NumPy with machine learning libraries - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import NumPy with its common alias.

NumPy

import [1] as np

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

Asklearn

Bnumpy

Cpandas

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing pandas or sklearn instead of numpy.

Not using the alias np.

NumPy is commonly imported as np to simplify code when using its functions.

2fill in blank

medium

Complete the code to convert a Python list to a NumPy array.

NumPy

arr = np.[1]([1, 2, 3, 4])

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

Aarray

Bmatrix

Clist

Dasarray

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list which does not exist.

Using np.asarray which also works but is not the common first choice here.

The np.array() function converts a list into a NumPy array.

3fill in blank

hard

Fix the error in the code to import the train_test_split function from scikit-learn.

NumPy

from sklearn.model_selection import [1]

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

Atrain_test_split

BtrainSplit

Csplit_train_test

Dtrain_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like train_test or trainSplit.

Misspelling the function name.

The correct function name is train_test_split from sklearn.model_selection.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

{word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B<

C>

Dword

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 maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values only if values are positive.

NumPy

result = [1]: [2] for k, v in data.items() if v [3] 0}

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() instead of uppercase.

Using wrong comparison operators like < or ==.

Swapping keys and values.

The comprehension uses k.upper() to convert keys to uppercase, keeps values as v, and filters for values greater than zero using >.

Practice

1. Why do machine learning libraries often use NumPy arrays for data input?

easy

A. Because NumPy arrays can only store text data

B. Because NumPy arrays are fast and easy to manipulate for numerical data

C. Because NumPy arrays automatically train machine learning models

D. Because NumPy arrays are the only data type in Python

Solution

1. Step 1: Understand NumPy array properties

   NumPy arrays store numerical data efficiently and support fast operations.

2. Step 2: Connect to machine learning needs

   Machine learning needs fast numerical data processing, which NumPy arrays provide.

3. Final Answer:

   Because NumPy arrays are fast and easy to manipulate for numerical data -> Option B

4. Quick Check:

   NumPy arrays speed up numerical data handling = A [OK]

Hint: Remember: ML needs fast number crunching, NumPy fits well [OK]

Common Mistakes:

• Thinking NumPy arrays store only text
• Believing NumPy trains models automatically
• Assuming Python has only one data type

2. Which of the following is the correct way to convert a Python list [1, 2, 3] into a NumPy array?

easy

A. np.toarray([1, 2, 3])

B. np.list([1, 2, 3])

C. np.convert([1, 2, 3])

D. np.array([1, 2, 3])

Solution

1. Step 1: Recall NumPy array creation syntax

   The function to create arrays from lists is np.array().

2. Step 2: Check each option

   Only np.array([1, 2, 3]) is valid syntax; others are not NumPy functions.

3. Final Answer:

   np.array([1, 2, 3]) -> Option D

4. Quick Check:

   Use np.array() to convert lists = A [OK]

Hint: Use np.array() to convert lists to arrays [OK]

Common Mistakes:

• Using non-existent functions like np.list()
• Confusing array creation with conversion functions
• Forgetting to import NumPy as np

3. What will be the output of this code snippet?

import numpy as np
from sklearn.preprocessing import StandardScaler

X = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(np.round(X_scaled, 2))

medium

A. [[-1.22 -1.22]
[ 0. 0. ]
[ 1.22 1.22]]

B. [[1 2]
[3 4]
[5 6]]

C. [[0. 0.]
[0. 0.]
[0. 0.]]

D. Error: StandardScaler not imported

Solution

1. Step 1: Understand StandardScaler effect

   StandardScaler centers data to mean 0 and scales to unit variance.

2. Step 2: Calculate scaled values

   For each column, mean is 3 and 4; std dev approx 1.63. Values transform to approx -1.22, 0, 1.22.

3. Final Answer:

   [[-1.22 -1.22] [ 0. 0. ] [ 1.22 1.22]] -> Option A

4. Quick Check:

   StandardScaler centers and scales data = D [OK]

Hint: StandardScaler centers data to mean 0 and scales variance 1 [OK]

Common Mistakes:

• Expecting original data output
• Confusing StandardScaler with MinMaxScaler
• Missing import causing runtime error

4. Identify the error in this code that uses NumPy with scikit-learn:

import numpy as np
from sklearn.linear_model import LinearRegression

X = [[1, 2], [3, 4], [5, 6]]
y = [1, 2, 3]
model = LinearRegression()
model.fit(X, y)
print(model.coef_)

medium

A. X should be a NumPy array, not a list

B. y should be a 2D array, not a list

C. LinearRegression requires data scaling before fitting

D. model.fit() is missing required parameters

Solution

1. Step 1: Check data types for model.fit()

   scikit-learn expects NumPy arrays or similar, not plain Python lists.

2. Step 2: Identify the issue in X

   X is a list of lists; converting to np.array is needed for proper fitting.

3. Final Answer:

   X should be a NumPy array, not a list -> Option A

4. Quick Check:

   Use NumPy arrays for input data = B [OK]

Hint: Always convert input data to NumPy arrays before fitting models [OK]

Common Mistakes:

• Thinking y must be 2D array
• Believing scaling is mandatory before fitting
• Assuming fit() needs extra parameters

5. You have a dataset as a Python list of lists representing features. You want to prepare it for a machine learning model that requires normalized NumPy arrays. Which steps should you perform?

hard

A. Convert the list to a Python tuple, then normalize manually with loops

B. Directly pass the list to the model without conversion or scaling

C. Convert the list to a NumPy array, then use a scaler like MinMaxScaler to normalize

D. Use pandas DataFrame without converting to NumPy arrays

Solution

1. Step 1: Convert list to NumPy array

   Machine learning models expect NumPy arrays for efficient computation.

2. Step 2: Normalize data using scaler

   Use MinMaxScaler or similar to scale features between 0 and 1 for better model performance.

3. Final Answer:

   Convert the list to a NumPy array, then use a scaler like MinMaxScaler to normalize -> Option C

4. Quick Check:

   Convert then scale data before modeling = C [OK]

Hint: Always convert data to NumPy arrays and scale before modeling [OK]

Common Mistakes:

• Passing raw lists directly to models
• Using tuples which are not efficient for ML
• Skipping normalization hurting model accuracy