Análisis de los 25 retailers más grandes de Estados Unidos DataAcademy 2022 Platzi.
# Importar librerías aquí
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Importar datos aquí
df=pd.read_csv('/work/largest_us_retailers_9b00dc73-a938-46cd-af17-fcb2bd67301f.csv')
df
Companyobject
Walmart US4%
Kroger4%
23 others92%
Salesint64
16592 - 658119
0
Walmart US
658119
1
Kroger
115037
2
Costco
90048
3
Home Depot
83976
4
Walgreen Boots
78924
5
CVS incl. Target
77792
6
Amazon
71687
7
Target
71208
8
Lowe's
60311
9
Albertsons
56829
10
Sam's Club
56828
11
Apple incl. Online
37664
12
Best Buy
34980
13
Publix
34408
14
Rite Aid
27486
15
Ahold
26903
16
Macy's
26028
17
TJX
25012
18
Aldi
24402
19
Dollar General
22234
20
Dollar Tree
21464
21
HEB
21384
22
Kohl's
19060
23
Delhaize
18201
24
Meijer
16592
df.mean()
I. Preguntas del negocio
df.columns
DF=df[['Company','Sales']]
filtro=DF[DF.Company != 'Walmart US']
filtro.mean()
Sc=df[['Company','Sales']]
Sc.median()
td=df[['Company','Stores']]
sns.histplot(x=td.Stores,y=td.Company,bins=15)
plt.title('COMPANY VS STORES ')
plt.show()
td.median()
td.min()
StvsSt=df[['Sales','Stores','Company']]
sns.jointplot(x=StvsSt.Stores ,y=StvsSt.Sales ,hue_order=StvsSt.Company)
plt.title('Ventas Vs Tiendas', loc='left')
plt.show()
sns.barplot(x=StvsSt.Stores ,y=StvsSt.Sales ,order=StvsSt.sort_values('Sales',ascending=False).Stores,ci=False)
plt.xticks(rotation=90)
plt.title('Sales Vs Stores')
plt.legend(bbox_to_anchor = (1 , 1 ), loc = 'upper left', borderaxespad = 2 )
plt.show()
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
DFR=df['Sales'].max()-df['Sales'].min()
DFR
mt=df[['Company','Sales','Stores']]
mt.sort_values('Stores',ascending=False).head(5)
Companyobject
Salesint64
20
Dollar Tree
21464
19
Dollar General
22234
5
CVS incl. Target
77792
4
Walgreen Boots
78924
0
Walmart US
658119
mt.sort_values(['Sales','Stores'],ascending=[False,False]).head(5)
Companyobject
Salesint64
0
Walmart US
658119
1
Kroger
115037
2
Costco
90048
3
Home Depot
83976
4
Walgreen Boots
78924
Cm=df[['Company','Sales/Avg. Store']].sort_values('Sales/Avg. Store',ascending=False)
Cm
Companyobject
Costco4%
Sam's Club4%
23 others92%
Sales/Avg. Storefloat64
1.56391854 - 187.7956204
2
Costco
187.7956204
10
Sam's Club
87.29339478
24
Meijer
74.73873874
21
HEB
66.825
0
Walmart US
65.64972497
3
Home Depot
42.73587786
7
Target
39.98203257
15
Ahold
34.446863
8
Lowe's
33.31179232
16
Macy's
30.44210526
Ratio_pad.plot(kind='bar')
plt.xlabel('Companies')
plt.ylabel('Ratio Sales/Stores')
plt.title('Ratio Sales/Stores')