Pandas Series: isna() function

Detect missing values in the given Pandas series

The isna() function is used to detect missing values.

Syntax:

Series.isna(self)

Returns: Series- Mask of bool values for each element in Series that indicates whether an element is not an NA value.

Example - Show which entries in a DataFrame are NA:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'age': [6, 7, np.NaN],
                   'born': [pd.NaT, pd.Timestamp('1998-04-25'),
                            pd.Timestamp('1940-05-27')],
                   'name': ['Alfred', 'Spiderman', ''],
                   'toy': [None, 'Spidertoy', 'Joker']})
df

Output:

  age	   born	    name	    toy
0	6.0	    NaT	     Alfred	    None
1	7.0	 1998-04-25 Spiderman	Spidertoy
2	NaN	 1940-05-27	            Joker

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'age': [6, 7, np.NaN],
                   'born': [pd.NaT, pd.Timestamp('1998-04-25'),
                            pd.Timestamp('1940-05-27')],
                   'name': ['Alfred', 'Spiderman', ''],
                   'toy': [None, 'Spidertoy', 'Joker']})
df.isna()

Output:

  age	    born	 name	 toy
0	False	True	False	True
1	False	False	False	False
2	True	False	False	False

Example - Show which entries in a Series are NA:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([6, 7, np.NaN])
s

Output:

0    6.0
1    7.0
2    NaN
dtype: float64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([6, 7, np.NaN])
s.isna()

Output:

0    False
1    False
2     True
dtype: bool

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