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# Finding columns with missing values (NaNs) in Pandas DataFrame

schedule Aug 12, 2023
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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 B0 3.0 41 NaN 5 ```

To find columns with at least one `NaN`:

``` df.isna().any() A TrueB Falsedtype: bool ```

## Explanation

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

``` df.isna() A B0 False False1 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 TrueB Falsedtype: 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 B0 NaN 41 NaN 5 ```

To find columns that contain only `NaN`:

``` df.isna().all() A TrueB Falsedtype: bool ```

## Explanation

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

``` df.isna().all() A B0 True False1 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 TrueB Falsedtype: bool ```
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