# A veces necesitamos instalar nuevas librerías en nuestros proyectos
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# imports
import numpy as np
import pandas as pd
import sqlite3 as sql3
import openpyxl
import matplotlib.pyplot as plt
import seaborn as sns
# Levanto los datos en 3 diferentes dataframes
# Articulos
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'])
# Vendedores
df_sellers = pd.read_excel('/work/data/sellers.xlsx', index_col=0)
# Ordenes
df_orders = pd.read_csv('/work/data/orders.csv')
# Exploración del df de artículos
print("Muestra de datos")
print(df_articles.head()) # head() 5 Filas por defecto
print('\nFormato del dataframe')
print(df_articles.shape)
print('\nBusqueda de valores nulls por columna')
print(df_articles.isnull().sum())
print('\nFormato de los datos por columna')
print(df_articles.dtypes)
Muestra de datos
article_id article_name unit_price
0 20015 Smartphone 525.00
1 20016 Full Pc 2127.81
2 20017 Monitor 230.00
3 20018 Tablet 130.00
4 20019 Desk 130.10
Formato del dataframe
(31, 3)
Busqueda de valores nulls por columna
article_id 0
article_name 0
unit_price 0
dtype: int64
Formato de los datos por columna
article_id int64
article_name object
unit_price object
dtype: object
# Exploración del df de vendedores
print('Muestra de datos')
print(df_sellers.head()) # head() 5 Filas por defecto
print('\nFormato del dataframe')
print(df_sellers.shape)
print('\nBusqueda de valores nulls por columna')
print(df_sellers.isnull().sum())
print('\nFormato de los datos por columna')
print(df_sellers.dtypes)
Muestra de datos
seller_name
seller_id
1 Aveline Swanwick
2 Jase Doy
3 Oliviero Charkham
4 Cornie Wynrehame
5 Ewell Peres
Formato del dataframe
(15, 1)
Busqueda de valores nulls por columna
seller_name 0
dtype: int64
Formato de los datos por columna
seller_name object
dtype: object
# Exploración del df de órdenes
print('Muestra de datos')
print(df_orders.head()) # head() 5 Filas por defecto
print('\nFormato del dataframe')
print(df_orders.shape)
print('\nBusqueda de valores nulls por columna')
print(df_orders.isnull().sum())
print('\nFormato de los datos por columna')
print(df_orders.dtypes)
Muestra de datos
order_id week article_id quantity seller_id country_name
0 15024 1 20039 10 10 Peru
1 15025 1 20029 15 5 Peru
2 15026 1 20024 5 14 Bolivia
3 15027 1 20018 9 14 Brazil
4 15028 1 20035 6 15 Mexico
Formato del dataframe
(1000, 6)
Busqueda de valores nulls por columna
order_id 0
week 0
article_id 0
quantity 0
seller_id 0
country_name 0
dtype: int64
Formato de los datos por columna
order_id int64
week int64
article_id int64
quantity int64
seller_id int64
country_name object
dtype: object
df_articles['unit_price'] = df_articles['unit_price'].astype(float)
print(df_articles.dtypes)
article_id int64
article_name object
unit_price float64
dtype: object
# https://pandas.pydata.org/docs/user_guide/indexing.html
# https://towardsdatascience.com/how-to-use-loc-and-iloc-for-selecting-data-in-pandas-bd09cb4c3d79
# https://stackoverflow.com/questions/28754603/indexing-pandas-data-frames-integer-rows-named-columns
my_df = df_orders.copy() # Shallow copy
# Cambio el índice del df de artículos
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'])
# print(df_articles)
# print()
# my_df
for i in range(max(my_df.count())):
# print(i)
# SINTAXIS: df_articles.loc[indice][columna]
# [indice]: va a ser el dato que obtengo de [my_df.loc[i]['article_id']]
# o sea, tomo registro a registro el article_id y lo uso para extraer el nombre del artículo (article_name) de df_articles
# print(df_articles.loc[my_df[i]['article_id']]['article_name'])
article = df_articles.loc[my_df.loc[i]['article_id']]['article_name']
# print(article)
my_df.loc[i, 'article_name'] = article
# my_df
# hacemos lo mismo con 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_id']]['seller_name']
# elimino las columnas que no necesito del df
my_df.drop(['order_id', 'article_id', 'seller_id'], axis='columns', inplace=True)
print(my_df)
week quantity country_name article_name total_amount seller_name
0 1 10 Peru Water Cooling 675.0 Cirilo Grandham
1 1 15 Peru Mouse 454.5 Ewell Peres
2 1 5 Bolivia Netbook 725.0 Janel O'Curran
3 1 9 Brazil Tablet 1170.0 Janel O'Curran
4 1 6 Mexico Case 227.4 Daisie Slograve
.. ... ... ... ... ... ...
995 4 1 Brazil Modem 67.5 Kati Innot
996 4 15 Brazil Heatsink 150.0 Daisie Slograve
997 4 2 Colombia Heatsink 20.0 Vasily Danilyuk
998 4 14 Brazil Tablet 1820.0 Vasily Danilyuk
999 4 12 Brazil SDD 264.0 Onida Cosely
[1000 rows x 6 columns]
# my_df.loc[:, 'quantity']
# my_df.loc[0, ['quantity','country_name']]
# my_df.loc[0:5, ['quantity','country_name']]
# my_df.iloc[:,1]
# my_df.iloc[0,[1,2]]
my_df.iloc[0:5,[1,2]]
quantityint64
country_nameobject
0
10
Peru
1
15
Peru
2
5
Bolivia
3
9
Brazil
4
6
Mexico
# RESOLUCIÓN ANALÍTICA
# df7 = my_df.groupby(by='article_name').sum().sort_values('quantity', ascending=False)
# print(df7.head())
# print(df7[['quantity']].head())
# print()
# df7 = my_df.groupby(by='article_name').sum().sort_values('quantity', ascending=False).reset_index()
# print(df7.head())
# df7_2 = df7[['article_name', 'quantity']].groupby('article_name').sum('quantity').sort_values('quantity', ascending=False)
# print(df7_2.head())
# print()
# print(df7_2.head().index)
# print(df7_2.head().index[0])
# df7 = my_df.groupby(by='article_name')['quantity'].sum().sort_values(ascending=False).head()
# df7 = my_df.groupby(by='article_name')[['quantity'] + ['total_amount']].sum().sort_values('quantity',ascending=False).head()
# print(df7)
# print(df7.head().index)
# df7 = my_df.groupby(by='article_name')['quantity'].sum().sort_values(ascending=False)
# print(df7.iloc[0:5])
# df7 = my_df.groupby(by='article_name')['quantity'].sum().sort_values(ascending=False).reset_index()
# print(df7.iloc[0:5])
# df7 = my_df.groupby(by='article_name')['quantity'].sum().sort_values(ascending=False)
# df7 = my_df.groupby(by='article_name').sum().sort_values('quantity',ascending=False)
# print(df7)
# print(df7.loc['HDD':'Netbook'])
# print(df7.loc['HDD':'Netbook']['quantity'])
# df7 = my_df.groupby(by='article_name').agg({'quantity':'sum'}).sort_values('quantity', ascending=False)
# print(df7.head())
# df7 = my_df.groupby(by='article_name').agg({'quantity':'sum'}).sort_values('quantity', ascending=False).rename(columns={'quantity':'Cantidad'}).sort_values('Cantidad', ascending=False)
# print(df7.head())
# df7 = my_df.groupby(by='article_name').agg({'quantity':'sum'}).sort_values('quantity', ascending=False).reset_index().rename(columns={'quantity':'Cantidad'}).sort_values('Cantidad', ascending=False)
# print(df7.head())
# df7 = my_df.groupby(by='article_name').sum().sort_values('quantity', ascending=False).head()
df7 = my_df.groupby(by='article_name').sum().sort_values('quantity', ascending=False)
pd.options.display.float_format= '$ {:,.2f}'.format
# print(df7[['quantity']].head())
print(df7[['quantity','total_amount']].head())
# print(df7[['quantity','total_amount']])
quantity total_amount
article_name
HDD 413 $ 22,558.06
Tablet 374 $ 48,620.00
SDD 372 $ 8,184.00
Mouse 322 $ 9,756.60
Netbook 320 $ 46,400.00
# RESOLUCIÓN GRÁFICA
# Opción 1 - Count
# sns.countplot(my_df, x='article_name')
# Opción 2 - barplot
sns.barplot(data=df7, x=df7.index, y='quantity')
# Opción 3 - barplot
# xs = ["HDD", "Tablet", "SDD", "Mouse", "Netbook"]
# ys = [413, 374, 372, 322, 320]
# sns.barplot(x=xs, y=ys)
plt.xticks(rotation=90) # 'vertical'
plt.show()
# RESOLUCIÓN ANALÍTICA
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'])
# print(df2)
# print()
# print(df2.index[0])
# print()
# print(df2.reset_index().iloc[0])
# print()
# for i in range(5):
# # print(df2.index[i])
# print(df2.reset_index().iloc[i])
# print()
# print()
# print(df2.reset_index())
article_name
Full Pc $ 538,335.93
Notebook $ 251,000.00
Smartphone $ 152,250.00
Chair $ 69,477.48
Tablet $ 48,620.00
Name: total_amount, dtype: float64
# RESOLUCIÓN GRÁFICA
plt.pie(x=df2['total_amount'], labels=df2.index, autopct='%1.2f%%')
plt.show()
# RESOLUCIÓN ANALÍTICA
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))
quantity total_amount
seller_name
Janel O'Curran 703 $ 192,832.47
Brockie Patience 441 $ 142,709.88
Oliviero Charkham 555 $ 141,329.76
Vasily Danilyuk 521 $ 129,157.55
Daisie Slograve 554 $ 120,520.11
# RESOLUCIÓN GRÁFICA
plt.bar(df4.index, df4['total_amount'])
plt.xticks(rotation=90)
plt.show()
# RESOLUCIÓN ANALÍTICA
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']])
quantity total_amount
week
1 2449 $ 507,458.81
2 2444 $ 415,364.44
3 2114 $ 329,140.03
4 1058 $ 223,844.56
# RESOLUCIÓN GRÁFICA
plt.bar(df5.index, df5['total_amount'])
plt.show()
# 'week' es el index
# RESOLUCIÓN ANALITICA
my_df5 = (my_df.groupby(by='country_name').sum()).sort_values('total_amount',ascending=False).head(7)
print(my_df5['total_amount'])
country_name
Brazil $ 441,271.85
Argentina $ 205,832.78
Colombia $ 177,514.29
Peru $ 161,421.12
Mexico $ 138,619.99
Venezuela $ 77,684.52
El Salvador $ 57,391.26
Name: total_amount, dtype: float64
# RESOLUCIÓN GRAFICA
sns.barplot(y=my_df5['total_amount'], x=my_df5.index, palette='flare')
plt.suptitle('TOP 10 Paises Mayores Compras Totales',fontsize='x-large')
for pos in ['right', 'top', 'bottom', 'left']:
plt.gca().spines[pos].set_visible(False)
ingresos = my_df5['total_amount'].values
for i, ingreso in enumerate(ingresos):
v = '$ ' + str(int(ingreso))
plt.text(s=v, x=i, y=my_df5['total_amount'][i] + 15000, ha='center')
plt.xlabel('')
plt.yticks([])
plt.ylabel(ylabel='')
plt.show()
# RESOLUCIÓN ANÁLITICA
# Obtener Monto total desglosado por Pais
df_pais_articulo = my_df.groupby(['country_name', 'article_name']).sum()
# Obtener la lista de paises
paises = my_df.groupby('country_name').sum().index.values
# Seteo la estructura del DF
my_df6 = pd.DataFrame(columns=['ARTICULO', 'MONTO'], index=paises)
# Calcular los articulos mas vendidos por pais y guardarlas en df
for p in paises:
df = df_pais_articulo.loc[p]
v = df.max()['total_amount']
a = df[(df['total_amount'] == v)].index[0]
my_df6.loc[p] = {'ARTICULO': a, 'MONTO': v}
my_df6 = my_df6.sort_values(['ARTICULO', 'MONTO'])
my_df6.reset_index(inplace=True, drop=False)
my_df6.columns = ['PAIS','ARTICULO', 'MONTO']
print(my_df6)
PAIS ARTICULO MONTO
0 Puerto Rico CPU $ 1,256.58
1 Uruguay Full Pc $ 8,511.24
2 Bolivia Full Pc $ 10,639.05
3 Costa Rica Full Pc $ 23,405.91
4 Guatemala Full Pc $ 27,661.53
5 El Salvador Full Pc $ 40,428.39
6 Venezuela Full Pc $ 44,684.01
7 Peru Full Pc $ 51,067.44
8 Colombia Full Pc $ 72,345.54
9 Argentina Full Pc $ 95,751.45
10 Brazil Full Pc $ 134,052.03
11 Paraguay Monitor $ 4,370.00
12 Honduras Notebook $ 14,000.00
13 Mexico Notebook $ 44,000.00
14 Chile Smartphone $ 6,300.00
15 Ecuador Smartphone $ 7,875.00
# RESOLUCIÓN GRÁFICA
c = sns.color_palette('rocket', n_colors=my_df6.nunique(0)[1])
df_pivot = pd.pivot_table(my_df6, index='PAIS', columns='ARTICULO', values='MONTO', aggfunc='sum')
df_pivot = df_pivot.sort_values(['CPU','Full Pc', 'Monitor', 'Notebook','Smartphone'], ascending=False)
df_pivot.plot.barh(stacked=True, color=c, width=0.8)
plt.suptitle(t='PRODUCTO QUE MAS SE VENDE EN CADA PAIS Y SU MONTO TOTAL', y=1.4, fontsize='x-large')
for pos in ['right', 'top', 'bottom', 'left']:
plt.gca().spines[pos].set_visible(False)
ingresos = my_df6.values
for ingreso in ingresos:
v = '$ ' + str(int(ingreso[2]))
i = df_pivot.index.values
plt.text(s=v, x=ingreso[2] + 1000, y=np.where(i==ingreso[0])[0][0], va='center')
plt.xlabel('')
plt.xticks([])
plt.ylabel(ylabel='')
plt.show()
# RESOLUCIÓN ANÁLITICA
#Filtro del País que quiero analizar, Brasil
filter_brasil=my_df['country_name']=='Brazil'
brasil_df = my_df[filter_brasil]
#Busco los articulos más vendidos correspondientes al país en cuestión
brasil_agrupado_df=brasil_df.groupby("article_name").sum().sort_values('total_amount',ascending=False).drop(['week'], axis=1).head(10)
print (brasil_agrupado_df)
quantity total_amount
article_name
Full Pc 63 $ 134,052.03
Notebook 83 $ 83,000.00
Smartphone 79 $ 41,475.00
Chair 112 $ 37,591.68
Tablet 156 $ 20,280.00
Scanner 78 $ 14,430.00
Motherboard 101 $ 13,976.38
Desk 97 $ 12,619.70
Netbook 81 $ 11,745.00
CPU 82 $ 11,448.84
# RESOLUCIÓN GRÁFICA
colors = sns.color_palette('Set3')[0:9]
plt.pie(brasil_agrupado_df['total_amount'], labels = brasil_agrupado_df.index, colors = colors, autopct='%.0f%%')
plt.title('Desglose de Ventas Totales en el Brasil')
plt.show()
df6 = my_df[my_df['country_name'] == 'Brazil']
#A partir del anterior, ordenar vendedores por total de ventas
vendedores_pais = df6.groupby('seller_name').sum().sort_values('total_amount', ascending=False)
vendedores_pais.head()
weekint64
quantityint64
Daisie Slograve
60
226
Aveline Swanwick
66
227
Arnold Kilkenny
47
184
Kati Innot
52
151
Tobin Roselli
54
211
plt.bar(vendedores_pais.index,vendedores_pais['total_amount'],
color = ['green' if x in vendedores_pais.index[0:5] else 'blue' for x in vendedores_pais.index]) #Color verde para los mejores 5
plt.xticks(rotation = 90)
plt.title("Desempeño vendedores del país que más compra")
plt.xlabel("Vendedores")
plt.ylabel("Total de Ventas")
plt.show()
df_brazil = my_df[my_df['country_name'] == 'Brazil']
print(df_brazil[['seller_name','total_amount', 'quantity']].groupby('seller_name').sum().sort_values('total_amount', ascending=False))
print()
df_brazil_bars = df_brazil[['seller_name','total_amount']].groupby('seller_name').sum('total_amount').sort_values('total_amount', ascending=False)
print(df_brazil_bars)
print()
df_brazil_line = df_brazil[['seller_name','quantity']].groupby('seller_name').sum('quantity').sort_values('quantity', ascending=False).reset_index()
print(df_brazil_line)
fig, ax1 = plt.subplots()
# ax1: axes1
# sns.barplot(data = df_brazil_bars, x='seller_name', y='total_amount', ax=ax1, color='C3')
sns.barplot(data = df_brazil_bars, x = df_brazil_bars.index, y='total_amount', ax=ax1, color='C3')
# ax: axes
plt.xticks(rotation=90)
ax2 = ax1.twinx()
# ax2: axes2 = ax1.twinx()
sns.lineplot(data = df_brazil_line, x='seller_name', y='quantity', ax=ax2, color='C11')
plt.xticks(rotation=90)
plt.title('Top sellers in Brazil')
ax1.set_xlabel("Seller Name")
ax1.set_ylabel("Income ($)")
ax2.set_ylabel("Quantity (line)")
plt.show()
total_amount quantity
seller_name
Daisie Slograve $ 65,283.28 226
Aveline Swanwick $ 58,771.48 227
Arnold Kilkenny $ 47,243.54 184
Kati Innot $ 32,087.32 151
Tobin Roselli $ 31,997.93 211
Janel O'Curran $ 31,562.86 182
Onida Cosely $ 29,354.31 206
Jase Doy $ 28,493.25 149
Vasily Danilyuk $ 27,495.77 124
Milly Christoffe $ 21,247.38 161
Cirilo Grandham $ 21,061.74 158
Ewell Peres $ 15,253.22 91
Oliviero Charkham $ 14,951.72 195
Brockie Patience $ 10,756.96 125
Cornie Wynrehame $ 5,711.09 125
total_amount
seller_name
Daisie Slograve $ 65,283.28
Aveline Swanwick $ 58,771.48
Arnold Kilkenny $ 47,243.54
Kati Innot $ 32,087.32
Tobin Roselli $ 31,997.93
Janel O'Curran $ 31,562.86
Onida Cosely $ 29,354.31
Jase Doy $ 28,493.25
Vasily Danilyuk $ 27,495.77
Milly Christoffe $ 21,247.38
Cirilo Grandham $ 21,061.74
Ewell Peres $ 15,253.22
Oliviero Charkham $ 14,951.72
Brockie Patience $ 10,756.96
Cornie Wynrehame $ 5,711.09
seller_name quantity
0 Aveline Swanwick 227
1 Daisie Slograve 226
2 Tobin Roselli 211
3 Onida Cosely 206
4 Oliviero Charkham 195
5 Arnold Kilkenny 184
6 Janel O'Curran 182
7 Milly Christoffe 161
8 Cirilo Grandham 158
9 Kati Innot 151
10 Jase Doy 149
11 Brockie Patience 125
12 Cornie Wynrehame 125
13 Vasily Danilyuk 124
14 Ewell Peres 91