A dashboard built using Plotly-Dash for interactive visualization of Dex-connected individuals across the country.

Overview

Dashboard For The DexConnect Platform of Dexterity Global

Working prototype submission for internship at Dexterity Global Group. Dashboard for real time analysis of data of connected individuals and institutes across the country.

Requirements

  • Python
  • Plotly
  • Dash
  • Flask

How to run

  • Install the dependencies by running the command pip install -r requirements.txt
  • Once dependencies are installed, just run python app.py to see it in your browser.

Features

  • Map to visualize the connected individuals and institutes

  • Demographics and age of connected individuals

  • Data table, gender, institutes

Deployment

  • Azure cloud platform

If you found this repo useful, please the repo.

You might also like...
An interactive dashboard for visualisation, integration and classification of data using Active Learning.
An interactive dashboard for visualisation, integration and classification of data using Active Learning.

AstronomicAL An interactive dashboard for visualisation, integration and classification of data using Active Learning. AstronomicAL is a human-in-the-

YOPO is an interactive dashboard which generates various standard plots.
YOPO is an interactive dashboard which generates various standard plots.

YOPO is an interactive dashboard which generates various standard plots.you can create various graphs and charts with a click of a button. This tool uses Dash and Flask in backend.

This is a super simple visualization toolbox (script) for transformer attention visualization ✌
This is a super simple visualization toolbox (script) for transformer attention visualization ✌

Trans_attention_vis This is a super simple visualization toolbox (script) for transformer attention visualization ✌ 1. How to prepare your attention m

Interactive Data Visualization in the browser, from  Python
Interactive Data Visualization in the browser, from Python

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords hi

Interactive Data Visualization in the browser, from  Python
Interactive Data Visualization in the browser, from Python

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords hi

Interactive Data Visualization in the browser, from  Python
Interactive Data Visualization in the browser, from Python

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords hi

SummVis is an interactive visualization tool for text summarization.
SummVis is an interactive visualization tool for text summarization.

SummVis is an interactive visualization tool for analyzing abstractive summarization model outputs and datasets.

Learning Convolutional Neural Networks with Interactive Visualization.
Learning Convolutional Neural Networks with Interactive Visualization.

CNN Explainer An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs) For more information,

GUI for visualization and interactive editing of SMPL-family body models ie. SMPL, SMPL-X, MANO, FLAME.

Body Model Visualizer Introduction This is a simple Open3D-based GUI for SMPL-family body models. This GUI lets you play with the shape, expression, a

Comments
  • Bump numpy from 1.18.5 to 1.22.0

    Bump numpy from 1.18.5 to 1.22.0

    Bumps numpy from 1.18.5 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Bump jinja2 from 2.11.2 to 2.11.3

    Bump jinja2 from 2.11.2 to 2.11.3

    Bumps jinja2 from 2.11.2 to 2.11.3.

    Release notes

    Sourced from jinja2's releases.

    2.11.3

    This contains a fix for a speed issue with the urlize filter. urlize is likely to be called on untrusted user input. For certain inputs some of the regular expressions used to parse the text could take a very long time due to backtracking. As part of the fix, the email matching became slightly stricter. The various speedups apply to urlize in general, not just the specific input cases.

    Changelog

    Sourced from jinja2's changelog.

    Version 2.11.3

    Released 2021-01-31

    • Improve the speed of the urlize filter by reducing regex backtracking. Email matching requires a word character at the start of the domain part, and only word characters in the TLD. :pr:1343
    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
Releases(Plotly)
Owner
Yashasvi Misra
Contribute more than you criticize...
Yashasvi Misra
HiPlot makes understanding high dimensional data easy

HiPlot - High dimensional Interactive Plotting HiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and

Facebook Research 2.4k Jan 04, 2023
Standardized plots and visualizations in Python

Standardized plots and visualizations in Python pltviz is a Python package for standardized visualization. Routine and novel plotting approaches are f

Andrew Tavis McAllister 0 Jul 09, 2022
Here I plotted data for the average test scores across schools and class sizes across school districts.

HW_02 Here I plotted data for the average test scores across schools and class sizes across school districts. Average Test Score by Race This graph re

7 Oct 27, 2021
Create a visualization for Trump's Tweeted Words Using Python

Data Trump's Tweeted Words This plot illustrates twitter word occurences. We already did the coding I needed for this plot, so I was very inspired to

7 Mar 27, 2022
Open-questions - Open questions for Bellingcat technical contributors

Open questions for Bellingcat technical contributors These are difficult, long-term projects that would contribute to open source investigations at Be

Bellingcat 234 Dec 31, 2022
This is a Web scraping project using BeautifulSoup and Python to scrape basic information of all the Test matches played till Jan 2022.

Scraping-test-matches-data This is a Web scraping project using BeautifulSoup and Python to scrape basic information of all the Test matches played ti

Souradeep Banerjee 4 Oct 10, 2022
Colormaps for astronomers

cmastro: colormaps for astronomers 🔭 This package contains custom colormaps that have been used in various astronomical applications, similar to cmoc

Adrian Price-Whelan 12 Oct 11, 2022
Simple python implementation with matplotlib to manually fit MIST isochrones to Gaia DR2 color-magnitude diagrams

Simple python implementation with matplotlib to manually fit MIST isochrones to Gaia DR2 color-magnitude diagrams

Karl Jaehnig 7 Oct 22, 2022
2D maze path solver visualizer implemented with python

2D maze path solver visualizer implemented with python

SS 14 Dec 21, 2022
Data Visualization Guide for Presentations, Reports, and Dashboards

This is a highly practical and example-based guide on visually representing data in reports and dashboards.

Anton Zhiyanov 395 Dec 29, 2022
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

AutoViz Automatically Visualize any dataset, any size with a single line of code. AutoViz performs automatic visualization of any dataset with one lin

AutoViz and Auto_ViML 1k Jan 02, 2023
Apache Superset is a Data Visualization and Data Exploration Platform

Apache Superset is a Data Visualization and Data Exploration Platform

The Apache Software Foundation 49.9k Jan 02, 2023
A python script to visualise explain plans as a graph using graphviz

README Needs to be improved Prerequisites Need to have graphiz installed on the machine. Refer to https://graphviz.readthedocs.io/en/stable/manual.htm

Edward Mallia 1 Sep 28, 2021
阴阳师后台全平台(使用网易 MuMu 模拟器)辅助。支持御魂,觉醒,御灵,结界突破,秘闻副本,地域鬼王。

阴阳师后台全平台辅助 Python 版本:Python 3.8.3 模拟器:网易 MuMu | 雷电模拟器 模拟器分辨率:1024*576 显卡渲染模式:兼容(OpenGL) 兼容 Windows 系统和 MacOS 系统 思路: 利用 adb 截图后,使用 opencv 找图找色,模拟点击。使用

简讯 27 Jul 09, 2022
Datapane is the easiest way to create data science reports from Python.

Datapane Teams | Documentation | API Docs | Changelog | Twitter | Blog Share interactive plots and data in 3 lines of Python. Datapane is a Python lib

Datapane 744 Jan 06, 2023
Customizing Visual Styles in Plotly

Customizing Visual Styles in Plotly Code for a workshop originally developed for an Unconference session during the Outlier Conference hosted by Data

Data Design Dimension 9 Aug 03, 2022
Library for exploring and validating machine learning data

TensorFlow Data Validation TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be hig

688 Jan 03, 2023
This is a super simple visualization toolbox (script) for transformer attention visualization ✌

Trans_attention_vis This is a super simple visualization toolbox (script) for transformer attention visualization ✌ 1. How to prepare your attention m

Mingyu Wang 3 Jul 09, 2022
Lightweight data validation and adaptation Python library.

Valideer Lightweight data validation and adaptation library for Python. At a Glance: Supports both validation (check if a value is valid) and adaptati

Podio 258 Nov 22, 2022
Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database

SpiderFoot Neo4j Tools Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database Step 1: Installation NOTE: This installs the sf

Black Lantern Security 42 Dec 26, 2022