2D interpolation (interp2d, griddata) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function used for 2D interpolation from scipy.interpolate.

SciPy

from scipy.interpolate import [1]

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

Ainterp2d

Binterp1d

Cgriddata1d

Dgriddata2d

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing interp1d which is for 1D interpolation.

Using griddata1d or griddata2d which are not valid function names.

The interp2d function is used for 2D interpolation in scipy.

2fill in blank

medium

Complete the code to create a 2D interpolation function using interp2d with linear method.

SciPy

f = interp2d(x, y, z, kind=[1])

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

A'nearest'

B'cubic'

C'linear'

D'quadratic'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cubic' or 'quadratic' which are more complex interpolation methods.

Using 'nearest' which picks the nearest point without interpolation.

The kind='linear' argument specifies linear interpolation.

3fill in blank

hard

Fix the error in the code to interpolate values at new points using griddata.

SciPy

from scipy.interpolate import griddata
points = np.array([[0, 0], [1, 1], [2, 2]])
values = np.array([0, 1, 4])
new_points = np.array([[1, 0], [2, 1]])
result = griddata(points, values, [1], method='linear')

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

Anew_points

Bpoints

Cvalues

Dmethod

Attempts:

3 left

💡 Hint

Common Mistakes

Passing known points or values instead of new points to interpolate.

Passing method as the third argument instead of as a keyword argument.

The third argument to griddata is the points where we want to interpolate values, here new_points.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

SciPy

{word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word in the condition instead of checking its length.

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values only if value is positive.

SciPy

{ [1]: [2] for ([3], [2]) in data.items() if [2] > 0 }

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

Ak.upper()

Bv

Ck

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of uppercase.

Using v in the loop variable instead of k, v.

Not filtering values greater than zero.

The dictionary comprehension converts keys to uppercase, keeps values, and filters for positive values.