Analysis of the 25 largest retailers in the United States

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

df = pd.read_csv('/work/largest_us_retailers_9b00dc73-a938-46cd-af17-fcb2bd67301f.csv') df.sort_values('Sales',ascending=0); df = df.replace(to_replace ="Warehouse Club",value ="Warehouse Clubs"); df

dfnw = df.drop(df.index[0]) mean_sales = dfnw['Sales'].mean() plt.figure(figsize=(19,8)); plt.xticks(rotation=90); plt.title('sales by company') ax = plt.subplot() sn.barplot(data=dfnw,y='Sales', x ='Company' ) ax.axhline(mean_sales,color='red',label='average sales '+ str(round(mean_sales, 3))) plt.legend();

plt.figure(figsize=(19,8)); plt.xticks(rotation=90); plt.title('sales by company') ax = plt.subplot() sn.barplot(data=df,y='Sales', x ='Company' ) ax.axhline(mean_sales,color='red',label='Average sales excluding Walmart '+ str(round(mean_sales, 3))) plt.legend();

plt.figure(figsize=(19,8)); sn.histplot(data=df,x='Sales' ,binrange = [0,700000],binwidth=50000) plt.title("Distribution of company sales") plt.xlabel('Sales') plt.ylabel('frequency')

plt.figure(figsize=(19,8)); sn.histplot(data=df,x='Stores' ,binrange = [0,15000],binwidth=2000) plt.title("Distribution of stores by companies") plt.xlabel('stores') plt.ylabel('frequency') dfs = df[df.Stores < 2000] mean = dfs['Stores'].mean() ax = plt.subplot() ax.axvline(mean,color='red',label='Average Stores of the first 13 companies '+ str(round(mean, 0)) + ' tiendas') plt.legend();

plt.figure(figsize=(10,8)); sn.scatterplot(data=df,x='Stores',y='Sales',hue='Company') plt.title('Number of physical stores vs Sales') plt.grid()

plt.figure(figsize=(19,5)); sn.boxplot(df['Sales']) sales_range = df['Sales'].max()-df['Sales'].min() print(sales_range)

plt.figure(figsize=(19,5)); sn.boxplot(dfnw['Sales'],color='red') sales_range = dfnw['Sales'].max()-dfnw['Sales'].min() print(sales_range)

dfstores = df.sort_values('Stores',ascending=0) dfstores= dfstores[0:5] dfsales = df.sort_values('Sales',ascending=0) dfsales = dfsales[0:5] fig, (ax,bx)= plt.subplots(2,1, figsize=(15,9)) sn.barplot(data=dfstores,x='Company',y='Stores',ax=ax) sn.barplot(data=dfsales,x='Company',y='Sales',ax=bx)

fig, (ax,bx)= plt.subplots(2,1, figsize=(15,9)); sn.barplot(data=df,x='Sales',y='Category',ax=ax); sn.barplot(data=dfnw,x='Sales',y='Category',ax=bx);

plt.figure(figsize=(19,9)); dfsast = df.sort_values('Sales/Avg. Store') sn.barplot(data= dfsast,x='Sales/Avg. Store',y='Company');

fig, (ax,bx)= plt.subplots(1,2, figsize=(14,7)) dfnw['company type']=['online' if i else 'física' for i in dfnw.Stores.isna()] df['company type']=['online' if i else 'física' for i in df.Stores.isna()] sn.barplot(data= dfnw,x='company type',y='Sales',ax=ax); sn.barplot(data= df,x='company type',y='Sales',ax=bx); ax.set(title='Excluding Walmart data') bx.set(title='Including Walmart data') print('Companies that sell exclusively online are') print(df[df.Stores.isna()]['Company'])

plt.figure (figsize = (16,8)) plt.title('company growth', fontsize = 15) plt.xticks(rotation = 90) sn.barplot (x= 'Company', y= 'Store Count Growth', data = df.sort_values('Store Count Growth')) df['Store Count Growth'].mean()

plt.figure (figsize = (16,8)) plt.xticks(rotation = 45) sn.barplot(data=df,y='Stores',x='Category')

plt.figure (figsize = (16,8)) plt.xticks(rotation = 45) sn.histplot(data=df,x='Category') dfg = df[df['Category'] == 'Grocery']

plt.figure(figsize=(10,8)) w = 100* dfg['Sales']/dfg['Sales'].sum() patches, _ =plt.pie(dfg['Sales'], labels=dfg['Company'],) labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(dfg['Company'], w)] plt.legend(patches, labels, loc='best', bbox_to_anchor=(0, 1)) plt.show()