np.concatenate() for joining arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.concatenate() do in NumPy?

np.concatenate() joins two or more arrays into one bigger array along a specified axis.

beginner

What is the default axis for np.concatenate()?

The default axis is axis=0, which means arrays are joined row-wise (stacked vertically).

beginner

Can np.concatenate() join arrays of different shapes?

No, arrays must have the same shape except in the dimension along the joining axis.

beginner

How do you join two 1D arrays a = [1, 2] and b = [3, 4] using np.concatenate()?

Use np.concatenate([a, b]) to get [1, 2, 3, 4].

intermediate

What happens if you set axis=1 when concatenating 2D arrays?

Arrays are joined column-wise (side by side), increasing the number of columns.

What is the result of np.concatenate([np.array([1,2]), np.array([3,4])])?

A [[1 2] [3 4]]

B [[1 3] [2 4]]

C [1 2 3 4]

D Error

Arrays are joined end-to-end along axis 0 by default, resulting in a 1D array [1 2 3 4].

Which axis joins arrays side by side (columns) in 2D with np.concatenate()?

A axis=1

B axis=-1

C axis=0

D axis=2

axis=1 joins arrays column-wise (side by side) in 2D arrays.

If two arrays have shapes (2,3) and (2,4), can they be concatenated along axis=0?

A Yes

B No

C Only if axis=1

D Only if reshaped

Along axis=0, arrays must have the same number of columns. Here, 3 != 4, so no.

What type of object must be passed to np.concatenate()?

A A scalar value

B A single array

C A dictionary

D A list or tuple of arrays

np.concatenate() requires a list or tuple of arrays to join.

What happens if arrays have different shapes on non-concatenation axes?

A An error is raised

B They concatenate successfully

C Arrays are padded automatically

D Arrays are truncated

Arrays must match in all dimensions except the concatenation axis; otherwise, an error occurs.

Explain how np.concatenate() works and what rules arrays must follow to be joined.

Think about stacking arrays like stacking blocks either vertically or horizontally.

Describe a real-life example where you might use np.concatenate() to combine data.

Imagine putting together pieces of a puzzle to see the full picture.

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