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Counting number of missing values (NaN) in each column of a Pandas DataFrame

Pandas
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Handling Missing Values
schedule Jul 1, 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":[6,7,8]})
df
   A    B    C
0  NaN  4.0  6
1  3.0  NaN  7
2  NaN  5.0  8

In each column

To count the number of NaNs of each column of df:

df.isna().sum()
A 2
B 1
C 0
dtype: int64

Explanation

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

df.isna()
   A      B      C
0  True   False  False
1  False  True   False
2  True   False  False

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

df.isna().sum()
A 2
B 1
C 0
dtype: int64

In a particular column

To count the number of NaNs in just column A:

df["A"].isna().sum()
2

In multiple columns

To count the number of NaNs in columns A and B:

df[["A","B"]].isna().sum()
A 1
B 1
dtype: int64
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Published by Isshin Inada
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