# 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()) 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)

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 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('\nBúsqueda 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)

Búsqueda 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('\nBúsqueda 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)

Búsqueda 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) print(df_articles.head())

           article_name  unit_price
article_id                         
20015        Smartphone      525.00
20016           Full Pc     2127.81
20017           Monitor      230.00
20018            Tablet      130.00
20019              Desk      130.10

   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 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())

   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         675.0  Cirilo Grandham  
1          Mouse         454.5      Ewell Peres  
2        Netbook         725.0   Janel O'Curran  
3         Tablet        1170.0   Janel O'Curran  
4           Case         227.4  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         675.0  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

# 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))

article_name
HDD    413
Name: quantity, dtype: int64

# 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'])

article_name
Full Pc       538335.93
Notebook      251000.00
Smartphone    152250.00
Chair          69477.48
Tablet         48620.00
Name: total_amount, dtype: float64

# 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']])

                   quantity  total_amount
seller_name                              
Janel O'Curran          703     192832.47
Brockie Patience        441     142709.88
Oliviero Charkham       555     141329.76
Vasily Danilyuk         521     129157.55
Daisie Slograve         554     120520.11
Aveline Swanwick        629     118874.33
Arnold Kilkenny         583      94552.04
Kati Innot              512      83704.62
Jase Doy                582      80628.31
Ewell Peres             496      78144.32
Onida Cosely            535      77373.37
Milly Christoffe        442      61733.69
Tobin Roselli           519      56984.42
Cornie Wynrehame        523      52253.57
Cirilo Grandham         470      45009.40

# 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)

      quantity  total_amount
week                        
1         2449     507458.81
2         2444     415364.44
3         2114     329140.03
4         1058     223844.56

# 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()

                           quantity
article_name country_name          
Tablet       Brazil             156
Notebook     Brazil              83
Smartphone   Brazil              79
Full Pc      Brazil              63
Smartphone   Colombia            52
Tablet       Peru                52
             Colombia            50
Full Pc      Argentina           45
Notebook     Argentina           43
Full Pc      Colombia            34
Notebook     Peru                32
Smartphone   Argentina           25
Full Pc      Peru                24
Notebook     Colombia            18
Smartphone   Peru                15
/shared-libs/python3.9/py/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

# 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()

                        quantity
seller_name       week          
Janel O'Curran    3          236
Arnold Kilkenny   2          218
Janel O'Curran    1          211
Aveline Swanwick  2          202
Tobin Roselli     1          201
Onida Cosely      2          201
Arnold Kilkenny   1          201
Jase Doy          3          195
Cornie Wynrehame  1          189
Janel O'Curran    2          185
Oliviero Charkham 3          183
Vasily Danilyuk   1          183
Jase Doy          1          178
Daisie Slograve   2          176
Kati Innot        2          171

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

              total_amount
country_name              
Puerto Rico        1265.43
Paraguay           8195.12
Ecuador           17475.30
Uruguay           17843.09
Bolivia           22682.80

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()

              quantity  total_amount
country_name                        
Puerto Rico         12       1265.43
Paraguay           123       8195.12
Ecuador            129      17475.30
Uruguay             92      17843.09
Bolivia            181      22682.80

Zona Experimental

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)

     week  quantity country_name article_name  total_amount        seller_name
16      1         6       Brazil      Full Pc      12766.86    Arnold Kilkenny
38      1         2       Brazil      Full Pc       4255.62           Jase Doy
70      1         5       Brazil      Full Pc      10639.05           Jase Doy
108     1        13       Brazil     Notebook      13000.00    Arnold Kilkenny
112     1         9       Brazil      Full Pc      19150.29     Janel O'Curran
114     1         4       Brazil     Notebook       4000.00        Ewell Peres
142     1        11    Argentina     Notebook      11000.00    Vasily Danilyuk
160     1        12       Brazil      Full Pc      25533.72    Daisie Slograve
162     1         4       Brazil     Notebook       4000.00   Aveline Swanwick
193     1         6    Argentina     Notebook       6000.00   Aveline Swanwick
212     1         5    Argentina      Full Pc      10639.05       Onida Cosely
283     1        10    Argentina      Full Pc      21278.10  Oliviero Charkham
299     2        15    Argentina      Full Pc      31917.15   Brockie Patience
313     2         8       Brazil      Full Pc      17022.48    Vasily Danilyuk
330     2         1    Argentina     Notebook       1000.00         Kati Innot
350     2        10    Argentina     Notebook      10000.00      Tobin Roselli
413     2        12       Brazil      Full Pc      25533.72   Aveline Swanwick
420     2         9       Brazil     Notebook       9000.00    Daisie Slograve
440     2        11    Argentina      Full Pc      23405.91     Janel O'Curran
523     2         2       Brazil      Full Pc       4255.62   Aveline Swanwick
644     3         2       Brazil      Full Pc       4255.62    Arnold Kilkenny
652     3         8    Argentina     Notebook       8000.00    Daisie Slograve
702     3         2    Argentina     Notebook       2000.00  Oliviero Charkham
785     3         5       Brazil      Full Pc      10639.05   Aveline Swanwick
867     3        12       Brazil     Notebook      12000.00       Onida Cosely
915     4         5    Argentina     Notebook       5000.00    Arnold Kilkenny
930     4         4    Argentina      Full Pc       8511.24    Vasily Danilyuk
933     4        14       Brazil     Notebook      14000.00      Tobin Roselli
935     4        14       Brazil     Notebook      14000.00    Daisie Slograve
936     4        13       Brazil     Notebook      13000.00         Kati Innot

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)

                   quantity  total_amount
article_name week                        
HDD          2          158       8629.96
SDD          1          153       3366.00
Tablet       3          148      19240.00
HDD          3          129       7045.98
Mouse        2          125       3787.50
...                     ...           ...
Keyboard     4           14        316.40
Case         4           13        492.70
Wi-Fi Card   4           10        596.10
Desk         4            6        780.60
Scanner      4            1        185.00

[124 rows x 2 columns]

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()

              quantity  total_amount
country_name                        
Brazil            2515     441271.85
Argentina          947     205832.78
Colombia           881     177514.29
Peru              1027     161421.12
Mexico             846     138619.99

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)

     week  quantity country_name article_name  total_amount        seller_name
16      1         6       Brazil      Full Pc      12766.86    Arnold Kilkenny
38      1         2       Brazil      Full Pc       4255.62           Jase Doy
70      1         5       Brazil      Full Pc      10639.05           Jase Doy
103     1        13         Peru     Notebook      13000.00   Cornie Wynrehame
108     1        13       Brazil     Notebook      13000.00    Arnold Kilkenny
112     1         9       Brazil      Full Pc      19150.29     Janel O'Curran
114     1         4       Brazil     Notebook       4000.00        Ewell Peres
142     1        11    Argentina     Notebook      11000.00    Vasily Danilyuk
160     1        12       Brazil      Full Pc      25533.72    Daisie Slograve
162     1         4       Brazil     Notebook       4000.00   Aveline Swanwick
167     1         6     Colombia     Notebook       6000.00   Milly Christoffe
193     1         6    Argentina     Notebook       6000.00   Aveline Swanwick
212     1         5    Argentina      Full Pc      10639.05       Onida Cosely
215     1        15    Guatemala     Notebook      15000.00     Janel O'Curran
262     1         7     Colombia     Notebook       7000.00         Kati Innot
282     1        14     Honduras     Notebook      14000.00  Oliviero Charkham
283     1        10    Argentina      Full Pc      21278.10  Oliviero Charkham
289     1         2    Guatemala     Notebook       2000.00     Janel O'Curran
299     2        15    Argentina      Full Pc      31917.15   Brockie Patience
301     2         5     Colombia     Notebook       5000.00   Aveline Swanwick
313     2         8       Brazil      Full Pc      17022.48    Vasily Danilyuk
330     2         1    Argentina     Notebook       1000.00         Kati Innot
350     2        10    Argentina     Notebook      10000.00      Tobin Roselli
363     2         7       Mexico     Notebook       7000.00        Ewell Peres
413     2        12       Brazil      Full Pc      25533.72   Aveline Swanwick
420     2         9       Brazil     Notebook       9000.00    Daisie Slograve
440     2        11    Argentina      Full Pc      23405.91     Janel O'Curran
491     2         5         Peru     Notebook       5000.00   Brockie Patience
523     2         2       Brazil      Full Pc       4255.62   Aveline Swanwick
633     3         5       Mexico     Notebook       5000.00    Arnold Kilkenny
644     3         2       Brazil      Full Pc       4255.62    Arnold Kilkenny
652     3         8    Argentina     Notebook       8000.00    Daisie Slograve
696     3         6         Peru     Notebook       6000.00   Cornie Wynrehame
702     3         2    Argentina     Notebook       2000.00  Oliviero Charkham
703     3         8         Peru     Notebook       8000.00   Milly Christoffe
785     3         5       Brazil      Full Pc      10639.05   Aveline Swanwick
867     3        12       Brazil     Notebook      12000.00       Onida Cosely
894     4         9       Mexico     Notebook       9000.00         Kati Innot
896     4         9       Mexico     Notebook       9000.00   Aveline Swanwick
915     4         5    Argentina     Notebook       5000.00    Arnold Kilkenny
930     4         4    Argentina      Full Pc       8511.24    Vasily Danilyuk
933     4        14       Brazil     Notebook      14000.00      Tobin Roselli
935     4        14       Brazil     Notebook      14000.00    Daisie Slograve
936     4        13       Brazil     Notebook      13000.00         Kati Innot
989     4        14       Mexico     Notebook      14000.00    Vasily Danilyuk

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

              week  quantity  total_amount
country_name                              
Brazil          37       146     217052.03
Argentina       26        88     138751.45

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

              week  quantity  total_amount
country_name                              
Brazil          19        79       41475.0
Colombia        13        52       27300.0
Venezuela       10        32       16800.0
Mexico           6        29       15225.0
Argentina        5        25       13125.0
El Salvador      7        22       11550.0
Ecuador          4        15        7875.0
Peru             5        15        7875.0
Chile            3        12        6300.0
Uruguay          2         9        4725.0

# 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')

             article_name  week  quantity  total_amount
country_name                                           
Brazil            Full Pc    17        63     134052.03
Argentina         Full Pc    10        45      95751.45
Brazil           Notebook    20        83      83000.00
Argentina        Notebook    16        43      43000.00
/shared-libs/python3.9/py/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

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'])

                           week  quantity  total_amount
country_name article_name                              
Brazil       Tablet          41       156      20280.00
Peru         Mouse           23       125       3787.50
Brazil       HDD             45       119       6499.78
             SDD             33       117       2574.00
             Chair           32       112      37591.68
...                         ...       ...           ...
Ecuador      SDD              2         1         22.00
             Modem            1         1         67.50
             Heatsink         2         1         10.00
Paraguay     Video Card       3         1        131.50
Chile        Netbook          2         1        145.00

[306 rows x 3 columns]
125.0

# 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)

     week  quantity country_name article_name  total_amount        seller_name
3       1         9       Brazil       Tablet       1170.00     Janel O'Curran
16      1         6       Brazil      Full Pc      12766.86    Arnold Kilkenny
20      1        10     Colombia       Tablet       1300.00    Daisie Slograve
22      1        15    Venezuela       Tablet       1950.00    Arnold Kilkenny
38      1         2       Brazil      Full Pc       4255.62           Jase Doy
..    ...       ...          ...          ...           ...                ...
985     4        10       Brazil       Tablet       1300.00   Milly Christoffe
988     4        13     Colombia      Full Pc      27661.53     Janel O'Curran
989     4        14       Mexico     Notebook      14000.00    Vasily Danilyuk
993     4         8       Brazil        Chair       2685.12  Oliviero Charkham
998     4        14       Brazil       Tablet       1820.00    Vasily Danilyuk

[163 rows x 6 columns]
                                week  quantity  total_amount
seller_name       article_name                              
Janel O'Curran    Full Pc         13        56     119157.36
Brockie Patience  Tablet          11        50       6500.00
Oliviero Charkham Tablet          13        49       6370.00
Milly Christoffe  Smartphone      12        47      24675.00
Brockie Patience  Full Pc         10        46      97879.26
...                              ...       ...           ...
Aveline Swanwick  Tablet           4         4        520.00
Cornie Wynrehame  Chair            2         2        671.28
Vasily Danilyuk   Chair            4         1        335.64
Cirilo Grandham   Full Pc          3         1       2127.81
Jase Doy          Smartphone       1         1        525.00

[65 rows x 3 columns]

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

          seller_name article_name  week  quantity  total_amount
0      Janel O'Curran      Full Pc    13        56     119157.36
1    Brockie Patience       Tablet    11        50       6500.00
2   Oliviero Charkham       Tablet    13        49       6370.00
3    Milly Christoffe   Smartphone    12        47      24675.00
4    Brockie Patience      Full Pc    10        46      97879.26
..                ...          ...   ...       ...           ...
60   Aveline Swanwick       Tablet     4         4        520.00
61   Cornie Wynrehame        Chair     2         2        671.28
62    Vasily Danilyuk        Chair     4         1        335.64
63    Cirilo Grandham      Full Pc     3         1       2127.81
64           Jase Doy   Smartphone     1         1        525.00

[65 rows x 5 columns]

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()