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.