https://github.com/anas436/dash-callbacks-with-python
https://github.com/anas436/dash-callbacks-with-python
css3 dash dashhtmlcomponent html5 jupyerlab jupyter-dash pandas plotly python3
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/anas436/dash-callbacks-with-python
- Owner: Anas436
- Created: 2022-08-02T18:43:20.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-03T17:13:40.000Z (over 3 years ago)
- Last Synced: 2025-02-01T15:30:56.916Z (12 months ago)
- Topics: css3, dash, dashhtmlcomponent, html5, jupyerlab, jupyter-dash, pandas, plotly, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 2.65 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Dash-Callbacks-with-Python
## Objectives
After completing the lab you will be able to:
* Work with Dash Callbacks
## Theme
Extract average monthly arrival delay time and see how it changes over the year. Year range is from 2010 to 2020.
## Expected Output
Below is the expected result from the lab. Our dashboard application consists of three components:
* Title of the application
* Component to enter input year
* Chart conveying the average monthly arrival delay
__`Later in the browser address bar use this`:__
http://localhost:8090
#### Airline Reporting Carrier On-Time Performance Dataset
The Reporting Carrier On-Time Performance Dataset contains information on approximately 200 million domestic US flights reported to the United States Bureau of Transportation Statistics. The dataset contains basic information about each flight (such as date, time, departure airport, arrival airport) and, if applicable, the amount of time the flight was delayed and information about the reason for the delay. This dataset can be used to predict the likelihood of a flight arriving on time.
Preview data, dataset metadata, and data glossary [here.](https://dax-cdn.cdn.appdomain.cloud/dax-airline/1.0.1/data-preview/index.html)