Building a Python Dashboard for Amazon Seller Insights with SellerMagnetAPI

Unlock Amazon Seller Insights with a Custom Python Dashboard

In today's competitive e-commerce landscape, Amazon businesses and market analysts require robust tools for competitive analysis, inventory management, and market research. SellerMagnet's enterprise-grade Amazon data API ( https://sellermagnet-api.com ) offers a powerful solution. This blog post will guide you through building a custom Python dashboard to visualize and analyze critical Amazon seller insights using the SellerMagnetAPI.

Note: The thumbnail image for this post will be auto-generated using SellerMagnet’s internal image generation API.

Why Build a Custom Dashboard?

While pre-built analytics platforms offer general insights, a custom dashboard tailored to your specific needs provides several advantages:

• Focus: Display only the metrics that matter most to your business.
• Integration: Seamlessly integrate Amazon data with other internal systems.
• Customization: Create visualizations and reports that meet your unique analytical requirements.
• Efficiency: Automate data retrieval and analysis, saving time and resources.

Getting Started with SellerMagnetAPI

Before diving into code, ensure you have:

• A SellerMagnetAPI account (sign up for a free trial).
• Python 3.6+ installed.
• Necessary Python libraries: requests, pandas, plotly (install with pip install requests pandas plotly).

Practical Use Cases and Examples

1. Product Performance Tracking

Monitor key metrics like sales rank, pricing, and review counts for your products using the Amazon Product Statistics endpoint.

import requests
import pandas as pd
import plotly.express as px

API_KEY = "YOUR_API_KEY" # Replace with your actual API key
ASIN = "B08N5WRWNW" # Example ASIN
MARKETPLACE_ID = "ATVPDKIKX0DER" # Example: Amazon.com

url = f"https://sellermagnet-api.com/amazon-product-statistics?api_key={API_KEY}&asin={ASIN}&marketplaceId={MARKETPLACE_ID}&graphs=true"

try:
    response = requests.get(url)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
    data = response.json()

    if data["success"]:
        # Accessing the root data object
        product_data = data["data"]

        # Price Trend
        price_df = pd.DataFrame(product_data["graphs"]["priceTrend"])
        if not price_df.empty:
            price_df['timestamp'] = pd.to_datetime(price_df['timestamp'])
            fig_price = px.line(price_df, x='timestamp', y='price', title=f'Price Trend for {ASIN}')
            fig_price.show()

        # Sales Rank Trend
        rank_df = pd.DataFrame(product_data["graphs"]["rankTrend"])
        if not rank_df.empty:
            rank_df['timestamp'] = pd.to_datetime(rank_df['timestamp'])
            fig_rank = px.line(rank_df, x='timestamp', y='rank', title=f'Sales Rank Trend for {ASIN}')
            fig_rank.show()

    else:
        print(f"API request failed: {data.get('errors', 'No errors provided')}")

except requests.exceptions.RequestException as e:
    print(f"Request failed: {e}")
except KeyError as e:
    print(f