From the course: Python Data Analysis (2020)

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Smoothing time series

Smoothing time series - Python Tutorial

From the course: Python Data Analysis (2020)

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Smoothing time series

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- [Instructor] Now we know how to load temperature data from any station, how to compute basic summaries such as mean, min and max, and how to integrate missing data points using interpolation. We'll continue with more data analysis in NumPy. I've copied your fill NaNs function here since we will need it. We looked at data for Pasadena in the last video, now let's move to even sunnier skies by looking at weather in the town of Hilo, big island Hawaii. We use our custom loader and again, I encourage you to go look under the hood. This is data in fact from Hilo International Airport we now fill the missing data for both T min and T max. Once more two pole unpacking is very useful. Let's look at some data summaries. The yearly average which gives us a sense of the typical value for T min, and it's min and max. Will span the range of variation of these measurements. We can plot the summaries together with the time series.…

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