Why interpolation estimates between data points in SciPy - Test Your Understanding

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'

Ainterp1d

Binterpolate

Cinterpolator

Dinterpolate1d

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'interpolate' which is the module, not the function.

Using 'interpolator' which is not a valid function in scipy.

Using 'interpolate1d' which is not the correct function name.

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

2fill in blank

medium

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

SciPy

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

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

A'random'

B'smooth'

C'linear'

D'step'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'smooth' which is not a valid kind in interp1d.

Using 'random' which does not make sense for interpolation.

Using 'step' which is a valid kind but not the basic linear interpolation.

The 'kind' parameter specifies the type of interpolation. 'linear' means straight lines between points.

3fill in blank

hard

Fix the error in the code to correctly interpolate new x values.

SciPy

y_new = f([1])

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

Ax_new

By

Cx

Df

Attempts:

3 left

💡 Hint

Common Mistakes

Passing y instead of new x values.

Passing x which are original points, not new points.

Passing f itself which is a function, not data.

To get interpolated values, you must pass new x values (x_new) to the interpolation function f.

4fill in blank

hard

Fill both blanks to create a dictionary of interpolated values for x_new points greater than 2.

SciPy

result = {xi: f(xi) for xi in [1] if xi [2] 2}

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

Ax_new

Bx

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using original x instead of x_new.

Using '<' instead of '>' which selects wrong values.

We want to use the new x values (x_new) and select those greater than 2 for interpolation.

5fill in blank

hard

Fill all three blanks to create a dictionary of squared values for x_new points less than 5.

SciPy

squares = { [1]: [2]**2 for [1] in x_new if [1] [3] 5 }

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

Axi

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using '>' instead of '<' which selects wrong values.

We use 'xi' as the loop variable, square it, and filter for values less than 5.