Store Sales and Profit Analysis using Python

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.