The latest articles on DEV Community by joelm69 (@joelm69). https://dev.to/joelm69 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4138219%2F3b1a7e9d-f442-4350-acba-a81cd9cdbb7b.jpg https://dev.to/joelm69 en joelm69 Tue, 22 Sep 2026 20:13:34 +0000 https://dev.to/joelm69/building-an-interactive-excel-dashboard-for-e-commerce-product-analysis-a-case-study-of-jumia-4pif https://dev.to/joelm69/building-an-interactive-excel-dashboard-for-e-commerce-product-analysis-a-case-study-of-jumia-4pif <p>I started with 115 Jumia product records. Before analysing anything, I needed to understand the quality of the data. During the cleaning stage, I found three records with invalid review values, so I removed them. The final dataset contained 112 products.</p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fs4b6ixpdnqc3hzk866l3.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fs4b6ixpdnqc3hzk866l3.png" alt=" " width="800" height="229"></a></p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fiaj8wyai9k77o82hsenl.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fiaj8wyai9k77o82hsenl.png" alt=" " width="800" height="145"></a></p> <p>I used Python and Pandas to inspect and clean the raw Jumia dataset before moving into the analysis and dashboard stages.</p> <p>The original dataset contained 115 product records. During the cleaning process, I investigated the review field and found that the source data represented review counts as negative values. I also identified three invalid records and removed them.</p> <p>After cleaning, the dataset contained 112 product records.<br> I also created additional analytical fields, including:<br> Review count<br> Current price midpoint<br> Old price midpoint<br> Discount amount<br> Calculated discount percentage<br> Rating category<br> Discount category<br> Price category</p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F3j9jp1qsarcpf04qehsw.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F3j9jp1qsarcpf04qehsw.png" alt=" " width="800" height="418"></a></p> <p>After cleaning the dataset with Python, I loaded the cleaned data into PostgreSQL for structured analysis.</p> <p>I used SQL to investigate the main business questions around:</p> <p>Product discounts<br> Customer review activity<br> Product ratings<br> Product prices<br> Highly discounted products<br> Highly reviewed products<br> I also used SQL queries to calculate summary statistics and identify products that stood out based on their ratings, review activity and pricing.<br> This stage allowed me to move from simply cleaning the data to analysing it from a business perspective.</p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F87kqwm9fpw2kmdnes8f3.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F87kqwm9fpw2kmdnes8f3.png" alt=" " width="800" height="661"></a></p> <p>Excel Analysis and Pivot Tables</p> <p>After completing the data cleaning in Python and the structured analysis in PostgreSQL, I moved the relevant data into Microsoft Excel to build the reporting and visualization layer.</p> <p>I used Excel to organize the analysis into summary tables and pivot tables. This made it easier to compare products based on discounts, ratings, review activity and pricing.</p> <p>The pivot tables helped me identify patterns such as:</p> <p>Products with the highest discount percentages<br> Products with the highest review activity<br> Rating categories across the dataset<br> Discount categories across products</p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fbi0pi4z1e44rxfcp2d0p.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fbi0pi4z1e44rxfcp2d0p.png" alt=" " width="799" height="366"></a></p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F5x0ob25zhi8yotcaz5mf.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F5x0ob25zhi8yotcaz5mf.png" alt=" " width="799" height="233"></a></p> <p>Full project details can be found on:<br> <a href="https://github.com/joelm69/jumia-data-analysis" rel="noopener noreferrer">https://github.com/joelm69/jumia-data-analysis</a></p> analytics data python tutorial