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1 | | -# Basic-Plotly-Charts-Using-Python |
| 1 | +# Basic-Plotly-Charts-Using-Python |
| 2 | + |
| 3 | + |
| 4 | +## Objectives |
| 5 | + |
| 6 | +In this lab, you will learn about creating plotly charts using plotly.graph_objects and plotly.express. |
| 7 | + |
| 8 | +Learn more about: |
| 9 | + |
| 10 | +* [Plotly python](https://plotly.com/python/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022年01月01日) |
| 11 | +* [Plotly Graph Objects](https://plotly.com/python/graph-objects/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022年01月01日) |
| 12 | +* [Plotly Express](https://plotly.com/python/plotly-express/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022年01月01日) |
| 13 | +* Handling data using [Pandas](https://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022年01月01日) |
| 14 | + |
| 15 | +We will be using the [airline dataset](https://developer.ibm.com/exchanges/data/all/airline/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022年01月01日) from [Data Asset eXchange](https://developer.ibm.com/exchanges/data/). |
| 16 | + |
| 17 | +#### Airline Reporting Carrier On-Time Performance Dataset |
| 18 | + |
| 19 | +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. |
| 20 | + |
| 21 | +Preview data, dataset metadata, and data glossary [here.](https://dax-cdn.cdn.appdomain.cloud/dax-airline/1.0.1/data-preview/index.html) |
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