Adidas Sales Analysis - Project Code

Project Report for STAT 303-1 Fall 2025

Abstract

This file contains the code for the project.

3) Data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
adidas = pd.read_csv('Adidas US Sales Datasets.csv', skiprows = 3, header = 0)
adidas.head()

	Retailer	Retailer ID	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Total Sales	Operating Profit	Operating Margin	Sales Method
0	Foot Locker	1185732	1/1/20	Northeast	New York	New York	Men's Street Footwear	$50.00	1,200	$600,000	$300,000	50%	In-store
1	Foot Locker	1185732	1/2/20	Northeast	New York	New York	Men's Athletic Footwear	$50.00	1,000	$500,000	$150,000	30%	In-store
2	Foot Locker	1185732	1/3/20	Northeast	New York	New York	Women's Street Footwear	$40.00	1,000	$400,000	$140,000	35%	In-store
3	Foot Locker	1185732	1/4/20	Northeast	New York	New York	Women's Athletic Footwear	$45.00	850	$382,500	$133,875	35%	In-store
4	Foot Locker	1185732	1/5/20	Northeast	New York	New York	Men's Apparel	$60.00	900	$540,000	$162,000	30%	In-store

adidas.shape

(9648, 13)

adidas.isnull().sum()

Retailer            0
Retailer ID         0
Invoice Date        0
Region              0
State               0
City                0
Product             0
Price per Unit      0
Units Sold          0
Total Sales         0
Operating Profit    0
Operating Margin    0
Sales Method        0
dtype: int64

#adidas.dtypes
adidas

	Retailer	Retailer ID	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Total Sales	Operating Profit	Operating Margin	Sales Method
0	Foot Locker	1185732	1/1/20	Northeast	New York	New York	Men's Street Footwear	$50.00	1,200	$600,000	$300,000	50%	In-store
1	Foot Locker	1185732	1/2/20	Northeast	New York	New York	Men's Athletic Footwear	$50.00	1,000	$500,000	$150,000	30%	In-store
2	Foot Locker	1185732	1/3/20	Northeast	New York	New York	Women's Street Footwear	$40.00	1,000	$400,000	$140,000	35%	In-store
3	Foot Locker	1185732	1/4/20	Northeast	New York	New York	Women's Athletic Footwear	$45.00	850	$382,500	$133,875	35%	In-store
4	Foot Locker	1185732	1/5/20	Northeast	New York	New York	Men's Apparel	$60.00	900	$540,000	$162,000	30%	In-store
...	...	...	...	...	...	...	...	...	...	...	...	...	...
9643	Foot Locker	1185732	1/24/21	Northeast	New Hampshire	Manchester	Men's Apparel	$50.00	64	$3,200	$896	28%	Outlet
9644	Foot Locker	1185732	1/24/21	Northeast	New Hampshire	Manchester	Women's Apparel	$41.00	105	$4,305	$1,378	32%	Outlet
9645	Foot Locker	1185732	2/22/21	Northeast	New Hampshire	Manchester	Men's Street Footwear	$41.00	184	$7,544	$2,791	37%	Outlet
9646	Foot Locker	1185732	2/22/21	Northeast	New Hampshire	Manchester	Men's Athletic Footwear	$42.00	70	$2,940	$1,235	42%	Outlet
9647	Foot Locker	1185732	2/22/21	Northeast	New Hampshire	Manchester	Women's Street Footwear	$29.00	83	$2,407	$650	27%	Outlet

9648 rows × 13 columns

adidas.columns = adidas.columns.str.strip()
adidas['Units Sold'] = pd.to_numeric(adidas['Units Sold'].replace(',', '', regex=True))
adidas['Price per Unit'] = pd.to_numeric(adidas['Price per Unit'].replace(r'[\$]', '', regex=True))
adidas['Total Sales'] = pd.to_numeric(adidas['Total Sales'].replace(r'[\$,]', '', regex=True))
adidas['Operating Profit'] = pd.to_numeric(adidas['Operating Profit'].replace(r'[\$,]', '', regex=True))
adidas['Operating Margin'] = pd.to_numeric(adidas['Operating Margin'].replace(r'[\%]', '', regex=True)) / 100
adidas['Invoice Date'] = pd.to_datetime(adidas['Invoice Date'].str.strip(),format='%m/%d/%y')
adidas.head()

	Retailer	Retailer ID	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Total Sales	Operating Profit	Operating Margin	Sales Method
0	Foot Locker	1185732	2020-01-01	Northeast	New York	New York	Men's Street Footwear	50.0	1200	600000	300000	0.50	In-store
1	Foot Locker	1185732	2020-01-02	Northeast	New York	New York	Men's Athletic Footwear	50.0	1000	500000	150000	0.30	In-store
2	Foot Locker	1185732	2020-01-03	Northeast	New York	New York	Women's Street Footwear	40.0	1000	400000	140000	0.35	In-store
3	Foot Locker	1185732	2020-01-04	Northeast	New York	New York	Women's Athletic Footwear	45.0	850	382500	133875	0.35	In-store
4	Foot Locker	1185732	2020-01-05	Northeast	New York	New York	Men's Apparel	60.0	900	540000	162000	0.30	In-store

adidas.dtypes

Retailer                    object
Retailer ID                  int64
Invoice Date        datetime64[ns]
Region                      object
State                       object
City                        object
Product                     object
Price per Unit             float64
Units Sold                   int64
Total Sales                  int64
Operating Profit             int64
Operating Margin           float64
Sales Method                object
dtype: object

5) Data Cleaning

a) Cleaning - Question 1

clean1 = adidas[['City', 'State', 'Total Sales', 'Operating Profit', 'Sales Method']]
numcols = clean1.select_dtypes(include=[np.number]).columns #find numeric columns
catcols = clean1.select_dtypes(exclude=[np.number]).columns
outliersc = {}

for col in numcols:
    Q1 = adidas[col].quantile(0.25)
    Q3 = adidas[col].quantile(0.75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5 * IQR
    upper = Q3 + 1.5 * IQR
    outliers = ((adidas[col] < lower) | (adidas[col] > upper)).sum()
    outliersc[col] = outliers

print("Outliers:", outliersc)

Outliers: {'Total Sales': 653, 'Operating Profit': 706}

catnum = {}

for col in catcols:
    unique_count = adidas[col].nunique()
    catnum[col] = unique_count

print("# of categories for each cat var:", catnum)

for col in catcols:
    print(f"\nCategory counts for {col}:")
    print(adidas[col].value_counts())

# of categories for each cat var: {'City': 52, 'State': 50, 'Sales Method': 3}

Category counts for City:
City
Portland          360
Charleston        288
Orlando           216
Salt Lake City    216
Houston           216
Boise             216
Phoenix           216
Albuquerque       216
Atlanta           216
New York          216
Jackson           216
Little Rock       216
Oklahoma City     216
Hartford          216
Providence        216
Boston            216
Burlington        216
Richmond          216
New Orleans       216
Manchester        216
Dallas            216
Philadelphia      216
Knoxville         216
Birmingham        216
Las Vegas         216
Los Angeles       216
San Francisco     216
Chicago           144
Newark            144
Baltimore         144
Indianapolis      144
Milwaukee         144
Des Moines        144
Fargo             144
Sioux Falls       144
Wichita           144
Wilmington        144
Honolulu          144
Albany            144
Louisville        144
Columbus          144
Charlotte         144
Seattle           144
Miami             144
Minneapolis       144
Billings          144
Omaha             144
St. Louis         144
Detroit           144
Anchorage         144
Cheyenne          144
Denver            144
Name: count, dtype: int64

Category counts for State:
State
California        432
Texas             432
New York          360
Florida           360
Mississippi       216
Oregon            216
Louisiana         216
Idaho             216
New Mexico        216
Georgia           216
Arkansas          216
Virginia          216
Oklahoma          216
Connecticut       216
Rhode Island      216
Massachusetts     216
Vermont           216
Utah              216
Arizona           216
New Hampshire     216
Pennsylvania      216
Nevada            216
Alabama           216
Tennessee         216
South Dakota      144
Illinois          144
Colorado          144
New Jersey        144
Delaware          144
Maryland          144
West Virginia     144
Indiana           144
Wisconsin         144
Iowa              144
North Dakota      144
Michigan          144
Kansas            144
Missouri          144
Minnesota         144
Montana           144
Kentucky          144
Ohio              144
North Carolina    144
South Carolina    144
Nebraska          144
Maine             144
Alaska            144
Hawaii            144
Wyoming           144
Washington        144
Name: count, dtype: int64

Category counts for Sales Method:
Sales Method
Online      4889
Outlet      3019
In-store    1740
Name: count, dtype: int64

incorrectval = {}
for col in numcols:
    neg = adidas[(adidas[col] < 0)]  # negative values problem
    if len(neg) > 0:
        incorrectval[col] = neg

print("Incorrect numeric values:", incorrectval)

Incorrect numeric values: {}

b) Cleaning - Question 2

clean2 = adidas[['Retailer', 'City', 'State', 'Total Sales', 'Operating Profit']]
numcols2 = clean2.select_dtypes(include=[np.number]).columns #find numeric columns
catcols2 = clean2.select_dtypes(exclude=[np.number]).columns
outliersc2 = {}

for col in numcols2:
    Q1 = adidas[col].quantile(0.25)
    Q3 = adidas[col].quantile(0.75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5 * IQR
    upper = Q3 + 1.5 * IQR
    outliers = ((adidas[col] < lower) | (adidas[col] > upper)).sum()
    outliersc2[col] = outliers

print("Outliers:", outliersc2)

Outliers: {'Total Sales': 653, 'Operating Profit': 706}

catnum2 = {}

for col in catcols2:
    unique_count = adidas[col].nunique()
    catnum2[col] = unique_count

print("# of categories for each cat var:", catnum2)

for col in catcols2:
    print(f"\nCategory counts for {col}:")
    print(adidas[col].value_counts())

# of categories for each cat var: {'Retailer': 6, 'City': 52, 'State': 50}

Category counts for Retailer:
Retailer
Foot Locker      2637
West Gear        2374
Sports Direct    2032
Kohl's           1030
Amazon            949
Walmart           626
Name: count, dtype: int64

Category counts for City:
City
Portland          360
Charleston        288
Orlando           216
Salt Lake City    216
Houston           216
Boise             216
Phoenix           216
Albuquerque       216
Atlanta           216
New York          216
Jackson           216
Little Rock       216
Oklahoma City     216
Hartford          216
Providence        216
Boston            216
Burlington        216
Richmond          216
New Orleans       216
Manchester        216
Dallas            216
Philadelphia      216
Knoxville         216
Birmingham        216
Las Vegas         216
Los Angeles       216
San Francisco     216
Chicago           144
Newark            144
Baltimore         144
Indianapolis      144
Milwaukee         144
Des Moines        144
Fargo             144
Sioux Falls       144
Wichita           144
Wilmington        144
Honolulu          144
Albany            144
Louisville        144
Columbus          144
Charlotte         144
Seattle           144
Miami             144
Minneapolis       144
Billings          144
Omaha             144
St. Louis         144
Detroit           144
Anchorage         144
Cheyenne          144
Denver            144
Name: count, dtype: int64

Category counts for State:
State
California        432
Texas             432
New York          360
Florida           360
Mississippi       216
Oregon            216
Louisiana         216
Idaho             216
New Mexico        216
Georgia           216
Arkansas          216
Virginia          216
Oklahoma          216
Connecticut       216
Rhode Island      216
Massachusetts     216
Vermont           216
Utah              216
Arizona           216
New Hampshire     216
Pennsylvania      216
Nevada            216
Alabama           216
Tennessee         216
South Dakota      144
Illinois          144
Colorado          144
New Jersey        144
Delaware          144
Maryland          144
West Virginia     144
Indiana           144
Wisconsin         144
Iowa              144
North Dakota      144
Michigan          144
Kansas            144
Missouri          144
Minnesota         144
Montana           144
Kentucky          144
Ohio              144
North Carolina    144
South Carolina    144
Nebraska          144
Maine             144
Alaska            144
Hawaii            144
Wyoming           144
Washington        144
Name: count, dtype: int64

incorrectval2 = {}
for col in numcols2:
    neg2 = adidas[(adidas[col] < 0)]  # negative values problem
    if len(neg) > 0:
        incorrectval2[col] = neg2

print("Incorrect numeric values:", incorrectval2)

Incorrect numeric values: {}

c) Cleaning - Question 3

clean3 = adidas[['Product', 'City', 'State', 'Total Sales', 'Operating Profit']]
numcols3 = clean3.select_dtypes(include=[np.number]).columns #find numeric columns
catcols3 = clean3.select_dtypes(exclude=[np.number]).columns
outliersc3 = {}

for col in numcols3:
    Q1 = adidas[col].quantile(0.25)
    Q3 = adidas[col].quantile(0.75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5 * IQR
    upper = Q3 + 1.5 * IQR
    outliers = ((adidas[col] < lower) | (adidas[col] > upper)).sum()
    outliersc3[col] = outliers

print("Outliers:", outliersc3)

Outliers: {'Total Sales': 653, 'Operating Profit': 706}

catnum3 = {}

for col in catcols3:
    unique_count = adidas[col].nunique()
    catnum3[col] = unique_count

print("# of categories for each cat var:", catnum3)

for col in catcols3:
    print(f"\nCategory counts for {col}:")
    print(adidas[col].value_counts())

# of categories for each cat var: {'Product': 6, 'City': 52, 'State': 50}

Category counts for Product:
Product
Men's Street Footwear        1610
Men's Athletic Footwear      1610
Women's Street Footwear      1608
Women's Apparel              1608
Women's Athletic Footwear    1606
Men's Apparel                1606
Name: count, dtype: int64

Category counts for City:
City
Portland          360
Charleston        288
Orlando           216
Salt Lake City    216
Houston           216
Boise             216
Phoenix           216
Albuquerque       216
Atlanta           216
New York          216
Jackson           216
Little Rock       216
Oklahoma City     216
Hartford          216
Providence        216
Boston            216
Burlington        216
Richmond          216
New Orleans       216
Manchester        216
Dallas            216
Philadelphia      216
Knoxville         216
Birmingham        216
Las Vegas         216
Los Angeles       216
San Francisco     216
Chicago           144
Newark            144
Baltimore         144
Indianapolis      144
Milwaukee         144
Des Moines        144
Fargo             144
Sioux Falls       144
Wichita           144
Wilmington        144
Honolulu          144
Albany            144
Louisville        144
Columbus          144
Charlotte         144
Seattle           144
Miami             144
Minneapolis       144
Billings          144
Omaha             144
St. Louis         144
Detroit           144
Anchorage         144
Cheyenne          144
Denver            144
Name: count, dtype: int64

Category counts for State:
State
California        432
Texas             432
New York          360
Florida           360
Mississippi       216
Oregon            216
Louisiana         216
Idaho             216
New Mexico        216
Georgia           216
Arkansas          216
Virginia          216
Oklahoma          216
Connecticut       216
Rhode Island      216
Massachusetts     216
Vermont           216
Utah              216
Arizona           216
New Hampshire     216
Pennsylvania      216
Nevada            216
Alabama           216
Tennessee         216
South Dakota      144
Illinois          144
Colorado          144
New Jersey        144
Delaware          144
Maryland          144
West Virginia     144
Indiana           144
Wisconsin         144
Iowa              144
North Dakota      144
Michigan          144
Kansas            144
Missouri          144
Minnesota         144
Montana           144
Kentucky          144
Ohio              144
North Carolina    144
South Carolina    144
Nebraska          144
Maine             144
Alaska            144
Hawaii            144
Wyoming           144
Washington        144
Name: count, dtype: int64

incorrectval3 = {}
for col in numcols3:
    neg3 = adidas[(adidas[col] < 0)]  # negative values problem
    if len(neg) > 0:
        incorrectval3[col] = neg3

print("Incorrect numeric values:", incorrectval3)

Incorrect numeric values: {}

6) Data Analysis

a) Analysis 1

nationalsm = adidas.groupby('Sales Method').agg(Total_Sales=('Total Sales', 'sum'),
                                                Total_Operating_Profit=('Operating Profit', 'sum')).reset_index()

nationalsm = nationalsm.rename(columns={"Total_Sales": "Total Sales","Total_Operating_Profit": "Total Operating Profit"})
nationalsm #compare sales method disregarding city/state
#in-store are most popular

	Sales Method	Total Sales	Total Operating Profit
0	In-store	356643750	127591382
1	Online	247672882	96555337
2	Outlet	295585493	107988403

profits = nationalsm.set_index('Sales Method')['Total Operating Profit'].to_dict()

comparisons = {"In-store vs Online": profits['In-store'] - profits['Online'], 
"In-store vs Outlet": profits['In-store'] - profits['Outlet'],
"Online vs Outlet": profits['Online'] - profits['Outlet']} 

#turn it in to a df
comparisons_df = pd.DataFrame(list(comparisons.items()), columns=["Comparison", "Total Operating Profit Difference"])
comparisons_df

	Comparison	Total Operating Profit Difference
0	In-store vs Online	31036045
1	In-store vs Outlet	19602979
2	Online vs Outlet	-11433066

labels = list(comparisons.keys())
values = list(comparisons.values())

plt.bar(labels, values, color=["#BDD7EE", "#C9E2F4", "#D6EAF8"])

plt.axhline(0, color='black', linewidth=1)
plt.yticks(values, [f"{v:,}" for v in values])   #give exact values
plt.ylabel("Total Operating Profit Difference ($)")
plt.title("Total Operating Profit Differences Between Sales Methods")

plt.tight_layout()
plt.show()

combosm = adidas.groupby(['Sales Method', 'City', 'State']).agg(Total_Sales=('Total Sales', 'sum'), 
Total_Operating_Profit=('Operating Profit', 'sum')).reset_index()
#.reset_index() to make the multiindex --> columns

combosm = combosm.rename(columns={"Total_Sales": "Total Sales","Total_Operating_Profit": "Total Operating Profit"})
combosm = combosm.sort_values("State").reset_index(drop=True)

combosm.head(10)

	Sales Method	City	State	Total Sales	Total Operating Profit
0	Outlet	Birmingham	Alabama	17191868	8863634
1	Online	Birmingham	Alabama	441556	283947
2	Outlet	Anchorage	Alaska	14380750	4340225
3	Online	Anchorage	Alaska	372353	158134
4	Outlet	Phoenix	Arizona	356709	135066
5	Online	Phoenix	Arizona	15425512	5209612
6	Outlet	Little Rock	Arkansas	284365	122671
7	Online	Little Rock	Arkansas	12354982	4793032
8	Online	Los Angeles	California	629877	296413
9	Outlet	Los Angeles	California	25005036	8748518

best_city_state = (
    combosm.loc[combosm.groupby(["City", "State"])["Total Operating Profit"].idxmax()].sort_values(["State", "City"])
    .reset_index(drop=True))

best_city_state #for every city/state, only kept the sales method that has greated Total Operating Profit

	Sales Method	City	State	Total Sales	Total Operating Profit
0	Outlet	Birmingham	Alabama	17191868	8863634
1	Outlet	Anchorage	Alaska	14380750	4340225
2	Online	Phoenix	Arizona	15425512	5209612
3	Online	Little Rock	Arkansas	12354982	4793032
4	Outlet	Los Angeles	California	25005036	8748518
5	Outlet	San Francisco	California	33680352	9900809
6	In-store	Denver	Colorado	20475000	7458942
7	In-store	Hartford	Connecticut	11030000	3911402
8	In-store	Wilmington	Delaware	11988750	4373366
9	In-store	Miami	Florida	30807500	11765821
10	In-store	Orlando	Florida	20997500	6544939
11	Online	Atlanta	Georgia	18575819	6723339
12	Outlet	Honolulu	Hawaii	21720000	5633381
13	Online	Boise	Idaho	18840711	7920946
14	Outlet	Chicago	Illinois	9548250	3790803
15	In-store	Indianapolis	Indiana	8612750	3266316
16	In-store	Des Moines	Iowa	7238000	2566213
17	Online	Wichita	Kansas	9972864	3510159
18	Online	Louisville	Kentucky	10072848	3935833
19	Online	New Orleans	Louisiana	19837823	7841032
20	Outlet	Portland	Maine	8962500	3077681
21	In-store	Baltimore	Maryland	7563250	2664616
22	In-store	Boston	Massachusetts	10553750	3151648
23	In-store	Detroit	Michigan	18153500	7872830
24	Outlet	Minneapolis	Minnesota	5355500	1928218
25	Online	Jackson	Mississippi	15240524	6211421
26	In-store	St. Louis	Missouri	9437500	3778630
27	Outlet	Billings	Montana	15311250	6023630
28	Outlet	Omaha	Nebraska	5778000	2358530
29	In-store	Las Vegas	Nevada	11558750	3657127
30	Outlet	Manchester	New Hampshire	10380394	3595323
31	In-store	Newark	New Jersey	9682000	3533704
32	Online	Albuquerque	New Mexico	19424023	6569822
33	In-store	Albany	New York	23815000	9121071
34	Outlet	New York	New York	35277364	12121563
35	Online	Charlotte	North Carolina	23956531	9756425
36	In-store	Fargo	North Dakota	6742500	2322475
37	Online	Columbus	Ohio	18484583	7528843
38	Online	Oklahoma City	Oklahoma	10378110	4003232
39	In-store	Portland	Oregon	20336250	7127816
40	Outlet	Philadelphia	Pennsylvania	10102069	4023655
41	In-store	Providence	Rhode Island	8049500	2869454
42	Online	Charleston	South Carolina	29285637	11324247
43	Online	Sioux Falls	South Dakota	8495576	2943322
44	Outlet	Knoxville	Tennessee	17625079	8233073
45	Outlet	Dallas	Texas	20384362	8558821
46	Outlet	Houston	Texas	24831905	9528682
47	In-store	Salt Lake City	Utah	9232000	3649541
48	In-store	Burlington	Vermont	13679250	5456531
49	In-store	Richmond	Virginia	20556250	7271816
50	In-store	Seattle	Washington	25676250	6737442
51	In-store	Charleston	West Virginia	10420000	4142252
52	In-store	Milwaukee	Wisconsin	7531750	2706338
53	In-store	Cheyenne	Wyoming	18105750	6320027

method_counts = best_city_state["Sales Method"].value_counts()

plt.figure(figsize=(6,6))

plt.pie(method_counts,labels=method_counts.index,
    autopct=lambda pct: f"{pct:.1f}%\n({int(round(pct/100 * method_counts.sum()))})",
    colors=["#BDD7EE", "#C9E2F4", "#D6EAF8"],
    startangle=90)

plt.title("Percentage of Cities Where Each Sales Method Leads in Performance")
plt.tight_layout()
plt.show()

unique_city_state_count = adidas.groupby(["City", "State"]).ngroups
unique_city_state_count
#sanity check

54

top_profitable = combosm.sort_values('Total Operating Profit', ascending=False).reset_index(drop=True) 
top_profitable.head() #there should be 3 new yorks

	Sales Method	City	State	Total Sales	Total Operating Profit
0	Outlet	New York	New York	35277364	12121563
1	In-store	Miami	Florida	30807500	11765821
2	Online	Charleston	South Carolina	29285637	11324247
3	Outlet	San Francisco	California	33680352	9900809
4	Online	Charlotte	North Carolina	23956531	9756425

plottop = top_profitable.head(25)

labels = plottop['City'] + ", " + plottop['State'] + " (" + plottop['Sales Method'] + ")"
plt.figure(figsize=(15, 7))
plt.barh(labels, plottop['Total Operating Profit'], color="#BDD7EE")
plt.xlabel("Total Operating Profit")
plt.ylabel("City, State (Sales Method)")
xticks = plt.xticks()[0]
plt.xticks(xticks, [f"{int(x):,}" for x in xticks])
plt.title("Top 25 Most Profitable City/State (Sales Method)")

plt.tight_layout()
plt.show()

b) Analysis 2

city_state_profit = adidas.groupby(["City", "State"]).agg(Total_Sales=("Total Sales", "sum"),
                                                          Total_Operating_Profit=("Operating Profit", "sum")).reset_index().sort_values("Total_Operating_Profit", ascending=False)
city_state_profit = city_state_profit.rename(columns={"Total_Sales": "Total Sales","Total_Operating_Profit": "Total Operating Profit"})
city_state_profit.head(10)

	City	State	Total Sales	Total Operating Profit
36	New York	New York	39801235	13899981
32	Miami	Florida	31600863	12168628
10	Charleston	South Carolina	29285637	11324247
48	San Francisco	California	34539220	10256252
23	Houston	Texas	25456882	9845140
12	Charlotte	North Carolina	23956531	9756425
0	Albany	New York	24427804	9429864
35	New Orleans	Louisiana	23750781	9417239
6	Birmingham	Alabama	17633424	9147581
29	Los Angeles	California	25634913	9044931

state_profit = adidas.groupby(["State"]).agg(Total_Sales=("Total Sales", "sum"),
                                             Total_Operating_Profit=("Operating Profit", "sum")).reset_index().sort_values("Total_Operating_Profit", ascending=False)
state_profit = state_profit.rename(columns={"Total_Sales": "Total Sales","Total_Operating_Profit": "Total Operating Profit"})
state_profit.head(10)

	State	Total Sales	Total Operating Profit
31	New York	64229039	23329845
8	Florida	59283714	20926219
4	California	60174133	19301183
42	Texas	46359746	18688214
39	South Carolina	29285637	11324247
32	North Carolina	23956531	9756425
17	Louisiana	23750781	9417239
0	Alabama	17633424	9147581
41	Tennessee	18067440	8493670
21	Michigan	18625433	8135902

city_state_list = [
    ('New York', 'New York'),
    ('Houston', 'Texas'),
    ('San Francisco', 'California'),
    ('Los Angeles', 'California'),
    ('Chicago', 'Illinois'),
    ('Dallas', 'Texas'),
    ('Philadelphia', 'Pennsylvania'),
    ('Las Vegas', 'Nevada'),
    ('Denver', 'Colorado'),
    ('Seattle', 'Washington'),
    ('Miami', 'Florida'),
    ('Minneapolis', 'Minnesota'),
    ('Billings', 'Montana'),
    ('Knoxville', 'Tennessee'),
    ('Omaha', 'Nebraska'),
    ('Birmingham', 'Alabama'),
    ('Portland', 'Maine'),
    ('Anchorage', 'Alaska'),
    ('Honolulu', 'Hawaii'),
    ('Orlando', 'Florida'),
    ('Albany', 'New York'),
    ('Cheyenne', 'Wyoming'),
    ('Richmond', 'Virginia'),
    ('Detroit', 'Michigan'),
    ('St. Louis', 'Missouri'),
    ('Salt Lake City', 'Utah'),
    ('Portland', 'Oregon'),
    ('New Orleans', 'Louisiana'),
    ('Boise', 'Idaho'),
    ('Phoenix', 'Arizona'),
    ('Albuquerque', 'New Mexico'),
    ('Atlanta', 'Georgia'),
    ('Charleston', 'South Carolina'),
    ('Charlotte', 'North Carolina'),
    ('Columbus', 'Ohio'),
    ('Louisville', 'Kentucky'),
    ('Jackson', 'Mississippi'),
    ('Little Rock', 'Arkansas'),
    ('Oklahoma City', 'Oklahoma'),
    ('Wichita', 'Kansas'),
    ('Sioux Falls', 'South Dakota'),
    ('Fargo', 'North Dakota'),
    ('Des Moines', 'Iowa'),
    ('Milwaukee', 'Wisconsin'),
    ('Indianapolis', 'Indiana'),
    ('Charleston', 'West Virginia'),
    ('Baltimore', 'Maryland'),
    ('Wilmington', 'Delaware'),
    ('Newark', 'New Jersey'),
    ('Hartford', 'Connecticut'),
    ('Providence', 'Rhode Island'),
    ('Boston', 'Massachusetts'),
    ('Burlington', 'Vermont'),
    ('Manchester', 'New Hampshire')]

city_df = pd.DataFrame(city_state_list, columns=["City", "State"])

state_map = {
    "New York": "NY", "Texas": "TX", "California": "CA", "Illinois": "IL",
    "Pennsylvania": "PA", "Nevada": "NV", "Colorado": "CO", "Washington": "WA",
    "Florida": "FL", "Minnesota": "MN", "Montana": "MT", "Tennessee": "TN",
    "Nebraska": "NE", "Alabama": "AL", "Maine": "ME", "Alaska": "AK",
    "Hawaii": "HI", "Wyoming": "WY", "Virginia": "VA", "Michigan": "MI",
    "Missouri": "MO", "Utah": "UT", "Oregon": "OR", "Louisiana": "LA",
    "Idaho": "ID", "Arizona": "AZ", "New Mexico": "NM", "Georgia": "GA",
    "South Carolina": "SC", "North Carolina": "NC", "Ohio": "OH",
    "Kentucky": "KY", "Mississippi": "MS", "Arkansas": "AR", "Oklahoma": "OK",
    "Kansas": "KS", "South Dakota": "SD", "North Dakota": "ND",
    "Iowa": "IA", "Wisconsin": "WI", "Indiana": "IN", "Maryland": "MD",
    "Delaware": "DE", "New Jersey": "NJ", "Connecticut": "CT",
    "Rhode Island": "RI", "Massachusetts": "MA", "Vermont": "VT",
    "New Hampshire": "NH"}

city_df["State_Abbrev"] = city_df["State"].map(state_map)
us_cities = pd.read_csv("uscities.csv")

city_coords = city_df.merge(us_cities[["city", "state_id", "lat", "lng"]],left_on=["City", "State_Abbrev"],
    right_on=["city", "state_id"],
    how="left")

city_coords = city_coords.drop(columns=["city", "state_id"])
city_coords = city_coords.rename(columns={"lat": "Latitude", "lng": "Longitude"})

#city_coords (city, state, state_abbrev, latitude, longitude)

city_profit = (adidas.groupby(["City", "State"])["Operating Profit"]
          .sum()
          .reset_index())
merged_city_map = city_profit.merge(
    city_coords,
    on=["City", "State"],
    how="left")
merged_city_map

	City	State	Operating Profit	State_Abbrev	Latitude	Longitude
0	Albany	New York	9429864	NY	42.6664	-73.7987
1	Albuquerque	New Mexico	6738070	NM	35.1054	-106.6465
2	Anchorage	Alaska	4498359	AK	61.1508	-149.1091
3	Atlanta	Georgia	6893299	GA	33.7628	-84.4220
4	Baltimore	Maryland	2757648	MD	39.3051	-76.6144
5	Billings	Montana	6232040	MT	45.7891	-108.5526
6	Birmingham	Alabama	9147581	AL	33.5279	-86.7971
7	Boise	Idaho	8121123	ID	43.6005	-116.2308
8	Boston	Massachusetts	3353884	MA	42.3188	-71.0852
9	Burlington	Vermont	5785973	VT	44.4876	-73.2316
10	Charleston	South Carolina	11324247	SC	32.8168	-79.9687
11	Charleston	West Virginia	4282958	NaN	NaN	NaN
12	Charlotte	North Carolina	9756425	NC	35.2083	-80.8303
13	Cheyenne	Wyoming	6544076	WY	41.1350	-104.7902
14	Chicago	Illinois	3920377	IL	41.8375	-87.6866
15	Columbus	Ohio	7528843	OH	39.9862	-82.9855
16	Dallas	Texas	8843074	TX	32.7935	-96.7667
17	Denver	Colorado	7713562	CO	39.7620	-104.8758
18	Des Moines	Iowa	2655220	IA	41.5725	-93.6105
19	Detroit	Michigan	8135902	MI	42.3834	-83.1024
20	Fargo	North Dakota	2688518	ND	46.8651	-96.8292
21	Hartford	Connecticut	4152327	CT	41.7661	-72.6834
22	Honolulu	Hawaii	5849802	HI	21.3294	-157.8460
23	Houston	Texas	9845140	TX	29.7860	-95.3885
24	Indianapolis	Indiana	3379262	IN	39.7771	-86.1458
25	Jackson	Mississippi	6369102	MS	32.3157	-90.2125
26	Knoxville	Tennessee	8493670	TN	35.9692	-83.9496
27	Las Vegas	Nevada	7060660	NV	36.2333	-115.2654
28	Little Rock	Arkansas	4915703	AR	34.7256	-92.3577
29	Los Angeles	California	9044931	CA	34.1141	-118.4068
30	Louisville	Kentucky	3935833	KY	38.1663	-85.6485
31	Manchester	New Hampshire	5786209	NH	42.9848	-71.4447
32	Miami	Florida	12168628	FL	25.7840	-80.2101
33	Milwaukee	Wisconsin	2800835	WI	43.0642	-87.9675
34	Minneapolis	Minnesota	2670235	MN	44.9635	-93.2678
35	New Orleans	Louisiana	9417239	LA	30.0687	-89.9288
36	New York	New York	13899981	NY	40.6943	-73.9249
37	Newark	New Jersey	3657247	NJ	40.7245	-74.1725
38	Oklahoma City	Oklahoma	4106535	OK	35.4676	-97.5136
39	Omaha	Nebraska	2439482	NE	41.2627	-96.0529
40	Orlando	Florida	8757591	FL	28.4773	-81.3370
41	Philadelphia	Pennsylvania	4156749	PA	40.0077	-75.1339
42	Phoenix	Arizona	5344678	AZ	33.5722	-112.0892
43	Portland	Maine	3187662	ME	43.6773	-70.2715
44	Portland	Oregon	7573151	OR	45.5371	-122.6500
45	Providence	Rhode Island	3045828	RI	41.8230	-71.4187
46	Richmond	Virginia	7719439	VA	37.5295	-77.4756
47	Salt Lake City	Utah	3873440	UT	40.7776	-111.9311
48	San Francisco	California	10256252	CA	37.7558	-122.4449
49	Seattle	Washington	6991412	WA	47.6211	-122.3244
50	Sioux Falls	South Dakota	2943322	SD	43.5396	-96.7311
51	St. Louis	Missouri	3907217	MO	38.6359	-90.2451
52	Wichita	Kansas	3510159	KS	37.6895	-97.3443
53	Wilmington	Delaware	4524358	DE	39.7415	-75.5416

import plotly.express as px
import plotly.io as pio
fig = px.scatter_geo(
    merged_city_map,
    lat="Latitude",
    lon="Longitude",
    scope="usa",
    projection="albers usa",
    color="Operating Profit",
    size="Operating Profit",
    text="City",
    hover_name="City",
    hover_data=["State", "Operating Profit"],
    color_continuous_scale="Blues")

fig.update_traces(textposition="top center")
fig.update_layout(
    title="Adidas Operating Profit by City/State",
    height=2000,
    width=1200)
fig.show()

## CHECK THIRD HTML (UNABLE TO RENDER UNITED STATES MAP INTO CURRENT HTML)!!!!

pio.write_html(fig,"adidas_operating_profit_map.html", full_html=True, include_plotlyjs="cdn")

retailer = adidas.groupby(["Retailer"]).agg(
    Total_Sales=("Total Sales", "sum"), Total_Operating_Profit=("Operating Profit", "sum")).reset_index().sort_values("Total_Operating_Profit", ascending=False)

retailers = retailer.rename(columns={"Total_Sales": "Total Sales",
    "Total_Operating_Profit": "Total Operating Profit"})
totals = retailers.set_index("Retailer")["Total Operating Profit"]

plt.figure(figsize=(8,6))
sns.barplot(
    x=totals.index, 
    y=totals.values, 
    palette="Blues_d"
)
plt.title("Total Operating Profit by Retailer")
plt.ylabel("Total Operating Profit ($)")
plt.xlabel("Retailer")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

retailers2 = adidas.groupby(["Retailer", "City", "State"]).agg(
    Total_Sales=("Total Sales", "sum"), Total_Operating_Profit=("Operating Profit", "sum")
).reset_index().sort_values("Total_Operating_Profit", ascending=False)

retailers2 = retailers2.rename(columns={"Total_Sales": "Total Sales","Total_Operating_Profit": "Total Operating Profit"})

store_counts = retailers2["Retailer"].value_counts()
print(store_counts)

retailers2["Normalized_Profit"] = retailers2.apply(lambda row: row["Total Operating Profit"] / store_counts[row["Retailer"]],
    axis=1)

plt.figure(figsize=(10,6))
sns.boxplot(
    data=retailers2,
    x="Retailer",
    y="Normalized_Profit",
    palette="Blues")

plt.title("Normalized Operating Profit per Store Across Cities/States by Retailer")
plt.xlabel("Retailer")
plt.ylabel("Normalized Operating Profit ($)")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Retailer
Foot Locker      33
Sports Direct    26
West Gear        24
Kohl's           12
Amazon            9
Walmart           6
Name: count, dtype: int64

c) Analysis 3

products = adidas.groupby("Product").agg(Total_Sales=("Total Sales", "sum"), 
                                         Total_Operating_Profit=("Operating Profit", "sum")).reset_index().sort_values("Total_Operating_Profit", ascending=False)
products = products.rename(columns={"Total_Sales": "Total Sales","Total_Operating_Profit": "Total Operating Profit"})
products

	Product	Total Sales	Total Operating Profit
2	Men's Street Footwear	208826244	82802323
3	Women's Apparel	179038860	68650996
1	Men's Athletic Footwear	153673680	51846964
5	Women's Street Footwear	128002813	45095897
0	Men's Apparel	123728632	44763099
4	Women's Athletic Footwear	106631896	38975843

products_count = adidas.groupby(["Product"]).ngroups
products_count
#sanity check

6

plt.figure(figsize=(12,15)) #change axis a little bit
plt.barh(products["Product"], products["Total Operating Profit"], color="#BDD7EE")

plt.xlabel("Total Operating Profit ($)")
plt.title("Total Operating Profit by Product Type")

for i, v in enumerate(products["Total Operating Profit"]):
    plt.text(v, i, f"${v:,.0f}", va="center")

plt.tight_layout()
plt.show()

product_city_state = (
    adidas.groupby(["Product", "City", "State"]).agg(Total_Operating_Profit=("Operating Profit", "sum")).reset_index())

plt.figure(figsize=(14,6))
sns.boxplot(data=product_city_state, x="Product", y="Total_Operating_Profit",palette="Blues")

plt.xticks(rotation=60, ha='right')
plt.ylabel("Total Operating Profit ($)")
plt.title("Distribution of Total Operating Profit Across Cities/States by Product Type")

plt.tight_layout()
plt.show()