Weather Feature Lag

Problem

Shift a weather feature series forward by lag: return temperatures[:n-lag].

For lag <= 0 return a copy unchanged; return an empty list when lag is at least the series length. Weather effects on demand are usually delayed.

Hint

The lagged feature at time t is the weather observed lag periods earlier.

case 1

in: [[10,11,12,13],1]

expected: [10,11,12]

case 2

in: [[10,11,12],2]

expected: [10]

case 3

in: [[1],2]

expected: []

case 4

in: [[1,2],0]

expected: [1,2]

Starter codePython

def weather_feature_lag(temperatures, lag):
    # Your code here
    pass

4 test cases · run your code against them in the editor