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Removing columns where some rows contain missing values (NaNs) in Pandas DataFrame

schedule Aug 11, 2023
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To remove columns where some rows contain missing values (NaN), use the DataFrame's dropna(~) method.

Example

Consider the following DataFrame:

df = pd.DataFrame({"A":[pd.np.NaN,2], "B":[3,4], "C":[5,pd.np.NaN]}, index=["a","b"])
df
A B C
a NaN 3 5
b 2.0 4 NaN

To remove columns where the value for row a is missing:

df.dropna(subset=["a"], axis=1)
B C
a 3 5.0
b 4 NaN

Here, axis=1 means that we are removing columns instead of rows.

To remove columns where the value for rows a or b is missing:

df.dropna(subset=["a","b"], axis=1)
B
a 3
b 4
NOTE

Click here for our full documentation on dropna(~).

robocat
Published by Isshin Inada
Edited by 0 others
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