Set operations on structured data in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a structured numpy array with fields 'name' and 'age'.

NumPy

import numpy as np

data = np.array([('Alice', 25), ('Bob', 30)], dtype=[1])

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

A('name', 'U10'), ('age', 'i4')

B['name', 'age']

C{'name': 'U10', 'age': 'i4'}

D[('name', 'U10'), ('age', 'i4')]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple instead of a list for dtype

Using a dictionary instead of a list of tuples

Not specifying the dtype at all

The dtype for a structured array must be a list of tuples specifying field names and types.

2fill in blank

medium

Complete the code to find the union of two structured numpy arrays 'a' and 'b'.

NumPy

import numpy as np

a = np.array([(1, 'A'), (2, 'B')], dtype=[('id', 'i4'), ('val', 'U1')])
b = np.array([(2, 'B'), (3, 'C')], dtype=[('id', 'i4'), ('val', 'U1')])

union = np.[1](a, b)

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Aunion1d

Bsetdiff1d

Cin1d

Dintersect1d

Attempts:

3 left

💡 Hint

Common Mistakes

Using intersect1d which finds common elements only

Using setdiff1d which finds difference

Using in1d which returns boolean mask

np.union1d returns the sorted unique values that are in either of the input arrays.

3fill in blank

hard

Fix the error in the code to find the intersection of two structured arrays 'x' and 'y'.

NumPy

import numpy as np

x = np.array([(1, 10), (2, 20)], dtype=[('id', 'i4'), ('score', 'i4')])
y = np.array([(2, 20), (3, 30)], dtype=[('id', 'i4'), ('score', 'i4')])

common = np.[1](x, y)

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Aunion1d

Bintersect1d

Csetxor1d

Dsetdiff1d

Attempts:

3 left

💡 Hint

Common Mistakes

Using union1d which returns all unique elements

Using setxor1d which returns symmetric difference

Using setdiff1d which returns difference

np.intersect1d returns the sorted unique values that are in both arrays.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {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 value instead of length

Checking if word > 3 which is invalid

Not using len(word) in condition

The dictionary comprehension maps each word to its length if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values only if the value is greater than 0.

NumPy

data = {'a': 1, 'b': -1, 'c': 3}
result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper()

Using '<' instead of '>' in condition

Mapping keys instead of values

The comprehension maps uppercase keys to their values only if the value is positive.