# imports import numpy as np import pandas as pd import sqlite3 as sql3 import matplotlib.pyplot as plt import seaborn as sns

# Levanto los datos en 3 diferentes dataframes # ARTÍCULOS conn = sql3.connect('/work/data/articles.db') sql_query = pd.read_sql_query('SELECT * FROM articles', conn) df_articles = pd.DataFrame(sql_query, columns=['article_id','article_name','unit_price']) print(df_articles)

# VENDEDORES df_sellers = pd.read_excel('/work/data/sellers.xlsx', index_col=0) print(df_sellers)

# ÓRDENES df_orders = pd.read_csv('/work/data/orders.csv') print(df_orders)

# Exploración del df de artículos print('Muestra de datos') print(df_articles.head()) print('\nFormato del dataframe') print(df_articles.shape) print('\nBúsqueda de valores nulos') print(df_articles.isnull().sum()) print('\nFormato de los datos') print(df_articles.dtypes)

# Exploración del df de vendedores print('Muestra de datos') print(df_sellers.head()) print('\nFormato del dataframe') print(df_sellers.shape) print('\nBúsqueda de valores nulos') print(df_sellers.isnull().sum()) print('\nFormato de los datos') print(df_sellers.dtypes)

# Exploración del df de órdenes print('Muestra de datos') print(df_orders.head()) print('\nFormato del dataframe') print(df_orders.shape) print('\nBúsqueda de valores nulos') print(df_orders.isnull().sum()) print('\nFormato de los datos') print(df_orders.dtypes)

df_articles['unit_price'] = df_articles['unit_price'].astype(float) print(df_articles.dtypes)

# Creo una copia del df_orders my_df = df_orders.copy() # Cambio el índice del df_articles df_articles.set_index('article_id', inplace=True) print(df_articles.head())

# agrego las columnas que me faltan 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']) print(my_df.head())

# reemplazar los datos ne las nuevas columnas for i in range(len(my_df.index)): # columna article_name # cargo el nombre del artículo en una variable article = df_articles.loc[my_df.loc[i]['article_name']]['article_name'] # se lo asigno a la columna y registro que corresponde my_df.loc[i,'article_name']= article # columna total_amount my_df.loc[i,'total_amount'] = my_df.loc[i,'quantity'] * df_articles.loc[my_df.loc[i]['article_id']]['unit_price'] # columna de seller_name my_df.loc[i,'seller_name'] = df_sellers.loc[my_df.loc[i]['seller_name']]['seller_name'] print(my_df.head())

# elimino las columnas que no necesito my_df.drop(['order_id', 'article_id','seller_id'], axis='columns', inplace=True) print(my_df.head())

# RESOLUCIÓN ANALÍTICA my_df2 = my_df.groupby('article_name').sum() por_cant = my_df2.sort_values('quantity', ascending=False) print(por_cant['quantity'].head(1))

# RESOLUCIÓN GRÁFICA sns.displot(my_df, x='article_name') plt.xticks(rotation=90, fontsize=8) plt.show()

# RESOLUCIÓN ANALÍTICA my_df3 = (my_df.groupby('article_name').sum()).sort_values('total_amount', ascending=False).head(5) print(my_df3['total_amount'])

# RESOLUCIÓN GRÁFICA plt.pie(x=my_df3['total_amount'],labels=my_df3.index) plt.show()

# RESOLUCIÓN ANALÍTICA df4 = (my_df.groupby('seller_name').sum()).sort_values('total_amount', ascending=False) print(df4[['quantity']+['total_amount']])

# RESOLUCIÓN GRÁFICA plt.bar(df4.index, df4['total_amount']) plt.xticks(rotation=60) plt.show()

# RESOLUCIÓN ANALÍTICA df5=(my_df.groupby('week').sum()).sort_values('total_amount',ascending=False) print(df5)

# RESOLUCIÓN GRÁFICA plt.bar(df5.index,df5['total_amount']) plt.show()

df6=my_df[((my_df['country_name']=='Argentina') | (my_df['country_name']=='Brazil') | (my_df['country_name']=='Peru') |(my_df['country_name']=='Colombia') ) & ((my_df['article_name']=='Full Pc')|(my_df['article_name']=='Notebook')|(my_df['article_name']=='Tablet') |(my_df['article_name']=='Smartphone')) ] df6 = df6.groupby(["article_name","country_name"]).sum().sort_values('quantity',ascending=False) print(df6[['quantity']]) df6.reset_index('article_name', inplace=True) sns.barplot(df6.index.get_level_values(0), 'quantity', data =df6, hue='article_name') rows = ['%d year' % x for x in (100, 50, 20, 10, 5)] values = np.arange(0, 2500, 500) value_increment = 1000 plt.title('Dispositivos', size=14, fontweight='bold') plt.ylabel('Unidades Vendidas',fontstyle='italic',fontsize=14, fontweight='bold') plt.xlabel('País',fontstyle='oblique',fontsize=14, fontweight='heavy') plt.show()

# RESOLUCIÓN df7 = (my_df.groupby(['seller_name','week']).sum()).sort_values('quantity', ascending=False).head(15) print(df7[['quantity']]) sns.displot(df7, x='seller_name') plt.xticks(rotation=90, fontsize=8) plt.ylabel('Semanas',fontstyle='italic',fontsize=14, fontweight='bold') plt.xlabel('Vendedores',fontstyle='oblique',fontsize=14, fontweight='heavy') plt.show()

df8 = (my_df.groupby('country_name').sum()).sort_values('total_amount', ascending=True).head(5) print(df8[['total_amount']])

df8=(my_df.groupby('country_name').sum()).sort_values('total_amount',ascending=True).head(5) print(df8[['quantity']+['total_amount']]) # RESOLUCIÓN GRÁFICA fig, graf5 = plt.subplots() graf5.bar(df8.index,df8['total_amount']) graf5.yaxis.set_tick_params(labelleft=False,labelright=True) graf5.xaxis.set_tick_params(rotation=60) plt.title('Paises con caidas de ventas', size=16, fontweight='bold') plt.ylabel('Total $ Vendido',fontstyle='italic',fontsize=14, fontweight='bold') plt.xlabel('País',fontstyle='oblique',fontsize=14, fontweight='heavy') plt.show()

ff = my_df[((my_df['article_name'] == 'Full Pc') |(my_df['article_name'] == 'Notebook')) & ((my_df['country_name'] == 'Argentina') | (my_df['country_name']=='Brazil'))] print(ff)

sns.barplot(x='country_name', y='quantity', hue='week', data=ff)

df40 = (my_df.groupby(['article_name','week']).sum()).sort_values('quantity', ascending=False) print(df40)

paises=df40.index.get_level_values(0) #df40.loc[('Brazil','Chair')] paises

my_df6=(my_df.groupby('country_name').sum()).sort_values('total_amount',ascending=False).head(5) print(my_df6[['quantity']+['total_amount']]) # RESOLUCIÓN GRÁFICA fig, graf5 = plt.subplots() graf5.bar(my_df6.index,my_df6['total_amount']) graf5.plot(my_df6['total_amount']) graf5.yaxis.set_tick_params(labelleft=False,labelright=True) graf5.xaxis.set_tick_params(rotation=30) sns.set_palette('coolwarm') plt.title('Top Mejores 5 Países en Ventas', size=18, fontweight='bold') plt.ylabel('Total $ Vendido',fontstyle='italic',fontsize=14, fontweight='bold') plt.xlabel('País',fontstyle='oblique',fontsize=14, fontweight='heavy') plt.show()

plt.pie(x=my_df3['total_amount'],labels=my_df3.index, startangle=90) plt.legend(my_df3['total_amount'],loc='upper left') centre_cicle = plt.Circle((0,0),0.4, fc='white') fig=plt.gcf() fig.gca().add_artist(centre_cicle) plt.title('Artículos que proporcionaron mayores ingresos') plt.show()

art2 = my_df[(my_df['article_name']== 'Notebook') | (my_df['article_name']== 'Full Pc') & ((my_df['country_name'] == 'Argentina') | (my_df['country_name']=='Brazil'))] print(art2)

my_df60=(ff.groupby('country_name').sum()).sort_values('total_amount',ascending=False) print(my_df60)

smarts = my_df[(my_df['article_name']== 'Smartphone')] sa=(smarts.groupby('country_name').sum()).sort_values('total_amount',ascending=False) print(sa)

# CONSULTA 13/7 # Graficando doble agrupamiento ff=my_df[((my_df['country_name']=='Argentina') | (my_df['country_name']=='Brazil')) & ((my_df['article_name']=='Full Pc')|(my_df['article_name']=='Notebook')) ] ff = ff.groupby(["article_name","country_name"]).sum().sort_values('total_amount',ascending=False) # para facilitar la gráfica, uno de los índices que me quedaron después del agrupamiento, lo puedo convertir a columna nuevamente ff.reset_index('article_name', inplace=True) print(ff) sns.barplot(ff.index.get_level_values(0), 'total_amount', data =ff, hue='article_name')

df9 = my_df.groupby(["country_name","article_name"]).sum() prod_cant = df9.sort_values("quantity", ascending=False) print(prod_cant) print(prod_cant.loc[('Peru','Mouse')]['quantity'])

# GRÁFICA BARRAS APILADAS my_df8 = my_df[(my_df['article_name'] == 'Full Pc') | (my_df['article_name'] == 'Notebook')| (my_df['article_name'] == 'Tablet')| (my_df['article_name'] == 'Smartphone')| (my_df['article_name'] == 'Chair')] print(my_df8) my_df8b=(my_df8.groupby(['seller_name','article_name']).sum()).sort_values('quantity',ascending=False) print(my_df8b)

my_df8b.reset_index(inplace=True) print(my_df8b)

s1= sns.barplot(x='seller_name',y='quantity',data=my_df8b) s2= sns.barplot(x='seller_name',y='quantity',data=my_df8b) plt.xticks(rotation=90) plt.show()

vendedores = ['vendedor A', 'vendedor B', 'vendedor C', 'vendedor D'] full_pc = [120,544,254,235] notebook = [45,55,45,221] indice = np.arange(len(vendedores)) plt.bar(indice, full_pc, label='Full PC') plt.bar(indice, notebook, label='Full PC') plt.xticks(indice, vendedores) plt.xlabel("Vendedor") plt.show()