Send Bulk Emails with Ease Using Python: A Guide to Efficient Email API Integration

Joseph bwalya — Wed, 16 Jul 2025 14:58:47 +0000

In today’s digital world, email communication remains one of the most powerful tools for engaging with users. Whether you're sending newsletters, verification codes, or marketing campaigns, having the ability to send bulk emails programmatically can save time and increase efficiency.

In this article, you'll learn how to integrate a Bulk Email Sending API using Python to send transactional or mass emails with ease.

Why Use an Email API for Bulk Sending?
Using an Email API allows you to:

Automate sending large volumes of emails.

Track delivery and open rates.

Customize emails dynamically.

Improve deliverability using SMTP gateways and verified domains.

API Endpoint Overview
We’ll be working with the following API:

Endpoint:

https://send-bulk-emails.p.rapidapi.com/api/send/otp/mail

This endpoint sends a simple OTP or custom email to the specified recipient.

Required Headers
You’ll need to include the following headers with your request:

Content-Type: application/json  
x-rapidapi-host: send-bulk-emails.p.rapidapi.com  
x-rapidapi-key: YOUR_RAPIDAPI_KEY
Replace YOUR_RAPIDAPI_KEY with your actual key from RapidAPI.

**Sample Email JSON Payload**
Here’s the structure of the data you’ll send:

{
  "subject": "Account OTP",
  "from": "gateway.smtp587@gmail.com",
  "to": " gateway.smtp587@gmail.com ",
  "senders_name": "Company Name",
  "body": "Your OTP is 12453"
}

You can customize the fields to suit your business logic, e.g., dynamic names or codes.

Python Code to Send Bulk Emails
Here is a simple script using Python and the requests library:

import requests
import json

url = "https://send-bulk-emails.p.rapidapi.com/api/send/otp/mail"

payload = {
    "subject": "Account OTP",
    "from": "gateway.smtp587@gmail.com",
    "to": " gateway.smtp587@gmail.com",
    "senders_name": "Company Name",
    "body": "Your OTP is 12453"
}

headers = {
    "Content-Type": "application/json",
    "x-rapidapi-host": "send-bulk-emails.p.rapidapi.com",
    "x-rapidapi-key": "6fba124bddmsh85209fcbdd66c1bp11d81ajsn03828f70971d"  # Replace with your own key
}

response = requests.post(url, data=json.dumps(payload), headers=headers)

# Output the response
print("Status Code:", response.status_code)
print("Response Body:", response.json())

Expected Response
A successful response will typically return a confirmation message like:

{
  "success": true,
  "message": "Email sent successfully."
}

Security Note
Do not expose your API key in client-side code or public repositories. Store it securely using environment variables or secret management tools.

Conclusion
Integrating an email API into your application is a game-changer for business communication. Whether you're verifying users, sending alerts, or promoting services, this setup in Python gives you the foundation to build scalable email capabilities.

Loan Calculation Using Python: A Step-by-Step Guide

Joseph bwalya — Mon, 24 Feb 2025 13:37:56 +0000

In today's world, loans are an integral part of personal and business finance. Whether you're buying a home, financing a car, or starting a business, understanding how loans work is crucial. One of the most important aspects of a loan is the amortization schedule, which breaks down each payment into principal and interest components. In this blog post, we'll explore how to calculate loan amortization using Python and an API. We'll also create a dynamic table to display the results.

Understanding Loan Amortization
Before diving into the code, let's briefly discuss what loan amortization is. Amortization is the process of spreading out a loan into a series of fixed payments over time. Each payment consists of two parts:

Principal: The amount of the loan that is being paid off.

Interest: The cost of borrowing the money.

The amortization schedule is a table that shows the breakdown of each payment, including the remaining balance after each payment.

Setting Up the Environment
To get started, you'll need Python installed on your machine. You'll also need to install the requests library, which allows you to make HTTP requests in Python. You can install it using pip:

pip install requests

Making the API Request
We'll be using the Loan Amortization Calculator API from RapidAPI to calculate the loan schedule. The API requires the following parameters:

loan_amount: The total amount of the loan.

annual_interest_rate: The annual interest rate (in percentage).

num_payments: The number of payments.

extra_payment: Any extra payment made (default is 0).

frequency: The frequency of payments (e.g., monthly, quarterly).

Here's how you can make a POST request to the API using Python:

import requests

url = "https://loan-amortization-calculator1.p.rapidapi.com/api/calculate/amortization"

payload = {
    "loan_amount": 10000,
    "annual_interest_rate": 7,
    "num_payments": 3,
    "extra_payment": 0,
    "frequency": "monthly"
}

headers = {
    'Content-Type': 'application/json',
    'x-rapidapi-host': 'loan-amortization-calculator1.p.rapidapi.com',
    'x-rapidapi-key': 'cee0d12e3dmshd8cc8a3c884fcbdp1c2d92jsnd916f6092b82'
}

response = requests.post(url, json=payload, headers=headers)

if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print(f"Error: {response.status_code}")

Parsing the API Response
The API response will be in JSON format, which we can easily parse in Python. Here's a sample response:

{
    "data": {
        "loan_amount": 10000,
        "payments": [
            {
                "closing_balance": 6686.04,
                "interest": 58.33,
                "opening_balance": 10000,
                "payment": 3372.3,
                "period": 1,
                "principal_payment": 3313.96
            },
            {
                "closing_balance": 3352.74,
                "interest": 39.0,
                "opening_balance": 6686.04,
                "payment": 3372.3,
                "period": 2,
                "principal_payment": 3333.3
            },
            {
                "closing_balance": 0.0,
                "interest": 19.56,
                "opening_balance": 3352.74,
                "payment": 3372.3,
                "period": 3,
                "principal_payment": 3352.74
            }
        ],
        "periodic_payment": 3372.3,
        "total_amount": 10116.89,
        "total_interest": 116.89
    },
    "message": "Loan Schedule",
    "status": 200,
    "success": true
}

Creating a Dynamic Table for Results
To display the loan schedule in a user-friendly format, we'll create a dynamic table using Python's tabulate library. First, install the library:

pip install tabulate

Now, let's create a function to display the loan schedule:

from tabulate import tabulate

def display_loan_schedule(data):
    payments = data['data']['payments']
    table = []

    for payment in payments:
        row = [
            payment['period'],
            payment['opening_balance'],
            payment['payment'],
            payment['principal_payment'],
            payment['interest'],
            payment['closing_balance']
        ]
        table.append(row)

    headers = ["Period", "Opening Balance", "Payment", "Principal", "Interest", "Closing Balance"]
    print(tabulate(table, headers, tablefmt="pretty"))

# Assuming 'data' is the API response
display_loan_schedule(data)

Putting It All Together
Now that we have all the pieces, let's put them together in a single script:

import requests
from tabulate import tabulate

def calculate_loan_schedule():
    url = "https://loan-amortization-calculator1.p.rapidapi.com/api/calculate/amortization"

    payload = {
        "loan_amount": 10000,
        "annual_interest_rate": 7,
        "num_payments": 3,
        "extra_payment": 0,
        "frequency": "monthly"
    }

    headers = {
        'Content-Type': 'application/json',
        'x-rapidapi-host': 'loan-amortization-calculator1.p.rapidapi.com',
        'x-rapidapi-key': 'cee0d12e3dmshd8cc8a3c884fcbdp1c2d92jsnd916f6092b82'
    }

    response = requests.post(url, json=payload, headers=headers)

    if response.status_code == 200:
        data = response.json()
        display_loan_schedule(data)
    else:
        print(f"Error: {response.status_code}")

def display_loan_schedule(data):
    payments = data['data']['payments']
    table = []

    for payment in payments:
        row = [
            payment['period'],
            payment['opening_balance'],
            payment['payment'],
            payment['principal_payment'],
            payment['interest'],
            payment['closing_balance']
        ]
        table.append(row)

    headers = ["Period", "Opening Balance", "Payment", "Principal", "Interest", "Closing Balance"]
    print(tabulate(table, headers, tablefmt="pretty"))

if __name__ == "__main__":
    calculate_loan_schedule()

Running the Script
When you run the script, it will make a POST request to the Loan Amortization Calculator API, retrieve the loan schedule, and display it in a dynamic table. Here's what the output will look like:

+---------+-----------------+----------+------------+----------+-----------------+
| Period  | Opening Balance | Payment  | Principal  | Interest | Closing Balance |
+---------+-----------------+----------+------------+----------+-----------------+
|    1    |     10000.0     | 3372.3   |  3313.96   |  58.33   |     6686.04     |
|    2    |     6686.04     | 3372.3   |  3333.3    |   39.0   |     3352.74     |
|    3    |     3352.74     | 3372.3   |  3352.74   |  19.56   |       0.0       |
+---------+-----------------+----------+------------+----------+-----------------+

In this blog post, we've explored how to calculate loan amortization using Python and an API. We've also created a dynamic table to display the loan schedule in a user-friendly format. This approach can be easily extended to handle more complex loan scenarios, such as variable interest rates or additional payments.

Understanding loan amortization is essential for making informed financial decisions. By leveraging Python and APIs, you can automate the calculation process and gain deeper insights into your loan repayment strategy.

Happy coding!

DEV Community: Joseph bwalya

Send Bulk Emails with Ease Using Python: A Guide to Efficient Email API Integration

Loan Calculation Using Python: A Step-by-Step Guide