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

schedule Aug 12, 2023
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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 Ca NaN 4 NaNb 3.0 5 7.0c NaN 6 8.0 ```

## Solution - single column case

To get rows with missing values in column `C`:

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

### Explanation

We first fetch column `C` as a `Series`:

``` df["C"] a NaNb 7.0c 8.0Name: 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 Trueb Falsec FalseName: C, dtype: bool ```

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

``` df[df["C"].isna()] A B Ca 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 Ca NaN 4 NaNb 3.0 5 7.0c 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 Ca NaN 4 NaNc NaN 6 8.0 ```

### Explanation

We start off by extracting columns `A` and `C`:

``` df[["A","C"]] A Ca NaN NaNb 3.0 7.0c 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 Ca True Trueb False Falsec 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 Trueb Falsec Truedtype: 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 Ca NaN 4 NaNc 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 Ca 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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