Dropping missing values (dropna) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of dropna on DataFrame with default settings

What is the output of this code?

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'A': [1, 2, np.nan, 4],
  'B': [np.nan, 2, 3, 4]
})
result = df.dropna()
print(result)

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'A': [1, 2, np.nan, 4],
  'B': [np.nan, 2, 3, 4]
})
result = df.dropna()
print(result)

A

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

B

     A    B
0  1.0  NaN
1  2.0  2.0

C

     A    B
1  2.0  2.0
3  4.0  4.0

D

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

💡 Hint

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

By default, dropna() removes rows where any column has NaN. Only rows 1 and 3 have no NaN values.

❓ data_output

intermediate

Number of rows after dropna with subset parameter

Given this DataFrame:

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'X': [1, np.nan, 3, 4],
  'Y': [np.nan, 2, 3, np.nan],
  'Z': [1, 2, 3, 4]
})

How many rows remain after running:

df.dropna(subset=['X', 'Y'])

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'X': [1, np.nan, 3, 4],
  'Y': [np.nan, 2, 3, np.nan],
  'Z': [1, 2, 3, 4]
})
result = df.dropna(subset=['X', 'Y'])
print(len(result))

A 1

B 2

C 3

D 4

💡 Hint

Rows with NaN in columns X or Y are dropped.

Rows 0 (Y NaN), 1 (X NaN), and 3 (Y NaN) have NaN in X or Y. Only row 2 has no NaN in X or Y, so 1 row remains.

🔧 Debug

advanced

Identify the error in dropna usage

What error does this code raise?

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'A': [1, 2, np.nan],
  'B': [4, np.nan, 6]
})
result = df.dropna(axis=2)
print(result)

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'A': [1, 2, np.nan],
  'B': [4, np.nan, 6]
})
result = df.dropna(axis=2)
print(result)

A No error, prints DataFrame with dropped columns

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

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

D ValueError: No axis named 2 for object type DataFrame

💡 Hint

DataFrame axes are 0 (rows) and 1 (columns).

axis=2 is invalid for DataFrame. Valid axes are 0 or 1. This causes a ValueError.

❓ visualization

advanced

Visualize effect of dropna with thresh parameter

Given this DataFrame:

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'A': [1, np.nan, 3, 4],
  'B': [np.nan, 2, 3, np.nan],
  'C': [1, 2, np.nan, 4]
})

Which option shows the correct DataFrame after:

df.dropna(thresh=2)

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'A': [1, np.nan, 3, 4],
  'B': [np.nan, 2, 3, np.nan],
  'C': [1, 2, np.nan, 4]
})
result = df.dropna(thresh=2)
print(result)

A

     A    B    C
1  NaN  2.0  2.0
2  3.0  3.0  NaN

B

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

C

     A    B    C
0  1.0  NaN  1.0
1  NaN  2.0  2.0
2  3.0  3.0  NaN

D

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

💡 Hint

thresh=2 means keep rows with at least 2 non-NaN values.

All rows have at least 2 non-NaN values, so none are dropped.

🚀 Application

expert

Effect of dropna with how='all' on DataFrame

Consider this DataFrame:

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'X': [np.nan, np.nan, 3, 4],
  'Y': [np.nan, 2, 3, 4],
  'Z': [np.nan, np.nan, np.nan, 4]
})

What is the output of:

df.dropna(how='all')

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
  'X': [np.nan, np.nan, 3, 4],
  'Y': [np.nan, 2, 3, 4],
  'Z': [np.nan, np.nan, np.nan, 4]
})
result = df.dropna(how='all')
print(result)

A

     X    Y    Z
1  NaN  2.0  NaN
2  3.0  3.0  NaN
3  4.0  4.0  4.0

B

     X    Y    Z
2  3.0  3.0  NaN
3  4.0  4.0  4.0

C

     X    Y    Z
0  NaN  NaN  NaN
1  NaN  2.0  NaN
2  3.0  3.0  NaN
3  4.0  4.0  4.0

D

Empty DataFrame
Columns: [X, Y, Z]
Index: []

💡 Hint

how='all' drops rows only if all values are NaN.

Row 0 has all NaN values and is dropped. Other rows have at least one non-NaN value and remain.

Practice

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

easy

A. Duplicates rows with missing values

B. Fills missing values with zeros

C. Sorts the DataFrame by index

D. Removes rows or columns with missing values

Solution

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

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

2. Step 2: Identify what is removed

   It removes rows or columns that contain missing values, depending on parameters.

3. Final Answer:

   Removes rows or columns with missing values -> Option D

4. Quick Check:

   dropna() removes missing data [OK]

Hint: Remember: dropna means drop missing data [OK]

Common Mistakes:

• Confusing dropna with fillna
• Thinking it sorts data
• Assuming it duplicates data

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

easy

A. df.dropna(inplace=False, axis=2)

B. df.dropna(axis=1)

C. df.dropna(axis=0)

D. df.dropna(axis='columns')

Solution

1. Step 1: Understand axis parameter in dropna()

   axis=0 means drop rows, axis=1 means drop columns.

2. Step 2: Identify correct axis for dropping rows

   To drop rows with missing values, use axis=0.

3. Final Answer:

   df.dropna(axis=0) -> Option C

4. Quick Check:

   axis=0 drops rows [OK]

Hint: axis=0 drops rows, axis=1 drops columns [OK]

Common Mistakes:

• Using axis=1 to drop rows
• Using invalid axis=2
• Confusing inplace parameter

3. Given the DataFrame df below, what will df.dropna() return?

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

medium

A. Rows with index 0 only

B. Rows with index 0 and 1

C. Rows with index 1 and 2

D. Empty DataFrame

Solution

1. Step 1: Examine the DataFrame rows for missing values

   Row 0: A=1, B=4 (no missing)
   Row 1: A=2, B=NaN (missing in B)
   Row 2: A=NaN, B=6 (missing in A)

2. Step 2: Apply dropna() default behavior

   By default, dropna() drops rows with any missing values, so only row 0 remains.

3. Final Answer:

   Rows with index 0 only -> Option A

4. Quick Check:

   Only complete rows remain [OK]

Hint: dropna() keeps only rows without any NaN [OK]

Common Mistakes:

• Assuming it drops columns instead of rows
• Thinking it fills missing values
• Expecting multiple rows to remain

4. What is wrong with this code snippet if the goal is to drop columns with any missing values?

df.dropna(axis=2)

medium

A. axis=2 is invalid; axis should be 0 or 1

B. dropna cannot drop columns

C. Missing inplace=True to modify df

D. dropna only works on rows by default

Solution

1. Step 1: Check valid axis values for dropna

   Valid axis values are 0 (rows) and 1 (columns). axis=2 is invalid and causes an error.

2. Step 2: Understand error cause

   Using axis=2 will raise a ValueError because pandas does not support axis=2 for DataFrames.

3. Final Answer:

   axis=2 is invalid; axis should be 0 or 1 -> Option A

4. Quick Check:

   axis must be 0 or 1 [OK]

Hint: Only axis=0 or 1 allowed in dropna [OK]

Common Mistakes:

• Using axis=2 by mistake
• Forgetting axis parameter
• Confusing inplace with axis

5. You have a DataFrame df with some missing values. You want to drop only those rows where all columns are missing values. Which dropna() parameter should you use?

hard

A. dropna(axis=1)

B. dropna(how='all')

C. dropna(thresh=2)

D. dropna(how='any')

Solution

1. Step 1: Understand the how parameter in dropna()

   how='all' drops rows only if all values are missing.
   how='any' drops rows if any value is missing.

2. Step 2: Identify correct parameter for dropping rows with all missing

   Since the goal is to drop rows where all columns are missing, use how='all'.

3. Final Answer:

   dropna(how='all') -> Option B

4. Quick Check:

   how='all' drops rows with all NaN [OK]

Hint: Use how='all' to drop rows with all missing [OK]

Common Mistakes:

• Using how='any' which drops too many rows
• Confusing thresh with how
• Dropping columns instead of rows