Why statistics with NumPy matters - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the data array using NumPy.

NumPy

import numpy as np

data = np.array([10, 20, 30, 40, 50])
mean_value = np.[1](data)
print(mean_value)

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

Amax

Bmedian

Csum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of mean will give the total, not the average.

Using median calculates the middle value, not the average.

The mean function calculates the average value of the array elements.

2fill in blank

medium

Complete the code to calculate the standard deviation of the data array using NumPy.

NumPy

import numpy as np

data = np.array([5, 10, 15, 20, 25])
std_dev = np.[1](data)
print(std_dev)

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

Asum

Bstd

Cmean

Dvar

Attempts:

3 left

💡 Hint

Common Mistakes

Using var calculates variance, not standard deviation.

Using mean calculates average, not spread.

The std function calculates the standard deviation, which shows how spread out the numbers are.

3fill in blank

hard

Fix the error in the code to calculate the median of the data array using NumPy.

NumPy

import numpy as np

data = np.array([3, 1, 4, 1, 5])
median_value = np.[1](data)
print(median_value)

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

Amedian

Bmean

Cmode

Dstd

Attempts:

3 left

💡 Hint

Common Mistakes

Using mode is not available in NumPy and causes an error.

Using mean calculates average, not the middle value.

The median function finds the middle value when the data is sorted.

4fill in blank

hard

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

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
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>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using == filters only words with length exactly 3.

Using <= includes words with length 3 or less.

The dictionary comprehension uses len(word) to get the length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 10.

NumPy

data = {'a': 5, 'b': 15, 'c': 25}
result = { [1]: [2] for k, v in data.items() if v [3] 10 }
print(result)

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() does not change keys to uppercase.

Using < or == filters wrong values.

The dictionary comprehension creates keys as uppercase letters using k.upper(), keeps values as v, and filters values greater than 10 using >.

Practice

1. Why is it useful to calculate statistics like mean and median using NumPy?

easy

A. Because it deletes unnecessary data points

B. Because it helps summarize large data sets quickly and clearly

C. Because it makes data harder to understand

D. Because it changes the original data values

Solution

1. Step 1: Understand the purpose of statistics

   Statistics like mean and median give a simple summary of data, making it easier to understand large amounts of numbers.

2. Step 2: Recognize NumPy's role

   NumPy provides fast and easy functions to calculate these statistics, saving time and effort.

3. Final Answer:

   Because it helps summarize large data sets quickly and clearly -> Option B

4. Quick Check:

   Statistics summarize data = Because it helps summarize large data sets quickly and clearly [OK]

Hint: Statistics simplify data understanding fast with NumPy [OK]

Common Mistakes:

• Thinking statistics change original data
• Believing statistics delete data points
• Confusing summary with data removal

2. Which of the following is the correct way to calculate the mean of a NumPy array named data?

easy

A. np.mean(data)

B. np.mean[data]

C. data.mean()()

D. mean(data)

Solution

1. Step 1: Recall NumPy mean syntax

   The correct function to calculate mean in NumPy is np.mean() with the array passed inside parentheses.

2. Step 2: Check each option

   mean(data) misses the np. prefix, data.mean()() has extra parentheses, np.mean[data] uses square brackets incorrectly.

3. Final Answer:

   np.mean(data) -> Option A

4. Quick Check:

   Correct NumPy mean syntax = np.mean(data) [OK]

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

Common Mistakes:

• Forgetting to use np. prefix
• Using square brackets instead of parentheses
• Adding extra parentheses after method

3. What is the output of this code?

import numpy as np
data = np.array([1, 2, 3, 4, 5])
print(np.median(data))

medium

A. 2

B. 2.5

C. 3

D. 3.0

Solution

1. Step 1: Understand the median calculation

   The median is the middle value when data is sorted. For [1,2,3,4,5], the middle is 3.

2. Step 2: Check NumPy median output type

   NumPy returns median as a float, so output is 3.0, not integer 3.

3. Final Answer:

   3.0 -> Option D

4. Quick Check:

   Median of odd-length array = middle value as float [OK]

Hint: Median of odd list is middle number as float [OK]

Common Mistakes:

• Confusing median with mean
• Expecting integer instead of float output
• Choosing average of two numbers for odd-length array

4. The following code is meant to calculate the standard deviation of data, but it gives an error. What is the problem?

import numpy as np
data = np.array([10, 20, 30])
std_dev = np.std data
print(std_dev)

medium

A. Data array is not numeric

B. np.std does not exist in NumPy

C. Missing parentheses after np.std

D. print() cannot display std_dev

Solution

1. Step 1: Identify syntax error in function call

   The code calls np.std without parentheses, which is invalid syntax in Python.

2. Step 2: Correct the function call

   Adding parentheses and passing data inside fixes the error: np.std(data).

3. Final Answer:

   Missing parentheses after np.std -> Option C

4. Quick Check:

   Function calls need parentheses [OK]

Hint: Always use parentheses when calling functions [OK]

Common Mistakes:

• Forgetting parentheses on function calls
• Thinking np.std is not a NumPy function
• Assuming print cannot show numbers

5. You have a large dataset with some missing values represented as np.nan. Which NumPy function helps calculate the mean while ignoring these missing values?

hard

A. np.nanmean()

B. np.mean()

C. np.median()

D. np.std()

Solution

1. Step 1: Understand the problem with missing values

   Missing values as np.nan cause normal mean calculations to return nan instead of a number.

2. Step 2: Use the correct NumPy function

   np.nanmean() calculates the mean ignoring nan values, giving a valid average.

3. Final Answer:

   np.nanmean() -> Option A

4. Quick Check:

   Ignore NaNs with np.nanmean() [OK]

Hint: Use np.nanmean() to skip NaN values in mean [OK]

Common Mistakes:

• Using np.mean() which returns nan if any NaN present
• Confusing median with mean for missing data
• Trying to use np.std() for mean calculation