Dropping missing values with dropna() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of dropna() on rows with any missing values

What is the output DataFrame after running the code below?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': [None, 2, 3, 4],
    'C': [1, 2, 3, 4]
})
result = df.dropna()
print(result)

A

     A    B  C
1  2.0  2.0  2
3  4.0  4.0  4

B

     A    B  C
0  1.0  NaN  1
1  2.0  2.0  2
2  NaN  3.0  3
3  4.0  4.0  4

C

     A    B  C
0  1.0  NaN  1
2  NaN  3.0  3

D

Empty DataFrame
Columns: [A, B, C]
Index: []

💡 Hint

dropna() removes rows with any missing values by default.

The dropna() method removes rows where any column has a missing value. Only rows 1 and 3 have no missing values.

❓ data_output

intermediate

Number of rows after dropping columns with missing values

Given the DataFrame below, how many rows remain after dropping columns with any missing values?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [1, 2, 3, 4],
    'Y': [None, 2, None, 4],
    'Z': [1, 2, 3, 4]
})
result = df.dropna(axis=1)
print(len(result))

A 2

B 4

C 0

D 3

💡 Hint

dropna(axis=1) removes columns with missing values, not rows.

Column 'Y' has missing values and is dropped. Columns 'X' and 'Z' remain with all 4 rows.

🔧 Debug

advanced

Error raised by dropna() with invalid parameter

What error does the following code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, None, 3]})
result = df.dropna(axis=2)
print(result)

A No error, outputs DataFrame with missing values dropped

B TypeError: dropna() got an unexpected keyword argument 'axis'

C ValueError: No axis named 2 for object type DataFrame

D AttributeError: 'DataFrame' object has no attribute 'dropna'

💡 Hint

Valid axis values for DataFrame are 0 or 1.

The axis parameter must be 0 (rows) or 1 (columns). Using 2 causes a ValueError.

🚀 Application

advanced

Using dropna() to keep rows with at least 2 non-missing values

Which code correctly drops rows that have less than 2 non-missing values?

A df.dropna(thresh=2)

B df.dropna(min_count=2)

C df.dropna(how='any', thresh=2)

D df.dropna(how='all')

💡 Hint

The thresh parameter sets the minimum number of non-NA values required to keep a row.

thresh=2 keeps rows with at least 2 non-missing values. min_count is not a dropna parameter.

🧠 Conceptual

expert

Effect of dropna() with subset parameter on DataFrame

Given the DataFrame below, what is the output after running df.dropna(subset=['B', 'C'])?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [None, 2, None, 4],
    'C': [1, None, 3, 4]
})
result = df.dropna(subset=['B', 'C'])
print(result)

A

     A    B    C
1  2.0  2.0  NaN
3  4.0  4.0  4.0
0  1.0  NaN  1.0

B

     A    B    C
1  2.0  2.0  NaN
3  4.0  4.0  4.0

C

     A    B    C
1  2.0  2.0  NaN
3  4.0  4.0  4.0
2  3.0  NaN  3.0

D

     A    B    C
3  4  4  4

💡 Hint

dropna with subset only checks specified columns for missing values.

Rows 0, 1, and 2 have missing values in columns B or C, so only row 3 remains.

Practice

1. What does the dropna() function do in pandas?

easy

A. Sorts the DataFrame by index

B. Fills missing values with zeros

C. Removes rows or columns with missing values

D. Calculates the mean of columns

Solution

1. Step 1: Understand the purpose of dropna()

   The dropna() function is designed to remove data that contains missing values (NaN) from a DataFrame.

2. Step 2: Identify what dropna() removes

   It removes either rows or columns that have missing values, depending on the parameters used.

3. Final Answer:

   Removes rows or columns with missing values -> Option C

4. Quick Check:

   dropna() removes missing data [OK]

Hint: Remember: dropna() drops missing data, not fills it [OK]

Common Mistakes:

• Confusing dropna() with fillna()
• Thinking dropna() sorts data
• Assuming dropna() calculates statistics

2. Which of the following is the correct syntax to drop rows with any missing values in a DataFrame df?

easy

A. df.dropna(axis=0, how='any')

B. df.dropna(axis=1, how='any')

C. df.dropna(axis=0, how='all')

D. df.dropna(axis=1, how='all')

Solution

1. Step 1: Identify axis for rows

   In pandas, axis=0 means rows, so to drop rows, use axis=0.

2. Step 2: Use how='any' to drop rows with any missing values

   The how='any' parameter drops rows if any value is missing in that row.

3. Final Answer:

   df.dropna(axis=0, how='any') -> Option A

4. Quick Check:

   Drop rows with any NaN = axis=0, how='any' [OK]

Hint: axis=0 drops rows; how='any' drops if any NaN [OK]

Common Mistakes:

• Using axis=1 to drop rows
• Using how='all' to drop rows with any NaN
• Confusing axis and how parameters

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, 2, None], 'B': [4, None, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

What is the result of df.dropna(axis=0, how='any')?

medium

A. Rows with index 0 and 1 only

B. Rows with index 0 and 2 only

C. All rows dropped

D. Only row with index 0

Solution

1. Step 1: Identify rows with any missing values

   Row 0: A=1, B=4, C=7 (no NaN)
   Row 1: A=2, B=NaN, C=8 (has NaN)
   Row 2: A=NaN, B=6, C=9 (has NaN)

2. Step 2: Drop rows with any NaN

   Only row 0 has no missing values, so only it remains after dropna(axis=0, how='any').

3. Final Answer:

   Only row with index 0 -> Option D

4. Quick Check:

   Drop rows with any NaN leaves only complete rows [OK]

Hint: Check which rows have any NaN to keep them [OK]

Common Mistakes:

• Keeping rows with NaN
• Confusing axis=0 and axis=1
• Misreading how='any' vs how='all'

4. You run the code:

df.dropna(axis=2)

and get an error. What is the problem?

medium

A. axis=2 is invalid; only 0 (rows) or 1 (columns) allowed

B. dropna() cannot be used on DataFrames

C. Missing values must be filled before dropping

D. You must specify how='any' or how='all'

Solution

1. Step 1: Understand valid axis values for dropna()

   In pandas, axis can only be 0 (rows) or 1 (columns) for dropna().

2. Step 2: Identify the error cause

   Using axis=2 is invalid and causes an error because it is not a supported axis.

3. Final Answer:

   axis=2 is invalid; only 0 (rows) or 1 (columns) allowed -> Option A

4. Quick Check:

   Valid axis values = 0 or 1 only [OK]

Hint: axis must be 0 or 1 for dropna() [OK]

Common Mistakes:

• Using axis=2 mistakenly
• Thinking dropna() needs fillna() first
• Assuming how parameter is mandatory

5. You have a DataFrame df with some rows having all values missing and some rows having only some missing values. Which dropna() call will remove only rows where all values are missing, keeping rows with partial data?

hard

A. df.dropna(axis=0, how='any')

B. df.dropna(axis=0, how='all')

C. df.dropna(axis=1, how='any')

D. df.dropna(axis=1, how='all')

Solution

1. Step 1: Understand how='all' parameter

   Using how='all' drops rows only if all values in that row are missing.

2. Step 2: Use axis=0 to drop rows

   Since we want to drop rows, axis=0 is correct.

3. Step 3: Confirm this keeps rows with some data

   Rows with some non-missing values are kept because not all values are missing.

4. Final Answer:

   df.dropna(axis=0, how='all') -> Option B

5. Quick Check:

   Drop rows with all NaN = axis=0, how='all' [OK]

Hint: Use how='all' to drop only fully empty rows [OK]

Common Mistakes:

• Using how='any' which drops partial rows
• Using axis=1 which drops columns
• Confusing how parameter effects