From the course: Practical Python for Time Series Analysis

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Create a heat matrix with pivot tables

Create a heat matrix with pivot tables - Python Tutorial

From the course: Practical Python for Time Series Analysis

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Create a heat matrix with pivot tables

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- [Instructor] This is a heat matrix that represents the most significant values for the energy generation in California, based in hour and the fuel type. For example, you can observe that the solar energy during the daylight hours are the most significant. You're gonna learn the steps, starting from a raw dataset to create the heat matrix chart that highlights the most significant values of any given datasets according to its temporal properties and some other categorical column. We load the dataset, apply the same pre-processing we did in other lesson, calculate the temporal properties such as the year, month, day, hour. And now let's go with the steps. To aggregate the data with a pivot table, we start from these long datasets, which means that every record, it's uniquely identifying a timestamp and a unique combination of variables. And the goal with pivot tables is combining the same measure, in this case…

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