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Merge pull request #398 from Kotlin/fix_grammar
Fix missing articles and grammer in docs
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‎README.md

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| Kotlin DataFrame Version | Minimum Java Version | Kotlin Version | Kotlin Jupyter Version | OpenAPI version | Apache Arrow version |
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|--------------------------|----------------------|----------------|------------------------|-----------------|----------------------|
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| 0.10.0 | 8 |1.8.20 | 0.11.0-358 |2.1.13 | 11.0.0 |
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| 0.10.1 | 8 |1.8.20 | 0.11.0-358 |2.1.13 | 11.0.0 |
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| 0.10.0 | 8 |1.8.20 | 0.11.0-358 | 3.0.0 | 11.0.0 |
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| 0.10.1 | 8 |1.8.20 | 0.11.0-358 | 3.0.0 | 11.0.0 |
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## Usage example
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‎docs/StardustDocs/snippets/org.jetbrains.kotlinx.dataframe.samples.api.Analyze.describe.html

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/*<!--*/
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call_DataFrame(function() { DataFrame.addTable({ cols: [{ name: "<span title=\"name: String\">name</span>", children: [], rightAlign: false, values: ["firstName","lastName","age","city","weight","isHappy"] },
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{ name: "<span title=\"path: ColumnPath\">path</span>", children: [], rightAlign: false, values: ["<span class=\"formatted\" title=\"name\nfirstName\"><span class=\"structural\">[</span>name<span class=\"structural\">, </span>firstName<span class=\"structural\">]</span></span>","<span class=\"formatted\" title=\"name\nlastName\"><span class=\"structural\">[</span>name<span class=\"structural\">, </span>lastName<span class=\"structural\">]</span></span>","<span class=\"formatted\" title=\"age\"><span class=\"structural\">[</span>age<span class=\"structural\">]</span></span>","<span class=\"formatted\" title=\"city\"><span class=\"structural\">[</span>city<span class=\"structural\">]</span></span>","<span class=\"formatted\" title=\"weight\"><span class=\"structural\">[</span>weight<span class=\"structural\">]</span></span>","<span class=\"formatted\" title=\"isHappy\"><span class=\"structural\">[</span>isHappy<span class=\"structural\">]</span></span>"] },
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{ name: "<span title=\"type: Any\">type</span>", children: [], rightAlign: false, values: ["String","String","Int","String","Int","Boolean"] },
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{ name: "<span title=\"type: Any\">type</span>", children: [], rightAlign: false, values: ["String","String","Int","String?","Int?","Boolean"] },
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{ name: "<span title=\"count: Int\">count</span>", children: [], rightAlign: true, values: ["<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>"] },
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{ name: "<span title=\"unique: Int\">unique</span>", children: [], rightAlign: true, values: ["<span class=\"formatted\" title=\"\"><span class=\"numbers\">3</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">7</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">5</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">6</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">6</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">2</span></span>"] },
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{ name: "<span title=\"nulls: Int\">nulls</span>", children: [], rightAlign: true, values: ["<span class=\"formatted\" title=\"\"><span class=\"numbers\">0</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">0</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">0</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">1</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">2</span></span>","<span class=\"formatted\" title=\"\"><span class=\"numbers\">0</span></span>"] },

‎docs/StardustDocs/topics/ColumnSelectors.md

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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Access-->
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[`DataFrame`](DataFrame.md) provides DSL for selecting arbitrary set of columns.
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[`DataFrame`](DataFrame.md) provides a DSL for selecting an arbitrary set of columns.
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Column selectors are used in many operations:
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‎docs/StardustDocs/topics/access.md

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**Learn how to:**
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* [Access data by index](indexing.md)
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* [Iterate over data](iterate.md)
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* [Get single row](getRow.md)
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* [Get a single row](getRow.md)
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* [Get single column](getColumns.md)

‎docs/StardustDocs/topics/add.md

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Returns [`DataFrame`](DataFrame.md) which contains all columns from original [`DataFrame`](DataFrame.md) followed by newly added columns.
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Original [`DataFrame`](DataFrame.md) is not modified.
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## Create new column and add it to [`DataFrame`](DataFrame.md)
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## Create a new column and add it to [`DataFrame`](DataFrame.md)
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```text
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add(columnName: String) { rowExpression }
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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 preceding row. It is helpful for recurrent computations:
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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,... New column will be added in the beginning of columns list and will become the first column in [`DataFrame`](DataFrame.md).
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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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```
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addId(name: String = "id")

‎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 contents of [`DataFrame`](DataFrame.md),
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but marks [`DataFrame`](DataFrame.md) with a type that represents data schema that this [`DataFrame`](DataFrame.md) is supposed to have.
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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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Another place where this argument has a special role is in [interop with data classes](collectionsInterop.md#interop-with-data-classes):

‎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, column labels depend on the input source and also new columns could be added
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or deleted while wrangling. Kotlin, in contrast, is a statically typed language and all types are defined and verified
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ahead of execution. That's why creating a flexible, handy, and, at the same time, safe API to a data frame is tricky.
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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 where all types are defined and verified
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ahead of execution.
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In the Kotlin DataFrame library we provide four different ways to access columns, and, while they are essentially different, they
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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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In the Kotlin DataFrame library, we provide four different ways to access columns,
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and, while they are essentially different, they
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## List of Access APIs
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</tabs>
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The `titanic.csv` file could be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
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The `titanic.csv` file can be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
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# Comparing the APIs
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# Comparing APIs
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The [String API](stringApi.md) is the simplest and unsafest of them all. The main advantage of it is that it can be
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used at any time, including when accessing new columns in chain calls. So we can write something like:
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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 generation.
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To get new extension properties you have to run a cell in a notebook,
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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 compiler 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 [KProperties API](KPropertiesApi.md) is useful when you already have declared classed in your application business
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logic with fields that correspond columns of a data frame.
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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>
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<tr>

‎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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* `verify: Boolean = false` —
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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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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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‎docs/StardustDocs/topics/collectionsInterop.md

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Although [`DataFrame`](DataFrame.md) doesn't implement [`Collection`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-collection/#kotlin.collections.Collection) or [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) interface, it has many similar operations,
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Although [`DataFrame`](DataFrame.md)
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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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interface, it has many similar operations,
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such as [`filter`](filter.md), [`take`](sliceRows.md#take),
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[`first`](first.md), [`map`](map.md), [`groupBy`](groupBy.md) etc.
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can be accessed as [`List`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/),
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[`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/)
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annotation to get [extension properties](extensionPropertiesApi.md) and perform data transformations.
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<tip>
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for Gradle or the [Kotlin Jupyter kernel](installation.md)
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[unfold](unfold.md) can be used as [`toDataFrame()`](createDataFrame.md#todataframe) analogue for specific columns inside existing dataframes

‎docs/StardustDocs/topics/columnAccessorsApi.md

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<!---END-->
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<warning>
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Note that it still doesn’t solve the problem of whether the column actually exists in a data frame, but reduces type casting.

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