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Finding columns with missing values (NaNs) in Pandas DataFrame

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Handling Missing Values
schedule Mar 10, 2022
Last updated
local_offer PythonPandas
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Finding columns with at least one missing value

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,None],"B":[4,5]})
df
A B
0 3.0 4
1 NaN 5

To find columns with at least one NaN:

df.isna().any()
A True
B False
dtype: bool

Explanation

Here, isna() returns a DataFrame of booleans where True corresponds to an entry with NaN value:

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

We then call any(), which checks each column to see if there exists a non-zero value. If so, then True is returned for that column:

df.isna().any()
A True
B False
dtype: bool

Remember, booleans True and False are synonymous to integers 1 and 0 respectively.

Finding columns with only missing values

Consider the following DataFrame:

df = pd.DataFrame({"A":[pd.np.nan,pd.np.nan],"B":[4,5]})
df
A B
0 NaN 4
1 NaN 5

To find columns that contain only NaN:

df.isna().all()
A True
B False
dtype: bool

Explanation

Once again, we use isna() to fetch a DataFrame of booleans where True corresponds to entries with NaN:

df.isna().all()
A B
0 True False
1 True False

Now, instead of any(), we use all(), which checks each column and returns True for that column if all its values are non-zero:

df.isna().all()
A True
B False
dtype: bool
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Published by Isshin Inada
Edited by 0 others
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