Clothing Sales Exploratory Data Analysis¶

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In [1]:
# Load in some packages
import calendar
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")

# load dataset
clothing_df =pd.read_excel(r"C:\Users\jki\Downloads\clothing sales dataset.xlsx")  
clothing_df.head(6)
Out[1]:
Receipt no Location Customer name Session id Sale date Status Ordertype name Token number Product name Product Category Cost Per Unit Selling Per Unit Quantity Cost Amount Sales Amount Profit
0 BL11 Chennai Naga 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200
1 BL11 Chennai Mahesh 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200
2 BL12 Hyderabad Ganesh 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200
3 BL12 Chennai Kumar 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200
4 BL12 Bangalore Vijay 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200
5 BL12 Bangalore Sunil 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200

After getting a sense of the data's structure, it is a good idea to look at a statistical summary of the variables with df.describe()

In [3]:
clothing_df.describe()
Out[3]:
Session id Token number Cost Per Unit Selling Per Unit Quantity Cost Amount Sales Amount Profit
count 4972.0 4972.0 4972.000000 4972.000000 4972.000000 4972.000000 4972.000000 4972.000000
mean 28.0 0.0 668.825422 868.825422 1.038013 697.687047 905.289622 207.602574
std 0.0 0.0 206.379808 206.379808 0.248842 293.852867 330.276940 49.768368
min 28.0 0.0 100.000000 300.000000 1.000000 100.000000 300.000000 200.000000
25% 28.0 0.0 400.000000 600.000000 1.000000 400.000000 600.000000 200.000000
50% 28.0 0.0 700.000000 900.000000 1.000000 700.000000 900.000000 200.000000
75% 28.0 0.0 900.000000 1100.000000 1.000000 900.000000 1100.000000 200.000000
max 28.0 0.0 1000.000000 1200.000000 3.000000 2700.000000 3300.000000 600.000000
In [4]:
# lets  check for missing values
missing_values  = clothing_df.isna().sum()
print(missing_values)

Receipt no          0
Location            0
Customer name       0
Session id          0
Sale date           0
Status              0
Ordertype name      0
Token number        0
Product name        0
Product Category    0
Cost Per Unit       0
Selling Per Unit    0
Quantity            0
Cost Amount         0
Sales Amount        0
Profit              0
dtype: int64
In [5]:
# lets check the data types
clothing_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4972 entries, 0 to 4971
Data columns (total 16 columns):
 #   Column            Non-Null Count  Dtype         
---  ------            --------------  -----         
 0   Receipt no        4972 non-null   object        
 1   Location          4972 non-null   object        
 2   Customer name     4972 non-null   object        
 3   Session id        4972 non-null   int64         
 4   Sale date         4972 non-null   datetime64[ns]
 5   Status            4972 non-null   object        
 6   Ordertype name    4972 non-null   object        
 7   Token number      4972 non-null   int64         
 8   Product name      4972 non-null   object        
 9   Product Category  4972 non-null   object        
 10  Cost Per Unit     4972 non-null   int64         
 11  Selling Per Unit  4972 non-null   int64         
 12  Quantity          4972 non-null   int64         
 13  Cost Amount       4972 non-null   int64         
 14  Sales Amount      4972 non-null   int64         
 15  Profit            4972 non-null   int64         
dtypes: datetime64[ns](1), int64(8), object(7)
memory usage: 621.6+ KB
In [6]:
# Let's create a new variable, Month, from 'Order Date':

clothing_df['Month'] = clothing_df['Sale date'].dt.month
clothing_df['Month'].describe()
Out[6]:
count    4972.000000
mean        5.385358
std         3.208746
min         1.000000
25%         3.000000
50%         4.000000
75%         9.000000
max        12.000000
Name: Month, dtype: float64
In [7]:
clothing_df.head(5)
Out[7]:
Receipt no Location Customer name Session id Sale date Status Ordertype name Token number Product name Product Category Cost Per Unit Selling Per Unit Quantity Cost Amount Sales Amount Profit Month
0 BL11 Chennai Naga 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200 3
1 BL11 Chennai Mahesh 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200 3
2 BL12 Hyderabad Ganesh 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200 3
3 BL12 Chennai Kumar 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200 3
4 BL12 Bangalore Vijay 28 2020-03-06 Ordered On Shop 0 Jeans Levis Casual Wear 900 1100 1 900 1100 200 3
In [8]:
clothing_df['Year'] =clothing_df['Sale date'].dt.year
clothing_df['Year'].describe()
Out[8]:
count    4972.000000
mean     2019.286203
std         0.452031
min      2019.000000
25%      2019.000000
50%      2019.000000
75%      2020.000000
max      2020.000000
Name: Year, dtype: float64

1. What was the best month for sales? How much was earned that month?¶

In [9]:
# Load in some packages
import calendar
import warnings
import pandas as pd
import matplotlib.pyplot as plt
from itertools import combinations
from collections import Counter

warnings.filterwarnings("ignore")

# 1. What was the best month for sales? How much was earned that month?

# Replace NaN or inf values in the 'Month' column with a default value (e.g., 0)
clothing_df['Month'] = clothing_df['Month'].fillna(0).astype(int)

# Convert month numbers to abbreviated month names
clothing_df['Month Name'] = clothing_df['Month'].apply(lambda x: calendar.month_abbr[x])

# Group by month and calculate total sales for each month
sales_by_month = clothing_df.groupby('Month Name').sum()['Sales Amount']

# Find the best month for sales and the corresponding earnings
best_month = sales_by_month.idxmax()
earnings_for_best_month = sales_by_month.max()

print(f"The best month for sales was {best_month} with earnings of ${earnings_for_best_month:,.2f}")

The best month for sales was Mar with earnings of $1,142,600.00

2. What Location had the Highest number of Sales?¶

In [22]:
# Plot the highest number of sales for each Location

Sales_by_location = clothing_df.groupby('Location').sum()['Sales Amount'] # Take the number of sales for each Location
unique_location =  clothing_df.groupby('Location').sum().index # Take all Location

# Plot the highest number of sales for each Location
plt.bar(unique_location, Sales_by_location)
plt.xticks(unique_location, rotation = 90, size = 8)
plt.ylabel('Sales Amount)')
plt.xlabel('Location')
plt.show()

2. Which Customer bought the most?¶

In [25]:
# Plot the highest number of sales for each Customer

Sales_by_Customer_name = clothing_df.groupby('Customer name').sum()['Sales Amount'] # Take the number of sales for each Location
unique_Customer_name =  clothing_df.groupby('Customer name').sum().index # Take all Location

# Plot the highest number of sales for each Customer
plt.bar(unique_Customer_name,Sales_by_Customer_name )
plt.xticks(unique_Customer_name, rotation = 90, size = 8)
plt.ylabel('Sales Amount)')
plt.xlabel('Customer name')
plt.show()

3. Which Ordertype name had the most sales¶

In [30]:
# Plot the highest number of sales for each Order Type Name

Sales_by_Ordertype_name  = clothing_df.groupby('Ordertype name').sum()['Sales Amount'] # Take the number of sales for each Location
unique_Ordertype_name  =  clothing_df.groupby('Ordertype name').sum().index # Take all Location

# Plot the highest number of sales for each Order Type Name
plt.bar(unique_Ordertype_name,Sales_by_Ordertype_name)
plt.xticks(unique_Ordertype_name, rotation = 90, size = 8)
plt.ylabel('Sales Amount)')
plt.xlabel('Ordertype name')
plt.show()

4. Which Product had the most sales¶

In [33]:
# Plot the highest number of sales for each Product_name

Sales_by_Product_name = clothing_df.groupby('Product name').sum()['Sales Amount'] # Take the number of sales for each Product_name
unique_Product_name =  clothing_df.groupby('Product name').sum().index # Take all Product_name

# Plot the highest number of sales for each Product_name
plt.bar(unique_Product_name,Sales_by_Product_name)
plt.xticks(unique_Product_name, rotation = 90, size = 8)
plt.ylabel('Sales Amount)')
plt.xlabel('Product_name')
plt.show()

5. Which Product Category had the highest sales¶

In [34]:
# Plot the highest number of sales for each Product Category

Sales_by_Product_Category = clothing_df.groupby('Product Category').sum()['Sales Amount'] # Take the number of sales for each Product Category
unique_Product_Category =  clothing_df.groupby('Product Category').sum().index # Take all Product Category

# Plot the highest number of sales for each Product Category
plt.bar(unique_Product_Category,Sales_by_Product_Category )
plt.xticks(unique_Product_Category, rotation = 90, size = 8)
plt.ylabel('Sales Amount)')
plt.xlabel('Product Category')
plt.show()

5. What Location sold the most units¶

In [63]:
# Plot the highest number of sales for each Location

Sales_by_location = clothing_df.groupby('Location').sum()['Quantity'] # Take the number of sales for each Location
unique_location =  clothing_df.groupby('Location').sum().index # Take all Location

# Plot the highest number of sales for each Location
plt.bar(unique_location, Sales_by_location)
plt.xticks(unique_location, rotation = 90, size = 8)
plt.ylabel('Quantity Amount)')
plt.xlabel('Location')
plt.show()

6 Which Customer bought the most units?¶

In [64]:
# Plot the highest number of sales for each Customer

Sales_by_Customer_name = clothing_df.groupby('Customer name').sum()['Quantity'] # Take the number of sales for each Location
unique_Customer_name =  clothing_df.groupby('Customer name').sum().index # Take all Location

# Plot the highest number of sales for each Customer
plt.bar(unique_Customer_name,Sales_by_Customer_name )
plt.xticks(unique_Customer_name, rotation = 90, size = 8)
plt.ylabel('Quantity Amount)')
plt.xlabel('Customer name')
plt.show()

7 Which Ordertype name sold the most units¶

In [66]:
# Plot the highest number of sales for each Order Type

Sales_by_Ordertype_name  = clothing_df.groupby('Ordertype name').sum()['Quantity'] # Take the number of sales for each Location
unique_Ordertype_name  =  clothing_df.groupby('Ordertype name').sum().index # Take all Location

# Plot the highest number of sales for each Order Type
plt.bar(unique_Ordertype_name ,Sales_by_Ordertype_name)
plt.xticks(unique_Ordertype_name, rotation = 90, size = 8)
plt.ylabel('Quantity Amount)')
plt.xlabel('Ordertype_name')
plt.show()

8 Which Product sold the most units¶

In [57]:
# Plot the highest number of sales for each Product_name

Sales_by_Product_name = clothing_df.groupby('Product name').sum()['Quantity'] # Take the number of sales for each Product_name
unique_Product_name =  clothing_df.groupby('Product name').sum().index # Take all Product_name

# Plot the highest number of sales for each Product_name
plt.bar(unique_Product_name,Sales_by_Product_name)
plt.xticks(unique_Product_name, rotation = 90, size = 8)
plt.ylabel('Quantity Amount)')
plt.xlabel('Product_name')
plt.show()

9 Which Product Category had the most sales¶

In [58]:
# Plot the highest number of sales for each Product Category

Sales_by_Product_Category = clothing_df.groupby('Product Category').sum()['Quantity'] # Take the number of sales for each Product Category
unique_Product_Category =  clothing_df.groupby('Product Category').sum().index # Take all Product Category

# Plot the highest number of sales for each Product Category
plt.bar(unique_Product_Category,Sales_by_Product_Category )
plt.xticks(unique_Product_Category, rotation = 90, size = 8)
plt.ylabel('Quantity Amount)')
plt.xlabel('Product Category')
plt.show()
In [ ]: