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various docs fixes to lower the number of errors
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‎docs/README.md‎

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@@ -62,5 +62,3 @@ Code samples for the documentation website reside in [core/.../test/.../samples/
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and [tests/.../samples/api](../tests/src/test/kotlin/org/jetbrains/kotlinx/dataframe/samples/api) (for samples can depend on other I/O modules)
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and they are copied over to Markdown files in [docs/StardustDocs/topics](./StardustDocs/topics)
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by [Korro](https://github.com/devcrocod/korro).
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‎docs/StardustDocs/topics/ColumnSelectors.md‎

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@@ -51,15 +51,15 @@ or `ColumnSet` that adheres to the optional given condition. If no column adhere
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##### Col {collapsible="true"}
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`col(name)`, `col(5)`, `this[5]`
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Creates a [ColumnAccessor](DataColumn.md#column-accessors) (or `SingleColumn`) for a column with the given
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Creates a [ColumnAccessor](DataColumn.md) (or `SingleColumn`) for a column with the given
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argument from the top-level or specified [column group](DataColumn.md#columngroup). The argument can be either an
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index (`Int`) or a reference to a column (`String`, `ColumnPath`, `KProperty`, or `ColumnAccessor`;
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any [AccessApi](apiLevels.md)).
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##### Value Col, Frame Col, Col Group {collapsible="true"}
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`valueCol(name)`, `valueCol(5)`, `frameCol(name)`, `frameCol(5)`, `colGroup(name)`, `colGroup(5)`
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Creates a [ColumnAccessor](DataColumn.md#column-accessors) (or `SingleColumn`) for a
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Creates a [ColumnAccessor](DataColumn.md) (or `SingleColumn`) for a
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[value column](DataColumn.md#valuecolumn) / [frame column](DataColumn.md#framecolumn) /
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[column group](DataColumn.md#columngroup) with the given argument from the top-level or
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specified [column group](DataColumn.md#columngroup). The argument can be either an index (`Int`) or a reference

‎docs/StardustDocs/topics/Compiler-Plugin.md‎

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Kotlin DataFrame compiler plugin: available in Gradle projects, is coming to Kotlin Notebook and Maven projects soon.
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Check out this video that shows how expressions update the schema of a dataframe:
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<video src="compiler_plugin.mp4" controls/>
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<video src="compiler_plugin.mp4" controls=""/>
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## Setup
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‎docs/StardustDocs/topics/_shadow_resources.md‎

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<resource src="notebook_test_generate_docs_1.html"></resource>
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<resource src="notebook_test_rename_3.html"></resource>
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<resource src="notebook_test_rename_4.html"></resource>
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<resource src="notebook_test_rename_5.html"></resource>
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<resource src="notebook_test_rename_5.html"></resource>

‎docs/StardustDocs/topics/gettingStarted/gettingStartedGradleAdvanced.md‎

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</tab>
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<tab title=".editorconfig">
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```.editorconfig
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```editorconfig
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[{**/*.Generated.kt,**/*$Extensions.kt}]
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ktlint = disabled
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```

‎docs/StardustDocs/topics/guides/Kotlin-DataFrame-Features-in-Kotlin-Notebook.md‎

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You can quickly load data into `DataFrame` into a notebook by simply dragging and dropping a file
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(.csv/.json/.xlsx and .geojson/.shp) directly into the notebook editor:
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<video src="ktnb_drag_n_drop.mp4" controls/>
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<video src="ktnb_drag_n_drop.mp4" controls=""/>
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### Visual Data Exploration
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**Page through your data**:
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**Go straight to the data you need**:
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You can jump directly to a particular row or column if you want something specific.
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This makes working with large datasets more straightforward.
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<video src="https://github.com/user-attachments/assets/aeae1c79-9755-4558-bac4-420bf1331f39" controls></video>
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<video src="https://github.com/user-attachments/assets/aeae1c79-9755-4558-bac4-420bf1331f39" controls=""/>
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### Drill down into nested data
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When your data has multiple layers, like a table within a table,
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you can now click on a cell containing a nested table to view these details directly.
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This makes it easy to go deeper into your data and then return to where you were.
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<video src="https://github.com/user-attachments/assets/ef9509be-e19b-469c-9bad-0ce81eec36b0" controls></video>
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<video src="https://github.com/user-attachments/assets/ef9509be-e19b-469c-9bad-0ce81eec36b0" controls=""/>
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### Visualize multiple tables via tabs
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You can open and visualize multiple tables in separate tabs.
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This feature is tailored to those who need to compare, contrast, or monitor different datasets simultaneously.
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<video src="https://github.com/user-attachments/assets/51b7a6e3-0187-49b3-bf5e-0c4d60f8b769" controls></video>
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<video src="https://github.com/user-attachments/assets/51b7a6e3-0187-49b3-bf5e-0c4d60f8b769" controls=""/>
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### Exporting to files
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This simplifies sharing and further analysis.
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The interface supports exporting data to JSON for web applications,
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CSV for spreadsheet tools, and XML for data interchange.
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<video src="https://github.com/user-attachments/assets/ec28c59a-1555-44ce-98f6-a60d8feae347" controls></video>
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<video src="https://github.com/user-attachments/assets/ec28c59a-1555-44ce-98f6-a60d8feae347" controls=""/>
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### Convenient copying of data from tables
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and then copy what’s needed with a simple right-click or another shortcut.
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It’s designed to feel intuitive,
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like copying text from a document, but with the structure and format of your data preserved.
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<video src="https://github.com/user-attachments/assets/88e53dfb-361f-40f8-bffb-52a512cdd3cd" controls></video>
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<video src="https://github.com/user-attachments/assets/88e53dfb-361f-40f8-bffb-52a512cdd3cd" controls=""/>
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To get started, ensure you have the latest version of the Kotlin Notebook Plugin installed in IntelliJ IDEA,

‎docs/StardustDocs/topics/median.md‎

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The following automatic type conversions are performed for the `median` operation.
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(Note that `null` only appears in the return type when using `-orNull` overloads).
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| Conversion | Result for Empty Input |
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|--------------------------------|------------------------|
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| T -> T where T : Comparable<T> | null |
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| Int -> Double | null |
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| Byte -> Double | null |
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| Short -> Double | null |
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| Long -> Double | null |
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| Double -> Double | null |
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| Float -> Double | null |
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| Nothing -> Nothing | null |
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| Conversion | Result for Empty Input |
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|----------------------------------|------------------------|
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| T -> T where T : Comparable\<T\> | null |
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| Int -> Double | null |
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| Byte -> Double | null |
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| Short -> Double | null |
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| Long -> Double | null |
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| Double -> Double | null |
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| Float -> Double | null |
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| Nothing -> Nothing | null |

‎docs/StardustDocs/topics/minmax.md‎

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The following automatic type conversions are performed for the `min` and `max` operations.
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(Note that `null` only appears in the return type when using `-orNull` overloads).
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| Conversion | Result for Empty Input |
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|--------------------------------|------------------------|
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| T -> T where T : Comparable<T> | null |
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| Int -> Int | null |
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| Byte -> Byte | null |
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| Short -> Short | null |
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| Long -> Long | null |
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| Double -> Double | null |
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| Float -> Float | null |
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| Nothing -> Nothing | null |
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| Conversion | Result for Empty Input |
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|----------------------------------|------------------------|
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| T -> T where T : Comparable\<T\> | null |
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| Int -> Int | null |
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| Byte -> Byte | null |
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| Short -> Short | null |
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| Long -> Long | null |
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| Double -> Double | null |
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| Float -> Float | null |
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| Nothing -> Nothing | null |

‎docs/StardustDocs/topics/parse.md‎

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```
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<!---END-->
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For `locale`, this means that the one being used by the parser is defined as:
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↪ The locale given as function argument directly, or in `parserOptions`, if it is not `null`, else
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&nbsp;&nbsp;&nbsp;&nbsp;↪ The locale set by `DataFrame.parser.locale = ...`, if it is not `null`, else
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&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;↪ `Locale.getDefault()`, which is the system's default locale that can be changed with `Locale.setDefault()`.
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### Parsing Doubles
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DataFrame has a new fast and powerful double parser enabled by default.
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It is based on [the FastDoubleParser library](https://github.com/wrandelshofer/FastDoubleParser) for its
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high performance and configurability
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(in the future, we might expand this support to `Float`, `BigDecimal`, and `BigInteger` as well).
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The parser is locale-aware; it will use the locale set by the
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[(global)](#global-parser-options) [parser options](#parser-options) to parse the doubles.
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It also has a fallback mechanism built in, meaning it can recognize characters from
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all other locales (and some from [Wikipedia](https://en.wikipedia.org/wiki/Decimal_separator))
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and parse them correctly as long as they don't conflict with the current locale.
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For example, if your locale uses ',' as decimal separator, it will not recognize ',' as thousands separator, but it will
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recognize ''', ' ', '٬', '_', ' ', etc. as such.
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The same holds for characters like "e", "inf", "×10^", "NaN", etc. (ignoring case).
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This means you can safely parse `"123'456 789,012.345×10^6"` with a US locale but not `"1.234,5"`.
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Aside from this, DataFrame also explicitly recognizes "∞", "inf", "infinity", and "infty" as `Double.POSITIVE_INFINITY`
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(as well as their negative counterparts), "nan", "na", and "n/a" as `Double.NaN`,
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and all forms of whitespace are treated equally.
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If `FastDoubleParser` fails to parse a `String` as `Double`, DataFrame will try
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to parse it using the standard `NumberFormat.parse()` function as a last resort.
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If you experience any issues with the new parser, you can turn it off by setting
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`useFastDoubleParser = false`, which will use the old `NumberFormat.parse()` function instead.
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Please [report](https://github.com/Kotlin/dataframe/issues) any issues you encounter.

‎docs/StardustDocs/topics/percentile.md‎

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The following automatic type conversions are performed for the `percentile` operation.
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| Conversion | Result for Empty Input |
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|--------------------------------|------------------------|
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| T -> T where T : Comparable<T> | null |
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| Int -> Double | null |
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| Byte -> Double | null |
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| Short -> Double | null |
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| Long -> Double | null |
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| Double -> Double | null |
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| Float -> Double | null |
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| Nothing -> Nothing | null |
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| Conversion | Result for Empty Input |
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|----------------------------------|------------------------|
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| T -> T where T : Comparable\<T\> | null |
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| Int -> Double | null |
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| Byte -> Double | null |
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| Short -> Double | null |
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| Long -> Double | null |
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| Double -> Double | null |
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| Float -> Double | null |
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| Nothing -> Nothing | null |

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