Aggregation along specific axes in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does aggregation along a specific axis mean in numpy?

It means combining values in an array along a chosen direction (axis), like summing all rows or columns separately.

beginner

How do you sum all elements in each column of a 2D numpy array?

Use np.sum(array, axis=0). Axis 0 means moving down rows, summing column-wise.

beginner

What is the difference between axis=0 and axis=1 in numpy aggregation?

axis=0 aggregates down the rows (column-wise), axis=1 aggregates across columns (row-wise).

beginner

How to find the maximum value in each row of a 2D numpy array?

Use np.max(array, axis=1). This finds the max value moving across columns for each row.

beginner

Why is specifying the axis important in numpy aggregation functions?

Because it tells numpy which direction to combine values, affecting the shape and meaning of the result.

What does np.sum(array, axis=0) do on a 2D array?

A Returns the original array

B Sums each row separately

C Sums all elements into one number

D Sums each column separately

Axis 0 means summing down rows, so each column's values are added together.

If you want to find the minimum value in each row, which axis should you use?

A axis=1

B axis=2

C axis=0

D No axis needed

Axis 1 moves across columns, so it finds the min value in each row.

What shape will the result have after np.mean(array, axis=1) on a 2D array with shape (4,5)?

A (4,5)

B (5,)

C (4,)

D (1,5)

Aggregating along axis 1 reduces columns, so result shape is (4,), one mean per row.

Which numpy function can you use to get the sum of all elements regardless of axis?

A np.sum(array)

B np.sum(array, axis=0)

C np.sum(array, axis=1)

D np.sum(array, axis=2)

Without axis, np.sum adds all elements into a single number.

What happens if you use an invalid axis number in numpy aggregation?

A Returns zeros

B Raises an error

C Ignores the axis and sums all

D Returns the original array

Numpy raises an error because the axis does not exist in the array shape.

Explain how aggregation along axis 0 and axis 1 differ in a 2D numpy array.

Think about summing columns vs summing rows.

Describe why specifying the axis is important when using numpy aggregation functions.

Consider what happens if you don't specify axis.

Practice

1. What does setting axis=0 do when aggregating a 2D NumPy array?

easy

A. Aggregates values down each column (per column)

B. Aggregates values across each row (per row)

C. Flattens the array before aggregation

D. Aggregates only the diagonal elements

Solution

1. Step 1: Understand axis parameter meaning

   In NumPy, axis=0 means aggregation happens down the rows, combining values in each column.

2. Step 2: Apply to 2D array

   Aggregating with axis=0 summarizes each column separately.

3. Final Answer:

   Aggregates values down each column (per column) -> Option A

4. Quick Check:

   axis=0 means down rows = per column aggregation [OK]

Hint: axis=0 aggregates columns, axis=1 aggregates rows [OK]

Common Mistakes:

• Confusing axis=0 with axis=1
• Thinking axis=0 aggregates rows instead of columns
• Assuming axis=0 flattens the array

2. Which of the following is the correct syntax to sum a 2D NumPy array arr along rows (per row)?

easy

A. np.sum(arr, axis=1)

B. np.sum(arr, axis=0)

C. arr.sum(axis=2)

D. np.sum(arr)

Solution

1. Step 1: Identify axis for row-wise sum

   Summing across columns (per row) requires axis=1.

2. Step 2: Check syntax correctness

   np.sum(arr, axis=1) correctly sums each row's elements.

3. Final Answer:

   np.sum(arr, axis=1) -> Option A

4. Quick Check:

   Sum per row uses axis=1 [OK]

Hint: Use axis=1 to sum across columns (per row) [OK]

Common Mistakes:

• Using axis=0 to sum rows
• Using invalid axis like 2 for 2D array
• Forgetting to specify axis

3. Given the array:

arr = np.array([[1, 2, 3], [4, 5, 6]])

What is the output of np.sum(arr, axis=0)?

medium

A. [3 7 11]

B. [5 7 9]

C. [6 15]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand axis=0 sum

   Sum down rows means add elements in each column: (1+4), (2+5), (3+6).

2. Step 2: Calculate sums

   Calculations: 1+4=5, 2+5=7, 3+6=9, so result is [5 7 9].

3. Final Answer:

   [5 7 9] -> Option B

4. Quick Check:

   Sum axis=0 adds columns down rows [OK]

Hint: Sum axis=0 adds columns down rows [OK]

Common Mistakes:

• Adding rows instead of columns
• Confusing axis=0 with axis=1
• Returning flattened array instead of summed

4. What is wrong with this code snippet?

arr = np.array([[1, 2], [3, 4]])
result = np.mean(arr, axis=2)

medium

A. No error, code runs fine

B. np.mean cannot be used on arrays

C. Missing parentheses in np.array

D. axis=2 is invalid for 2D array

Solution

1. Step 1: Check array dimensions

   The array arr is 2D with shape (2,2), so valid axes are 0 and 1.

2. Step 2: Validate axis parameter

   Using axis=2 is invalid and causes an error because axis 2 does not exist.

3. Final Answer:

   axis=2 is invalid for 2D array -> Option D

4. Quick Check:

   Axis must be less than array dimensions [OK]

Hint: Axis must be less than array dimensions [OK]

Common Mistakes:

• Using axis number too large for array
• Thinking np.mean can't be used on arrays
• Ignoring error messages

5. You have a 3D NumPy array data with shape (4, 3, 2). You want to compute the sum of each 2D slice along the first axis (axis=0), resulting in a 2D array. Which code correctly does this?

hard

A. np.sum(data)

B. np.sum(data, axis=1)

C. np.sum(data, axis=0)

D. np.sum(data, axis=2)

Solution

1. Step 1: Understand array shape and axis

   Array shape is (4,3,2). Summing along axis=0 collapses the first dimension (4), resulting in shape (3,2).

2. Step 2: Apply sum along axis=0

   Using np.sum(data, axis=0) sums all 4 slices element-wise, producing a 2D array.

3. Final Answer:

   np.sum(data, axis=0) -> Option C

4. Quick Check:

   Sum axis=0 collapses first dimension to 2D [OK]

Hint: Sum axis=0 collapses first dimension in 3D arrays [OK]

Common Mistakes:

• Using wrong axis and getting wrong shape
• Summing all elements without axis
• Confusing axis numbering in 3D arrays