Give example code for SEMrush API to compare my website's performance with competitors

python
import requests

# Your SEMrush API key
API_KEY = 'YOUR_API_KEY'

# Your domain and competitor domains to compare
domains = [
    'yourdomain.com',   # Replace with your website
    'competitor1.com',
    'competitor2.com',
    'competitor3.com',
    'competitor4.com',
    'competitor5.com'
]

# Base URL for the SEMrush Traffic Summary API
url = 'https://api.semrush.com/analytics/ta/api/v3/summary'

# Comma-separated string of target domains
targets = ','.join(domains)

# Columns to retrieve
export_columns = 'target,visits,users,time_on_site,pages_per_visit,bounce_rate'

# Optional parameters
params = {
    'key': API_KEY,
    'targets': targets,
    'export_columns': export_columns,
    # Optional: 'display_date': '2025-08-01',  # Format YYYY-MM-01 to get data for specific month
    # Optional: 'country': 'us'  # Use country code to get data for a specific country
}

# Make the GET request
response = requests.get(url, params=params)

# Check for request success
if response.status_code == 200:
    # The response text contains CSV data
    data = response.text
    print("SEMrush Traffic Summary Data:\n")
    print(data)
else:
    print(f"Error fetching data from SEMrush API. Status code: {response.status_code}")
    print(response.text)

This script performs the following steps:

- Defines your SEMrush API key and the list of your website plus competitor domains.
- Builds the API endpoint request with the domains and desired columns.
- Optionally allows specifying a month and country to narrow the analysis.
- Sends the request and prints out the CSV-formatted traffic summary data for all domains.

Further Expansion: Extracting and Comparing Data in Python

You can parse the CSV result into a structured format for further analysis or reporting. Example using pandas:

python
import pandas as pd
from io import StringIO

# Assuming response.text holds the CSV string data
csv_data = response.text

# Parse CSV into DataFrame
df = pd.read_csv(StringIO(csv_data))

# Clean or convert data types if needed
df['visits'] = pd.to_numeric(df['visits'], errors='coerce')
df['users'] = pd.to_numeric(df['users'], errors='coerce')
df['time_on_site'] = pd.to_numeric(df['time_on_site'], errors='coerce')
df['pages_per_visit'] = pd.to_numeric(df['pages_per_visit'], errors='coerce')
df['bounce_rate'] = pd.to_numeric(df['bounce_rate'], errors='coerce')

# Sort domains by visits descending to see leader
df_sorted = df.sort_values(by='visits', ascending=False)

print("Traffic Summary Data Sorted by Visits:")
print(df_sorted)

Using Other SEMrush API Endpoints to Deepen Analysis

- Domain Organic Search Overview
To compare organic search visibility, keyword rankings, and CPC data for domains.

Example request URL:

https://api.semrush.com/analytics/v1/?type=domain_organic&key=YOUR_API_KEY&domain=yourdomain.com&export_columns=Ph,Po,Nq,Cp,Ur,Tr,Tc

Where columns include information on keywords, positions, search volume, competition, URL distribution, traffic, and traffic cost.

- Competitors Discovery
To discover and compare the main organic competitors for your domain based on keyword overlap.

- Position Tracking API
For tracking keyword ranking positions over time for your site and competitors.

General Tips for SEMrush API Usage

- Use your company's or personal SEMrush API key with proper permissions and units.
- Requests consume API units; plan calls considering your subscription limits.
- Automate data gathering in scripts for periodic competitor performance monitoring.
- Combine various API endpoints for comprehensive performance and SEO analysis.
- Export data and visualize trends with tools like Excel, Power BI, or custom dashboards.
- Use Python libraries such as `requests`, `pandas`, and `matplotlib` for data handling and visualization.