Phonepe_Pulse_Data_Visualization

I have created a dashboard to visualize Phonepe pulse Github repository data(https://github.com/PhonePe/pulse) using Streamlit and Plotly in Python

Introduction

• PhonePe has become one of the most popular digital payment platforms in India, with millions of users relying on it for their day-to-day transactions. The app is known for its simplicity, user-friendly interface, and fast and secure payment processing. It has also won several awards and accolades for its innovative features and contributions to the digital payments industry.

• We create a web app to analyse the Phonepe transaction and users depending on various Years, Quarters, States, and Types of transaction and give a Geographical and Geo visualization output based on given requirements.

Developer Guide

1.Tools

• virtual code.
• Jupyter notebook.
• Python 3.11.0 or higher.
• MySQL

2.Requirement Libraries to Install

• pip install pandas numpy os json requests subprocess mysql.connector sqlalchemy pymysql streamlit plotly.express

3.Import Libraries

clone libraries

• import requests
• import subprocess

pandas, numpy and file handling libraries

• import pandas as pd
• import numpy as np
• import os
• import json

SQL libraries

• import mysql.connector
• import sqlalchemy
• from sqlalchemy import create_engine
• import pymysql

Dash board libraries

• import streamlit as st
• import plotly.express as px

4. E T L Process

a) Extract data

• Initially, we Clone the data from the Phonepe GitHub repository by using Python libraries. https://github.com/PhonePe/pulse.git

b) Process and Transform the data

• Process the clone data by using Python algorithms and transform the processed data into DataFrame formate.

c) Load data

• Finally, create a connection to the MySQL server and create a Database and stored the Transformed data in the MySQL server by using the given method. df.to_sql('table_name', connection, if_exists = 'replace', index = False, dtype={'Col_name':sqlalchemy.types.datatype()})

5. E D A Process and Frame work

a) Access MySQL DB

• Create a connection to the MySQL server and access the specified MySQL DataBase by using pymysql library

b) Filter the data

• Filter and process the collected data depending on the given requirements by using SQL queries

c) Visualization

• Finally, create a Dashboard by using Streamlit and applying selection and dropdown options on the Dashboard and show the output are Geo visualization, bar chart, and Dataframe Table

User Guide

Step 1.

• Select any one option fron All India or State wise or Top Ten categories.

Step 2.

• Select any one option fron Transaction or User.

Step 3.

• Select any Year, Quarter and additional required option.

Step 4.

• Finally, You get the Geo Visualization Analysis or Bar chart Analysis and Table format Analysis