Spline interpolation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the spline interpolation function from scipy.

SciPy

from scipy.interpolate import [1]

Drag options to blanks, or click blank then click option'

AUnivariateSpline

Bspline

Cinterp1d

Dgriddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'interp1d' which is for linear or other simple interpolations, not splines.

Using 'spline' which is deprecated in scipy.

Using 'griddata' which is for scattered data interpolation.

The UnivariateSpline function from scipy.interpolate is used for spline interpolation of 1D data.

2fill in blank

medium

Complete the code to create a spline interpolation object for given x and y data.

SciPy

spline = UnivariateSpline(x, [1])

Drag options to blanks, or click blank then click option'

Az

Bw

Ct

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Passing unrelated variables instead of y data.

Confusing x and y variables.

The UnivariateSpline function takes the x and y data points to create the spline.

3fill in blank

hard

Fix the error in the code to evaluate the spline at new points.

SciPy

y_new = spline([1])

Drag options to blanks, or click blank then click option'

Ax

Bx_new

Cy_new

Dnew_x

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the original x or y data instead of new points.

Using undefined variables.

To evaluate the spline, pass the new x-values where you want predictions, usually stored in x_new.

4fill in blank

hard

Fill both blanks to create a dictionary of squared errors for points where y is greater than 0.

SciPy

errors = {x: (y - spline(x))[1]2 for x, y in zip(x_vals, y_vals) if y [2] 0}

Drag options to blanks, or click blank then click option'

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of power for squaring.

Using wrong comparison operators.

The squared error is calculated using the power operator **. The condition filters points where y is greater than zero using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of absolute errors for points where y is less than 5.

SciPy

abs_errors = [1]: abs([2] - spline([1])) for [1] , [2] in zip(x_vals, y_vals) if [2] [3] 5}

Drag options to blanks, or click blank then click option'

Ax

By

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up x and y in the dictionary comprehension.

Using wrong comparison operators.

The dictionary keys are the x values. The absolute error is the absolute difference between y and the spline evaluated at x. The condition filters points where y is less than 5 using <.