Missing data strategies decision in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

Asum

Bcount

Cmean

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

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

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

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

2fill in blank

medium

Complete the code to drop rows with any missing values.

Pandas

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

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

Afillna

Breplace

Cisnull

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using isnull() which only detects missing values but does not remove them.

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

3fill in blank

hard

Fix the error in the code to fill missing values with the column mean.

Pandas

df_filled = df.fillna(df.[1]())

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

Amean

Bmode

Cmedian

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using median() or mode() which are different statistics.

Using sum() which adds values but does not calculate average.

The mean() function calculates the average of each column, which is used to fill missing values.

4fill in blank

hard

Fill both blanks to create a dictionary for filling missing values: fill numeric columns with mean and categorical with 'Unknown'.

Pandas

fill_values = {col: (df[col].[1]() if df[col].dtype == 'float64' else [2]) for col in df.columns}

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

Amean

B'Unknown'

C'NaN'

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using median() instead of mean() for numeric columns.

Using 'NaN' string instead of 'Unknown' for categorical columns.

Numeric columns use mean() to fill missing values. Categorical columns are filled with the string 'Unknown'.

5fill in blank

hard

Fill all three blanks to create a new DataFrame with missing values filled: numeric columns with median, categorical with mode, and others with 'Missing'.

Pandas

fill_map = {col: (df[col].[1]() if df[col].dtype == 'float64' else (df[col].[2]()[0] if df[col].dtype == 'object' else [3])) for col in df.columns}
df_filled = df.fillna(fill_map)

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

Amedian

Bmode

C'Missing'

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of median() for numeric columns.

Not selecting the first mode value with [0].

Using 'Unknown' instead of 'Missing' for other types.

Numeric columns use median(), categorical columns use the first value of mode(), and other types are filled with the string 'Missing'.

Practice

1.

What is the main purpose of using dropna() in pandas?

easy

A. To remove rows or columns that contain missing data

B. To replace missing data with zeros

C. To fill missing data with the mean value

D. To convert data types of columns

Solution

1. Step 1: Understand dropna() function

   dropna() removes rows or columns that have missing values (NaN).

2. Step 2: Compare with other options

   Replacing missing data or converting data types are done by other functions like fillna() or astype().

3. Final Answer:

   To remove rows or columns that contain missing data -> Option A

4. Quick Check:

   dropna() removes missing data rows/columns [OK]

Hint: Remember: dropna drops missing data, fillna fills it [OK]

Common Mistakes:

• Confusing dropna with fillna
• Thinking dropna fills missing values
• Assuming dropna changes data types

2.

Which of the following is the correct syntax to fill missing values in a DataFrame df with the value 0?

?

easy

A. df.fillna(0)

B. df.dropna(0)

C. df.fill(na=0)

D. df.replace(na=0)

Solution

1. Step 1: Recall fillna syntax

   The correct method to fill missing values is fillna() with the value as argument.

2. Step 2: Check each option

   df.fillna(0) uses df.fillna(0) which is correct. Others have wrong method names or parameters.

3. Final Answer:

   df.fillna(0) -> Option A

4. Quick Check:

   Use fillna(value) to fill missing values [OK]

Hint: fillna fills missing values; dropna removes rows/columns [OK]

Common Mistakes:

• Using dropna instead of fillna
• Wrong method name like fill() or replace()
• Passing parameters incorrectly

3.

Given the DataFrame df below, what will be the output of df.fillna(method='ffill')?

import pandas as pd
df = pd.DataFrame({'A': [1, None, 3, None], 'B': [None, 2, None, 4]})
print(df.fillna(method='ffill'))

medium

A. Removes rows with missing values

B. Fills missing values with zero

C. Fills missing values by propagating last valid observation forward

D. Raises an error due to wrong method

Solution

1. Step 1: Understand forward fill method

   method='ffill' fills missing values by copying the last known non-missing value forward.

2. Step 2: Apply to DataFrame

   For column A: 1 stays, None becomes 1, 3 stays, None becomes 3. For B: None stays None (no previous), 2 stays, None becomes 2, 4 stays.

3. Final Answer:

   Fills missing values by propagating last valid observation forward -> Option C

4. Quick Check:

   ffill copies last valid value forward [OK]

Hint: ffill copies previous value forward to fill NaNs [OK]

Common Mistakes:

• Thinking fillna(method='ffill') fills with zero
• Confusing ffill with dropping rows
• Expecting error from method parameter

4.

What is wrong with the following code if the goal is to remove all rows with missing data from DataFrame df?

df.fillna(drop=True)

medium

A. The code should use df.dropna(inplace=True) to modify in place

B. fillna() does not have a drop parameter; use dropna() instead

C. The parameter drop=True should be dropna=True

D. The code is correct and will remove rows with missing data

Solution

1. Step 1: Check method and parameters

   fillna() is for filling missing values and does not accept a drop parameter.

2. Step 2: Correct method for removing rows

   To remove rows with missing data, use dropna() instead.

3. Final Answer:

   fillna() does not have a drop parameter; use dropna() instead -> Option B

4. Quick Check:

   Use dropna() to remove missing data rows [OK]

Hint: Use dropna() to remove rows, fillna() to replace values [OK]

Common Mistakes:

• Using fillna() with invalid parameters
• Confusing fillna and dropna functions
• Expecting fillna to remove rows

5.

You have a DataFrame df with many missing values scattered randomly. You want to keep as much data as possible but avoid rows with more than 2 missing values. Which pandas method and parameter combination is best?

hard

A. Use df.fillna(method='bfill') to fill missing values backward

B. Use df.fillna(0) to replace all missing values with zero

C. Use df.dropna(axis=1) to drop columns with missing values

D. Use df.dropna(thresh=len(df.columns)-2) to drop rows with more than 2 missing values

Solution

1. Step 1: Understand the threshold parameter

   dropna(thresh=n) keeps rows with at least n non-missing values.

2. Step 2: Calculate threshold for max 2 missing values

   If total columns are len(df.columns), then rows must have at least len(df.columns) - 2 non-missing values to keep.

3. Step 3: Evaluate other options

   Filling with zero or backward fill changes data but does not drop rows. Dropping columns removes data unnecessarily.

4. Final Answer:

   Use df.dropna(thresh=len(df.columns)-2) to drop rows with more than 2 missing values -> Option D

5. Quick Check:

   dropna(thresh) keeps rows with enough non-NaNs [OK]

Hint: Use dropna(thresh) to keep rows with few missing values [OK]

Common Mistakes:

• Using fillna instead of dropping rows
• Dropping columns instead of rows
• Misunderstanding thresh parameter