Linear vs cubic interpolation in SciPy - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the interpolation function from scipy.

SciPy

from scipy.interpolate import [1]

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

Ainterp

Binterpolate

Cinterp1d

Dinterpolator

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'interpolate' instead of 'interp1d'.

Trying to import a non-existent function like 'interp'.

The function interp1d is used for 1-dimensional interpolation in scipy.

2fill in blank

medium

Complete the code to create a linear interpolation function for given x and y data.

SciPy

f_linear = interp1d(x, y, kind=[1])

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

A'nearest'

B'cubic'

C'quadratic'

D'linear'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cubic' when linear interpolation is needed.

Omitting quotes around the kind value.

To create a linear interpolation, the kind parameter should be set to 'linear'.

3fill in blank

hard

Fix the error in the code to create a cubic interpolation function.

SciPy

f_cubic = interp1d(x, y, kind=[1])

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

A'cubic'

B'linear'

C'nearest'

D'quadratic'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'linear' instead of 'cubic'.

Using 'quadratic' which is different from cubic.

The kind parameter must be set to 'cubic' to perform cubic interpolation.

4fill in blank

hard

Fill both blanks to create a dictionary of interpolated values using linear and cubic methods for new x points.

SciPy

results = {
    'linear': f_linear([1]),
    'cubic': f_cubic([2])
}

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

Ax_new

Bx_old

Cy_new

Dy_old

Attempts:

3 left

💡 Hint

Common Mistakes

Using old x or y values instead of new x values.

Mixing y values as input to interpolation functions.

Both interpolation functions should be called with the new x values (x_new) to get interpolated y values.

5fill in blank

hard

Fill all three blanks to plot original data points and both linear and cubic interpolation curves.

SciPy

plt.plot(x, y, 'o', label='data')
plt.plot([1], results['linear'], label='linear')
plt.plot([2], results[[3]], label='cubic')
plt.legend()
plt.show()

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

Ax_new

B'linear'

C'cubic'

Dx_old

Attempts:

3 left

💡 Hint

Common Mistakes

Using old x points for interpolation plots.

Using wrong label keys in the results dictionary.

We plot the interpolated values against the new x points. The label for cubic interpolation is 'cubic'.