Visualization Website by using Dash and Heroku

Overview

Visualization Website by using Dash and Heroku

You can visit the website https://payroll-expense-analysis.herokuapp.com/

In this project, I am interested in studying the top 10 departments with the highest total payroll expense in each county in Massachusetts in 2020. The link to this dashboard is:

Dashboard Description

Users can click on one or multiple counties to study the departments with the highest total payroll expenses in the state of Massachusetts. Moreover, the pie chart would allow us to compare the proportion of total payroll earnings across the selected counties. By using the checkbox interactive element, users could also generate the range of counties they want to study the top 10 departments with the highest payroll expense among the selected counties. Users who are interested in discovering high payroll expense on the department and county level could utilize this dashboard as an initial observation to generate idea for further research directions.

Dashboard elements:

The dropdown box is an interactive element where the users have the option to choose the counties they are interested in. It will generate a bar plot that reflects the sum of total earnings on the Y-axis, the top 10 department names with the highest pay in the county on the x-axis. The check box element creates an interactive platform for users to compare the percentage of total earnings across counties. For example, if we choose Suffolk and Middlesex as the base of our analysis, then we can see that Suffolk is 86.9 percent compared to the sum of Suffolk and Middlesex. If we had chosen all counties, we would be able to see how much funds were dedicated to the city employee payroll in each county across the state of Massachusetts. The check box element also generates a table of the top departments with the most payroll spendings within the selected counties.

Data Sources

The data collected from:

the City of Boston: The City of Boston US geo data: US geo data

The original dataset contained the following columns:

Name: The name of the city employee Department Name: The name of the department the employee work at Title: The title or position the individual has in the respective department Postal: The postal code of where the payroll is expensed

The definition the payroll component rest of the variables is provided by the City of Boston:

Definition

The other dataset we had used is from "http://download.geonames.org/export/zip/US.zip"

This data is a txt. List that contains geographic information of each postal code, including the state, statecode, city, county, longitude, latitude, etc. I transformed this list into a dataset. This dataset would be merged with our payroll 2020 dataset to locate each payroll’s county.

Data Cleaning Process:

The first step is to input the original payroll data and the US geo data from the website. Then, I eliminated the rows in the payroll data where postal code is null. Furthermore, I selected only the department name, total earnings, and the county columns to use as the dashboard data source. In addition. I eliminated the rows that is not within the State of Massachusetts. For the bar plot and table, I sorted the data through grouping the dataset by department name and county and summarizing the total earnings for each respective group. For the pie chart, I will sort the data by grouping the dataset solely by county and summarize the total earnings.

##Additional Comments

It is interesting to discover that the Boston Police Department is the highest across all departments. I think it is worth the future investigation for more detailed understanding of the payroll components.

Payroll-Expense

Owner
YF Liu
YF Liu
Type-safe YAML parser and validator.

StrictYAML StrictYAML is a type-safe YAML parser that parses and validates a restricted subset of the YAML specification. Priorities: Beautiful API Re

Colm O'Connor 1.2k Jan 04, 2023
UNMAINTAINED! Renders beautiful SVG maps in Python.

Kartograph is not maintained anymore As you probably already guessed from the commit history in this repo, Kartograph.py is not maintained, which mean

1k Dec 09, 2022
The windML framework provides an easy-to-use access to wind data sources within the Python world, building upon numpy, scipy, sklearn, and matplotlib. Renewable Wind Energy, Forecasting, Prediction

windml Build status : The importance of wind in smart grids with a large number of renewable energy resources is increasing. With the growing infrastr

Computational Intelligence Group 125 Dec 24, 2022
Create 3d loss surface visualizations, with optimizer path. Issues welcome!

MLVTK A loss surface visualization tool Simple feed-forward network trained on chess data, using elu activation and Adam optimizer Simple feed-forward

7 Dec 21, 2022
Flow-based visual scripting for Python

A simple visual node editor for Python Ryven combines flow-based visual scripting with Python. It gives you absolute freedom for your nodes and a simp

Leon Thomm 3.1k Jan 06, 2023
A small tool to test and visualize protein embeddings and amino acid proportions.

polyprotein_stats A small tool to test and visualize protein embeddings and amino acid proportions. Currently deployed on streamlit.io. Given a set of

2 Jan 07, 2023
🗾 Streamlit Component for rendering kepler.gl maps

streamlit-keplergl 🗾 Streamlit Component for rendering kepler.gl maps in a streamlit app. 🎈 Live Demo 🎈 Installation pip install streamlit-keplergl

Christoph Rieke 39 Dec 14, 2022
HM02: Visualizing Interesting Datasets

HM02: Visualizing Interesting Datasets This is a homework assignment for CSCI 40 class at Claremont McKenna College. Go to the project page to learn m

Qiaoling Chen 11 Oct 26, 2021
🌀❄️🌩️ This repository contains some examples for creating 2d and 3d weather plots using matplotlib and cartopy libraries in python3.

Weather-Plotting 🌀 ❄️ 🌩️ This repository contains some examples for creating 2d and 3d weather plots using matplotlib and cartopy libraries in pytho

Giannis Dravilas 21 Dec 10, 2022
a simple REPL display lib for circuitpython

Circuitpython-termio-lib a simple REPL display lib for circuitpython Fonctions cls clear terminal screen and set cursor on top left : coords 0,0 usage

BeBoXoS 1 Nov 17, 2021
Tools for calculating and visualizing Elo-like ratings of MLB teams using Retosheet data

Overview This project uses historical baseball games data to calculate an Elo-like rating for MLB teams based on regular season match ups. The Elo rat

Lukas Owens 0 Aug 25, 2021
Generate the report for OCULTest.

Sample report generated in this function Usage example from utils.gen_report import generate_report if __name__ == '__main__': # def generate_rep

Philip Guo 1 Mar 10, 2022
Rockstar - Makes you a Rockstar C++ Programmer in 2 minutes

Rockstar Rockstar is one amazing library, which will make you a Rockstar Programmer in just 2 minutes. In last decade, people learned C++ in 21 days.

4k Jan 05, 2023
Leyna's Visualizing Data With Python

Leyna's Visualizing Data Below is information on the number of bilingual students in three school districts in Massachusetts. You will also find infor

11 Oct 28, 2021
又一个云探针

ServerStatus-Murasame 感谢ServerStatus-Hotaru,又一个云探针诞生了(大雾 本项目在ServerStatus-Hotaru的基础上使用fastapi重构了服务端,部分修改了客户端与前端 项目还在非常原始的阶段,可能存在严重的问题 演示站:https://stat

6 Oct 19, 2021
A curated list of awesome Dash (plotly) resources

Awesome Dash A curated list of awesome Dash (plotly) resources Dash is a productive Python framework for building web applications. Written on top of

Luke Singham 1.7k Dec 26, 2022
A tool for creating SVG timelines from simple JSON input.

A tool for creating SVG timelines from simple JSON input.

Jason Reisman 432 Dec 30, 2022
Analysis and plotting for motor/prop/ESC characterization, thrust vs RPM and torque vs thrust

esc_test This is a Python package used to plot and analyze data collected for the purpose of characterizing a particular propeller, motor, and ESC con

Alex Spitzer 1 Dec 28, 2021
Visualize large time-series data in plotly

plotly_resampler enables visualizing large sequential data by adding resampling functionality to Plotly figures. In this Plotly-Resampler demo over 11

PreDiCT.IDLab 604 Dec 28, 2022
Some problems of SSLC ( High School ) before outputs and after outputs

Some problems of SSLC ( High School ) before outputs and after outputs 1] A Python program and its output (output1) while running the program is given

Fayas Noushad 3 Dec 01, 2021