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Getting rows with missing values (NaNs) in certain columns in Pandas DataFrame

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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:

df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,5,6],"C":[np.nan,7,8]}, index=["a","b","c"])
df
A B C
a NaN 4 NaN
b 3.0 5 7.0
c NaN 6 8.0

Solution - single column case

To get rows with missing values in column C:

df[df["C"].isna()]
A B C
a NaN 4 NaN

Explanation

We first fetch column C as a Series:

df["C"]
a NaN
b 7.0
c 8.0
Name: C, dtype: float64

We then use the isna() method, which returns a Series of booleans where True indicates the presence of a missing value:

df["C"].isna()
a True
b False
c False
Name: C, dtype: bool

With this boolean mask, we can then extract rows that correspond to True using [] syntax:

df[df["C"].isna()]
A B C
a NaN 4 NaN

Solution - multiple columns case (OR)

Consider the same df as above:

df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,5,6],"C":[np.nan,7,8]}, index=["a","b","c"])
df
A B C
a NaN 4 NaN
b 3.0 5 7.0
c NaN 6 8.0

To get rows with missing values in columns A or C:

df[df[["A","C"]].isna().any(axis=1)]
A B C
a NaN 4 NaN
c NaN 6 8.0

Explanation

We start off by extracting columns A and C:

df[["A","C"]]
A C
a NaN NaN
b 3.0 7.0
c NaN 8.0

We then use the isna() method, which returns a Series of booleans where True indicates the presence of a missing value:

df[["A","C"]].isna()
A C
a True True
b False False
c True False

We then use any(axis=1) to obtain a Series where True represents the presence of at least one True in each row:

df[["A","C"]].isna().any(axis=1)
a True
b False
c True
dtype: bool

The parameter axis=1 is needed here since the default behaviour of any(~) is to scan through each column (as opposed to each row).

Finally, with this boolean mask, we can then extract rows that correspond to True using [] syntax:

df[df[["A","C"]].isna().any(axis=1)]
A B C
a NaN 4 NaN
c NaN 6 8.0

Solution - multiple columns case (AND)

The solution is identical to the OR case except that we use all(axis=1) instead of any(~).

For instance, to find rows with missing values in both columns A and C:

df[df[["A","C"]].isna().all(axis=1)]
A B C
a NaN 4 NaN

Here's a quick comparison between all(~) and any(~):

  • all(~) scans each row (when axis=1) and returns a True for that row if all its entires are True.

  • any(~) scans each row (when axis=1) and returns a True for that row if at least one entry is True.

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Published by Isshin Inada
Edited by 0 others
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