Correlation coefficient with np.corrcoef() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the correlation coefficient matrix of two arrays using numpy.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([5, 4, 3, 2, 1])
result = np.[1](x, y)
print(result)

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Acov

Bcorrelate

Ccorrcoef

Dcorr

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.correlate() which computes cross-correlation, not correlation coefficient.

Using np.cov() which computes covariance, not correlation coefficient.

The function np.corrcoef() calculates the correlation coefficient matrix between arrays.

2fill in blank

medium

Complete the code to extract the correlation coefficient between x and y from the correlation matrix.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([5, 4, 3, 2, 1])
corr_matrix = np.corrcoef(x, y)
corr_xy = corr_matrix[1][0, 1]
print(corr_xy)

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A[]

B()

C{}

D<>

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses which is for function calls.

Using curly braces which are for dictionaries or sets.

The correlation matrix is a 2D numpy array, so we use square brackets [] to index elements.

3fill in blank

hard

Fix the error in the code to correctly calculate the correlation coefficient between two lists.

NumPy

import numpy as np
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]
corr = np.corrcoef([1], y)
print(corr[0, 1])

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Ax

Bnp.array(x)

Clist(x)

Dnp.tolist(x)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing Python lists directly to np.corrcoef which expects numpy arrays.

Using incorrect conversion functions like np.tolist().

np.corrcoef requires numpy arrays, so convert the list x to a numpy array first.

4fill in blank

hard

Fill both blanks to create a dictionary of correlation coefficients for words with length greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml', 'python']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

Bword

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using the wrong comparison operator.

We want the length of each word as the value, and only include words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values for words longer than 2 characters.

NumPy

words = ['cat', 'dog', 'a', 'bird']
result = [1]: [2] for w in words if len(w) [3] 2}
print(result)

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Aw.upper()

Bw

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using wrong comparison operator or filtering condition.

The keys are uppercase words, values are original words, and we filter words longer than 2 characters.