reverted changes from d-z.

Author: Jolanrensen

docs/StardustDocs/topics/DataColumn.md

@@ -7,9 +7,9 @@ See [how to create columns](createColumn.md)
 
 ### Properties
 * `name: String` — name of the column, should be unique within containing dataframe
-* `path: ColumnPath` — path to the column, depends on the way column was retrieved from the dataframe
+* `path: ColumnPath` — path to the column, depends on the way column was retrieved from dataframe
 * `type: KType` — type of elements in the column
-* `hasNulls: Boolean` — flag indicating whether a column contains `null` values
+* `hasNulls: Boolean` — flag indicating whether column contains `null` values
 * `values: Iterable<T>` — column data
 * `size: Int` — number of elements in the column
 
@@ -24,7 +24,7 @@ It can store values of primitive (integers, strings, decimals etc.) or reference
 
 #### ColumnGroup
 
-Container for nested columns. It is used to create column hierarchy. 
+Container for nested columns. Is used to create column hierarchy. 
 
 #### FrameColumn
 
@@ -36,11 +36,7 @@ Special case of [`ValueColumn`](#valuecolumn) that stores other [`DataFrames`](D
 
 ## Column accessors
 
-`ColumnAccessors` are used for [typed data access](columnAccessorsApi.md) in [`DataFrame`](DataFrame.md).
-`ColumnAccessor`
-stores column [`name`](#properties) (for top-level columns) or column path (for nested columns),
-has type argument that corresponds to [`type`](#properties) of the column,
-but it doesn't contain any actual data.
+`ColumnAccessors` are used for [typed data access](columnAccessorsApi.md) in [`DataFrame`](DataFrame.md). `ColumnAccessor` stores column [`name`](#properties) (for top-level columns) or column path (for nested columns), has type argument that corresponds to [`type`](#properties) of thep column, but it doesn't contain any actual data.
 
 <!---FUN columnAccessorsUsage-->
 
@@ -49,11 +45,11 @@ val age by column<Int>()
 
 // Access fourth cell in the "age" column of dataframe `df`.
 // This expression returns `Int` because variable `age` has `ColumnAccessor<Int>` type.
-// If dataframe `df` has no column "age" or column "age" has a type which is incompatible with `Int`,
-//  a runtime exception will be thrown.
+// If dataframe `df` has no column "age" or column "age" has type which is incompatible with `Int`,
+// runtime exception will be thrown.
 df[age][3] + 5
 
-// Access the first cell in the "age" column of dataframe `df`.
+// Access first cell in the "age" column of dataframe `df`.
 df[0][age] * 2
 
 // Returns new dataframe sorted by age column (ascending)
@@ -78,7 +74,7 @@ val name by column<String>()
 
 <!---END-->
 
-To assign a column name explicitly, pass it as an argument.
+To assign column name explicitly, pass it as an argument.
 
 <!---FUN createColumnAccessorRenamed-->
 
@@ -110,7 +106,7 @@ val firstName by name.column<String>()
 
 <!---END-->
 
-You can also create a virtual accessor that doesn't point to a real column but computes some expression on every data access:
+You can also create virtual accessor that doesn't point to a real column but computes some expression on every data access:
 
 <!---FUN columnAccessorComputed-->
 <tabs>

docs/StardustDocs/topics/DataRow.md

@@ -10,12 +10,12 @@
 * `next(): DataRow?` — next row (`null` for the last row)
 * `diff { rowExpression }: T` — difference between results of [row expression](#row-expressions) calculated for current and previous rows
 * `values(): List<Any?>` — list of all cell values from the current row
-* `valuesOf<T>(): List<T>` — list of values of a given type 
+* `valuesOf<T>(): List<T>` — list of values of given type 
 * `columnsCount(): Int` — number of columns
 * `columnNames(): List<String>` — list of all column names
 * `columnTypes(): List<KType>` — list of all column types 
 * `namedValues(): List<NameValuePair<Any?>>` — list of name-value pairs where `name` is a column name and `value` is cell value
-* `namedValuesOf<T>(): List<NameValuePair<T>>` — list of name-value pairs where value has given a type 
+* `namedValuesOf<T>(): List<NameValuePair<T>>` — list of name-value pairs where value has given type 
 * `transpose(): DataFrame<NameValuePair<*>>` — dataframe of two columns: `name: String` is column names and `value: Any?` is cell values
 * `transposeTo<T>(): DataFrame<NameValuePair<T>>`— dataframe of two columns: `name: String` is column names and `value: T` is cell values
 * `getRow(Int): DataRow` — row from [`DataFrame`](DataFrame.md) by row index
@@ -54,7 +54,7 @@ Row condition is a special case of [row expression](#row-expressions) that retur
 // Row condition is used to filter rows by index
 df.filter { index() % 5 == 0 }
 
-// Row condition is used to drop rows where `age` is the same as in the previous row
+// Row condition is used to drop rows where `age` is the same as in previous row
 df.drop { diff { age } == 0 }
 
 // Row condition is used to filter rows for value update
@@ -76,11 +76,10 @@ The following [statistics](summaryStatistics.md) are available for `DataRow`:
 * `rowStd`
 * `rowMedian`
 
-These statistics will be applied only to values of appropriate types, and incompatible values will be ignored.
-For example, if [`DataFrame`](DataFrame.md) has columns of a type `String` and `Int`,
-`rowSum()` will successfully compute a sum of `Int` values in a row and ignore `String` values.
+These statistics will be applied only to values of appropriate types and incompatible values will be ignored.
+For example, if [`DataFrame`](DataFrame.md) has columns of type `String` and `Int`, `rowSum()` will successfully compute sum of `Int` values in a row and ignore `String` values.
 
-To apply statistics only to values of a particular type, use `-Of` versions:
+To apply statistics only to values of particular type use `-Of` versions:
 * `rowMaxOf<T>`
 * `rowMinOf<T>`
 * `rowSumOf<T>`

docs/StardustDocs/topics/KPropertiesApi.md

@@ -3,7 +3,7 @@
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.ApiLevels-->
 
 [`DataFrame`](DataFrame.md) can be used as an intermediate structure for data transformation between two data formats. 
-If either source or destination is a Kotlin object, e.g., data class, it is convenient to use its properties 
+If either source or destination is a Kotlin object, e.g. data class, it is convenient to use its properties 
 for typed data access in [`DataFrame`](DataFrame.md).
 This can be done using `::` expression that provides [property references](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.reflect/-k-property/)
 
@@ -31,7 +31,7 @@ val passengers = DataFrame.read("titanic.csv")
 <!---END-->
 
 By default, [`DataFrame`](DataFrame.md) uses `name` and `returnType` of `KProperty` for typed access to data. 
-When the column name differs from the property name, use `@ColumnName` annotation:  
+When column name differs from property name, use `@ColumnName` annotation:  
 
 <!---FUN kproperties2-->
 
@@ -49,4 +49,4 @@ val passengers = DataFrame.read("titanic.csv")
 
 <!---END-->
 
-The `titanic.csv` file can be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
+The `titanic.csv` file could be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).

docs/StardustDocs/topics/distinct.md

@@ -14,7 +14,7 @@ df.distinct()
 <dataFrame src="org.jetbrains.kotlinx.dataframe.samples.api.Access.distinct.html"/>
 <!---END-->
 
-If columns are specified, the resulting [`DataFrame`](DataFrame.md) will have only given columns with distinct values.
+If columns are specified, resulting [`DataFrame`](DataFrame.md) will have only given columns with distinct values.
 
 <!---FUN distinctColumns-->
 <tabs>

docs/StardustDocs/topics/duplicate.md

@@ -6,14 +6,12 @@ DataRow.duplicate(n): DataFrame
 ```
 
 Returns [`FrameColumn`](DataColumn.md#framecolumn) with original [`DataFrame`](DataFrame.md) repeated `n` times. 
-The resulting [`FrameColumn`](DataColumn.md#framecolumn) will have an empty [`name`](DataColumn.md#properties).
+Resulting [`FrameColumn`](DataColumn.md#framecolumn) will have an empty [`name`](DataColumn.md#properties).
 ```text
 DataFrame.duplicate(n): FrameColumn
 ```
 
-Returns [`DataFrame`](DataFrame.md)
-where rows that satisfy the given [condition](DataRow.md#row-conditions) are repeated `n` times.
-If `rowCondition` is not specified, all rows will be duplicated.
+Returns [`DataFrame`](DataFrame.md) where rows that satisfy to the given [condition](DataRow.md#row-conditions) are repeated `n` times. If `rowCondition` is not specified all rows will be duplicated.
 ```text
 DataFrame.duplicateRows(n) [ { rowCondition } ]: DataFrame
 ```

docs/StardustDocs/topics/extensionPropertiesApi.md

@@ -2,8 +2,8 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.ApiLevels-->
 
-When [`DataFrame`](DataFrame.md) is used within Jupyter Notebooks or Datalore with Kotlin Kernel,
-after every cell execution all new global variables of type DataFrame are analyzed and replaced
+When [`DataFrame`](DataFrame.md) is used within Jupyter Notebooks or Datalore with Kotlin Kernel, 
+after every cell execution all new global variables of type DataFrame are analyzed and replaced 
 with typed [`DataFrame`](DataFrame.md) wrapper with auto-generated extension properties for data access:
 
 <!---FUN extensionProperties1-->
@@ -26,14 +26,11 @@ df.add("lastName") { name.split(",").last() }
 
 <!---END-->
 
-The `titanic.csv` file can be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
+The `titanic.csv` file could be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
 
-In notebooks, extension properties are generated for [`DataSchema`](schemas.md)
-that is extracted from [`DataFrame`](DataFrame.md)
+In notebooks, extension properties are generated for [`DataSchema`](schemas.md) that is extracted from [`DataFrame`](DataFrame.md) 
 instance after REPL line execution. 
-After that [`DataFrame`](DataFrame.md) variable is typed with its own [`DataSchema`](schemas.md),
-so only valid extension properties corresponding to actual columns in DataFrame will be allowed by the compiler
-and suggested by completion.
+After that [`DataFrame`](DataFrame.md)  variable is typed with its own [`DataSchema`](schemas.md), so only valid extension properties corresponding to actual columns in DataFrame will be allowed by the compiler and suggested by completion.
 
 Extension properties can be generated in IntelliJ IDEA using the [Kotlin Dataframe Gradle plugin](schemasGradle.md#configuration).
 

docs/StardustDocs/topics/filter.md

@@ -38,7 +38,7 @@ df.filter { "age"<Int>() > 18 && "name"["firstName"]<String>().startsWith("A") }
 
 ## filterBy
 
-Returns [`DataFrame`](DataFrame.md) with rows that have value `true` in given column of a type `Boolean`.
+Returns [`DataFrame`](DataFrame.md) with rows that have value `true` in given column of type `Boolean`.
 
 <!---FUN filterBy-->
 <tabs>

docs/StardustDocs/topics/first.md

@@ -1,9 +1,7 @@
 [//]: # (title: first)
 
-Returns the first [row](DataRow.md) that matches the given [condition](DataRow.md#row-conditions),
-or throws exception if there are no matching rows.
+Returns the first [row](DataRow.md) that matches the given [condition](DataRow.md#row-conditions), or throws exception if there is no matching rows.
 
 ## firstOrNull
 
-Returns the first [row](DataRow.md) that matches the given [condition](DataRow.md#row-conditions),
-or `null` if there are no matching rows.
+Returns the first [row](DataRow.md) that matches the given [condition](DataRow.md#row-conditions), or `null` if there is no matching rows.

docs/StardustDocs/topics/gather.md

@@ -2,7 +2,7 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
 
-Converts several columns into two columns `key` and `value`. `Key` column will contain names of original columns, `value` column will contain values from original columns.
+Converts several columns into two columns `key` and `value`. `key` column will contain names of original columns, `value` column will contain values from original columns.
 
 This operation is reverse to [pivot](pivot.md)
 
@@ -24,7 +24,7 @@ valueTransform: (value) -> R
 See [column selectors](ColumnSelectors.md)
 
 Configuration options:
-* `explodeLists` — gathered values of a type [`List`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/) will be exploded into their elements, so `where`, `cast`, `notNull` and `mapValues` will be applied to list elements instead of lists themselves
+* `explodeLists` — gathered values of type [`List`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/) will be exploded into their elements, so `where`, `cast`, `notNull` and `mapValues` will be applied to list elements instead of lists themselves
 * `cast` — inform compiler about the expected type of gathered elements. This type will be passed to `where` and `mapKeys` lambdas
 * `notNull` — skip gathered `null` values
 * `where` — filter gathered values

docs/StardustDocs/topics/getColumn.md

@@ -2,8 +2,7 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Access-->
 
-Return column by column name or [column selector](ColumnSelectors.md) as [`DataColumn`](DataColumn.md). 
-Throw exception if the requested column doesn't exist.
+Return column by column name or [column selector](ColumnSelectors.md) as [`DataColumn`](DataColumn.md). Throws exception if requested column doesn't exist.
 
 <!---FUN getColumn-->
 <tabs>
@@ -65,8 +64,7 @@ df.getColumnOrNull("age")
 
 ## getColumnGroup
 
-Return top-level column by column name or [column selector](ColumnSelectors.md) as [`ColumnGroup`](DataColumn.md#columngroup). 
-Throws exception if the requested column doesn't exist or is not a `ColumnGroup`.
+Return top-level column by column name or [column selector](ColumnSelectors.md) as [`ColumnGroup`](DataColumn.md#columngroup). Throws exception if requested column doesn't exist or is not a `ColumnGroup`.
 
 <!---FUN getColumnGroup-->
 <tabs>
@@ -97,7 +95,7 @@ df.getColumnGroup("name")
 
 ## getColumns
 
-Return a list of selected columns.
+Return list of selected columns.
 
 <!---FUN getColumns-->
 <tabs>