From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

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Leveraging Power Query

Leveraging Power Query

From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

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Leveraging Power Query

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- [Instructor] To get our data into Excel, we can either start with our data already in the model or connect to external data sources. It's important to emphasize that we cannot connect directly to a database or API within Python code in Excel. Instead, we want to leverage the power of Power Query to bring our data into the Excel model. Power Query is a built-in part of Power BI in Excel that enables us to easily set up ETL frameworks to read the data into our Excel models. It enables us to efficiently replicate and update our queries to create data tables. In Excel, one way we can access Power Query is from the Data ribbon tab. Then we'll choose Launch Power Query Editor from the dropdown menu. I've already set up two data queries in Power Query on the left side, one for the average temperatures by city and the other for the daily temperatures for the Santa Barbara airport. We're not going to get into the granular detail for the actual steps for creating the data tables ultimately…

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