NumPy with SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SciPy stats module.

NumPy

from scipy import [1]

print(stats.norm.cdf(0))

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Aoptimize

Bstats

Cintegrate

Dlinalg

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong SciPy submodule like optimize or integrate.

Trying to use stats without importing it.

The stats module in SciPy provides statistical functions like the normal cumulative distribution function (cdf).

2fill in blank

medium

Complete the code to create a NumPy array of 5 random numbers from a normal distribution using SciPy.

NumPy

import numpy as np
from scipy import stats

random_nums = stats.norm.[1](size=5)
print(random_nums)

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Arvs

Bppf

Ccdf

Dpdf

Attempts:

3 left

💡 Hint

Common Mistakes

Using cdf or pdf which compute probabilities, not samples.

Using ppf which is the percent point function (inverse cdf).

The rvs function generates random variates (random samples) from the specified distribution.

3fill in blank

hard

Fix the error in the code to calculate the mean of a NumPy array.

NumPy

import numpy as np

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

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Amean

Baverage

Cmedian

Dmode

Attempts:

3 left

💡 Hint

Common Mistakes

Using average() which is a NumPy function but not an array method.

Confusing mean with median or mode.

The correct method to calculate the mean of a NumPy array is mean().

4fill in blank

hard

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

NumPy

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong comparison operator like <= instead of >.

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 of uppercase words and their lengths for words longer than 2 characters.

NumPy

words = ['apple', 'to', 'banana', 'go']
result = { [1]: [2] for w in words if len(w) [3] 2 }
print(result)

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Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase for keys.

Using wrong comparison operator or value for filtering.

The dictionary comprehension uses w.upper() as keys, len(w) as values, and filters words with length greater than 2 using >.