various docs fixes to lower the number of errors

docs/README.md

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

docs/StardustDocs/topics/ColumnSelectors.md

@@ -49,17 +49,17 @@ or `ColumnSet` that adheres to the optional given condition. If no column adhere
 `NoSuchElementException` is thrown.
 
 ##### Col {collapsible="true"}
-`col(name)`, `col(5)`, `this[5]`
+`col(name)`, `col(5)`
 
-Creates a [ColumnAccessor](DataColumn.md#column-accessors) (or `SingleColumn`) for a column with the given 
+Creates a [ColumnAccessor](DataColumn.md) (or `SingleColumn`) for a column with the given 
 argument from the top-level or specified [column group](DataColumn.md#columngroup). The argument can be either an 
 index (`Int`) or a reference to a column (`String`, `ColumnPath`, `KProperty`, or `ColumnAccessor`;
 any [AccessApi](apiLevels.md)).
 
 ##### Value Col, Frame Col, Col Group {collapsible="true"}
 `valueCol(name)`, `valueCol(5)`, `frameCol(name)`, `frameCol(5)`, `colGroup(name)`, `colGroup(5)`
 
-Creates a [ColumnAccessor](DataColumn.md#column-accessors) (or `SingleColumn`) for a 
+Creates a [ColumnAccessor](DataColumn.md) (or `SingleColumn`) for a 
 [value column](DataColumn.md#valuecolumn) / [frame column](DataColumn.md#framecolumn) / 
 [column group](DataColumn.md#columngroup) with the given argument from the top-level or
 specified [column group](DataColumn.md#columngroup). The argument can be either an index (`Int`) or a reference

docs/StardustDocs/topics/Compiler-Plugin.md

@@ -3,7 +3,9 @@
 Kotlin DataFrame compiler plugin: available in Gradle projects, is coming to Kotlin Notebook and Maven projects soon.
 
 Check out this video that shows how expressions update the schema of a dataframe: 
-<video src="compiler_plugin.mp4" controls/>
+
+
+<video src="compiler_plugin.mp4" controls=""/>
 
 ## Setup
 

docs/StardustDocs/topics/_shadow_resources.md

@@ -164,4 +164,4 @@
 <resource src="notebook_test_generate_docs_1.html"></resource>
 <resource src="notebook_test_rename_3.html"></resource>
 <resource src="notebook_test_rename_4.html"></resource>
-<resource src="notebook_test_rename_5.html"></resource>
+<resource src="notebook_test_rename_5.html"></resource>

docs/StardustDocs/topics/gettingStarted/gettingStartedGradleAdvanced.md

@@ -224,7 +224,7 @@ tasks.withType(org.jmailen.gradle.kotlinter.tasks.LintTask).all {
 </tab>
 <tab title=".editorconfig">
 
-```.editorconfig
+```editorconfig
 [{**/*.Generated.kt,**/*$Extensions.kt}]
 ktlint = disabled
 ```

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

@@ -22,7 +22,7 @@ Here are the key features:
 You can quickly load data into `DataFrame` into a notebook by simply dragging and dropping a file 
 (.csv/.json/.xlsx and .geojson/.shp) directly into the notebook editor:
 
-<video src="ktnb_drag_n_drop.mp4" controls/>
+<video src="ktnb_drag_n_drop.mp4" controls=""/>
 
 ### Visual Data Exploration
 **Page through your data**:
@@ -35,18 +35,26 @@ This is a convenient alternative to using `sortBy` in separate cells.
 **Go straight to the data you need**:
 You can jump directly to a particular row or column if you want something specific.
 This makes working with large datasets more straightforward.
-<video src="https://github.com/user-attachments/assets/aeae1c79-9755-4558-bac4-420bf1331f39" controls></video>
+
+
+<video src="https://github.com/user-attachments/assets/aeae1c79-9755-4558-bac4-420bf1331f39" controls=""/>
+
 
 ### Drill down into nested data
 When your data has multiple layers, like a table within a table,
 you can now click on a cell containing a nested table to view these details directly.
 This makes it easy to go deeper into your data and then return to where you were.
-<video src="https://github.com/user-attachments/assets/ef9509be-e19b-469c-9bad-0ce81eec36b0" controls></video>
+
+
+<video src="https://github.com/user-attachments/assets/ef9509be-e19b-469c-9bad-0ce81eec36b0" controls=""/>
+
 
 ### Visualize multiple tables via tabs
 You can open and visualize multiple tables in separate tabs.
 This feature is tailored to those who need to compare, contrast, or monitor different datasets simultaneously.
-<video src="https://github.com/user-attachments/assets/51b7a6e3-0187-49b3-bf5e-0c4d60f8b769" controls></video>
+
+
+<video src="https://github.com/user-attachments/assets/51b7a6e3-0187-49b3-bf5e-0c4d60f8b769" controls=""/>
 
 
 ### Exporting to files
@@ -55,7 +63,9 @@ You can export data directly from the dataframe into various file formats.
 This simplifies sharing and further analysis.
 The interface supports exporting data to JSON for web applications,
 CSV for spreadsheet tools, and XML for data interchange.
-<video src="https://github.com/user-attachments/assets/ec28c59a-1555-44ce-98f6-a60d8feae347" controls></video>
+
+
+<video src="https://github.com/user-attachments/assets/ec28c59a-1555-44ce-98f6-a60d8feae347" controls=""/>
 
 
 ### Convenient copying of data from tables
@@ -64,7 +74,9 @@ or you can use keyboard shortcuts for quicker selection
 and then copy what’s needed with a simple right-click or another shortcut.
 It’s designed to feel intuitive,
 like copying text from a document, but with the structure and format of your data preserved.
-<video src="https://github.com/user-attachments/assets/88e53dfb-361f-40f8-bffb-52a512cdd3cd" controls></video>
+
+
+<video src="https://github.com/user-attachments/assets/88e53dfb-361f-40f8-bffb-52a512cdd3cd" controls=""/>
 
 
 To get started, ensure you have the latest version of the Kotlin Notebook Plugin installed in IntelliJ IDEA,

docs/StardustDocs/topics/median.md

@@ -58,13 +58,13 @@ See [column selectors](ColumnSelectors.md) for how to select the columns for thi
 The following automatic type conversions are performed for the `median` operation.
 (Note that `null` only appears in the return type when using `-orNull` overloads).
 
-| Conversion                     | Result for Empty Input |
-|--------------------------------|------------------------|
-| T -> T where T : Comparable<T> | null                   |
-| Int -> Double                  | null                   |
-| Byte -> Double                 | null                   |
-| Short -> Double                | null                   |
-| Long -> Double                 | null                   |
-| Double -> Double               | null                   |
-| Float -> Double                | null                   |
-| Nothing -> Nothing             | null                   |
+| Conversion                       | Result for Empty Input |
+|----------------------------------|------------------------|
+| T -> T where T : Comparable\<T\> | null                   |
+| Int -> Double                    | null                   |
+| Byte -> Double                   | null                   |
+| Short -> Double                  | null                   |
+| Long -> Double                   | null                   |
+| Double -> Double                 | null                   |
+| Float -> Double                  | null                   |
+| Nothing -> Nothing               | null                   |

docs/StardustDocs/topics/minmax.md

@@ -49,13 +49,13 @@ See [column selectors](ColumnSelectors.md) for how to select the columns for thi
 The following automatic type conversions are performed for the `min` and `max` operations.
 (Note that `null` only appears in the return type when using `-orNull` overloads).
 
-| Conversion                     | Result for Empty Input |
-|--------------------------------|------------------------|
-| T -> T where T : Comparable<T> | null                   |
-| Int -> Int                     | null                   |
-| Byte -> Byte                   | null                   |
-| Short -> Short                 | null                   |
-| Long -> Long                   | null                   |
-| Double -> Double               | null                   |
-| Float -> Float                 | null                   |
-| Nothing -> Nothing             | null                   |
+| Conversion                       | Result for Empty Input |
+|----------------------------------|------------------------|
+| T -> T where T : Comparable\<T\> | null                   |
+| Int -> Int                       | null                   |
+| Byte -> Byte                     | null                   |
+| Short -> Short                   | null                   |
+| Long -> Long                     | null                   |
+| Double -> Double                 | null                   |
+| Float -> Float                   | null                   |
+| Nothing -> Nothing               | null                   |

docs/StardustDocs/topics/parse.md

@@ -96,3 +96,42 @@ DataFrame.parser.addDateTimePattern("dd.MM.uuuu HH:mm:ss")
 ```
 
 <!---END-->
+
+For `locale`, this means that the one being used by the parser is defined as:
+
+↪ The locale given as function argument directly, or in `parserOptions`, if it is not `null`, else
+
+&nbsp;&nbsp;&nbsp;&nbsp;↪ The locale set by `DataFrame.parser.locale = ...`, if it is not `null`, else
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;↪ `Locale.getDefault()`, which is the system's default locale that can be changed with `Locale.setDefault()`.
+
+### Parsing Doubles
+
+DataFrame has a new fast and powerful double parser enabled by default.
+It is based on [the FastDoubleParser library](https://github.com/wrandelshofer/FastDoubleParser) for its
+high performance and configurability
+(in the future, we might expand this support to `Float`, `BigDecimal`, and `BigInteger` as well).
+
+The parser is locale-aware; it will use the locale set by the
+[(global)](#global-parser-options) [parser options](#parser-options) to parse the doubles.
+It also has a fallback mechanism built in, meaning it can recognize characters from
+all other locales (and some from [Wikipedia](https://en.wikipedia.org/wiki/Decimal_separator))
+and parse them correctly as long as they don't conflict with the current locale.
+
+For example, if your locale uses ',' as decimal separator, it will not recognize ',' as thousands separator, but it will
+recognize ''', ' ', '٬', '_', ' ', etc. as such.
+The same holds for characters like "e", "inf", "×10^", "NaN", etc. (ignoring case).
+
+This means you can safely parse `"123'456 789,012.345×10^6"` with a US locale but not `"1.234,5"`.
+
+Aside from this, DataFrame also explicitly recognizes "∞", "inf", "infinity", and "infty" as `Double.POSITIVE_INFINITY`
+(as well as their negative counterparts), "nan", "na", and "n/a" as `Double.NaN`,
+and all forms of whitespace are treated equally.
+
+If `FastDoubleParser` fails to parse a `String` as `Double`, DataFrame will try
+to parse it using the standard `NumberFormat.parse()` function as a last resort.
+
+If you experience any issues with the new parser, you can turn it off by setting
+`useFastDoubleParser = false`, which will use the old `NumberFormat.parse()` function instead.
+
+Please [report](https://github.com/Kotlin/dataframe/issues) any issues you encounter. 

docs/StardustDocs/topics/percentile.md

@@ -80,13 +80,13 @@ See [column selectors](ColumnSelectors.md) for how to select the columns for thi
 The following automatic type conversions are performed for the `percentile` operation.
 (Note that `null` only appears in the return type when using `-orNull` overloads).
 
-| Conversion                     | Result for Empty Input |
-|--------------------------------|------------------------|
-| T -> T where T : Comparable<T> | null                   |
-| Int -> Double                  | null                   |
-| Byte -> Double                 | null                   |
-| Short -> Double                | null                   |
-| Long -> Double                 | null                   |
-| Double -> Double               | null                   |
-| Float -> Double                | null                   |
-| Nothing -> Nothing             | null                   |
+| Conversion                       | Result for Empty Input |
+|----------------------------------|------------------------|
+| T -> T where T : Comparable\<T\> | null                   |
+| Int -> Double                    | null                   |
+| Byte -> Double                   | null                   |
+| Short -> Double                  | null                   |
+| Long -> Double                   | null                   |
+| Double -> Double                 | null                   |
+| Float -> Double                  | null                   |
+| Nothing -> Nothing               | null                   |