Non-linear curve fitting in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the curve fitting function from scipy.

SciPy

from scipy.optimize import [1]

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

Acurve_fit

Bminimize

Clinregress

Droot

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'minimize' which is for optimization but not specifically curve fitting.

Using 'linregress' which is for linear regression, not non-linear fitting.

The curve_fit function from scipy.optimize is used for non-linear curve fitting.

2fill in blank

medium

Complete the code to define a model function for fitting a quadratic curve.

SciPy

def model(x, a, b, c):
    return a * x[1]2 + b * x + c

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

A//

B**

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' which multiplies but does not square.

Using '+' which adds instead of exponentiation.

The ** operator is used to raise x to the power of 2, representing a quadratic term.

3fill in blank

hard

Fix the error in the code to call curve_fit correctly with the model and data arrays x_data and y_data.

SciPy

params, covariance = curve_fit([1], x_data, y_data)

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

Amodel()

Bcurve_fit

Cmodel

Dx_data

Attempts:

3 left

💡 Hint

Common Mistakes

Calling the model function immediately with parentheses.

Passing the wrong variable like 'curve_fit' or 'x_data' instead of the model.

The model function should be passed without parentheses to curve_fit, so it can call it internally with parameters.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if the 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 instead of its length as the dictionary value.

Comparing the word string directly to 3, which is invalid.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each key uppercased to its value if the value is positive.

SciPy

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

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

Ak.upper()

Bv

Cv > 0

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of uppercase.

Filtering with incorrect conditions like k > 0.

The dictionary comprehension uses k.upper() to uppercase keys, keeps the value v, and filters only positive values with v > 0.