# A veces necesitamos instalar nuevas librerías en nuestros proyectos
!pip install openpyxl==3.0.10
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WARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.
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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('\nBúsqueda de valores null 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)
Búsqueda de valores null 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('\nBúsqueda de valores null 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)
Búsqueda de valores null 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('\nBúsqueda de valores null 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)
Búsqueda de valores null 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 indice 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 (tabla)
# print(df_articles.loc[my_df.loc[i ,'article_id']]['article_name'])
article = df_articles.loc[my_df.loc[i ,'article_id']]['article_name']
# print(article)
# Asignar a cada valor id de la columna 'article_name' de my_df, el nombre del artículo
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']
#my_df
# 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 de my_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[:, ['quantity','country_name']]
# my_df.loc[0:5, ['quantity','country_name']]
# my_df.iloc[:, 1]
# my_df.iloc[:, [1,2]]
# my_df.iloc[0:6, [1,2]]
my_df.iloc[0:5, [1,2]]
0
10
Peru
1
15
Peru
2
5
Bolivia
3
9
Brazil
4
6
Mexico
5 rows, showing
per page
Page of 1
# RESOLUCIÓN ANALÍTICA
# Si no filtro por 'quantity' toma las series numéricas
# df7 = my_df.groupby(by='article_name').sum().sort_values('quantity', ascending=False)
# print(df7.head())
# print(df7[['quantity']].head())
# print()
# Tomamos article_name como una columna más
# 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.index[0]) # toma correctamente el index
# Vista tipo Serie
# 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)
# Vista tipo Serie
# df7 = my_df.groupby(by='article_name')['quantity'].sum().sort_values(ascending=False)
# print(df7.iloc[0:5])
# Vista tipo Serie (reset index)
# df7 = my_df.groupby(by='article_name')['quantity'].sum().sort_values(ascending=False).reset_index()
# print(df7.iloc[0:5])
# Vista tipo Serie
# 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.loc['HDD':'Netbook'])
# print(df7.loc['HDD':'Netbook']['quantity'])
# print(df7.head())
# 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'}).rename(columns={'quantity':'Cantidad'}).sort_values('Cantidad',ascending=False)
# print(df7.head())
# df7 = my_df.groupby(by='article_name').agg({'quantity':'sum'}).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()
pd.options.display.float_format= '$ {:,.2f}'.format
# print(df7[['quantity']].head())
# print(df7[['quantity', 'total_amount']].head(5))
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 - 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'])
# 'article_name' es el index
# 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()
paises = pd.unique(my_df["country_name"])
#print(paises)
vendedores = pd.unique(my_df["seller_name"])
#print(vendedores)
productos = np.sort(pd.unique(my_df["article_name"]))
print(productos)
['CPU' 'Case' 'Chair' 'Desk' 'Fan Cooler' 'Full Pc' 'HDD' 'Headphones'
'Heatsink' 'Keyboard' 'Mesh Wi-Fi X 2' 'Modem' 'Monitor' 'Motherboard'
'Mouse' 'Netbook' 'Notebook' 'Pci Express Port' 'Power Supply'
'Ram Memory' 'Range Extender' 'SDD' 'Sata Cable' 'Scanner' 'Smartphone'
'Tablet' 'Usb Cable' 'Video Card' 'Water Cooling' 'Webcam' 'Wi-Fi Card']
# RESOLUCIÓN
# Ventas filtrado por país. Comparar 2 países
# Cuál es el producto que más se vende, en qué cantidad
# Resolución Analítica y Gráfica
Pais_1
Pais_2
¿Cuál es el producto que más se vende, en qué cantidad?
#!pip install colorama==0.4.6
dfPreg5 = my_df[(my_df['country_name'] == Pais_1) | (my_df['country_name'] == Pais_2)]
dfPreg5_1 = dfPreg5.groupby(by=['article_name', 'country_name']).sum().sort_values('quantity', ascending=False).reset_index()
dfPreg5Pais1 = dfPreg5_1[dfPreg5_1['country_name'] == Pais_1].sort_values('quantity', ascending=False)
dfPreg5Pais2 = dfPreg5_1[dfPreg5_1['country_name'] == Pais_2].sort_values('quantity', ascending=False)
umbralPais1 = dfPreg5Pais1.head().iloc[4]['quantity']
umbralPais2 = dfPreg5Pais2.head().iloc[4]['quantity']
#import colorama
#colorama.init()
print(f'\nCantidad de ventas por producto')
print(f'{"Producto" :<20}{Pais_1 :^10}{Pais_2 :^10}')
for art in productos:
if art in dfPreg5Pais1['article_name'].values:
cantidadPais1 = dfPreg5Pais1[dfPreg5Pais1['article_name'] == art]['quantity'].iloc[0]
else:
cantidadPais1 = 0
if art in dfPreg5Pais2['article_name'].values:
cantidadPais2 = dfPreg5Pais2[dfPreg5Pais2['article_name'] == art]['quantity'].iloc[0]
else:
cantidadPais2 = 0
if (cantidadPais1 >= umbralPais1) & (cantidadPais2 >= umbralPais2):
print(f'{art :<20} {cantidadPais1 :>10} {cantidadPais2 :>10}')
elif cantidadPais1 >= umbralPais1:
print(f'{art :<20} {cantidadPais1 :>10}{cantidadPais2 :>10}')
elif cantidadPais2 >= umbralPais2:
print(f'{art :<20}{cantidadPais1 :>10} {cantidadPais2 :>10}')
else:
print(f'{art :<20}{cantidadPais1 :>10} {cantidadPais2 :>10}')
Cantidad de ventas por producto
Producto Peru Bolivia
CPU 16 0
Case 19 0
Chair 26 0
Desk 28 16
Fan Cooler 39 0
Full Pc 24 5
HDD 56 0
Headphones 17 0
Heatsink 59 0
Keyboard 7 13
Mesh Wi-Fi X 2 4 12
Modem 42 0
Monitor 54 0
Motherboard 13 19
Mouse 125 0
Netbook 57 7
Notebook 32 0
Pci Express Port 18 34
Power Supply 13 0
Ram Memory 36 28
Range Extender 39 0
SDD 34 0
Sata Cable 62 0
Scanner 21 15
Smartphone 15 0
Tablet 52 0
Usb Cable 35 21
Video Card 29 11
Water Cooling 23 0
Webcam 5 0
Wi-Fi Card 27 0
top5ProductosPais1 = dfPreg5Pais1.sort_values('quantity', ascending=False).head()["article_name"]
top5ProductosPais2 = dfPreg5Pais2.sort_values('quantity', ascending=False).head()["article_name"]
top5ProductosPais = pd.unique(top5ProductosPais1.append(top5ProductosPais2))
#print(top5ProductosPais1)
#print(top5ProductosPais2)
#print(top5ProductosPais)
print(f'\nCantidad de ventas por producto')
print(f'{"Producto" :<20}{Pais_1 :^10}{Pais_2 :^10}')
for art in top5ProductosPais:
if art in dfPreg5Pais1['article_name'].values:
cantidadPais1 = dfPreg5Pais1[dfPreg5Pais1['article_name'] == art]['quantity'].iloc[0]
else:
cantidadPais1 = "-"
if art in dfPreg5Pais2['article_name'].values:
cantidadPais2 = dfPreg5Pais2[dfPreg5Pais2['article_name'] == art]['quantity'].iloc[0]
else:
cantidadPais2 = "-"
print(f'{art :<20}{cantidadPais1 :>10} {cantidadPais2 :>10}')
Cantidad de ventas por producto
Producto Peru Bolivia
Mouse 125 -
Sata Cable 62 -
Heatsink 59 -
Netbook 57 7
HDD 56 -
Pci Express Port 18 34
Ram Memory 36 28
Usb Cable 35 21
Motherboard 13 19
Desk 28 16
figP5 = sns.catplot(data= dfPreg5_1, x='article_name', y = 'quantity', kind= 'bar', hue= 'country_name',
width=1, aspect=1.5, legend_out=False)
sns.move_legend(figP5,'upper right', title='País')
figP5.set_xticklabels(
rotation=90,
horizontalalignment='center'
)
# RESOLUCIÓN
# Evolución de Ventas por producto.
# Tomar 2 países y 'week' como variable categórica
# Resolución Analítica y Gráfica
top5VentasPais1 = dfPreg5Pais1.sort_values('total_amount', ascending=False).head()["article_name"]
top5VentasPais2 = dfPreg5Pais2.sort_values('total_amount', ascending=False).head()["article_name"]
top5VentasPais = pd.unique(top5VentasPais1.append(top5VentasPais2))
#print(top5VentasPais1)
#print(top5VentasPais2)
#print(top5VentasPais)
print(f'\nCantidad de ventas por producto')
print(f'{"Producto" :<20}{Pais_1 :^10}{Pais_2 :^10}')
for art in top5VentasPais:
if art in dfPreg5Pais1['article_name'].values:
cantidadPais1 = dfPreg5Pais1[dfPreg5Pais1['article_name'] == art]['total_amount'].iloc[0].round()
else:
cantidadPais1 = "-"
if art in dfPreg5Pais2['article_name'].values:
cantidadPais2 = dfPreg5Pais2[dfPreg5Pais2['article_name'] == art]['total_amount'].iloc[0].round()
else:
cantidadPais2 = "-"
print(f'{art :<20}{cantidadPais1 :>10} {cantidadPais2 :>10}')
Cantidad de ventas por producto
Producto Peru Bolivia
Full Pc 51067.0 10639.0
Notebook 32000.0 -
Monitor 12420.0 -
Chair 8727.0 -
Netbook 8265.0 1015.0
Scanner 3885.0 2775.0
Motherboard 1799.0 2629.0
Desk 3643.0 2082.0
Video Card 3814.0 1446.0
Preg6 = my_df[(my_df['country_name'] == Pais_1) | (my_df['country_name'] == Pais_2)]
dfPreg6_1 = dfPreg5.groupby(by=['article_name', 'country_name', 'week']).sum().reset_index()
dfPreg6_2 = dfPreg6_1[dfPreg6_1['article_name'].isin(top5VentasPais1)]
dataFig6 = dfPreg6_2[dfPreg6_2['country_name'] == Pais_1]
figP6 = sns.lineplot(data=dataFig6, x='week', y='total_amount', hue='article_name').set(title=Pais_1)
#sns.move_legend(figP6,'upper right')
dfPreg6_2
dfPreg6_1 = dfPreg5.groupby(by=['article_name', 'country_name', 'week']).sum().reset_index()
dfPreg6_2 = dfPreg6_1[dfPreg6_1['article_name'].isin(top5VentasPais2)]
dataFig6 = dfPreg6_2[dfPreg6_2['country_name'] == Pais_2]
figP6 = sns.lineplot(data=dataFig6, x='week', y='total_amount', hue='article_name').set(title=Pais_2)
#sns.move_legend(figP6,'upper right')
# RESOLUCIÓN
# Mejor vendedor/producto. Mostrar importe/cantidad. Comparar 2 países (variable categórica)
# Resolución Analítica y Gráfica
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()
Daisie Slograve
60
226
Aveline Swanwick
66
227
Arnold Kilkenny
47
184
Kati Innot
52
151
Tobin Roselli
54
211
5 rows, showing
per page
Page of 1
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