Are charging durations getting longer?

The Data Science team currently has a model that predicts the number of open chargers based on average vehicle charging duration. This allows for them to send notifications to previous users when it might be a good time to charge. However, the team believes that average charging duration may have changed since the last time they trained their model. Can you validate this for them?

Diese Übung ist Teil des Kurses

Window Functions in Snowflake

Anleitung zur Übung

Use a window function to find the running average of vehicle charging_duration.
Partition the results by charging_station_location.
Sequence the window by charging_start_time in ascending order.
Create the window of records to always be between the first row and the CURRENT ROW.

Interaktive Übung

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SELECT
	user_id,
    charging_station_location,
	TO_DATE(charging_start_time),
    charging_duration,

    -- Find the running average of charging duration
    ___ OVER(
      	-- Partition the results by charging_station_location
      	___

        -- Sequence the results by charging start time in ascending order
        ORDER BY ___

        -- Create the window of records to always be between the 
      	-- first row and the current row
        ___

    ) AS running_average
FROM ELECTRIC_VEHICLES.charging;

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Diese Übung ist Teil des Kurses

Window Functions in Snowflake

Mittlere SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

Open the window to a world of possibilities with Snowflake window functions! You'll get the ball rolling by differentiating window functions from traditional functions. Then, you'll learn how to provide a row number and ranking for each record in a query. Once you've nailed down the basics, you'll put the "window" in window functions, using PARTITION BY. You'll explore how to find and use the first and last value of a certain window before wrapping up with a sneak peek into aggregation functions.

Exercise 1: Assigning row numbers Exercise 2: Building a window function Exercise 3: Adding row numbers to charging sessions Exercise 4: Riding the heat wave Exercise 5: Ranking window functions Exercise 6: Was that a fast charge?Exercise 7: Comparing energy consumed Exercise 8: Partitioning data in a window function Exercise 9: Follow the money!Exercise 10: Digging into charging costs

Time to crank it up! In this chapter, you’ll take ranking functions to the next level. You’ll start with a variant of RANK, called DENSE_RANK, which handles ties in a bit of a different way. You’ll also explore a more robust version of the functions you saw in the previous lesson using NTH_VALUE. Next, you’ll create “buckets” of data using NTILE, which is more useful than you may think. You’ll also pick up a nifty little tool called CUME_DIST to find the number of records less than or equal to a certain record in a window. You’ll wrap up the chapter with one of the most powerful applications of window functions you’ve seen so far; LAG and LEAD.

Exercise 1: More Ranking Functions Exercise 2: Marketing the right vehicles Exercise 3: Optimizing charging scheduling Exercise 4: Setting a minimum charging cost Exercise 5: NTILE and CUME_DIST Exercise 6: Surveying vehicle owners Exercise 7: Evaluating charging duration Exercise 8: How long are vehicle owners going between charges?Exercise 9: LAG and LEAD Exercise 10: Looking into the past!Exercise 11: Maintaining charger efficiency Exercise 12: Lightning charger load

You’ll start this final chapter with aggregation functions like AVG, COUNT, and SUM. You’ll compare the output of these functions to individual records in a window, as well as to perform additional calculations. After this, you’ll master the most exciting application of window functions; running and moving calculations! You’ll start by calculating running averages and totals for different metrics for electric vehicle charging. Finally, you’ll wrap up the course by generating moving totals and averages with a sliding window!

Exercise 1: Aggregate window functions Exercise 2: Engaging vehicle owners with metrics Exercise 3: Visualizing charging session duration and cost Exercise 4: Who's consuming the least?Exercise 5: Window frames Exercise 6: Are charging durations getting longer?

Aktuelle Übung

Exercise 7: Identifying inefficient chargers Exercise 8: Moving Averages and Totals Exercise 9: Moving vs. running Exercise 10: Impact of temperature on charging rate Exercise 11: Putting it all together!Exercise 12: Congratulations!