TP Integrador – Módulo II (Andreyna Araujo Núñez - Big Data

# Artículos conexion = sql3.connect('/work/data/articles.db') sql_query = pd.read_sql_query('SELECT * FROM articles', conexion) 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 los datos') print(df_articles.head()) print('\nFormato del dataframe') print(df_articles.shape)

Muestra de los 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)

# Exploración del df de vendedores print('Muestra de los datos') print(df_sellers.head()) print('\nFormato del dataframe') print(df_sellers.shape)

Muestra de los 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)

# Exploración del df de órdenes print('Muestra de los datos') print(df_orders.head()) print('\nFormato del dataframe') print(df_orders.shape)

Muestra de los 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)

# Cambio el tipo de dato de 'unit_price' a float 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

# Cambio el índice del df_articles por la columna article_id 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

# Creo una copia del df_orders mi_df = df_orders.copy() mi_df = mi_df.assign(article_name = mi_df['article_id']) mi_df = mi_df.assign(total_amount = mi_df['article_id']) mi_df = mi_df.assign(seller_name = mi_df['seller_id']) print(mi_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

# Reemplazo los valores en las nuevas columnas del df for i in range(len(mi_df.index)): article = df_articles.loc[mi_df.loc[i]['article_name']]['article_name'] mi_df.loc[i,'article_name'] = article mi_df.loc[i,'total_amount'] = mi_df.loc[i,'quantity'] * df_articles.loc[mi_df.loc[i]['total_amount']]['unit_price'] mi_df.loc[i,'seller_name'] = df_sellers.loc[mi_df.loc[i]['seller_name']]['seller_name'] print(mi_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

# Borrar las columnas que no voy a utilizar mi_df.drop(['order_id','article_id','seller_id'], axis='columns', inplace=True) print(mi_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 df = mi_df.groupby('article_name').sum() orden_cantidad = df.sort_values('quantity', ascending=False) print(orden_cantidad['quantity'].head(1))

article_name
HDD    413
Name: quantity, dtype: int64

# Resolución Gráfica r1= sns.barplot(x=df.index,y=df['quantity'],data=df,order=df.sort_values('quantity', ascending=False).index) r1.set_xlabel('Artículos') r1.set_ylabel('Unidades vendidas') plt.xticks(rotation=90) plt.show()

# Resolución Analítica df2 = (mi_df.groupby('article_name').sum()).sort_values('total_amount', ascending=False).head() print(df2['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 e = (0.05,0,0,0,0) c = ['gold','yellowgreen', 'lightcoral','lightskyblue','plum'] plt.pie(x=df2['total_amount'], explode=e, labels=df2.index, colors=c, startangle=90) plt.show()

# Resolución Analítica df3 = (mi_df.groupby('seller_name').sum()).sort_values('total_amount', ascending=False) print('Respuesta:') print(df3.head(1))

Respuesta:
                week  quantity  total_amount
seller_name                                 
Janel O'Curran   174       703     192832.47

# Resolución Gráfica r3 = plt.bar(df3.index, df3['total_amount']) plt.ylabel('Monto total de ventas') plt.xlabel('Vendedores') plt.xticks(rotation=60) plt.show()

# Resolución Analítica df4 = (mi_df.groupby('week').sum()).sort_values('total_amount',ascending=False) print(df4)

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

# Resolución Gráfica plt.bar(df4.index,df4['total_amount']) plt.show()

# Resolución Analítica df5 = (mi_df.groupby('country_name').sum()).sort_values('total_amount', ascending=False).head() print(df5['total_amount'])

country_name
Brazil       441271.85
Argentina    205832.78
Colombia     177514.29
Peru         161421.12
Mexico       138619.99
Name: total_amount, dtype: float64

# Resolución Gráfica r5 = plt.bar(df5.index, df5['total_amount']) plt.ylabel('Monto total de ventas') plt.xlabel('Países') plt.show()

# Resolución Gráfica df6 = mi_df[(mi_df['article_name'] == 'Full Pc') | (mi_df['article_name'] == 'Notebook') | (mi_df['article_name'] == 'Smartphone')] df6 = df6[['article_name','country_name','total_amount']] df6 = df6.groupby(['article_name','country_name'])['total_amount'].sum().reset_index() plt_order = df6.groupby('country_name')['total_amount'].sum().sort_values(ascending=False).index.values[0:5] sns.catplot(y='country_name', x='total_amount', hue='article_name', data=df6, kind='bar', order=plt_order) plt.xticks(rotation=90) plt.title('Ingresos generados del top 3 de productos por países') plt.ylabel('Países') plt.xlabel('Ingresos') plt.show()

# Resolución Analítica df7 = (mi_df.groupby('country_name').sum()).sort_values('quantity', ascending=True).head() print('Repuesta:') print(df7['quantity'])

Repuesta:
country_name
Puerto Rico     12
Uruguay         92
El Salvador    111
Paraguay       123
Ecuador        129
Name: quantity, dtype: int64

# Resolución Gráfica e = (0.05,0,0,0,0) c = ['gold','yellowgreen', 'lightcoral','lightskyblue','plum'] plt.pie(x=df7['quantity'], explode=e, labels=df7.index, colors=c, startangle=90) plt.show()