Step by Step Guide to Building a WhatConverts API Integration in Python

Introduction

Hey there, fellow developer! Ready to dive into the world of WhatConverts API integration? You're in for a treat. This guide will walk you through the process of building a robust integration using Python. WhatConverts API is a powerful tool for tracking and analyzing lead data, and with this integration, you'll be able to harness its full potential in your projects.

Prerequisites

Before we jump in, make sure you've got these basics covered:

• A Python environment (3.6+ recommended)
• requests library installed (pip install requests)
• Your WhatConverts API credentials (if you don't have them, grab them from your account settings)

Setting up the API Connection

Let's kick things off by setting up our connection:

import requests
import json

API_KEY = 'your_api_key_here'
BASE_URL = 'https://app.whatconverts.com/api/v1'

headers = {
    'Authorization': f'Bearer {API_KEY}',
    'Content-Type': 'application/json'
}

Basic API Requests

Now, let's make our first API call:

def get_leads():
    endpoint = f'{BASE_URL}/leads'
    response = requests.get(endpoint, headers=headers)
    return response.json()

leads = get_leads()
print(json.dumps(leads, indent=2))

Easy peasy, right? This will fetch your leads and print them out nicely.

Implementing Key Functionalities

Let's step it up a notch. Here's how you can filter and paginate your results:

def get_filtered_leads(start_date, end_date, page=1, per_page=100):
    endpoint = f'{BASE_URL}/leads'
    params = {
        'start_date': start_date,
        'end_date': end_date,
        'page': page,
        'per_page': per_page
    }
    response = requests.get(endpoint, headers=headers, params=params)
    return response.json()

filtered_leads = get_filtered_leads('2023-01-01', '2023-12-31')

Error Handling and Best Practices

Always be prepared for things to go sideways. Here's a simple way to handle errors:

def make_api_request(endpoint, params=None):
    try:
        response = requests.get(f'{BASE_URL}/{endpoint}', headers=headers, params=params)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"An error occurred: {e}")
        return None

Advanced Usage

Ready for some advanced stuff? Let's set up a webhook listener:

from flask import Flask, request

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def webhook():
    data = request.json
    # Process the webhook data here
    print(f"Received webhook: {data}")
    return '', 200

if __name__ == '__main__':
    app.run(port=5000)

Data Processing and Analysis

Got your data? Great! Let's do something cool with it:

import pandas as pd

def analyze_leads(leads):
    df = pd.DataFrame(leads)
    # Perform your analysis here
    return df.groupby('source').agg({'id': 'count', 'value': 'sum'})

lead_analysis = analyze_leads(filtered_leads['leads'])
print(lead_analysis)

Putting It All Together

Here's a script that ties everything together:

import requests
import json
from datetime import datetime, timedelta

API_KEY = 'your_api_key_here'
BASE_URL = 'https://app.whatconverts.com/api/v1'

headers = {
    'Authorization': f'Bearer {API_KEY}',
    'Content-Type': 'application/json'
}

def get_leads(start_date, end_date):
    endpoint = f'{BASE_URL}/leads'
    params = {
        'start_date': start_date,
        'end_date': end_date,
        'per_page': 1000
    }
    
    all_leads = []
    page = 1
    
    while True:
        params['page'] = page
        response = requests.get(endpoint, headers=headers, params=params)
        data = response.json()
        
        if not data['leads']:
            break
        
        all_leads.extend(data['leads'])
        page += 1
    
    return all_leads

# Get leads for the last 30 days
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')

leads = get_leads(start_date, end_date)
print(f"Total leads: {len(leads)}")

# Do something with the leads...

Conclusion

And there you have it! You've just built a solid WhatConverts API integration in Python. From basic requests to advanced data analysis, you're now equipped to make the most of your lead data.

Remember, this is just the beginning. The WhatConverts API has a lot more to offer, so don't be afraid to explore and experiment. Happy coding!