Copy of DM16 LAB: Visualize Data Using Matplotlib and Seaborn

Business Scenario

Welcome!

Today is your 17th day as a Junior Data Analyst at a Retail Analytics Company.

The management team wants to build a Retail Sales Analysis Dashboard that provides a complete overview of business performance. The company generates thousands of transactions across different cities, categories, and sales channels. Management needs a single analytical solution that can:

Build Final Retail Sales Analysis Project

Monitor sales performance
Identify top-performing products and categories
Analyze profitability and discounts
Compare sales across cities and customer segments
Make data-driven business decisions

Therefore, management has assigned the analytics team to build a Final Retail Sales Analysis Project that combines data analysis, visualization, and business reporting.

Git Pull

Click here to download previous lab file: DM LAB 16

git pull origin branchName

Click to download Dataset : Retail_Dataset_Cleaned

Task 1: Analyze Retail Dataset

Before creating dashboards and generating insights, analysts must ensure that the dataset is accurate, complete, and ready for analysis. Raw retail data often contains missing values, duplicate records, incorrect data types, and inconsistent values that can affect business decisions.

Import Required Libraries

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

Load Dataset

df = pd.read_csv("/content/Retail_Dataset_Modified.csv")
print("Dataset Loaded Successfully")

Open Google Colab

Display Dataset Information

df.head()

Display First Five Records

Check Dataset Information

df.info()

Display Statistical Summary

df.describe()

Check Missing Values

df.isnull().sum()

Convert Data Types Correctly

numeric_cols = [
    'Quantity_Available',
    'Units_Sold',
    'Revenue',
    'Shipping_Cost'
    ]
for col in numeric_cols:
    df[col] = pd.to_numeric(df[col], errors='coerce')

Handle Missing Values

Numerical Columns

num_cols = ['Quantity_Available', 'Units_Sold', 
			'Revenue','Customer_Satisfaction', 
            'Delivery_Time', 'Shipping_Cost']

for col in num_cols:
    df[col].fillna(df[col].median(), inplace=True)

Categorical Columns

cat_cols = ['City', 'Product_Name','Supplier']

for col in cat_cols:
    df[col].fillna(df[col].mode()[0], inplace=True)

Remove Duplicate Records

Check Duplicate Rows

df.duplicated().sum()

Remove Duplicate Rows

df.drop_duplicates(inplace=True)

Verify Dataset Shape

df.shape

Standardize Text Formatting by Removing Extra Spaces and Correcting Capitalization

df['Category'] = df['Category'].str.strip().str.title()

df['Category'].unique()

Create New Columns

Create Profit Amount Column

df["Profit_Amount"] = (
    df["Revenue"] *
    df["Profit_Margin"] / 100
)

df[["Revenue",
    "Profit_Margin",
    "Profit_Amount"]].sample(5)

Transform Revenue into Revenue Category

df["Revenue_Category"] = np.where(
    df["Revenue"] < 5000,
    "Low Revenue",
    np.where(
        df["Revenue"] < 10000,
        "Medium Revenue",
        "High Revenue"
    )
)

df[["Revenue", "Revenue_Category"]].head()

Verify Final Dataset

df.info()

df.isnull().sum()

df.shape

Task 2: Create Visual Dashboards

The retail company receives thousands of sales transactions every month from different cities, product categories, and sales channels. Although the data is available in tables, management finds it difficult to identify trends, compare performance, and make quick

business decisions by looking at raw records.

Therefore, management has asked the analytics team to create visual dashboards that transform raw retail data into meaningful and actionable business insights.

Create Monthly Revenue Trend

monthly_revenue = df.groupby('Month')['Revenue'].sum() 
plt.figure(figsize=(12,6)) 
plt.plot( monthly_revenue.index, monthly_revenue.values, marker='o', linewidth=2 ) 
plt.title('Monthly Revenue Trend') 
plt.xlabel('Month') 
plt.ylabel('Total Revenue') 
plt.grid(True) 
plt.show()

Create Revenue by Product Category Bar Chart

category_revenue = df.groupby('Category')['Revenue'].sum() 
plt.figure(figsize=(12,6)) 
sns.barplot( x=category_revenue.index, y=category_revenue.values ) 
plt.title('Revenue by Product Category') 
plt.xlabel('Category') 
plt.ylabel('Revenue') 
plt.xticks(rotation=45) 
plt.show()

Create Revenue Distribution Histogram

plt.figure(figsize=(10,6)) 
plt.hist( df['Revenue'], bins=20 ) 
plt.title('Distribution of Revenue') 
plt.xlabel('Revenue') 
plt.ylabel('Frequency') 
plt.show()

Create Units Sold vs Revenue Scatter Plot

plt.figure(figsize=(10,6)) 
sns.scatterplot( data=df, x='Units_Sold', y='Revenue' ) 
plt.title('Units Sold vs Revenue') 
plt.xlabel('Units Sold') 
plt.ylabel('Revenue') 
plt.show()

Create Profit Margin Distribution Box Plot

plt.figure(figsize=(8,6)) 
sns.boxplot( y=df['Profit_Margin'] ) 
plt.title('Profit Margin Distribution') 
plt.ylabel('Profit Margin') 
plt.show()

Task 3: Present Business Insights

After cleaning the retail dataset and creating visual dashboards, management wants the analytics team to present meaningful business insights instead of only displaying charts. Business insights help decision-makers understand what is happening in the business and what actions should be taken to improve performance.

Identify the Month Generating the Highest Revenue

monthly_revenue = df.groupby('Month')['Revenue'].sum() 
highest_month = monthly_revenue.idxmax() 
highest_revenue = monthly_revenue.max() 
print("Highest Revenue Month:", highest_month) 
print("Revenue:", highest_revenue)

Identify the Top-Performing Product Category

category_revenue = df.groupby('Category')['Revenue'].sum() 
top_category = category_revenue.idxmax() 
top_revenue = category_revenue.max() 
print("Top Category:", top_category) 
print("Revenue:", top_revenue)

Great job!

You have successfully completed your lab on Build Final Retail Sales Analysis Project

In this lab, you have: Create visual dashboards using Matplotlib and Seaborn, generate a Monthly Revenue Trend chart to analyze sales performance over time, compare category-wise revenue using bar charts, Analyze the distribution of revenue using histograms, interpret charts and convert visual findings into meaningful business insights, Present data-driven recommendations to support business decision-making.

You are now ready to move to the next stage of Junior Data Analyst.

Checkpoint

Git Push

git push origin branchName