Fixed grammar in the second part

Author: zaleslaw

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 column name differs from property name, use `@ColumnName` annotation:  
+When the column name differs from the property name, use `@ColumnName` annotation:  
 
 <!---FUN kproperties2-->
 
@@ -49,4 +49,4 @@ val passengers = DataFrame.read("titanic.csv")
 
 <!---END-->
 
-The `titanic.csv` file could be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
+The `titanic.csv` file can be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).

docs/StardustDocs/topics/getColumn.md

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

docs/StardustDocs/topics/getRow.md

@@ -13,7 +13,7 @@ df[2]
 <dataFrame src="org.jetbrains.kotlinx.dataframe.samples.api.Access.getRowByIndex.html"/>
 <!---END-->
 
-Get single [`DataRow`](DataRow.md) by [row condition](DataRow.md#row-conditions):
+Get a single [`DataRow`](DataRow.md) by [row condition](DataRow.md#row-conditions):
 
 <!---FUN getRowByCondition-->
 <tabs>

docs/StardustDocs/topics/gettingStarted.md

@@ -14,7 +14,7 @@ To use the Kotlin DataFrame library with Jupyter notebooks or Datalore, follow t
 Kotlin is included in each IntelliJ IDEA release.
 Download and install [IntelliJ IDEA](https://www.jetbrains.com/idea/download/) to start using Kotlin.
 
-## Create Kotlin project
+## Create a Kotlin project
 
 1. In IntelliJ IDEA, select **File** | **New** | **Project**.
 2. In the panel on the left, select **New Project**.
@@ -82,7 +82,8 @@ Download the file `movies.csv` from [here](https://github.com/Kotlin/dataframe/b
 
 Delete the `println()` functions and comments from your main function in `Main.kt`.
 
-To import the movie sample data into a data frame and print it, inside your main function in `Main.kt`, add the following code:
+To import the movie sample data into a data frame and print it inside your main function in `Main.kt`,
+add the following code:
 
 ```kotlin
     // Import your data to a data frame
@@ -104,7 +105,7 @@ To print some information about your data frame and sort your data, add the foll
     // Sort your data alphabetically by title
     df = df.sortBy("title")
 
-    // Filter your data so that only comedy films remain, and print
+    // Filter your data so that only comedy films remain and print
     df = df.filter { "genres"<String>().contains("Comedy") }
     df.print()
 ```
@@ -150,7 +151,7 @@ movieId                                    title                       genres
 3 9b30aff7943f44579e92c261f3adc193                    Women in Black (1997)   Fantasy|Suspenseful|Comedy
 </code-block>
 
-Congratulations! You have successfully used the Kotlin DataFrame library to import, manipulate and export data.
+Congratulations! You have successfully used the Kotlin DataFrame library to import, manipulate, and export data.
 
 ## Next steps
 * Learn more about how to [import and export data](io.md)

docs/StardustDocs/topics/gradleReference.md

@@ -10,7 +10,7 @@ dataframes {
     }
 }
 ```
-Note than name of the file and the interface are normalized: split by '_' and ' ' and joined to camel case.
+Note the name of the file and the interface are normalized: split by '_' and ' ' and joined to camel case.
 You can set parsing options for CSV:
 ```kotlin
 dataframes {
@@ -23,8 +23,8 @@ dataframes {
     }
 }
 ```
-In this case output path will depend on your directory structure. For project with package `org.example` path will be `build/generated/dataframe/main/kotlin/org/example/dataframe/Securities.Generated.kt
-`. Note that name of the Kotlin file is derived from the name of the data file with the suffix `.Generated` and the package 
+In this case, the output path will depend on your directory structure. For project with package `org.example` a path will be `build/generated/dataframe/main/kotlin/org/example/dataframe/Securities.Generated.kt
+`. Note that the name of the Kotlin file is derived from the name of the data file with the suffix `.Generated` and the package
 is derived from the directory structure with child directory `dataframe`. The name of the **data schema** itself is `Securities`. You could specify it explicitly:
 ```kotlin
 schema {
@@ -33,7 +33,7 @@ schema {
     name = "MyName"
 }
 ```
-If you want to change default package for all schemas:
+If you want to change the default package for all schemas:
 ```kotlin
 dataframes {
     packageName = "org.example"
@@ -60,7 +60,8 @@ dataframes {
     }
 }
 ```
-By default, plugin will generate output in specified source set. Source set could be specified for all schemas or for specific schema:
+By default, the plugin will generate output in a specified source set.
+Source set could be specified for all schemas or for specific schema:
 ```kotlin
 dataframes {
     packageName = "org.example"
@@ -76,7 +77,7 @@ dataframes {
     }
 }
 ```
-But if you need generated files in other directory, set `src`:
+But if you need generated files in another directory, set `src`:
 ```kotlin
 dataframes {
     // output: schemas/org/example/test/OtherName.Generated.kt
@@ -90,7 +91,7 @@ dataframes {
 
 ## DSL reference
 Inside `dataframes` you can configure parameters that will apply to all schemas. Configuration inside `schema` will override these defaults for specific schema.
-Here is full DSL for declaring data schemas:
+Here is the full DSL for declaring data schemas:
 
 ```kotlin
 dataframes {
@@ -101,10 +102,10 @@ dataframes {
     // KOTLIN SCRIPT: DataSchemaVisibility.INTERNAL DataSchemaVisibility.IMPLICIT_PUBLIC, DataSchemaVisibility.EXPLICIT_PUBLIC
     // GROOVY SCRIPT: 'internal', 'implicit_public', 'explicit_public'
 
-    withoutDefaultPath() // disable default path for all schemas
-    // i.e. plugin won't copy "data" property of the schemas to generated companion objects
+    withoutDefaultPath() // disable a default path for all schemas
+    // i.e., plugin won't copy "data" property of the schemas to generated companion objects
 
-    // split property names by delimiters (arguments of this method), lowercase parts and join to camel case
+    // split property names by delimiters (arguments of this method), lowercase parts, and join to camel case
     // enabled by default
     withNormalizationBy('_') // [optional: default: ['\t', '_', ' ']]
     withoutNormalization() // disable property names normalization
@@ -125,8 +126,8 @@ dataframes {
         withNormalizationBy('_') // enable property names normalization for this schema and use these delimiters
         withoutNormalization() // disable property names normalization for this schema
 
-        withoutDefaultPath() // disable default path for this schema
-        withDefaultPath() // enable default path for this schema
+        withoutDefaultPath() // disable a default path for this schema
+        withDefaultPath() // enable a default path for this schema
     }
 }
 ```

docs/StardustDocs/topics/groupUngroupFlatten.md

@@ -1,7 +1,7 @@
 [//]: # (title: Group/ungroup/flatten columns)
 
 * [`group`](group.md) — groups given columns into [`ColumnGroups`](DataColumn.md#columngroup).
-* [`ungroup`](ungroup.md) — ungroups given [`ColumnGroups`](DataColumn.md#columngroup) by replacing them with their children columns
+* [`ungroup`](ungroup.md) — ungroups given [`ColumnGroups`](DataColumn.md#columngroup) by replacing them with their children's columns
 * [`flatten`](flatten.md) — recursively removes all column groupings under given [`ColumnGroups`](DataColumn.md#columngroup), remaining only [`ValueColumns`](DataColumn.md#valuecolumn) and [`FrameColumns`](DataColumn.md#framecolumn)
 
 These operations are special cases of general [`move`](move.md) operation.

docs/StardustDocs/topics/hierarchical.md

@@ -5,7 +5,8 @@
 * [`ColumnGroup`](DataColumn.md#columngroup) is a group of [columns](DataColumn.md)
 * [`FrameColumn`](DataColumn.md#framecolumn) is a column of [dataframes](DataFrame.md)
 
-You can read [`DataFrame`](DataFrame.md) [from json](read.md#reading-json) or [from in-memory object graph](createDataFrame.md#todataframe) preserving original tree structure.
+You can read [`DataFrame`](DataFrame.md) [from json](read.md#reading-json) or [from the in-memory object graph](createDataFrame.md#todataframe)
+preserving the original tree structure.
 
 Hierarchical columns can also appear as a result of some [modification operations](modify.md):
 * [group](group.md) produces [`ColumnGroup`](DataColumn.md#columngroup) 

docs/StardustDocs/topics/inferType.md

@@ -2,7 +2,7 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
 
-Changes type of the selected columns based on actual values stored in these columns. Resulting type of the column will be a nearest common supertype of all column values.
+Changes a type of the selected columns based on actual values stored in these columns. Resulting type of the column will be a nearest common supertype of all column values.
 
 ```text
 inferType [ { columns } ]

docs/StardustDocs/topics/insert.md

@@ -2,7 +2,7 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
 
-Inserts new column at specific position in [`DataFrame`](DataFrame.md). 
+Insert a new column at a specific position in [`DataFrame`](DataFrame.md). 
 
 ```text
 insert 
@@ -14,7 +14,7 @@ rowExpression: DataRow.(DataRow) -> Value
 
 Similar to [add](add.md), but supports column positioning.
 
-Create new column based on existing columns and insert it into [`DataFrame`](DataFrame.md):
+Create a new column based on existing columns and insert it into [`DataFrame`](DataFrame.md):
 
 <!---FUN insert-->
 <tabs>

docs/StardustDocs/topics/installation.md

@@ -1,7 +1,7 @@
 [//]: # (title: Installation)
 
 You can use the Kotlin DataFrame library in different environments — as any other JVM library.
-The following sections will show how to use the Kotlin DataFrame library in [Jupyter](#jupyter-notebook), [Datalore](#datalore) and in a [Gradle project](#gradle).
+The following sections will show how to use the Kotlin DataFrame library in [Jupyter](#jupyter-notebook), [Datalore,](#datalore) and in a [Gradle project](#gradle).
 
 ## Jupyter Notebook
 
@@ -13,21 +13,21 @@ the command line, for example:
 jupyter notebook
 ```
 
-In the notebook you only have to write single line to start using the Kotlin DataFrame library:
+In the notebook, you only have to write a single line to start using the Kotlin DataFrame library:
 
 ```text
 %use dataframe
 ```
 
-In this case the version which is bundled with the kernel, will be used.
+In this case, the version which is bundled with the kernel will be used.
 If you want to always use the latest version, add another magic before `%use dataframe`:
 
 ```text
 %useLatestDescriptors
 %use dataframe
 ```
 
-If you want to use specific version of the Kotlin DataFrame library, you can specify it in brackets:
+If you want to use a specific version of the Kotlin DataFrame library, you can specify it in brackets:
 
 ```text
 %use dataframe(%dataFrameVersion%)
@@ -156,11 +156,11 @@ tasks.withType(org.jetbrains.kotlin.gradle.tasks.KotlinCompile).configureEach {
 </tabs>
 
 Note that it's better to use the same version for a library and plugin to avoid unpredictable errors.
-After plugin configuration you can try it out with [example](schemasGradle.md#annotation-processing).
+After plugin configuration, you can try it out, for [example](schemasGradle.md#annotation-processing).
 
 ### Custom configuration
 
-If you want to avoid adding unnecessary dependency, you can choose from following artifacts:
+If you want to avoid adding unnecessary dependencies, you can choose from the following artifacts:
 
 <tabs>
 <tab title="Kotlin DSL">
@@ -197,7 +197,7 @@ dependencies {
 
 #### Linter configuration
 
-We provide a Gradle plugin that generates interfaces by your data.
+We provide a Gradle plugin that generates interfaces based on your data structure.
 Use this configuration to prevent linter from complaining about formatting in the generated sources.
 
 <tabs>
@@ -236,5 +236,6 @@ tasks.withType(org.jmailen.gradle.kotlinter.tasks.LintTask).all {
 
 ## Other build systems
 
-If you are using Maven, Ivy or Bazel to configure your build, you can still use the Kotlin DataFrame library in your project.
+If you are using Maven, Ivy, or Bazel to configure your build,
+you can still use the Kotlin DataFrame library in your project.
 Just follow the instructions for your build system on [this page](https://search.maven.org/artifact/org.jetbrains.kotlinx/dataframe/0.8.1/jar).