np.mean() for average in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.mean() do in numpy?

np.mean() calculates the average value of numbers in an array. It adds all numbers and divides by how many there are.

beginner

How do you calculate the average of a list [2, 4, 6, 8] using np.mean()?

Use np.mean([2, 4, 6, 8]). It returns 5.0 because (2+4+6+8)/4 = 20/4 = 5.

intermediate

What is the difference between np.mean() and Python's built-in sum()/len() for average?

np.mean() works directly on numpy arrays and is faster for large data. sum()/len() works on lists but is slower and less flexible.

intermediate

How can you calculate the average along a specific axis in a 2D numpy array using np.mean()?

Use the axis parameter. For example, np.mean(arr, axis=0) averages columns, np.mean(arr, axis=1) averages rows.

advanced

What happens if you use np.mean() on an empty array?

It returns nan (not a number) and usually shows a warning because you cannot find an average of no numbers.

What does np.mean() calculate?

A The average of numbers

B The sum of numbers

C The maximum number

D The minimum number

np.mean() calculates the average (mean) of the numbers in an array.

How do you calculate the average of each row in a 2D numpy array arr?

A np.mean(arr, axis=0)

B np.mean(arr, axis=1)

C np.mean(arr)

D np.mean(arr, axis=2)

Using axis=1 calculates the mean across each row.

What is the output of np.mean([10, 20, 30])?

A 10

B 30

C 20

D 60

The average is (10+20+30)/3 = 60/3 = 20.

Which data type does np.mean() work best with?

A Python lists only

B Dictionaries

C Strings

D Numpy arrays

np.mean() is designed for numpy arrays but can also work with lists by converting them internally.

What does np.mean([]) return?

A nan

B 0

C Error

D 1

It returns nan because the mean of an empty array is undefined.

Explain how to use np.mean() to find the average of numbers in a numpy array and how to specify the axis parameter.

Think about how you find average in a list and how axis changes that in 2D arrays.

Describe what happens when you use np.mean() on an empty array and why.

Consider what average means and what happens if there are no numbers.

Practice

1. What does the function np.mean() do when applied to a list or array?

easy

A. Counts the number of elements in the list or array

B. Finds the maximum value in the list or array

C. Sorts the list or array in ascending order

D. Calculates the average (mean) value of the numbers

Solution

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

   The function np.mean() is designed to calculate the average value of numbers in a list or array.

2. Step 2: Compare with other options

   Other options describe different functions: maximum value, count, or sorting, which are not what np.mean() does.

3. Final Answer:

   Calculates the average (mean) value of the numbers -> Option D

4. Quick Check:

   np.mean() = average [OK]

Hint: Mean = sum of values ÷ number of values [OK]

Common Mistakes:

• Confusing mean with max or count
• Thinking it sorts the data
• Using sum instead of mean

2. Which of the following is the correct syntax for np.mean() to calculate the mean of a NumPy array named arr?

easy

A. np.mean(arr)

B. mean(arr)

C. arr.mean()

D. np.average(arr)

Solution

1. Step 1: Identify the standard NumPy function for mean

   The correct function to calculate mean in NumPy is np.mean(), which takes the array as an argument.

2. Step 2: Check other options

   mean(arr) is not defined without importing; arr.mean() is valid but not the function asked; np.average(arr) is similar but not the exact function requested.

3. Final Answer:

   np.mean(arr) -> Option A

4. Quick Check:

   Use np.mean(array) for average [OK]

Hint: Use np.mean(array) to get average quickly [OK]

Common Mistakes:

• Calling mean() without np prefix
• Using arr.mean() instead of np.mean(arr)
• Confusing np.mean() with np.average()

3. What is the output of the following code?

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

medium

A. [1.5 2.5 3.5]

B. [3. 4. 5.]

C. [2.5 3.5 4.5]

D. [3.5 4.5 5.5]

Solution

1. Step 1: Understand the array and axis parameter

   The array is 2 rows by 3 columns: [[1,2,3],[4,5,6]]. Using axis=0 means calculating the mean down each column.

2. Step 2: Calculate mean for each column

   Column 1: (1+4)/2 = 2.5, Column 2: (2+5)/2 = 3.5, Column 3: (3+6)/2 = 4.5. So the result is [2.5, 3.5, 4.5].

3. Step 3: Check options carefully

   [2.5 3.5 4.5] matches the calculated values exactly.

4. Final Answer:

   [2.5 3.5 4.5] -> Option C

5. Quick Check:

   Mean along axis=0 averages columns [OK]

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

Common Mistakes:

• Mixing axis=0 and axis=1
• Calculating mean of entire array instead of axis
• Misreading array shape

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

import numpy as np
arr = [1, 2, 3, 4]
result = np.mean(arr, axis=1)
print(result)

medium

A. np.mean() cannot be used on lists

B. The list is 1D, so axis=1 is invalid

C. Missing parentheses in np.mean

D. The variable name 'arr' is reserved

Solution

1. Step 1: Check the array shape

   The variable arr is a 1D list with shape (4,). Axis 1 does not exist in 1D data.

2. Step 2: Understand axis parameter

   Axis=1 means operate along columns in 2D or higher arrays. Using axis=1 on 1D data causes an error.

3. Final Answer:

   The list is 1D, so axis=1 is invalid -> Option B

4. Quick Check:

   Axis must exist in array shape [OK]

Hint: Check array dimensions before using axis [OK]

Common Mistakes:

• Using axis parameter on 1D arrays
• Thinking np.mean can't handle lists
• Confusing axis indexing

5. You have a 3D NumPy array representing daily temperatures for 2 cities over 3 days with 4 measurements each day:

temps = np.array([
  [[20, 22, 21, 23], [19, 20, 22, 21]],
  [[21, 23, 22, 24], [20, 21, 23, 22]],
  [[22, 24, 23, 25], [21, 22, 24, 23]]
])

Which code correctly calculates the average temperature for each city over all days and measurements?

hard

A. np.mean(temps, axis=(0,2))

B. np.mean(temps, axis=0)

C. np.mean(temps, axis=1)

D. np.mean(temps, axis=2)

Solution

1. Step 1: Understand the array shape and axes

   The array shape is (3, 2, 4): 3 days, 2 cities, 4 measurements each day.

2. Step 2: Identify axes to average over

   To get average per city over all days and measurements, we average over days (axis 0) and measurements (axis 2), keeping axis 1 (cities).

3. Step 3: Use axis=(0,2) in np.mean

   Using np.mean(temps, axis=(0,2)) averages over days and measurements, resulting in average per city.

4. Final Answer:

   np.mean(temps, axis=(0,2)) -> Option A

5. Quick Check:

   Average over days and measurements = axis 0 and 2 [OK]

Hint: Use tuple axis to average multiple dimensions [OK]

Common Mistakes:

• Averaging over wrong axes
• Using single axis instead of tuple
• Confusing city axis with day or measurement axis