TP Final Integrador

# A veces necesitamos instalar nuevas librerías en nuestros proyectos !pip install openpyxl==3.0.10

Requirement already satisfied: openpyxl==3.0.10 in /root/venv/lib/python3.9/site-packages (3.0.10)
Requirement already satisfied: et-xmlfile in /root/venv/lib/python3.9/site-packages (from openpyxl==3.0.10) (1.1.0)
WARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.

# 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)

    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
5        20020             Chair     335.64
6        20021             Modem      67.50
7        20022    Range Extender      20.45
8        20023          Notebook    1000.00
9        20024           Netbook     145.00
10       20025               HDD      54.62
11       20026               SDD      22.00
12       20027        Ram Memory      35.95
13       20028       Motherboard     138.38
14       20029             Mouse      30.30
15       20030        Fan Cooler       4.25
16       20031            Webcam      20.07
17       20032          Keyboard      22.60
18       20033        Headphones      23.30
19       20034           Scanner     185.00
20       20035              Case      37.90
21       20036        Video Card     131.50
22       20037               CPU     139.62
23       20038      Power Supply      43.95
24       20039     Water Cooling      67.50
25       20040          Heatsink      10.00
26       20041         Usb Cable       2.95
27       20042        Sata Cable       2.14
28       20043  Pci Express Port      10.12
29       20044        Wi-Fi Card      59.61
30       20045    Mesh Wi-Fi X 2      32.50

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

                 seller_name
seller_id                   
1           Aveline Swanwick
2                   Jase Doy
3          Oliviero Charkham
4           Cornie Wynrehame
5                Ewell Peres
6           Milly Christoffe
7                 Kati Innot
8              Tobin Roselli
9               Onida Cosely
10           Cirilo Grandham
11           Vasily Danilyuk
12          Brockie Patience
13           Arnold Kilkenny
14            Janel O'Curran
15           Daisie Slograve

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

     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
..        ...   ...         ...       ...        ...          ...
995     16019     4       20021         1          7       Brazil
996     16020     4       20040        15         15       Brazil
997     16021     4       20040         2         11     Colombia
998     16022     4       20018        14         11       Brazil
999     16023     4       20026        12          9       Brazil

[1000 rows x 6 columns]

# Exploración del df de artículos print("Muestra de datos") print(df_articles.head(5)) print("\nFormato del dataframe") print(df_articles.shape) print("\nBúqueda de valores nulos") print(df_articles.isnull().sum()) print("\nFormato de los datos") 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)

Búqueda de valores nulos
article_id      0
article_name    0
unit_price      0
dtype: int64

Formato de los datos
article_id       int64
article_name    object
unit_price      object
dtype: object

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

Búqueda de valores nulos
seller_name    0
dtype: int64

Formato de los datos
seller_name    object
dtype: object

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

Búqueda de valores nulos
order_id        0
week            0
article_id      0
quantity        0
seller_id       0
country_name    0
dtype: int64

Formato de los datos
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

# 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) # Le doy formato de $ pd.options.display.float_format = '$ {:,.2f}'.format print(df_articles.head())

           article_name  unit_price
article_id                         
20015        Smartphone    $ 525.00
20016           Full Pc  $ 2,127.81
20017           Monitor    $ 230.00
20018            Tablet    $ 130.00
20019              Desk    $ 130.10

print(my_df.head())

   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

# 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())

   order_id  week  article_id  quantity  seller_id country_name  article_name  \
0     15024     1       20039        10         10         Peru         20039   
1     15025     1       20029        15          5         Peru         20029   
2     15026     1       20024         5         14      Bolivia         20024   
3     15027     1       20018         9         14       Brazil         20018   
4     15028     1       20035         6         15       Mexico         20035   

   total_amount  seller_name  
0         20039           10  
1         20029            5  
2         20024           14  
3         20018           14  
4         20035           15

# reemplazar los datos en 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_ammount my_df.loc[i, "total_amount"] = my_df.loc[i, "quantity"] * df_articles.loc[my_df.loc[i]["total_amount"]]["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())

   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   

    article_name  total_amount      seller_name  
0  Water Cooling      $ 675.00  Cirilo Grandham  
1          Mouse      $ 454.50      Ewell Peres  
2        Netbook      $ 725.00   Janel O'Curran  
3         Tablet    $ 1,170.00   Janel O'Curran  
4           Case      $ 227.40  Daisie Slograve

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

   week  quantity country_name   article_name  total_amount      seller_name
0     1        10         Peru  Water Cooling      $ 675.00  Cirilo Grandham
1     1        15         Peru          Mouse      $ 454.50      Ewell Peres
2     1         5      Bolivia        Netbook      $ 725.00   Janel O'Curran
3     1         9       Brazil         Tablet    $ 1,170.00   Janel O'Curran
4     1         6       Mexico           Case      $ 227.40  Daisie Slograve

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

article_name
HDD    413
Name: quantity, dtype: int64

# RESOLUCIÓN GRÁFICA plt.bar(my_df2.index, my_df2["quantity"]) plt.title("Cantidad vendida por artículo") plt.xticks(rotation = 90) plt.ylabel('Cantidad vendida') plt.xlabel('Artículo') plt.show()

# RESOLUCIÓN ANALÍTICA my_df3 = (my_df.groupby("article_name").sum()).sort_values("total_amount", ascending = False) print(my_df3["total_amount"].head(1))

article_name
Full Pc   $ 538,335.93
Name: total_amount, dtype: float64

# RESOLUCIÓN GRÁFICA plt.bar(my_df3.index, my_df3["total_amount"]) plt.title("Monto de ventas por artículo") plt.xticks(rotation = 90) plt.ylabel('Monto de las ventas') plt.xlabel('Artículo') plt.show()

# RESOLUCIÓN ANALÍTICA my_df4 = (my_df.groupby("seller_name").sum()).sort_values("total_amount", ascending = False) print(my_df4[["quantity"]+["total_amount"]].head(1))

                quantity  total_amount
seller_name                           
Janel O'Curran       703  $ 192,832.47

# RESOLUCIÓN GRÁFICA plt.bar(my_df4.index, my_df4["total_amount"]) plt.title("Monto de ventas por vendedor") plt.xticks(rotation = 90) plt.ylabel('Monto de las ventas') plt.xlabel('Vendedor') plt.show()

# RESOLUCIÓN ANALÍTICA my_df5 = (my_df.groupby("week").sum()).sort_values("total_amount", ascending=False) print(my_df5)

      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.pie(my_df5["total_amount"], labels = ["Semana 1","Semana 2", "Semana 3", "Semana 4"], autopct='%1.2f%%') plt.title("Proporción de ventas por semana") plt.show()

# RESOLUCIÓN ANALÍTICA my_df6 = (my_df.groupby("country_name").sum()).sort_values("total_amount", ascending = False) print(my_df6["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
Guatemala      $ 52,579.25
Honduras       $ 36,763.56
Costa Rica     $ 34,606.50
Chile          $ 24,660.98
Bolivia        $ 22,682.80
Uruguay        $ 17,843.09
Ecuador        $ 17,475.30
Paraguay        $ 8,195.12
Puerto Rico     $ 1,265.43
Name: total_amount, dtype: float64

# RESOLUCIÓN GRÁFICA plt.bar(my_df6.index, my_df6["total_amount"]) plt.title("Monto de ventas por país") plt.xticks(rotation = 90) plt.ylabel('Monto de las ventas') plt.xlabel('País') plt.show()

# RESOLUCIÓN ANALÍTICA my_df7 = (my_df.groupby("article_name").sum()).sort_values("quantity", ascending = False) print(my_df7["quantity"])

article_name
HDD                 413
Tablet              374
SDD                 372
Mouse               322
Netbook             320
Ram Memory          293
Motherboard         291
Pci Express Port    291
Smartphone          290
Range Extender      281
Heatsink            280
Usb Cable           273
Power Supply        269
CPU                 266
Sata Cable          264
Full Pc             253
Water Cooling       252
Notebook            251
Modem               244
Headphones          239
Webcam              229
Desk                223
Scanner             221
Mesh Wi-Fi X 2      213
Video Card          209
Monitor             208
Chair               207
Case                206
Fan Cooler          205
Keyboard            165
Wi-Fi Card          141
Name: quantity, dtype: int64

# RESOLUCIÓN GRÁFICA plt.bar(my_df7.index, my_df7["quantity"]) plt.title("Cantidad vendida por artículo") plt.xticks(rotation = 90) plt.ylabel('Cantidad vendida') plt.xlabel('Artículo') plt.show()

# RESOLUCIÓN ANALÍTICA my_df8 = (my_df.groupby("seller_name").count()) print(my_df8["quantity"])

seller_name
Arnold Kilkenny      68
Aveline Swanwick     75
Brockie Patience     57
Cirilo Grandham      62
Cornie Wynrehame     70
Daisie Slograve      72
Ewell Peres          66
Janel O'Curran       79
Jase Doy             73
Kati Innot           63
Milly Christoffe     54
Oliviero Charkham    71
Onida Cosely         67
Tobin Roselli        60
Vasily Danilyuk      63
Name: quantity, dtype: int64

# RESOLUCIÓN GRÁFICA plt.bar(my_df8.index, my_df8["quantity"]) plt.title("Ventas realizadas por cada vendedor") plt.xticks(rotation = 90) plt.ylabel('Ventas realizadas') plt.xlabel('Vendedor') plt.show()