Handling missing values in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check for missing values in the DataFrame.

ML Python

missing = df.isnull().[1]()

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

Amean

Bsum

Ccount

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts non-missing values instead.

Using fillna() which replaces missing values instead of counting.

The sum() function counts the number of missing values in each column.

2fill in blank

medium

Complete the code to fill missing values with the mean of the column.

ML Python

df['age'] = df['age'].[1](df['age'].mean())

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

Afillna

Bisnull

Creplace

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() which removes rows instead of filling values.

Using replace() which replaces specific values but not missing ones by default.

The fillna() method replaces missing values with the given value, here the mean.

3fill in blank

hard

Fix the error in the code to drop rows with any missing values.

ML Python

cleaned_df = df.[1](axis=0, how='any')

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

Afillna

Bisnull

Creplace

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which fills missing values instead of dropping rows.

Using isnull() which only detects missing values.

The dropna() method removes rows or columns with missing values. Here, axis=0 means rows.

4fill in blank

hard

Fill both blanks to create a new DataFrame with missing values replaced by the median.

ML Python

df_clean = df.[1](df.[2]())

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

Afillna

Bmean

Cmedian

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean instead of median for the second blank.

Using dropna which removes rows instead of filling values.

First, calculate the median with median(), then fill missing values using fillna().

5fill in blank

hard

Fill all three blanks to create a dictionary of column means for filling missing values.

ML Python

fill_values = {col: df[col].[1]() for col in df.columns if df[col].[2]().any()}
df_filled = df.[3](fill_values)

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

Amean

Bisnull

Cfillna

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna instead of fillna for the third blank.

Using notnull instead of isnull for the second blank.

Calculate the mean of each column with missing values using mean(), detect missing values with isnull(), and fill them using fillna().

Practice

1. What is the main reason to handle missing values before training a machine learning model?

easy

A. Missing values are automatically ignored by all models

B. Missing values can cause errors or reduce model accuracy

C. Missing values improve model training speed

D. Missing values increase the size of the dataset

Solution

1. Step 1: Understand the impact of missing values

   Missing values can cause errors or confuse the model, leading to poor predictions.

2. Step 2: Recognize model requirements

   Most machine learning models require complete data without missing values to work properly.

3. Final Answer:

   Missing values can cause errors or reduce model accuracy -> Option B

4. Quick Check:

   Missing values harm model accuracy = D [OK]

Hint: Missing data breaks models, so fix it first [OK]

Common Mistakes:

• Thinking missing values speed up training
• Assuming models ignore missing data automatically
• Believing missing values increase dataset size

2. Which of the following is the correct way to import and use SimpleImputer from scikit-learn to fill missing values with the mean?

easy

A. from sklearn.impute import SimpleImputer\nimputer = SimpleImputer(method='mean')

B. import SimpleImputer from sklearn.impute\nimputer = SimpleImputer('mean')

C. from sklearn.imputer import SimpleImputer\nimputer = SimpleImputer(strategy=mean)

D. from sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='mean')

Solution

1. Step 1: Check import syntax

   The correct import is 'from sklearn.impute import SimpleImputer'.

2. Step 2: Check SimpleImputer initialization

   The strategy parameter must be a string like 'mean', so strategy='mean' is correct.

3. Final Answer:

   from sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='mean') -> Option D

4. Quick Check:

   Correct import and strategy syntax = A [OK]

Hint: Use strategy='mean' and correct import syntax [OK]

Common Mistakes:

• Using 'method' instead of 'strategy'
• Not quoting 'mean' as a string
• Wrong import statement syntax

3. Given the following code, what will be the output of print(X_imputed)?

import numpy as np
from sklearn.impute import SimpleImputer

X = np.array([[1, 2], [np.nan, 3], [7, np.nan]])
imputer = SimpleImputer(strategy='mean')
imputer.fit(X)
X_imputed = imputer.transform(X)
print(X_imputed)

medium

A. [[1. 2.]
[4. 3.]
[7. 2.5]]

B. [[1. 2.]
[nan 3.]
[7. nan]]

C. [[1. 2.]
[4. 3.]
[7. nan]]

D. Error due to missing values

Solution

1. Step 1: Calculate mean ignoring missing values

   Column 0 mean = (1 + 7) / 2 = 4; Column 1 mean = (2 + 3) / 2 = 2.5

2. Step 2: Replace missing values with column means

   Replace np.nan in row 2, col 0 with 4; replace np.nan in row 3, col 1 with 2.5

3. Final Answer:

   [[1. 2.] [4. 3.] [7. 2.5]] -> Option A

4. Quick Check:

   Missing values replaced by column means = B [OK]

Hint: Mean replaces NaN with average of column values [OK]

Common Mistakes:

• Not calculating mean correctly
• Leaving NaNs unchanged
• Expecting an error instead of imputation

4. Identify the error in the following code snippet for imputing missing values:

from sklearn.impute import SimpleImputer

X_train = [[1, 2], [None, 3], [7, None]]
imputer = SimpleImputer(strategy='median')
imputer.transform(X_train)

medium

A. Using null instead of np.nan causes error

B. Incorrect strategy name 'median'

C. Missing fit() call before transform()

D. SimpleImputer cannot handle 2D lists

Solution

1. Step 1: Check method sequence

   SimpleImputer requires fit() before transform() to learn statistics from data.

2. Step 2: Verify data and strategy

   Strategy 'median' is valid; None is accepted as missing; 2D list is acceptable.

3. Final Answer:

   Missing fit() call before transform() -> Option C

4. Quick Check:

   fit() must precede transform() = A [OK]

Hint: Always fit before transform with SimpleImputer [OK]

Common Mistakes:

• Calling transform without fit
• Confusing strategy names
• Thinking None is invalid missing value

5. You have a training set with missing values and a test set with missing values in the same columns. What is the best practice to handle missing values before training and evaluating a model?

hard

A. Fit the imputer on training data, then transform both training and test data

B. Fit the imputer separately on training and test data

C. Fill missing values in test data with zeros, training data with mean

D. Remove all rows with missing values from both datasets

Solution

1. Step 1: Understand data leakage risk

   Fitting imputer separately on test data leaks information from test set, causing biased evaluation.

2. Step 2: Apply consistent imputation

   Fit imputer only on training data to learn statistics, then apply same transformation to test data.

3. Final Answer:

   Fit the imputer on training data, then transform both training and test data -> Option A

4. Quick Check:

   Fit on train, transform both = C [OK]

Hint: Fit imputer on train only, then transform test [OK]

Common Mistakes:

• Fitting imputer separately on test data
• Filling test missing values with zeros arbitrarily
• Dropping too much data unnecessarily