NaN and None in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of DataFrame with NaN and null

What is the output of this code snippet?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, None, 3], 'B': [np.nan, 2, 3]})
print(df)

A

     A    B
0  1    NaN
1  null  2
2  3    3

B

     A    B
0  1.0  NaN
1  null  2.0
2  3.0  3.0

C

     A    B
0  1.0  NaN
1  NaN  2.0
2  3.0  3.0

D

     A    B
0  1    NaN
1  NaN  2
2  3    3

💡 Hint

Remember that null in numeric columns is converted to NaN.

In pandas, null in a numeric column is converted to NaN, so both null and np.nan appear as NaN in the DataFrame.

❓ data_output

intermediate

Count of missing values with isna()

What is the output of this code?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [None, np.nan, 5], 'Y': [1, 2, None]})
print(df.isna().sum())

A

X    2
Y    1
dtype: int64

B

X    2
Y    2
dtype: int64

C

X    1
Y    1
dtype: int64

D

X    1
Y    2
dtype: int64

💡 Hint

Both null and np.nan count as missing values.

Both null and np.nan are treated as missing values by isna(), so column X has 2 missing and Y has 1 missing.

🔧 Debug

advanced

Why does this fillna not replace null?

Consider this code: import pandas as pd df = pd.DataFrame({'A': [1, None, 3], 'B': ['x', None, 'z']}) df['B'] = df['B'].fillna('missing') print(df) Why does the null in column 'B' get replaced but not in column 'A'?

A Because fillna only works on string columns, not numeric columns.

B Because column 'A' is numeric and null is converted to NaN, fillna works only on object columns like 'B'.

C Because null in numeric columns is ignored by fillna.

D Because fillna cannot replace null values at all.

💡 Hint

Think about data types and how pandas treats null in numeric vs object columns.

In numeric columns, null is converted to NaN, so fillna replaces NaN. In object columns, null is a Python null and fillna replaces it directly.

🚀 Application

advanced

Filtering rows with null and NaN

Given this DataFrame: import pandas as pd import numpy as np df = pd.DataFrame({'col1': [1, None, 3, np.nan], 'col2': ['a', 'b', None, 'd']}) Which code filters rows where 'col1' is missing (null or NaN)?

A df[df['col1'].notna()]

B df[df['col1'] == None]

C df[df['col1'] == np.nan]

D df[df['col1'].isna()]

💡 Hint

Use the pandas function that detects all missing values.

isna() detects both null and NaN as missing values. Comparing with null or np.nan directly does not work as expected.

🧠 Conceptual

expert

Difference between null and NaN in pandas

Which statement correctly describes the difference between null and NaN in pandas?

A null is a Python singleton representing missing data, NaN is a special floating-point value representing missing numerical data; pandas converts null to NaN in numeric columns.

B null and NaN are identical in pandas and always interchangeable without any conversion.

C NaN is used only in string columns, null only in numeric columns in pandas.

D null is a floating-point value, NaN is a Python object representing missing data.

💡 Hint

Think about data types and how pandas stores missing values internally.

null is a Python object for missing data; NaN is a float value for missing numerical data. pandas converts null to NaN in numeric columns for consistency.