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)

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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 >.