Short Answer

  • isna() and isnull() in pandas are identical.
  • They both check for missing (NaN) values in a DataFrame or Series.

Detailed Explanation

1. Purpose

Both functions detect missing data (NaN, None, or NaT).

import pandas as pd
import numpy as np

data = {
    'name': ['Dipak', 'Sita', 'Ram', None],
    'age': [25, np.nan, 30, 28]
}

df = pd.DataFrame(data)
print(df)

Output:

    name   age
0  Dipak  25.0
1   Sita   NaN
2    Ram  30.0
3   None  28.0

2. Using isna()

print(df.isna())

Output:

    name    age
0  False  False
1  False   True
2  False  False
3   True  False

3. Using isnull()

print(df.isnull())

Output: identical to isna()

    name    age
0  False  False
1  False   True
2  False  False
3   True  False

4. They return the same result

In fact, pandas defines them like this in the source code:

pd.isnull  = pd.isna

That means both functions point to the same underlying implementation.

Comparison Table

Feature isna() isnull() Same?
Detects NaN / None / NaT ✅ ✅ ✅
Works on Series ✅ ✅ ✅
Works on DataFrame ✅ ✅ ✅
Introduced for readability ✅ (newer) Legacy ✅
Preferred in documentation ✅    

Conclusion

  • isna() = isnull()
  • Both detect missing values.
  • Use isna() for modern, clean, and consistent code.

Bonus Tip: To remove missing values:

df.dropna(inplace=True)

To fill missing values:

df.fillna(0, inplace=True)