\nFormato de los datos por columna') print(df_articles.dtypes)

print('Muestra de datos') print(df_sellers.head()) print('\nFormato del dataframe') print(df_sellers.shape) print('\nBúsqueda de valores nulls por columna') print(df_sellers.isnull().sum()) print('\nFormato de los datos por columna') print(df_sellers.dtypes)

print('Muestra de datos') print(df_orders.head()) print('\nFormato del dataframe') print(df_orders.shape) print('\nBúsqueda de valores nulls por columna') print(df_orders.isnull().sum()) print('\nFormato de los datos por columna') print(df_orders.dtypes)

my_df = df_orders.copy() df_articles.set_index('article_id', inplace=True) my_df = my_df.assign(article_name = my_df['article_id']) my_df = my_df.assign(total_amount = my_df['article_id']) my_df = my_df.assign(seller_name = my_df['seller_id']) for i in range(max(my_df.count())): article = df_articles.loc[my_df.loc[i]['article_id']]['article_name'] # print(article) my_df.loc[i, 'article_name'] = article my_df.loc[i, 'total_amount'] = my_df.loc[i, 'quantity']*df_articles.loc[my_df.loc[i]['article_id']]['unit_price'] my_df.loc[i, 'seller_name'] = df_sellers.loc[my_df.loc[i]['seller_id']]['seller_name'] my_df.drop(['order_id', 'article_id', 'seller_id'], axis='columns', inplace=True) print(my_df)

print(my_df['article_name'].value_counts())

sns.displot(my_df, x='article_name', color = 'teal',kde=True) plt.xticks(rotation=90, fontsize=9, fontweight='bold', color='black') plt.xlabel("Artículo", size = 10) plt.ylabel("Unidades vendidas", size = 10) plt.title("Unidades vendidas por artículo", size = 14) plt.show()

df2=(my_df.groupby(by='article_name').sum()).sort_values('total_amount', ascending=False).head(5) pd.options.display.float_format= '$ {:,.2f}'.format print(df2['total_amount'])

c = ['darkturquoise','teal','lightseagreen','aquamarine','paleturquoise'] plt.pie(x=df2['total_amount'],radius=1.1, autopct='%1.2f%%', colors = c,labels=df2.index, startangle=90) plt.title("Top 5 artículos más vendidos ", size = 11) plt.show()

df4 = (my_df.groupby(by='seller_name').sum()).sort_values('total_amount', ascending=False) pd.options.display.float_format= '$ {:,.2f}'.format print(df4[['quantity'] + ['total_amount']].head(5))

plt.bar(df4.index, df4['total_amount'],color=['darkslategrey','teal','lightseagreen','aquamarine','paleturquoise']) plt.xticks(rotation=60) plt.xlabel("Vendedor", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Top 5 mejores vendedores", size = 12) plt.show()

df5 = (my_df.groupby(by='week').sum()).sort_values('total_amount', ascending=False) pd.options.display.float_format= '$ {:,.2f}'.format print(df5[['quantity'] + ['total_amount']])

plt.bar(df5.index, df5['total_amount'],color=['darkslategrey','teal','lightseagreen','aquamarine']) plt.xticks([1,2,3,4], ['1', '2', '3', '4'], rotation=0, fontsize=10, fontweight='bold') plt.xlabel("Semana", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Ventas por semana", size = 12) plt.show()

print("TOP 3 PAISES CON MAYORES VENTAS") df6 = (my_df.groupby(by='country_name').sum()).sort_values('total_amount', ascending=False).head(3) print(df6[['quantity'] + ['total_amount']]) df7 = my_df[(my_df['country_name'] == 'Brazil')] df7 = (df7.groupby(by='article_name').sum()).sort_values('total_amount', ascending=False).head(3) df8 = my_df[(my_df['country_name'] == 'Argentina')] df8 = (df8.groupby(by='article_name').sum()).sort_values('total_amount', ascending=False).head(3) df9 = my_df[(my_df['country_name'] == 'Colombia')] df9 = (df9.groupby(by='article_name').sum()).sort_values('total_amount', ascending=False).head(3) print() print("ARTICULOS CON MAYORES VENTAS - BRASIL") print(df7[['quantity'] + ['total_amount']]) print() print("ARTICULOS CON MAYORES VENTAS - ARGENTINA") print(df8[['quantity'] + ['total_amount']]) print() print("ARTICULOS CON MAYORES VENTAS - COLOMBIA") print(df9[['quantity'] + ['total_amount']])

df10 = (my_df[(my_df['country_name'] == 'Brazil')]).head(3) df11 = (my_df[(my_df['country_name'] == 'Argentina')]).head(3) df12 = (my_df[(my_df['country_name'] == 'Colombia')]).head(3) plt.bar(df6.index, df6['total_amount'],color=['darkslategrey','teal','aquamarine']) plt.xticks([0,1,2], ['Brasil', 'Argentina', 'Colombia'], fontsize=10) plt.xlabel("País", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Top 3 países con mayores ventas", size = 12) plt.show() c = ['violet','pink','blueviolet'] print() print("Top 3 artículos más vendidos por país") plt.subplot(1, 3, 1) plt.pie(x=df10['quantity'],radius=1.0, autopct='%1.2f%%', colors = c,labels=['Notebook','Smartphone','Full Pc'], startangle=90) plt.title("Brasil", size = 12) plt.subplot(1, 3, 2) plt.pie(x=df11['quantity'],radius=1.0, autopct='%1.2f%%', colors = c,labels=['Notebook','CPU','Full Pc'], startangle=90) plt.title("Argentina", size = 12) plt.subplot(1, 3, 3) plt.pie(x=df12['quantity'],radius=1.0, autopct='%1.2f%%', colors = c,labels=['Smartphone','Notebook','Full Pc'], startangle=90) plt.title("Colombia", size = 12) plt.show()

print("Correlación entre ingresos y cantidad de unidades vendidas - Full Pc") df7_pc = my_df[(my_df['article_name'] == 'Full Pc')|(my_df['country_name'] == 'Brazil')] df8_pc = my_df[(my_df['article_name'] == 'Full Pc')|(my_df['country_name'] == 'Argentina')] df9_pc = my_df[(my_df['article_name'] == 'Full Pc')|(my_df['country_name'] == 'Colombia')] plt.title("Full Pc - Ventas vs Cantidad - Brasil,Argentina,Colombia", size = 12) sns.scatterplot(y= 'quantity', x='total_amount', data = df7_pc) sns.scatterplot(y= 'quantity', x='total_amount', data = df8_pc) sns.scatterplot(y= 'quantity', x='total_amount', data = df9_pc)

print("Correlación entre ingresos y cantidad de unidades vendidas - Notebook") df7_nb = my_df[(my_df['article_name'] == 'Notebook')|(my_df['country_name'] == 'Brazil')] df8_nb = my_df[(my_df['article_name'] == 'Notebook')|(my_df['country_name'] == 'Argentina')] df9_nb = my_df[(my_df['article_name'] == 'Notebook')|(my_df['country_name'] == 'Colombia')] plt.title("Notebook - Ventas vs Cantidad venidas - Brasil,Argentina,Colombia", size = 12) sns.scatterplot(y= 'quantity', x='total_amount', data = df7_nb) sns.scatterplot(y= 'quantity', x='total_amount', data = df8_nb) sns.scatterplot(y= 'quantity', x='total_amount', data = df9_nb)

print("Correlación Brasil,Argentina, Colombia") df_3p = my_df[(my_df['country_name'] == 'Brazil') | (my_df['country_name'] == 'Argentina')| (my_df['country_name'] == 'Colombia')] sns.pairplot(df_3p, hue='country_name')

print("5 ARTICULOS MÁS VENDIDOS") df13 = (my_df.groupby(by='article_name').sum()).sort_values('total_amount', ascending=False).head(5) print(df13[['quantity'] + ['total_amount']]) print() print("FULL PC") df14 = my_df.loc[my_df['article_name'] == 'Full Pc'] df14 = (df14.groupby(by='week').sum()).sort_values('total_amount', ascending=False) print(df14) print() print("NOTEBOOK") df15 = my_df.loc[my_df['article_name'] == 'Notebook'] df15 = (df15.groupby(by='week').sum()).sort_values('total_amount', ascending=False) print(df15) print() print("SMARTPHONE") df16 = my_df.loc[my_df['article_name'] == 'Smartphone'] df16 = (df16.groupby(by='week').sum()).sort_values('total_amount', ascending=False) print(df16) print() print("CHAIR") df17 = my_df.loc[my_df['article_name'] == 'Chair'] df17 = (df17.groupby(by='week').sum()).sort_values('total_amount', ascending=False) print(df17) print() print("TABLET") df18 = my_df.loc[my_df['article_name'] == 'Tablet'] df18 = (df18.groupby(by='week').sum()).sort_values('total_amount', ascending=False) print(df18) print() print("FULL PC - ARGENTINA") df19 = my_df.loc[(my_df['article_name'] == 'Full Pc')&(my_df['country_name'] == 'Argentina')] df19 = (df19.groupby(by='week').sum()).sort_values('week', ascending=True) print(df19) print() print("FULL PC - PERÚ") df20 = my_df.loc[(my_df['article_name'] == 'Full Pc')&(my_df['country_name'] == 'Peru')] df20 = (df20.groupby(by='week').sum()).sort_values('week', ascending=True) print(df20) print() print("NOTEBOOK - ARGENTINA") df21 = my_df.loc[(my_df['article_name'] == 'Notebook')&(my_df['country_name'] == 'Argentina')] df21 = (df21.groupby(by='week').sum()).sort_values('week', ascending=True) print(df21) print() print("NOTEBOOK - PERÚ") df22 = my_df.loc[(my_df['article_name'] == 'Notebook')&(my_df['country_name'] == 'Peru')] df22 = (df22.groupby(by='week').sum()).sort_values('week', ascending=True) print(df22) print() print("SMARTPHONE - ARGENTINA") df23 = my_df.loc[(my_df['article_name'] == 'Smartphone')&(my_df['country_name'] == 'Argentina')] df23 = (df23.groupby(by='week').sum()).sort_values('week', ascending=True) print(df23) print() print("SMARTPHONE - PERÚ") df24 = my_df.loc[(my_df['article_name'] == 'Smartphone')&(my_df['country_name'] == 'Peru')] df24 = (df24.groupby(by='week').sum()).sort_values('week', ascending=True) print(df24)

plt.subplot(1, 3, 1) plt.bar(df14.index, df14['total_amount'], color=['darkslategrey','teal','lightseagreen','aquamarine']) plt.xticks([1,2,3,4],['1', '2', '3', '4'], rotation=0, fontsize=10) plt.xlabel("Semana", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Ventas de Full Pc por semana", size = 12) plt.subplot(1, 3, 3) plt.bar(df15.index, df15['total_amount'], color=['darkslategrey','teal','lightseagreen','aquamarine']) plt.xticks([1,2,3,4], ['1', '2', '3', '4'], rotation=0, fontsize=10) plt.xlabel("Semana", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Ventas de Notebook por semana", size = 12) plt.show() plt.subplot(1, 3, 1) plt.bar(df16.index, df16['total_amount'], color=['darkslategrey','teal','lightseagreen','aquamarine']) plt.xticks([1,2,3,4], ['1', '2', '3', '4'], rotation=0, fontsize=10) plt.xlabel("Semana", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Ventas de Smartphone por semana", size = 12) plt.subplot(1, 3, 3) plt.bar(df17.index, df17['total_amount'], color=['darkslategrey','teal','lightseagreen','aquamarine']) plt.xticks([1,2,3,4], ['1', '2', '3', '4'], rotation=0, fontsize=10) plt.xlabel("Semana", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Ventas de Chair por semana", size = 12) plt.show() plt.subplot(1, 3, 2) plt.bar(df18.index, df18['total_amount'], color=['darkslategrey','teal','lightseagreen','aquamarine']) plt.xticks([1,2,3,4], ['1', '2', '3', '4'], rotation=0, fontsize=10) plt.xlabel("Semana", size = 10) plt.ylabel("Monto de ventas($)", size = 10) plt.title("Ventas de Tablet por semana", size = 12) plt.show()

df_f1 = my_df.loc[(my_df['article_name'] == 'Full Pc')&(my_df['country_name'] == 'Brazil')] df_f2 = my_df.loc[(my_df['article_name'] == 'Full Pc')&(my_df['country_name'] == 'Argentina')] df_f3 = my_df.loc[(my_df['article_name'] == 'Notebook')&(my_df['country_name'] == 'Brazil')] df_f4 = my_df.loc[(my_df['article_name'] == 'Notebook')&(my_df['country_name'] == 'Argentina')] plt.title("Vendedor vs Semana - Full Pc y Notebook - Argentina y Brasil", size = 12) sns.scatterplot(y= 'seller_name', x='week', data = df_f1) sns.scatterplot(y= 'seller_name', x='week', data = df_f2) sns.scatterplot(y= 'seller_name', x='week', data = df_f3) sns.scatterplot(y= 'seller_name', x='week', data = df_f4)

print("5 Mejores vendedores") df25 = (my_df.groupby(by='seller_name').sum()).sort_values('total_amount', ascending=False).head(5) pd.options.display.float_format= '$ {:,.2f}'.format print(df25[['quantity'] + ['total_amount']]) print() print("TOP 3 MEJORES VENDEDORES BRASIL") df26 = my_df.loc[(my_df['country_name'] == 'Brazil')] df26 = (df26.groupby(by='seller_name').sum()).sort_values('total_amount', ascending=False).head(3) print(df26[['quantity'] + ['total_amount']]) print() print("TOP 3 MEJORES VENDEDORES ARGENTINA") df27 = my_df.loc[(my_df['country_name'] == 'Argentina')] df27 = (df27.groupby(by='seller_name').sum()).sort_values('total_amount', ascending=False).head(3) print(df27[['quantity'] + ['total_amount']])

plt.title("TOP 5 MEJORES VENDEDORES", size = 11) c = ['darkslategrey','teal','lightseagreen','aquamarine','paleturquoise'] e=(0.3,0,0,0,0) plt.pie(x=df25['total_amount'],radius=1.0,explode = e, autopct='%1.2f%%', colors = c,labels=df25.index, startangle=90) plt.show() plt.subplot(1, 3, 1) plt.title("Brasil", size = 12) c = ['violet','pink','blueviolet'] e=(0.2,0,0) plt.pie(x=df26['total_amount'],radius=1.2, explode = e, autopct='%1.2f%%', colors = c,labels=df26.index, startangle=90) plt.subplot(1, 3, 3) plt.title("Argentina", size = 12) c = ['violet','pink','blueviolet'] e=(0.2,0,0) plt.pie(x=df27['total_amount'],radius=1.2, explode = e, autopct='%1.2f%%', colors = c,labels=df27.index, startangle=90) plt.show()

df_f5 = my_df.loc[(my_df['country_name'] == 'Chile')] df_f6 = my_df.loc[(my_df['country_name'] == 'Mexico')] plt.title("Vendedores por semana - Chile, México", size = 12) sns.scatterplot(y= 'seller_name', x='week', data = df_f5) sns.scatterplot(y= 'seller_name', x='week', data = df_f6) sns.pairplot(df_f5) sns.pairplot(df_f6)