diff --git a/docs/StardustDocs/c.list b/docs/StardustDocs/c.list
index 8339a6eb7..c6b6e7762 100644
--- a/docs/StardustDocs/c.list
+++ b/docs/StardustDocs/c.list
@@ -2,5 +2,5 @@
-
+
diff --git a/docs/StardustDocs/d.tree b/docs/StardustDocs/d.tree
index df5b05868..4bc8dabea 100644
--- a/docs/StardustDocs/d.tree
+++ b/docs/StardustDocs/d.tree
@@ -21,12 +21,16 @@
-
-
-
-
+
+
+
+
+
+
+
+
@@ -36,20 +40,16 @@
+
-
-
-
-
-
+
+
-
-
@@ -199,8 +199,6 @@
-
-
-
+
diff --git a/docs/StardustDocs/images/github-mark.svg b/docs/StardustDocs/images/github-mark.svg
new file mode 100644
index 000000000..37fa923df
--- /dev/null
+++ b/docs/StardustDocs/images/github-mark.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/docs/StardustDocs/topics/ColumnSelectors.md b/docs/StardustDocs/topics/ColumnSelectors.md
index 0f571f0cb..75f91fccb 100644
--- a/docs/StardustDocs/topics/ColumnSelectors.md
+++ b/docs/StardustDocs/topics/ColumnSelectors.md
@@ -260,7 +260,7 @@ For instance, excluding `userData.age`:
`df.select { colsAtAnyDepth { "a" in it.name() } except userData.age }`
Note that the selection of columns to exclude from column sets is always done relative to the outer scope.
-Use the [Extension Properties API](extensionPropertiesApi.md) to prevent scoping issues if possible.
+Use the [Extension Properties API](concepts/extensionPropertiesApi.md) to prevent scoping issues if possible.
> Special case: If a column that needs to be removed appears multiple times in the [`ColumnSet`](#column-resolvers),
> it is excepted each time it is encountered (including inside [Column Groups](DataColumn.md#columngroup)).
@@ -392,11 +392,11 @@ This function behaves the same as [`cols {}` and `[{}]`](ColumnSelectors.md#cols
Creates a [`ColumnSet`](#column-resolvers) containing the columns from both the left and right side of the function. This allows
you to combine selections or simply select multiple columns at once.
-Any combination of [AccessApi](apiLevels.md) can be used on either side of the `and` operator.
+Any combination of [AccessApi](concepts/apiLevels.md) can be used on either side of the `and` operator.
Note, while you can write `col1 and col2 and col3...`, it may be more concise to use
[`cols(col1, col2, col3...)`](ColumnSelectors.md#cols) instead. The only downside is that you can't mix
-[Access APIs](apiLevels.md) with that notation.
+[Access APIs](concepts/apiLevels.md) with that notation.
##### Rename {collapsible="true"}
`colA named "colB"`, `colA into namedColAccessor`
@@ -404,7 +404,7 @@ Note, while you can write `col1 and col2 and col3...`, it may be more concise to
Renaming a column in the Columns Selection DSL is done by calling the infix functions
`named` or `into`.
They behave exactly the same, so it's up to contextual preference which one to use.
-Any combination of [Access API](apiLevels.md) can be used to specify the column to rename
+Any combination of [Access API](concepts/apiLevels.md) can be used to specify the column to rename
and which name should be used instead.
##### Expr (Column Expression) {collapsible="true"}
diff --git a/docs/StardustDocs/topics/Home.topic b/docs/StardustDocs/topics/Home.topic
index b38d03a63..27cec9353 100644
--- a/docs/StardustDocs/topics/Home.topic
+++ b/docs/StardustDocs/topics/Home.topic
@@ -20,7 +20,7 @@
First steps
-
+ Reading from files: CSV, JSON, ApacheArrow
@@ -32,7 +32,6 @@
-
diff --git a/docs/StardustDocs/topics/Support.md b/docs/StardustDocs/topics/Support.md
new file mode 100644
index 000000000..3a54fb0b6
--- /dev/null
+++ b/docs/StardustDocs/topics/Support.md
@@ -0,0 +1,14 @@
+# Support
+
+*
+[**Issue Tracker**](https://github.com/Kotlin/dataframe/issues)
+
+ If you find a bug or have an idea for a new feature,
+ file an issue in our [DataFrame GitHub repository](https://github.com/Kotlin/dataframe).
+
+*
+[**Community**](https://github.com/Kotlin/dataframe/issues)
+
+ Peer-to-peer support is available on the Kotlin Slack
+ [#datascience](https://kotlinlang.slack.com/archives/C4W52CFEZ) channel
+ ([request an invite](https://surveys.jetbrains.com/s3/kotlin-slack-sign-up?_gl=1*1ssyqy3*_gcl_au*MTk5NzUwODYzOS4xNzQ2NzkxMDMz*FPAU*MTk5NzUwODYzOS4xNzQ2NzkxMDMz*_ga*MTE0ODQ1MzY3OS4xNzM4OTY1NzM3*_ga_9J976DJZ68*czE3NTE1NDUxODUkbzIyNyRnMCR0MTc1MTU0NTE4NSRqNjAkbDAkaDA.)).
diff --git a/docs/StardustDocs/topics/DataColumn.md b/docs/StardustDocs/topics/concepts/DataColumn.md
similarity index 100%
rename from docs/StardustDocs/topics/DataColumn.md
rename to docs/StardustDocs/topics/concepts/DataColumn.md
diff --git a/docs/StardustDocs/topics/DataFrame.md b/docs/StardustDocs/topics/concepts/DataFrame.md
similarity index 100%
rename from docs/StardustDocs/topics/DataFrame.md
rename to docs/StardustDocs/topics/concepts/DataFrame.md
diff --git a/docs/StardustDocs/topics/DataRow.md b/docs/StardustDocs/topics/concepts/DataRow.md
similarity index 100%
rename from docs/StardustDocs/topics/DataRow.md
rename to docs/StardustDocs/topics/concepts/DataRow.md
diff --git a/docs/StardustDocs/topics/gradleReference.md b/docs/StardustDocs/topics/concepts/DataSchemaGenerationGradle.md
similarity index 99%
rename from docs/StardustDocs/topics/gradleReference.md
rename to docs/StardustDocs/topics/concepts/DataSchemaGenerationGradle.md
index b5fe5f0f7..e912ebe1a 100644
--- a/docs/StardustDocs/topics/gradleReference.md
+++ b/docs/StardustDocs/topics/concepts/DataSchemaGenerationGradle.md
@@ -1,4 +1,4 @@
-[//]: # (title: Gradle plugin reference)
+[//]: # (title: Data Shemas Generation in Gradle)
This page describes the Gradle plugin that generates `@DataSchema` from data samples.
```Kotlin
diff --git a/docs/StardustDocs/topics/DataSchema-Data-Classes-Generation.md b/docs/StardustDocs/topics/concepts/DataSchemaGenerationMethods.md
similarity index 99%
rename from docs/StardustDocs/topics/DataSchema-Data-Classes-Generation.md
rename to docs/StardustDocs/topics/concepts/DataSchemaGenerationMethods.md
index d5faa6c62..602c66db6 100644
--- a/docs/StardustDocs/topics/DataSchema-Data-Classes-Generation.md
+++ b/docs/StardustDocs/topics/concepts/DataSchemaGenerationMethods.md
@@ -1,4 +1,4 @@
-# Data Schemas/Data Classes Generation
+# Data Schemas Generation From Existing DataFrame
Generate useful Kotlin definitions based on your DataFrame structure.
diff --git a/docs/StardustDocs/topics/apiLevels.md b/docs/StardustDocs/topics/concepts/apiLevels.md
similarity index 95%
rename from docs/StardustDocs/topics/apiLevels.md
rename to docs/StardustDocs/topics/concepts/apiLevels.md
index 1595c8c0b..aabbe0129 100644
--- a/docs/StardustDocs/topics/apiLevels.md
+++ b/docs/StardustDocs/topics/concepts/apiLevels.md
@@ -16,7 +16,7 @@ In the Kotlin DataFrame library, we provide two different ways to access columns
Here's a list of all APIs in order of increasing safety.
-* [**String API**](stringApi.md)
+* **String API**
Columns are accessed by `string` representing their name. Type-checking is done at runtime, name-checking too.
* [**Extension Properties API**](extensionPropertiesApi.md)
@@ -80,7 +80,7 @@ The `titanic.csv` file can be found [here](https://github.com/Kotlin/dataframe/b
# Comparing APIs
-The [String API](stringApi.md) is the simplest and unsafest of them all. The main advantage of it is that it can be
+The String API is the simplest and unsafest of them all. The main advantage of it is that it can be
used at any time, including when accessing new columns in chain calls. So we can write something like:
```kotlin
diff --git a/docs/StardustDocs/topics/concepts/concepts.md b/docs/StardustDocs/topics/concepts/concepts.md
new file mode 100644
index 000000000..03c521ec8
--- /dev/null
+++ b/docs/StardustDocs/topics/concepts/concepts.md
@@ -0,0 +1,62 @@
+# Concepts And Principles
+
+
+Learn what Kotlin DataFrame is about — its core concepts, design principles, and usage philosophy.
+
+
+
+Discover the fundamentals of the library —
+understand key concepts, motivation, and the overall structure of the library.
+
+
+
+Explore the fundamentals of Kotlin DataFrame —
+understand key concepts, motivation, and the overall structure of the library.
+
+
+
+
+
+
+## What is a dataframe
+
+A *dataframe* is an abstraction for working with structured data.
+Essentially, it’s a 2-dimensional table with labeled columns of potentially different types.
+You can think of it like a spreadsheet or SQL table, or a dictionary of series objects.
+
+The handiness of this abstraction is not in the table itself but in a set of operations defined on it.
+The Kotlin DataFrame library is an idiomatic Kotlin DSL defining such operations.
+The process of working with dataframe is often called *data wrangling* which
+is the process of transforming and mapping data from one "raw" data form into another format
+that is more appropriate for analytics and visualization.
+The goal of data wrangling is to ensure 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)**.
+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](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples)
+ for some conversions between DataFrame and [Apache Spark](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/spark), [Multik](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/multik), and [JetBrains Exposed](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/exposed).
+* **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 dataframe.
+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.
+* [**Polymorphic**](schemas.md) —
+ 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).
diff --git a/docs/StardustDocs/topics/hierarchical.md b/docs/StardustDocs/topics/concepts/hierarchical.md
similarity index 100%
rename from docs/StardustDocs/topics/hierarchical.md
rename to docs/StardustDocs/topics/concepts/hierarchical.md
diff --git a/docs/StardustDocs/topics/nanAndNa.md b/docs/StardustDocs/topics/concepts/nanAndNa.md
similarity index 100%
rename from docs/StardustDocs/topics/nanAndNa.md
rename to docs/StardustDocs/topics/concepts/nanAndNa.md
diff --git a/docs/StardustDocs/topics/numberUnification.md b/docs/StardustDocs/topics/concepts/numberUnification.md
similarity index 100%
rename from docs/StardustDocs/topics/numberUnification.md
rename to docs/StardustDocs/topics/concepts/numberUnification.md
diff --git a/docs/StardustDocs/topics/schemas.md b/docs/StardustDocs/topics/concepts/schemas.md
similarity index 100%
rename from docs/StardustDocs/topics/schemas.md
rename to docs/StardustDocs/topics/concepts/schemas.md
diff --git a/docs/StardustDocs/topics/schemasCustom.md b/docs/StardustDocs/topics/concepts/schemasCustom.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasCustom.md
rename to docs/StardustDocs/topics/concepts/schemasCustom.md
diff --git a/docs/StardustDocs/topics/schemasExternalJupyter.md b/docs/StardustDocs/topics/concepts/schemasExternalJupyter.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasExternalJupyter.md
rename to docs/StardustDocs/topics/concepts/schemasExternalJupyter.md
diff --git a/docs/StardustDocs/topics/schemasGradle.md b/docs/StardustDocs/topics/concepts/schemasGradle.md
similarity index 97%
rename from docs/StardustDocs/topics/schemasGradle.md
rename to docs/StardustDocs/topics/concepts/schemasGradle.md
index 0296bcc0f..609d43fac 100644
--- a/docs/StardustDocs/topics/schemasGradle.md
+++ b/docs/StardustDocs/topics/concepts/schemasGradle.md
@@ -131,7 +131,7 @@ dataframes {
}
```
-See [reference](gradleReference.md) and [examples](gradleReference.md#examples) for more details.
+See [reference](DataSchemaGenerationGradle.md) and [examples](DataSchemaGenerationGradle.md#examples) for more details.
diff --git a/docs/StardustDocs/topics/schemasImportOpenApiGradle.md b/docs/StardustDocs/topics/concepts/schemasImportOpenApiGradle.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasImportOpenApiGradle.md
rename to docs/StardustDocs/topics/concepts/schemasImportOpenApiGradle.md
diff --git a/docs/StardustDocs/topics/schemasImportOpenApiJupyter.md b/docs/StardustDocs/topics/concepts/schemasImportOpenApiJupyter.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasImportOpenApiJupyter.md
rename to docs/StardustDocs/topics/concepts/schemasImportOpenApiJupyter.md
diff --git a/docs/StardustDocs/topics/schemasImportSqlGradle.md b/docs/StardustDocs/topics/concepts/schemasImportSqlGradle.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasImportSqlGradle.md
rename to docs/StardustDocs/topics/concepts/schemasImportSqlGradle.md
diff --git a/docs/StardustDocs/topics/schemasInheritance.md b/docs/StardustDocs/topics/concepts/schemasInheritance.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasInheritance.md
rename to docs/StardustDocs/topics/concepts/schemasInheritance.md
diff --git a/docs/StardustDocs/topics/schemasJupyter.md b/docs/StardustDocs/topics/concepts/schemasJupyter.md
similarity index 100%
rename from docs/StardustDocs/topics/schemasJupyter.md
rename to docs/StardustDocs/topics/concepts/schemasJupyter.md
diff --git a/docs/StardustDocs/topics/types.md b/docs/StardustDocs/topics/concepts/types.md
similarity index 100%
rename from docs/StardustDocs/topics/types.md
rename to docs/StardustDocs/topics/concepts/types.md
diff --git a/docs/StardustDocs/topics/extensionPropertiesApi.md b/docs/StardustDocs/topics/extensionPropertiesApi.md
index 0df47d6aa..41c79591e 100644
--- a/docs/StardustDocs/topics/extensionPropertiesApi.md
+++ b/docs/StardustDocs/topics/extensionPropertiesApi.md
@@ -104,7 +104,7 @@ See the [](quickstart.md) in Kotlin Notebook with basic Extension Properties API
For now, if you read [`DataFrame`](DataFrame.md) from a file or URL, you need to define its schema manually.
-You can do it quickly with [`generate..()` methods](DataSchema-Data-Classes-Generation.md).
+You can do it quickly with [`generate..()` methods](DataSchemaGenerationMethods.md).
Define schemas:
```kotlin
diff --git a/docs/StardustDocs/topics/guides/quickstart.md b/docs/StardustDocs/topics/guides/quickstart.md
index 5b8ad63a7..06de61193 100644
--- a/docs/StardustDocs/topics/guides/quickstart.md
+++ b/docs/StardustDocs/topics/guides/quickstart.md
@@ -349,7 +349,7 @@ Ready to go deeper? Check out what’s next:
- 🛠️ **[Browse the operations overview](operations.md)** to learn what Kotlin DataFrame can do.
-- 🧠 **Understand the design** and core concepts in the [library overview](overview.md).
+- 🧠 **Understand the design** and core concepts in the [library overview](concepts.md).
- 🔤 **[Learn more about Extension Properties](extensionPropertiesApi.md)**
and make working with your data both convenient and type-safe.
diff --git a/docs/StardustDocs/topics/overview.md b/docs/StardustDocs/topics/overview.md
deleted file mode 100644
index d5d1cb4b8..000000000
--- a/docs/StardustDocs/topics/overview.md
+++ /dev/null
@@ -1,174 +0,0 @@
-[//]: # (title: Library Overview)
-
-
-Learn what Kotlin DataFrame is about — its core concepts, design principles, and usage philosophy.
-
-
-
-Discover the fundamentals of the library —
-understand key concepts, motivation, and the overall structure of the library.
-
-
-
-Explore the fundamentals of Kotlin DataFrame —
-understand key concepts, motivation, and the overall structure of the library.
-
-
-
-
-
-
-
-This documentation is written in such a way that it could be read sequentially and in this case, it provides all necessary information about the library, but at the same time the [Operations](operations.md) section could be used as an API reference
-
-
-
-## What is Data Frame
-
-Data frame is an abstraction for working with structured data. Essentially it’s a 2-dimensional table with labeled columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dictionary of series objects.
-
-The handiness of this abstraction is not in the table itself but in a set of operations defined on it.
-The Kotlin DataFrame library is an idiomatic Kotlin DSL defining such operations.
-The process of working with data frame is often called *data wrangling* which
-is the process of transforming and mapping data from one "raw" data form into another format
-that is more appropriate for analytics and visualization.
-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)**.
-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](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples)
- for some conversions between DataFrame and [Apache Spark](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/spark), [Multik](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/multik), and [JetBrains Exposed](https://github.com/Kotlin/dataframe/tree/master/examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/exposed).
-* **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.
-* [**Polymorphic**](schemas.md) —
- 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
-
-**Basics:**
-
-```kotlin
-val df = DataFrame.readCsv("titanic.csv", delimiter = ';')
-```
-
-```kotlin
-// filter rows
-val someSurvivors = df.filter { survived && home.endsWith("NY") && age in 10..20 }
-
-// add column
-val withBirthYearColumn = df.add("birthYear") { 1912 - age }
-
-// sort rows
-val sortedByAge = df.sortByDesc { age }
-
-// aggregate data
-val aggregated = df.groupBy { pclass }.aggregate {
- maxBy { age }.name into "oldest person"
- count { survived } into "survived"
-}
-```
-
-The `titanic.csv` file could be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
-
-**Create:**
-```kotlin
-// create columns
-val fromTo by columnOf("LoNDon_paris", "MAdrid_miLAN", "londON_StockhOlm", "Budapest_PaRis", "Brussels_londOn")
-val flightNumber by columnOf(10045.0, Double.NaN, 10065.0, Double.NaN, 10085.0)
-val recentDelays by columnOf("23,47", null, "24, 43, 87", "13", "67, 32")
-val airline by columnOf("KLM(!)", "{Air France} (12)", "(British Airways. )", "12. Air France", "'Swiss Air'")
-
-// create dataframe
-val df = dataFrameOf(fromTo, flightNumber, recentDelays, airline)
-```
-
-**Clean:**
-```kotlin
-// typed accessors for columns
-// that will appear during
-// dataframe transformation
-val origin by column()
-val destination by column()
-
-val dfClean = df
- // fill missing flight numbers
- .fillNA { flightNumber }.with { prev()!!.flightNumber + 10 }
-
- // convert flight numbers to int
- .convert { flightNumber }.toInt()
-
- // clean 'Airline' column
- .update { airline }.with { "([a-zA-Z\\s]+)".toRegex().find(it)?.value ?: "" }
-
- // split 'From_To' column into 'From' and 'To'
- .split { fromTo }.by("_").into(origin, destination)
-
- // clean 'From' and 'To' columns
- .update { origin and destination }.with { it.lowercase().replaceFirstChar(Char::uppercase) }
-
- // split lists of delays in 'RecentDelays' into separate columns
- // 'delay1', 'delay2'... and nest them inside original column `RecentDelays`
- .split { recentDelays }.inward { "delay$it" }
-
- // convert string values in `delay1`, `delay2` into ints
- .parse { recentDelays }
-```
-
-**Aggregate:**
-```kotlin
-// group by flight origin
-val analysed = dfClean.groupBy { From into "origin" }.aggregate {
- // we are in the context of single data group
-
- // number of flights from origin
- count() into "count"
-
- // list of flight numbers
- flightNumber into "flight numbers"
-
- // counts of flights per airline
- airline.valueCounts() into "airlines"
-
- // max delay across all delays in `delay1` and `delay2`
- recentDelays.maxOrNull { delay1 and delay2 } into "major delay"
-
- // separate lists of recent delays for `delay1`, `delay2` and `delay3`
- recentDelays.implode(dropNulls = true) into "recent delays"
-
- // total delay per city of destination
- pivot { To }.sum { recentDelays.intCols() } into "total delays to"
-}
-```
-
-## Contribute and give feedback
-
-If you find a bug, or have an idea for a new feature, [file an issue](https://github.com/Kotlin/dataframe/issues/new) in our DataFrame GitHub repository.
-
-Additionally, we welcome contributions. To get stared, choose an issue, and let us know that you're working on it. When you're ready, create a [pull request](https://github.com/Kotlin/dataframe/pulls).
-
-You can also contact us in the [#datascience](https://kotlinlang.slack.com/archives/C4W52CFEZ) channel of Kotlin Slack.
-
-For more information on how to contribute, see our [Contributing guidelines](https://github.com/Kotlin/dataframe/blob/master/CONTRIBUTING.md).
-
-Good luck!
diff --git a/docs/StardustDocs/topics/stringApi.md b/docs/StardustDocs/topics/stringApi.md
deleted file mode 100644
index a5fece401..000000000
--- a/docs/StardustDocs/topics/stringApi.md
+++ /dev/null
@@ -1,26 +0,0 @@
-[//]: # (title: String API)
-
-
-
-String column names are the easiest way to access data in [`DataFrame`](DataFrame.md):
-
-
-
-```kotlin
-DataFrame.read("titanic.csv")
- .add("lastName") { "name"().split(",").last() }
- .dropNulls("age")
- .filter {
- "survived"() &&
- "home"().endsWith("NY") &&
- "age"() in 10..20
- }
-```
-
-
-
-The `titanic.csv` file could be found [here](https://github.com/Kotlin/dataframe/blob/master/data/titanic.csv).
-
-
-Note that if data frame doesn’t contain column with the string provided, or you try to cast to the wrong type it will lead to runtime exception.
-
+[**Issue Tracker**](https://github.com/Kotlin/dataframe/issues)
+
+ If you find a bug or have an idea for a new feature,
+ file an issue in our [DataFrame GitHub repository](https://github.com/Kotlin/dataframe).
+
+* 
+[**Community**](https://github.com/Kotlin/dataframe/issues)
+
+ Peer-to-peer support is available on the Kotlin Slack
+ [#datascience](https://kotlinlang.slack.com/archives/C4W52CFEZ) channel
+ ([request an invite](https://surveys.jetbrains.com/s3/kotlin-slack-sign-up?_gl=1*1ssyqy3*_gcl_au*MTk5NzUwODYzOS4xNzQ2NzkxMDMz*FPAU*MTk5NzUwODYzOS4xNzQ2NzkxMDMz*_ga*MTE0ODQ1MzY3OS4xNzM4OTY1NzM3*_ga_9J976DJZ68*czE3NTE1NDUxODUkbzIyNyRnMCR0MTc1MTU0NTE4NSRqNjAkbDAkaDA.)).
diff --git a/docs/StardustDocs/topics/DataColumn.md b/docs/StardustDocs/topics/concepts/DataColumn.md
similarity index 100%
rename from docs/StardustDocs/topics/DataColumn.md
rename to docs/StardustDocs/topics/concepts/DataColumn.md
diff --git a/docs/StardustDocs/topics/DataFrame.md b/docs/StardustDocs/topics/concepts/DataFrame.md
similarity index 100%
rename from docs/StardustDocs/topics/DataFrame.md
rename to docs/StardustDocs/topics/concepts/DataFrame.md
diff --git a/docs/StardustDocs/topics/DataRow.md b/docs/StardustDocs/topics/concepts/DataRow.md
similarity index 100%
rename from docs/StardustDocs/topics/DataRow.md
rename to docs/StardustDocs/topics/concepts/DataRow.md
diff --git a/docs/StardustDocs/topics/gradleReference.md b/docs/StardustDocs/topics/concepts/DataSchemaGenerationGradle.md
similarity index 99%
rename from docs/StardustDocs/topics/gradleReference.md
rename to docs/StardustDocs/topics/concepts/DataSchemaGenerationGradle.md
index b5fe5f0f7..e912ebe1a 100644
--- a/docs/StardustDocs/topics/gradleReference.md
+++ b/docs/StardustDocs/topics/concepts/DataSchemaGenerationGradle.md
@@ -1,4 +1,4 @@
-[//]: # (title: Gradle plugin reference)
+[//]: # (title: Data Shemas Generation in Gradle)
This page describes the Gradle plugin that generates `@DataSchema` from data samples.
```Kotlin
diff --git a/docs/StardustDocs/topics/DataSchema-Data-Classes-Generation.md b/docs/StardustDocs/topics/concepts/DataSchemaGenerationMethods.md
similarity index 99%
rename from docs/StardustDocs/topics/DataSchema-Data-Classes-Generation.md
rename to docs/StardustDocs/topics/concepts/DataSchemaGenerationMethods.md
index d5faa6c62..602c66db6 100644
--- a/docs/StardustDocs/topics/DataSchema-Data-Classes-Generation.md
+++ b/docs/StardustDocs/topics/concepts/DataSchemaGenerationMethods.md
@@ -1,4 +1,4 @@
-# Data Schemas/Data Classes Generation
+# Data Schemas Generation From Existing DataFrame