Análisis de los 25 retailers más grandes de Estados Unidos

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

df = pd.read_csv("/work/largest_us_retailers_-2.csv")

df.head()

Companyobject

Salesint64

Walmart US

658119

Kroger

115037

Costco

90048

Home Depot

83976

Walgreen Boots

78924

df.describe()

Salesfloat64

Storesfloat64

count

mean

71063.08

3466.565217

std

125405.7276

4038.708714

min

16592

231

25%

24402

1029

50%

34980

1828

75%

71687

4242

max

658119

14250

df_sorted = df.sort_values('Sales', ascending=False) df_sorted

Companyobject

Walmart US4%

Kroger4%

23 others92%

Salesint64

16592 - 658119

Walmart US

658119

Kroger

115037

Costco

90048

Home Depot

83976

Walgreen Boots

78924

CVS incl. Target

77792

Amazon

71687

Target

71208

Lowe's

60311

Albertsons

56829

I. Preguntas del negocio

df_clipping2['Sales'].mean()

df["Sales/Avg. Store"].median()

df.groupby("Company").count()

Salesint64

1 - 1

Storesint64

0 - 1

Ahold

Albertsons

Aldi

Amazon

Apple incl. Online

Best Buy

CVS incl. Target

Costco

Delhaize

Dollar General

sns.scatterplot(data = df_sorted, x = 'Sales', y = 'Stores', size = 'Sales/Avg. Store', color = "yellow")

rango = df["Sales"].max() - df["Sales"].min() rango

df_clipping = df_sorted.iloc[ : 5 , : ] df_clipping

Companyobject

Salesint64

Walmart US

658119

Kroger

115037

Costco

90048

Home Depot

83976

Walgreen Boots

78924

chart = sns.barplot(data = df_clipping, x = 'Company', y = 'Sales') chart.set_title('Sales of the main 5 retailers in the USA') chart.set_xlabel('Top 5 retails in sales') chart.set_ylabel('Sales in millions of dollars') sns.set(rc = {'figure.figsize':(18,8)})

df.groupby("Sales").count()

Companyint64

1 - 1

Storesint64

0 - 1

16592

18201

19060

21384

21464

22234

24402

25012

26028

26903

df_sorted['Sales/Avg. Store_2'] = df_sorted['Sales'] / df_sorted['Stores'] df_sorted

Companyobject

Walmart US4%

Kroger4%

23 others92%

Salesint64

16592 - 658119

Walmart US

658119

Kroger

115037

Costco

90048

Home Depot

83976

Walgreen Boots

78924

CVS incl. Target

77792

Amazon

71687

Target

71208

Lowe's

60311

Albertsons

56829

df[df['Stores'].isna()]

Companyobject

Salesint64

Amazon

71687

Apple incl. Online

37664

df["Sales/Avg. Store"].mean()