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.