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# Spline Interpolation

Unsolved
###### Data Wrangling

Difficulty: 2 | Problem written by Mr. Umair
Spline interpolation uses low polynomial degrees and chooses a polynomial that will result in a smooth dataset. Our data consist of (x,y) coordinates of a train over time. Since motion is restricted for the train, we can expect that the points between (x,y) will be “smooth” rather than jagged.

By using scipy.interpolate, find univariate splines for the "values" list from the x and y axis data points.

Input:

xAxis ( Time for reaching station ) =  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

yAxis ( Train Number )=  [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]

values= [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9]

Output:

result= [5.2, 5.4, 5.599999999999998, 5.799999999999998, 5.999999999999999, 6.199999999999999, 6.399999999999999, 6.6, 6.799999999999997]

##### Sample Input:
<class 'list'>
xAxis: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
<class 'list'>
yAxis: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
<class 'list'>
values: [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9]

##### Expected Output:
<class 'list'>
[5.2, 5.4, 5.599999999999998, 5.799999999999998, 5.999999999999999, 6.199999999999999, 6.399999999999999, 6.6, 6.799999999999997]

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