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fixed up to ColumnSelectors.md
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‎docs/StardustDocs/topics/add.md

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@@ -44,7 +44,7 @@ df.add("year of birth") { 2021 - "age"<Int>() }
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See [row expressions](DataRow.md#row-expressions)
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You can use `newValue()` function to access value that was already calculated for the preceding row.
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You can use the `newValue()` function to access value that was already calculated for the preceding row.
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It is helpful for recurrent computations:
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<!---FUN addRecurrent-->
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## addId
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Adds column with sequential values 0, 1, 2,...
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New column will be added in the beginning of a column list
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Adds a column with sequential values 0, 1, 2,...
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The new column will be added in the beginning of the column list
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and will become the first column in [`DataFrame`](DataFrame.md).
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```

‎docs/StardustDocs/topics/adjustSchema.md

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[//]: # (title: Adjust schema)
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[`DataFrame`](DataFrame.md) interface has type argument `T` that doesn't affect the contents of [`DataFrame`](DataFrame.md),
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The [`DataFrame`](DataFrame.md) interface has type argument `T` that doesn't affect the contents of [`DataFrame`](DataFrame.md),
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but marks [`DataFrame`](DataFrame.md) with a type that represents the data schema that this [`DataFrame`](DataFrame.md) is supposed to have.
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This argument is used to generate [extension properties](extensionPropertiesApi.md) for typed data access.
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‎docs/StardustDocs/topics/apiLevels.md

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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.ApiLevels-->
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By nature, data frames are dynamic objects,
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column labels depend on the input source, and also new columns could be added
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By nature, data frames are dynamic objects;
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column labels depend on the input source and new columns can be added
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or deleted while wrangling.
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Kotlin, in contrast, is a statically typed language and all types are defined and verified
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Kotlin, in contrast, is a statically typed language where all types are defined and verified
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ahead of execution.
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That's why creating a flexible, handy, and, at the same time, safe API to a data frame is tricky.
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## List of Access APIs
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Here's a list of all APIs in order to increase safety.
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Here's a list of all APIs in order of increasing safety.
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* [**String API**](stringApi.md) <br/>
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Columns are accessed by `string` representing their name. Type-checking is done at runtime, name-checking too.
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We don't need to interrupt a function call chain and declare a column accessor or generate new properties.
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In contrast, generated [extension properties](extensionPropertiesApi.md) are the most convenient and the safest API.
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Using it, you can always be sure that you work with correct data and types.
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But its bottleneck is the moment of a generation.
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In contrast, generated [extension properties](extensionPropertiesApi.md) form the most convenient and the safest API.
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Using them, you can always be sure that you work with correct data and types.
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However, there's a bottleneck at the moment of generation.
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To get new extension properties, you have to run a cell in a notebook,
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which could lead to unnecessary variable declarations.
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Currently, we are working on a compiler with a plugin that generates these properties on the fly while typing!
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Currently, we are working on a compiler plugin that generates these properties on the fly while typing!
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The [Column Accessors API](columnAccessorsApi.md) is a kind of trade-off between safety and needs to be written ahead of
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the execution type declaration. It was designed to better be able to write code in an IDE without a notebook experience.
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It provides type-safe access to columns but doesn't ensure that the columns really exist in a particular data frame.
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The [KProperties API](KPropertiesApi.md) is useful when you already have declared classed in your application business
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The [KProperties API](KPropertiesApi.md) is useful when you already have declared classed in your business
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logic with fields that correspond to columns of a data frame.
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<table>

‎docs/StardustDocs/topics/cast.md

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[//]: # (title: cast)
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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
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Changes type argument of [`DataFrame`](DataFrame.md) without changing its contents.
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Changes the type argument of the [`DataFrame`](DataFrame.md) instance without changing its contents.
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```kotlin
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cast<T>(verify = false)
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```
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**Parameters:**
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* `verify: Boolean = false` —
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when `true`, throws exception if [`DataFrame`](DataFrame.md) doesn't match the given schema.
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Otherwise, just change a format type without actual data check.
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when `true`, the function throws an exception if the [`DataFrame`](DataFrame.md) instance doesn't match the given schema.
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Otherwise, it just changes the format type without actual data checks.
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Use this operation to change a formal type of [`DataFrame`](DataFrame.md)
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Use this operation to change the formal type of a [`DataFrame`](DataFrame.md) instance
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to match the expected schema and enable generated [extension properties](extensionPropertiesApi.md) for it.
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```kotlin

‎docs/StardustDocs/topics/collectionsInterop.md

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* `Collection` stores data by records/rows
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Although [`DataFrame`](DataFrame.md)
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doesn't implement [`Collection`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-collection/#kotlin.collections.Collection)
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doesn't implement the [`Collection`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-collection/#kotlin.collections.Collection)
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or [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/)
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interface, it has many similar operations,
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such as [`filter`](filter.md), [`take`](sliceRows.md#take),
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<tip>
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To enable extension properties generation you should use [dataframe plugin](schemasGradle.md)
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for Gradle or [Kotlin jupyter kernel](installation.md)
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To enable extension properties generation, you should use the [DataFrame plugin](schemasGradle.md)
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for Gradle or the [Kotlin Jupyter kernel](installation.md)
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</tip>
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After data is transformed, [`DataFrame`](DataFrame.md) can be exported
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After your data is transformed, [`DataFrame`](DataFrame.md) instances can be exported
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into [`List`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/) of another data class using [toList](toList.md) or [toListOf](toList.md#tolistof) extensions:
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<!---FUN listInterop4-->

‎docs/StardustDocs/topics/columnNames.md

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[//]: # (title: columnNames)
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Returns a list of names for top-level columns of [`DataFrame`](DataFrame.md).
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Returns a list of names for top-level columns of the [`DataFrame`](DataFrame.md) instance.

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