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))

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

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)

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

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)

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

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)

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

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)

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

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 >.

Practice

1. What is the main benefit of using SciPy together with NumPy?

easy

A. SciPy replaces NumPy for basic array operations.

B. SciPy provides extra math functions that NumPy does not have.

C. SciPy is used only for plotting graphs.

D. SciPy is a database for storing NumPy arrays.

Solution

1. Step 1: Understand NumPy's role

   NumPy is mainly for fast array operations and basic math.

2. Step 2: Understand SciPy's role

   SciPy adds many advanced math and scientific functions on top of NumPy.

3. Final Answer:

   SciPy provides extra math functions that NumPy does not have. -> Option B

4. Quick Check:

   Extra math tools = SciPy [OK]

Hint: SciPy adds advanced math tools beyond NumPy basics [OK]

Common Mistakes:

• Thinking SciPy replaces NumPy
• Confusing SciPy with plotting libraries
• Believing SciPy stores data like a database

2. Which of the following is the correct way to import the SciPy stats module alongside NumPy?

easy

A. import numpy as np; import scipy.stats as stats

B. import numpy; import scipy.stats

C. from numpy import stats; import scipy

D. import numpy.stats; import scipy as sp

Solution

1. Step 1: Check standard import conventions

   NumPy is usually imported as np, SciPy submodules like stats as stats or sp.stats.

2. Step 2: Validate each option

   import numpy as np; import scipy.stats as stats uses correct aliases and imports the stats submodule properly.

3. Final Answer:

   import numpy as np; import scipy.stats as stats -> Option A

4. Quick Check:

   Standard imports = import numpy as np; import scipy.stats as stats [OK]

Hint: Use 'import numpy as np' and 'import scipy.stats as stats' [OK]

Common Mistakes:

• Importing non-existent numpy.stats
• Using wrong aliases or missing 'as'
• Importing whole scipy without submodule

3. What is the output of this code?

import numpy as np
from scipy import stats
arr = np.array([1, 2, 2, 3, 4])
mode_result = stats.mode(arr)
print(mode_result.mode[0])

medium

A. 1

B. 4

C. 3

D. 2

Solution

1. Step 1: Understand the array values

   The array is [1, 2, 2, 3, 4], where 2 appears twice, others once.

2. Step 2: Understand stats.mode function

   stats.mode returns the most frequent value; here it is 2.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Most frequent value = 2 [OK]

Hint: Mode is the most frequent number in the array [OK]

Common Mistakes:

• Confusing mode with mean or median
• Not accessing mode_result.mode[0]
• Assuming first element is mode

4. Find the error in this code snippet:

import numpy as np
from scipy import stats
arr = np.array([1, 2, 3, 4])
mode_result = stats.mode(arr)
print(mode_result[0])

medium

A. mode_result is an object; correct access is mode_result.mode, not mode_result[0].

B. stats.mode returns a tuple, so indexing with [0] is correct.

C. The array must be a list, not a NumPy array.

D. Missing import for numpy as np.

Solution

1. Step 1: Check stats.mode return type

   stats.mode returns an object with attributes like mode and count, not a tuple.

2. Step 2: Identify correct attribute access

   Access the mode value with mode_result.mode, not by indexing mode_result[0].

3. Final Answer:

   mode_result is an object; correct access is mode_result.mode, not mode_result[0]. -> Option A

4. Quick Check:

   Use .mode attribute, not [0] index [OK]

Hint: Use .mode attribute to get mode, not indexing [OK]

Common Mistakes:

• Using indexing instead of attribute access
• Confusing return type as tuple
• Thinking array must be list

5. You have two NumPy arrays:

import numpy as np
from scipy import stats
arr1 = np.array([1, 2, 2, 3])
arr2 = np.array([2, 2, 3, 4])

How can you find the combined mode of all elements from both arrays using SciPy?

hard

A. Use stats.mode(arr1 + arr2)

B. Use stats.mode(arr1 * arr2)

C. Use stats.mode(np.concatenate((arr1, arr2)))

D. Use stats.mode(arr1) and stats.mode(arr2) separately and pick the higher

Solution

1. Step 1: Combine both arrays

   Use np.concatenate to join arr1 and arr2 into one array.

2. Step 2: Apply stats.mode on combined array

   Passing the combined array to stats.mode finds the overall mode.

3. Final Answer:

   Use stats.mode(np.concatenate((arr1, arr2))) -> Option C

4. Quick Check:

   Combine arrays first, then mode [OK]

Hint: Join arrays with concatenate before finding mode [OK]

Common Mistakes:

• Adding or multiplying arrays instead of concatenating
• Finding modes separately and comparing manually
• Not combining arrays before mode