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Removing rows from a DataFrame with missing values (NaNs) in Pandas

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Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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To remove rows with missing values (NaN), use the DataFrame's dropna(~) method.

Rows with at least one missing value

Consider the following DataFrame with some NaN:

df = pd.DataFrame({"A":[np.nan,4,5],"B":[6,7,np.nan],"C":[np.nan,8,9]}, index=["a","b","c"])
df
A B C
a NaN 6.0 NaN
b 4.0 7.0 8.0
c 5.0 NaN 9.0

To remove rows that contain at least one NaN:

df.dropna()
A B C
b 4.0 7.0 8.0

Note that dropna() creates and return a new DataFrame - the original df is kept intact. This can be changed by setting inplace=True.

Rows with missing values for certain columns

Consider the same DataFrame as above:

df
A B C
a NaN 6.0 NaN
b 4.0 7.0 8.0
c 5.0 NaN 9.0

To remove rows where the value for column C is NaN:

df.dropna(subset=["C"])
A B C
b 4.0 7.0 8.0
c 5.0 NaN 9.0

Rows with n missing values

Consider the same DataFrame as above:

df
A B C
a NaN 6.0 NaN
b 4.0 7.0 8.0
c 5.0 NaN 9.0

To remove rows that contain at least two NaN:

df.dropna(thresh=2)
A B C
b 4.0 7.0 8.0
c 5.0 NaN 9.0

Rows with all missing values

Consider the following DataFrame:

df = pd.DataFrame({"A":[np.nan,np.nan],"B":[6,np.nan]}, index=["a","b"])
A B
a NaN 6.0
b NaN NaN

To remove rows where all entries are missing:

df.dropna(how="all")
A B
a NaN 6.0
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Published by Isshin Inada
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