np.split() for dividing arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.split() do in NumPy?

np.split() divides a NumPy array into multiple smaller arrays based on specified indices or sections.

beginner

How do you split a 1D array into 3 equal parts using np.split()?

You provide the array and the number of splits. For example, np.split(arr, 3) splits into 3 equal parts if the array length is divisible by 3.

intermediate

What happens if the array size is not divisible by the number of splits in np.split()?

np.split() will raise an error because it expects equal-sized splits unless you use np.array_split() which allows uneven splits.

intermediate

How do you split a 2D array vertically using np.split()?

Use the axis=1 argument to split columns. For example, np.split(arr, 2, axis=1) splits the array into two parts along columns.

intermediate

What is the difference between np.split() and np.array_split()?

np.split() requires equal-sized splits and raises an error otherwise. np.array_split() allows uneven splits without error.

What argument do you use to split a 2D array horizontally with np.split()?

A axis=1

B axis=-1

C axis=2

D axis=0

axis=0 splits along rows (horizontal split), axis=1 splits along columns (vertical split).

If you want to split an array into 4 equal parts, which of these is correct?

A np.split(arr, [4])

B np.split(arr, 4)

C np.split(arr, 3)

D np.split(arr, [1,2,3])

Using np.split(arr, 4) splits the array into 4 equal parts if the length is divisible by 4.

What happens if you try np.split() on an array where equal splits are not possible?

A It raises an error

B It ignores extra elements

C It splits unevenly

D It returns the original array

np.split() raises a ValueError if equal splits are not possible.

Which function allows splitting arrays into unequal parts without error?

A np.array_split()

B np.split()

C np.divide()

D np.chunk()

np.array_split() allows uneven splits without raising errors.

How do you specify the indices where to split an array using np.split()?

A As a boolean mask

B As a single integer

C As a list of indices

D As a string

You provide a list of indices where the array will be split.

Explain how np.split() works for dividing arrays and what happens if the splits are not equal.

Think about how you cut a cake into equal slices.

Describe the difference between np.split() and np.array_split().

One is strict about equal parts, the other is flexible.

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