np.concatenate() for joining arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to join two numpy arrays along the default axis.

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = np.concatenate((arr1, arr2), axis=[1])
print(result)

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

A1

B2

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 causes an error because 1D arrays have only one axis (0).

Forgetting to put the arrays inside a tuple in np.concatenate.

The default axis for concatenation of 1D arrays is 0, which joins them end-to-end.

2fill in blank

medium

Complete the code to join two 2D numpy arrays vertically.

NumPy

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
result = np.concatenate((arr1, arr2), axis=[1])
print(result)

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

A0

B1

C-1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 stacks arrays horizontally (side by side), not vertically.

Using axis=2 causes an error because 2D arrays have only axes 0 and 1.

Axis 0 means stacking arrays vertically (row-wise) for 2D arrays.

3fill in blank

hard

Fix the error in the code to concatenate two arrays along columns.

NumPy

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
result = np.concatenate((arr1, arr2), axis=[1])
print(result)

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

A1

B-1

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 stacks arrays vertically, not horizontally.

Using axis=2 causes an error because 2D arrays have only axes 0 and 1.

Axis 1 means concatenating arrays side by side (column-wise) for 2D arrays.

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 letters.

NumPy

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

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) as dictionary value.

Using '<' instead of '>' in the condition.

The dictionary comprehension uses len(word) as value and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

NumPy

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

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

Using '<' instead of '>' in the condition.

Using k instead of v for values.

The dictionary comprehension uses uppercase keys, values v, and filters values greater than 0.

Practice

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

easy

A. Splits an array into multiple smaller arrays

B. Joins two or more arrays along an existing axis

C. Sorts the elements of an array in ascending order

D. Creates a new array filled with zeros

Solution

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

   It is used to join arrays along a specified axis, combining them into one larger array.

2. Step 2: Compare with other array functions

   Splitting, sorting, or creating zero arrays are different functions like np.split(), np.sort(), and np.zeros().

3. Final Answer:

   Joins two or more arrays along an existing axis -> Option B

4. Quick Check:

   np.concatenate() = join arrays [OK]

Hint: Remember: concatenate means join arrays along axis [OK]

Common Mistakes:

• Confusing concatenate with split or sort functions
• Thinking it modifies original arrays
• Assuming it creates arrays from scratch

2. Which of the following is the correct syntax to join two 1D arrays a and b using np.concatenate()?

easy

A. np.concatenate([a, b])

B. np.concatenate(a, axis=1)

C. np.concatenate(a + b)

D. np.concatenate(a, b)

Solution

1. Step 1: Check the function signature

   np.concatenate() expects a single argument: a sequence (like a list or tuple) of arrays to join.

2. Step 2: Analyze each option

   np.concatenate([a, b]) correctly passes a list of arrays [a, b]. np.concatenate(a, b) passes two separate arguments, which is invalid. np.concatenate(a + b) tries to add arrays before concatenation, which is not the function's purpose. np.concatenate(a, axis=1) passes axis=1 for 1D arrays, which is invalid.

3. Final Answer:

   np.concatenate([a, b]) -> Option A

4. Quick Check:

   Pass arrays as a list to concatenate() [OK]

Hint: Always pass arrays inside a list or tuple to concatenate [OK]

Common Mistakes:

• Passing arrays as separate arguments instead of a list
• Using plus (+) operator instead of concatenate
• Setting wrong axis for 1D arrays

3. What is the output of the following code?

import numpy as np
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6]])
result = np.concatenate((x, y), axis=0)
print(result)

medium

A. [[1 2]
[3 4]
[5 6]]

B. [[1 2 5 6]
[3 4 5 6]]

C. [[1 2]
[3 4]]

D. Error: shapes do not match

Solution

1. Step 1: Check shapes of arrays

   x has shape (2, 2), y has shape (1, 2). They match in columns (axis=1) but differ in rows (axis=0).

2. Step 2: Concatenate along axis=0 (rows)

   Joining along rows stacks y below x, resulting in a (3, 2) array with rows from x then y.

3. Final Answer:

   [[1 2] [3 4] [5 6]] -> Option A

4. Quick Check:

   Concatenate rows stacks arrays vertically [OK]

Hint: Concatenate axis=0 stacks rows; axis=1 stacks columns [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting horizontal stacking with axis=0
• Ignoring shape compatibility

4. What is wrong with this code snippet?

import numpy as np
x = np.array([[1, 2], [3, 4]])
y = np.array([5, 6, 7])
result = np.concatenate((x, y), axis=0)
print(result)

medium

A. Error because y must be reshaped to 3D

B. No error, output is concatenated array

C. Error because axis=0 is invalid for 2D arrays

D. Error because arrays have incompatible shapes along axis=0

Solution

1. Step 1: Check shapes of arrays

   x shape is (2, 2), y shape is (3,). They differ in both dimensions.

2. Step 2: Understand concatenate axis rules

   Arrays must match in all dimensions except the concatenation axis. Here, axis=0 means rows, but y has no matching columns.

3. Final Answer:

   Error because arrays have incompatible shapes along axis=0 -> Option D

4. Quick Check:

   Shapes must match except on concat axis [OK]

Hint: Check array shapes match except on concat axis [OK]

Common Mistakes:

• Ignoring shape mismatch errors
• Assuming 1D array can concatenate with 2D without reshape
• Misunderstanding axis parameter

5. You have three arrays:

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
c = np.array([[7, 8]])

You want to join them into one array with shape (4, 2). Which code correctly does this?

hard

A. np.concatenate((a, b), axis=0) + c

B. np.concatenate([a, b, c], axis=1)

C. np.concatenate((a, b, c), axis=0)

D. np.concatenate((a, b, c), axis=2)

Solution

1. Step 1: Check shapes and desired output

   a shape is (2, 2), b and c are (1, 2). Joining along rows (axis=0) stacks them vertically to get (4, 2).

2. Step 2: Analyze options

   np.concatenate((a, b, c), axis=0) correctly concatenates all three arrays along axis=0. np.concatenate([a, b, c], axis=1) tries axis=1 which would require matching rows, which is false. np.concatenate((a, b), axis=0) + c adds arrays after concatenation, which is invalid. np.concatenate((a, b, c), axis=2) uses axis=2 which is invalid for 2D arrays.

3. Final Answer:

   np.concatenate((a, b, c), axis=0) -> Option C

4. Quick Check:

   Stack arrays vertically with axis=0 [OK]

Hint: Use axis=0 to stack arrays vertically when shapes match columns [OK]

Common Mistakes:

• Using wrong axis for concatenation
• Trying to add arrays instead of concatenate
• Using invalid axis number for 2D arrays