Banks API in Express

_👀 Banks API having IFSC code of Banks in India

This application contains data of around 160K banks in India with fields such as name, branch, ifsc code, state, contact and district.

Following is the script used to migrate data - bankSeeder.js

import fs from 'fs';
import { parse } from 'csv-parse';
import dotenv from 'dotenv';
import Bank from "./models/Bank.js";
import connectDB from "./config/db.js";

dotenv.config();

connectDB();

const filePath = 'data/IFSC.csv';

await Bank.deleteMany();

fs.readFile(filePath, 'utf8', async (err, data) => {
    if (err) {
        console.error(err);
        return;
    }

    try {
        const records = await new Promise((resolve, reject) => {
            parse(data, { columns: true }, (err, records) => {
                if (err) {
                    reject(err);
                } else {
                    resolve(records);
                }
            });
        });

        // print only first 5 records
        records.forEach((record) => {
            Bank.create({
                name: record.BANK,
                ifsc: record.IFSC,
                branch: record.BRANCH,
                centre: record.CENTRE,
                district: record.DISTRICT,
                city: record.CITY,
                state: record.STATE,
                address: record.ADDRESS,
                contact: record.CONTACT,
                upi: record.UPI === 'true' ? true : false,
                rtgs: record.RTGS ? true : false,
                neft: record.NEFT ? true : false,
            }).then((record) => {
                // console.log(record);
            }).catch((err) => {
                console.error('failed to create record', err);
            });
        });
        
    } catch (err) {
        console.error(err);
    }
});

Properties of the bank as given the dataset used to create this API

const data = {
  BANK: 'Abhyudaya Co-operative Bank',
  IFSC: 'ABHY0065001',
  BRANCH: 'Abhyudaya Co-operative Bank IMPS',
  CENTRE: 'GREATER MUMBAI',
  DISTRICT: 'GREATER MUMBAI',
  STATE: 'MAHARASHTRA',
  ADDRESS: 'ABHYUDAYA BANK BLDG., B.NO.71, NEHRU NAGAR, KURLA (E), MUMBAI-400024',
  CONTACT: '+912225260173',
  IMPS: 'true',
  RTGS: 'true',
  CITY: 'MUMBAI',
  ISO3166: 'IN-MH',
  NEFT: 'true',
  MICR: '400065001',
  UPI: 'true',
  SWIFT: ''
};

Reading data using Pandas and Python - read_csv.py

import pandas as pd

# import IFSC.csv
data = pd.read_csv('data/IFSC.csv')

# get column names
print(data.columns)

# get number of entries, 164836 entries in the original database
print(len(data))

# group by BANK and get the number of entries for each bank, print the max 5 banks
print(data.groupby('BANK').size().nlargest(5))

# group by CITY and get the number of entries for city named LUCKNOW
print(data.groupby('CITY').size()['NOIDA'])

I was experimenting with advanced filters in the dataset for instance getting all banks in a given city. Grouping the data and more based on column names.

Requirements 🏃

• Node
• MongoDB
• MongoDB Compass (Optional, GUI for MongoDB databases)
• Postman (Optional, for API testing)

Install 💥

npm install
npm start

The Express app is configured to run by default on port 3000 but can be easily changed by tweaking server.js file. The front-end Svelte app is configured to run on port 4000 by default

Features

• User authentication 📦
• Search banks by city, branch and state
• Svelte animations and store

API Docs

Swagger docs with decorators are used for generating docs of some of the end-points of the application

app.use('/api/users', userRoutes)
app.use('/api/banks', bankRoutes)

Deployment

Planned to be deployed on Azure using Docker containers.

Development

Easily set up a local development environment!

• clone
• npm install
• npm start

Start coding! 🎉

Clone this repo on your machine, navigate to its location in the terminal and run:

Contributing

All contributions are welcome!
Please take a moment to review guidelines PR | Issues