np.split() for dividing arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split the array into 3 equal parts.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
splits = np.split(arr, [1])

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

A6

B2

C4

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length of the array instead of the number of splits.

Using a number that does not divide the array length evenly.

We want to split the array into 3 equal parts, so the second argument to np.split should be 3.

2fill in blank

medium

Complete the code to split the array at the specified indices.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
splits = np.split(arr, [1])

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

A[0, 2]

B[1, 3]

C[2, 4]

D[3, 5]

Attempts:

3 left

💡 Hint

Common Mistakes

Using indices that are out of range.

Confusing the indices with the number of splits.

The array will be split at indices 1 and 3, creating subarrays: [10], [20, 30], and [40, 50].

3fill in blank

hard

Fix the error in the code to split the array into 4 parts.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
splits = np.split(arr, [1])

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

A[2, 4, 6]

B4

C[1, 3, 5]

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the number 4 instead of indices for unequal splits.

Using indices that do not split the array evenly.

To split into 4 parts, we provide indices where to split: [2, 4, 6].

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1] : [2] for word in words if len(word) > 3 }

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

Aword

Blen(word)

Cword.upper()

Dlen(word) - 1

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length as the key instead of the word.

Using incorrect expressions for length.

The dictionary keys are the words themselves, and the values are their lengths.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1] : [2] for word in words if [3] }

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Not filtering words by length.

Keys are uppercase words, values are lengths, and condition filters words longer than 3 letters.

Practice

1. What does the np.split() function do in NumPy?

easy

A. It merges multiple arrays into one large array.

B. It divides an array into multiple smaller arrays at specified indices or into equal parts.

C. It sorts the elements of an array in ascending order.

D. It reshapes an array into a different shape without changing data.

Solution

1. Step 1: Understand the purpose of np.split()

   The function is designed to break a large array into smaller arrays either by specifying split points or by dividing into equal parts.

2. Step 2: Compare with other array functions

   Unlike merging, sorting, or reshaping, np.split specifically divides arrays.

3. Final Answer:

   It divides an array into multiple smaller arrays at specified indices or into equal parts. -> Option B

4. Quick Check:

   np.split() = divides array [OK]

Hint: Remember: split means break into parts [OK]

Common Mistakes:

• Confusing split with merge
• Thinking it sorts arrays
• Mixing with reshape

2. Which of the following is the correct syntax to split a 1D NumPy array arr into 3 equal parts using np.split()?

easy

A. np.split(arr, 3)

B. np.split(arr, [3])

C. np.split(arr, indices=3)

D. np.split(arr, sections=3)

Solution

1. Step 1: Recall np.split() syntax

   The function signature is np.split(array, indices_or_sections), where indices_or_sections can be an integer for equal parts.

2. Step 2: Identify correct usage

   Passing 3 as the second argument splits the array into 3 equal parts. Keyword arguments like 'indices_or_sections' are not used explicitly in common syntax.

3. Final Answer:

   np.split(arr, 3) -> Option A

4. Quick Check:

   np.split(arr, 3) splits into 3 parts [OK]

Hint: Use integer directly for equal parts [OK]

Common Mistakes:

• Using list instead of integer for equal parts
• Wrong keyword arguments
• Confusing indices with number of parts

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40, 50, 60])
splits = np.split(arr, [2, 4])
print(splits)

medium

A. [array([10]), array([20, 30, 40]), array([50, 60])]

B. [array([10, 20, 30]), array([40, 50, 60])]

C. [array([10, 20]), array([30, 40]), array([50, 60])]

D. [array([10, 20]), array([30]), array([40, 50, 60])]

Solution

1. Step 1: Understand split indices

   The list [2, 4] means split at index 2 and then at index 4.

2. Step 2: Divide the array accordingly

   arr[:2] = [10, 20], arr[2:4] = [30, 40], arr[4:] = [50, 60]

3. Final Answer:

   [array([10, 20]), array([30, 40]), array([50, 60])] -> Option C

4. Quick Check:

   Split at 2 and 4 gives 3 parts [OK]

Hint: Split indices mark where to cut array [OK]

Common Mistakes:

• Misunderstanding split points
• Off-by-one errors in slicing
• Confusing output format

4. The following code throws an error. What is the cause?

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
splits = np.split(arr, 3)
print(splits)

medium

A. The array length is not divisible by 3, so equal splits are impossible.

B. np.split() requires a list of indices, not an integer.

C. The array must be 2D to use np.split().

D. The print statement syntax is incorrect.

Solution

1. Step 1: Check array length and split parts

   The array length is 5, but we want to split into 3 equal parts.

2. Step 2: Understand np.split() behavior

   np.split() requires the array length to be divisible by the number of parts when splitting equally, otherwise it raises an error.

3. Final Answer:

   The array length is not divisible by 3, so equal splits are impossible. -> Option A

4. Quick Check:

   Length 5 not divisible by 3 causes error [OK]

Hint: Check divisibility of array length by parts [OK]

Common Mistakes:

• Using integer splits on incompatible length
• Confusing np.split with np.array_split
• Assuming print error

5. You have a 2D NumPy array data with shape (6, 4). You want to split it into 3 equal parts along the first axis (rows) for separate analysis. Which code correctly does this using np.split()?

hard

A. np.split(data, [1, 3, 5], axis=1)

B. np.split(data, 3, axis=1)

C. np.split(data, [2, 4], axis=1)

D. np.split(data, 3, axis=0)

Solution

1. Step 1: Understand array shape and axis

   data has 6 rows and 4 columns. Splitting into 3 equal parts along rows means each part has 2 rows.

2. Step 2: Use np.split() with correct axis and parts

   np.split(data, 3, axis=0) splits rows into 3 equal parts. axis=1 would split columns, which is not desired.

3. Final Answer:

   np.split(data, 3, axis=0) -> Option D

4. Quick Check:

   Split rows into 3 parts with axis=0 [OK]

Hint: Use axis=0 to split rows, axis=1 for columns [OK]

Common Mistakes:

• Splitting along wrong axis
• Using indices instead of number for equal parts
• Ignoring array shape