small docs and readme updates regarding main concepts

Author: Jolanrensen

README.md

@@ -11,14 +11,16 @@
 Kotlin DataFrame aims to reconcile Kotlin's static typing with the dynamic nature of data by utilizing both the full power of the Kotlin language and the opportunities provided by intermittent code execution in Jupyter notebooks and REPL.   
 
 * **Hierarchical** — represents hierarchical data structures, such as JSON or a tree of JVM objects.
-* **Functional** — data processing pipeline is organized in a chain of `DataFrame` transformation operations. Every operation returns a new instance of `DataFrame` reusing underlying storage wherever it's possible.
+* **Functional** — the data processing pipeline is organized in a chain of `DataFrame` transformation operations.
+* **Immutable** — every operation returns a new instance of `DataFrame` reusing underlying storage wherever it's possible.
 * **Readable** — data transformation operations are defined in DSL close to natural language.
 * **Practical** — provides simple solutions for common problems and the ability to perform complex tasks.
 * **Minimalistic** — simple, yet powerful data model of three column kinds.
-* **Interoperable** — convertable with Kotlin data classes and collections.
+* **Interoperable** — convertable with Kotlin data classes and collections. This also means conversion to/from other libraries' data structures is usually quite straightforward!
 * **Generic** — can store objects of any type, not only numbers or strings.
 * **Typesafe** — on-the-fly generation of extension properties for type safe data access with Kotlin-style care for null safety.
 * **Polymorphic** — type compatibility derives from column schema compatibility. You can define a function that requires a special subset of columns in a dataframe but doesn't care about other columns.
+  In notebooks this works out-of-the-box. In ordinary projects this requires casting (for now).
 
 Integrates with [Kotlin kernel for Jupyter](https://github.com/Kotlin/kotlin-jupyter). Inspired by [krangl](https://github.com/holgerbrandl/krangl), Kotlin Collections and [pandas](https://pandas.pydata.org/)
 

docs/StardustDocs/topics/overview.md

@@ -36,30 +36,32 @@ The goal of data wrangling is to assure quality and useful data.
 
 ## Main Features and Concepts
 
-* [**Hierarchical**](hierarchical.md) — the Kotlin DataFrame library provides an ability to read and present data from different sources including not only plain **CSV** but also **JSON** or **[SQL databases](readSqlDatabases.md)**.
-That’s why it has been designed hierarchical and allows nesting of columns and cells.
-
-* [**Interoperable**](collectionsInterop.md) — hierarchical data layout also opens a possibility of converting any objects 
-structure in application memory to a data frame and vice versa.
-
-* **Safe** — the Kotlin DataFrame library provides a mechanism of on-the-fly [**generation of extension properties**](extensionPropertiesApi.md) 
+* [**Hierarchical**](hierarchical.md) — the Kotlin DataFrame library provides an ability to read and present data from different sources, 
+including not only plain **CSV** but also **JSON** or **[SQL databases](readSqlDatabases.md)**.
+This is why it was designed to be hierarchical and allows nesting of columns and cells.
+* **Functional** — the data processing pipeline is organized in a chain of [`DataFrame`](DataFrame.md)  transformation operations.
+* **Immutable** — every operation returns a new instance of [`DataFrame`](DataFrame.md)  reusing underlying storage wherever it's possible.
+* **Readable** — data transformation operations are defined in DSL close to natural language.
+* **Practical** — provides simple solutions for common problems and the ability to perform complex tasks.
+* **Minimalistic** — simple, yet powerful data model of three [column kinds](DataColumn.md#column-kinds).
+* [**Interoperable**](collectionsInterop.md) — convertable with Kotlin data classes and collections.
+  This also means conversion to/from other libraries' data structures is usually quite straightforward!
+  See our examples for some conversions between DataFrame and [Apache Spark](TODO), [Multik](TODO), and [JetBrains Exposed](TODO).
+* **Generic** — can store objects of any type, not only numbers or strings.
+* **Typesafe** — the Kotlin DataFrame library provides a mechanism of on-the-fly [**generation of extension properties**](extensionPropertiesApi.md) 
 that correspond to the columns of a data frame. 
 In interactive notebooks like Jupyter or Datalore, the generation runs after each cell execution. 
 In IntelliJ IDEA there's a Gradle plugin for generation properties based on CSV file or JSON file. 
 Also, we’re working on a compiler plugin that infers and transforms [`DataFrame`](DataFrame.md) schema while typing.
 You can now clone this [project with many examples](https://github.com/koperagen/df-plugin-demo) showcasing how it allows you to reliably use our most convenient extension properties API.
 The generated properties ensure you’ll never misspell column name and don’t mess up with its type, and of course nullability is also preserved.
-
-* **Generic** — columns can store objects of any type, not only numbers or strings.
-
 * [**Polymorphic**](schemas.md) —
-  if all columns of [`DataFrame`](DataFrame.md) are presented in some other dataframes,
-  then the first one could be a superclass for latter. 
-Thus,
-  one can define a function on an interface with some set of columns
-  and then execute it in a safe way on any [`DataFrame`](DataFrame.md) which contains this set of columns.
-
-* **Immutable** — all operations on [`DataFrame`](DataFrame.md) produce new instance, while underlying data is reused wherever it's possible
+  if all columns of a [`DataFrame`](DataFrame.md) instance are presented in another dataframe,
+  then the first one will be seen as a superclass for the latter. 
+This means you can define a function on an interface with some set of columns
+  and then execute it safely on any [`DataFrame`](DataFrame.md) which contains this same set of columns.
+  In notebooks, this works out-of-the-box.
+  In ordinary projects, this requires casting (for now).
 
 ## Syntax
 

docs/StardustDocs/topics/schemasInheritance.md

@@ -18,7 +18,7 @@ New schema interface for `filtered` variable will be derived from previously gen
 interface DataFrameType1 : DataFrameType
 ```
 
-Extension properties for data access are generated only for new and overriden members of `DataFrameType1` interface:
+Extension properties for data access are generated only for new and overridden members of `DataFrameType1` interface:
 
 ```kotlin
 val ColumnsContainer<DataFrameType1>.age: DataColumn<Int> get() = this["age"] as DataColumn<Int>