Store sales and profit analysis is the task of analyzing the performance of a retail store in terms of its sales and profits. It helps businesses identify areas for improvement and make data-driven decisions to optimize their operations, pricing, marketing, and inventory management strategies to drive revenue and growth. So, if you want to learn how to analyze the sales and profit of a store, this article is for you. In this article, I will take you through the task of Store sales and profit analysis using Python.
Store Sales and Profit Analysis: Dataset
For the task of Sales and Profit Analysis, we need to have a dataset with time-period data, sales data, product data, pricing data, customer data, etc.
I found an ideal dataset for this task on Kaggle. You can download the dataset from here.
In the section below, I’ll take you through the task of Store Sales and Profit analysis using Python.
Store Sales and Profit Analysis using Python
Let’s start this task by importing the necessary Python libraries and the dataset(download the dataset from here):
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import plotly.colors as colors
pio.templates.default = "plotly_white"
data = pd.read_csv("Sample - Superstore.csv", encoding='latin-1')
print(data.head()) Row ID Order ID Order Date Ship Date Ship Mode Customer ID \
0 1 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520
1 2 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520
2 3 CA-2016-138688 6/12/2016 6/16/2016 Second Class DV-13045
3 4 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335
4 5 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335
Customer Name Segment Country City ... \
0 Claire Gute Consumer United States Henderson ...
1 Claire Gute Consumer United States Henderson ...
2 Darrin Van Huff Corporate United States Los Angeles ...
3 Sean O'Donnell Consumer United States Fort Lauderdale ...
4 Sean O'Donnell Consumer United States Fort Lauderdale ...
Postal Code Region Product ID Category Sub-Category \
0 42420 South FUR-BO-10001798 Furniture Bookcases
1 42420 South FUR-CH-10000454 Furniture Chairs
2 90036 West OFF-LA-10000240 Office Supplies Labels
3 33311 South FUR-TA-10000577 Furniture Tables
4 33311 South OFF-ST-10000760 Office Supplies Storage
Product Name Sales Quantity \
0 Bush Somerset Collection Bookcase 261.9600 2
1 Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3
2 Self-Adhesive Address Labels for Typewriters b... 14.6200 2
3 Bretford CR4500 Series Slim Rectangular Table 957.5775 5
4 Eldon Fold 'N Roll Cart System 22.3680 2
Discount Profit
0 0.00 41.9136
1 0.00 219.5820
2 0.00 6.8714
3 0.45 -383.0310
4 0.20 2.5164
[5 rows x 21 columns]
Let’s start by looking at the descriptive statistics of the dataset:
print(data.describe())
Row ID Postal Code Sales Quantity Discount \
count 9994.000000 9994.000000 9994.000000 9994.000000 9994.000000
mean 4997.500000 55190.379428 229.858001 3.789574 0.156203
std 2885.163629 32063.693350 623.245101 2.225110 0.206452
min 1.000000 1040.000000 0.444000 1.000000 0.000000
25% 2499.250000 23223.000000 17.280000 2.000000 0.000000
50% 4997.500000 56430.500000 54.490000 3.000000 0.200000
75% 7495.750000 90008.000000 209.940000 5.000000 0.200000
max 9994.000000 99301.000000 22638.480000 14.000000 0.800000
Profit
count 9994.000000
mean 28.656896
std 234.260108
min -6599.978000
25% 1.728750
50% 8.666500
75% 29.364000
max 8399.976000
The dataset has an order date column. We can use this column to create new columns like order month, order year, and order day, which will be very valuable for sales and profit analysis according to time periods. So let’s add these columns:
data['Order Date'] = pd.to_datetime(data['Order Date']) data['Ship Date'] = pd.to_datetime(data['Ship Date']) data['Order Month'] = data['Order Date'].dt.month data['Order Year'] = data['Order Date'].dt.year data['Order Day of Week'] = data['Order Date'].dt.dayofweek
Now let’s have a look at the monthly sales:
sales_by_month = data.groupby('Order Month')['Sales'].sum().reset_index()
fig = px.line(sales_by_month,
x='Order Month',
y='Sales',
title='Monthly Sales Analysis')
fig.show()
Now let’s have a look at the sales by category:
sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
fig = px.pie(sales_by_category,
values='Sales',
names='Category',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Sales Analysis by Category', title_font=dict(size=24))
fig.show()
Now let’s have a look at the sales by sub-category:
sales_by_subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
fig = px.bar(sales_by_subcategory,
x='Sub-Category',
y='Sales',
title='Sales Analysis by Sub-Category')
fig.show()
Now let’s have a look at the monthly profits:
profit_by_month = data.groupby('Order Month')['Profit'].sum().reset_index()
fig = px.line(profit_by_month,
x='Order Month',
y='Profit',
title='Monthly Profit Analysis')
fig.show()
Now let’s have a look at the profit by category:
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
fig = px.pie(profit_by_category,
values='Profit',
names='Category',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Profit Analysis by Category', title_font=dict(size=24))
fig.show()
Now let’s have a look at the profit by sub-category:
profit_by_subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
fig = px.bar(profit_by_subcategory, x='Sub-Category',
y='Profit',
title='Profit Analysis by Sub-Category')
fig.show()
Now let’s have a look at the sales and profit analysis by customer segments:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
color_palette = colors.qualitative.Pastel
fig = go.Figure()
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'],
y=sales_profit_by_segment['Sales'],
name='Sales',
marker_color=color_palette[0]))
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'],
y=sales_profit_by_segment['Profit'],
name='Profit',
marker_color=color_palette[1]))
fig.update_layout(title='Sales and Profit Analysis by Customer Segment',
xaxis_title='Customer Segment', yaxis_title='Amount')
fig.show()
So the store has higher profits from the product sales for consumers, but the profit from corporate product sales is better in the sales-to-profit ratio. Let’s have a look at it to validate our findings:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
sales_profit_by_segment['Sales_to_Profit_Ratio'] = sales_profit_by_segment['Sales'] / sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment', 'Sales_to_Profit_Ratio']])Segment Sales_to_Profit_Ratio 0 Consumer 8.659471 1 Corporate 7.677245 2 Home Office 7.125416
So this is how you can analyze the sales and profit of a store using Python.
Summary
Store sales and profit analysis help businesses identify areas for improvement and make data-driven decisions to optimize their operations, pricing, marketing, and inventory management strategies to drive revenue and growth. I hope you liked this article on the task of analyzing the sales and profit of a store using Python. Feel free to ask valuable questions in the comments section below.
