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Pandas | notnull method

Pandas
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Documentation
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General Functions
schedule Jul 1, 2022
Last updated
local_offer PandasPython
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Pandas notnull(~) method returns a boolean mask where True is set for non-NaN values and False for NaN.

Parameters

1. obj | array-like or object

The array-like (e.g. Series, DataFrames, Numpy arrays, lists and so on) in which to check for non-missing values.

Return Value

  • If a single scalar object (e.g. a string and a numeric) is provided, then a single boolean is returned.

  • Otherwise, a boolean mask where True represents non-NaN value, and False representing NaN, is returned.

Examples

Scalars

pd.notnull("A")
True
pd.notnull(np.NaN)
False
pd.notnull(None)
False

Array-likes

Series

s = pd.Series([1,np.NaN,3])
pd.notnull(s)
0 True
1 False
2 True
dtype: bool

The return type is a Series of booleans.

NOTE

Series have the notna(~) method, which serves the exact same purpose:

s = pd.Series([1,np.NaN,3])
s.notna()
0 True
1 False
2 True
dtype: bool

DataFrame

Consider the following DataFrame:

df = pd.DataFrame({"A":[np.NaN,2], "B":[3,np.NaN]})
df
   A    B
0  NaN  3.0
1  2.0  NaN

To check for existing values (non-NaN values):

pd.notnull(df)
   A      B
0  False  True
1  True   False

The return type is a DataFrame of booleans.

NOTE

DataFrames have the notna(~) method, which serves the exact same purpose:

df = pd.DataFrame({"A":[np.NaN,2], "B":[3,np.NaN]})
df.notna()
A B
0 False True
1 True False
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Published by Isshin Inada
Edited by 0 others
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