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

df.mean()

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()

DFR=df['Sales'].max()-df['Sales'].min() DFR

mt=df[['Company','Sales','Stores']] mt.sort_values('Stores',ascending=False).head(5)

mt.sort_values(['Sales','Stores'],ascending=[False,False]).head(5)

Cm=df[['Company','Sales/Avg. Store']].sort_values('Sales/Avg. Store',ascending=False) Cm

Ratio_pad.plot(kind='bar') plt.xlabel('Companies') plt.ylabel('Ratio Sales/Stores') plt.title('Ratio Sales/Stores')