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docs: Improve docs about NaN #20310

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Dec 16, 2024
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docs: Improve docs about NaN #20310

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13 changes: 9 additions & 4 deletions docs/source/user-guide/expressions/missing-data.md
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Original file line number Diff line number Diff line change
Expand Up @@ -136,17 +136,19 @@ instead of the function `fill_null`:

## Not a Number, or `NaN` values

Missing data in a series is represented by the value `null`, regardless of the data type of the
series. However, in columns that have a floating point data type, the value `NaN` can be used. These
values can be created directly:
Missing data in a series is only ever represented by the value `null`, regardless of the data type
of the series. Columns with a floating point data type can sometimes have the value `NaN`, which
might be confused with `null`.

The special value `NaN` can be created directly:

{{code_block('user-guide/expressions/missing-data','nan',['DataFrame'])}}

```python exec="on" result="text" session="user-guide/missing-data"
--8<-- "python/user-guide/expressions/missing-data.py:nan"
```

The special value `NaN` might also arise as the result of a computation:
And it might also arise as the result of a computation:

{{code_block('user-guide/expressions/missing-data','nan-computed',[])}}

Expand Down Expand Up @@ -180,3 +182,6 @@ can be avoided by replacing the occurrences of the value `NaN` with the value `n
```python exec="on" result="text" session="user-guide/missing-data"
--8<-- "python/user-guide/expressions/missing-data.py:nanfill"
```

You can learn more about the value `NaN` in
[the section about floating point number data types](../concepts/data-types-and-structures.md#floating-point-numbers).
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