Interpolation for smoothing data in SciPy - Interactive Code 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'

Ainterp1d

Binterpolate

Csmooth

Dinterpolator

Attempts:

3 left

💡 Hint

Common Mistakes

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

Trying to import 'smooth' which does not exist in scipy.interpolate.

Using 'interpolator' which is not a valid function.

The interp1d function from scipy.interpolate is used for 1-dimensional interpolation, which helps smooth data.

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'nearest'

B'linear'

C'average'

D'smooth'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'smooth' which is not a valid kind.

Using 'average' which is not a valid kind.

Confusing 'nearest' with smoothing, but it creates step-like interpolation.

The kind='linear' argument creates a linear interpolation function, which is a common choice for smoothing data.

3fill in blank

hard

Fix the error in the code to evaluate the interpolation function at new points.

SciPy

y_new = f([1])

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

Ax_new

By_new

Cx

Df

Attempts:

3 left

💡 Hint

Common Mistakes

Passing y_new which is undefined or wrong.

Passing x which is original data, not new points.

Passing the function f itself instead of input values.

To get interpolated values, you call the function f with new x-values, here x_new.

4fill in blank

hard

Fill both blanks to create a smooth interpolation using cubic method and evaluate at new points.

SciPy

f = interp1d(x, y, kind=[1])
y_smooth = f([2])

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

A'cubic'

Bx_new

C'linear'

Dy_new

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'linear' instead of 'cubic' for smooth interpolation.

Passing y_new instead of x_new to the function.

Confusing the order of blanks.

Using kind='cubic' creates a smooth cubic interpolation. Then, calling f with x_new evaluates the smooth values.

5fill in blank

hard

Fill all three blanks to create a dictionary of smoothed values for words longer than 3 letters.

SciPy

smoothed = [1]: f([2]) for [3] in words if len([3]) > 3}

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

Aword

Bx_new

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for key and loop variable.

Passing x or word to the function instead of x_new.

Not filtering words by length correctly.

This dictionary comprehension creates keys from words longer than 3 letters and values by applying the interpolation function f to x_new. The variable word is used to iterate over words.