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Counting the number of missing values (NaNs) in each row of a Pandas DataFrame

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Handling Missing Values
schedule Mar 10, 2022
Last updated
local_offer PythonPandas
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Example

Consider the following DataFrame with some NaN values:

df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,np.nan,5],"C":[np.nan,7,8]}, index=["a","b","c"])
df
A B C
a NaN 4.0 NaN
b 3.0 NaN 7
c NaN 5.0 8

Of each row

To count the number of NaN values in each row of df:

df.isna().sum(axis=1)
a 2
b 1
c 1
dtype: int64

Explanation

Here, the df.isna() returns a DataFrame of booleans where True indicates entries that are NaN:

df.isna()
A B C
a True False True
b False True False
c True False False

Internally, True is represented by 1 while a False is represented by 0. Therefore, summing up the booleans for each row is equivalent to counting the number of True (NaN values) per row:

df.isna().sum(axis=1)
a 2
b 1
c 1
dtype: int64

Here, we must specify axis=1 so that we are summing each row, and not each column.

Of a particular row

Consider the same df as before:

df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,np.nan,5],"C":[np.nan,7,8]}, index=["a","b","c"])
df
A B C
a NaN 4.0 NaN
b 3.0 NaN 7
c NaN 5.0 8

To count the number of NaN value of just row a:

df.loc["a"].isna().sum()
2

Explanation

Here, the DataFrame's loc property is first used to extract the row a:

df.loc["a"]
A NaN
B 4.0
C NaN
Name: a, dtype: float64

We then use the same tactic as described above to count the number of NaNs in this row.

robocat
Published by Isshin Inada
Edited by 0 others
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