Update docs about supported formats

Author: koperagen

docs/StardustDocs/topics/io.md

@@ -1,4 +1,4 @@
 [//]: # (title: Input/output)
 
 When you work with data, you have to [read](read.md) it from disk or from remote URLs and [write](write.md) it on disk.
-This section describes how to do it. For now, only CSV, TSV, JSON, XLS and XLSX formats are supported.
+This section describes how to do it. For now, CSV, TSV, JSON, XLS, XLSX, Apache Arrow formats are supported.

docs/StardustDocs/topics/read.md

@@ -1,7 +1,7 @@
 [//]: # (title: Read)
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Read-->
 
-`DataFrame` supports CSV, TSV, JSON, XLS and XLSX input formats.
+`DataFrame` supports CSV, TSV, JSON, XLS and XLSX, Apache Arrow input formats.
 
 `read` method automatically detects input format based on file extension and content
 
@@ -136,17 +136,24 @@ D: Boolean?
 
 Column A has `String` type because all values are string literals, no implicit conversion is performed. Column C has `Number` type because it's the least common type for `Int` and `Double`.
 
-### Reading spreadsheets
+### Reading Excel
 
-Right now DataFrame only supports reading Excel formats: xls, xlsx.
+Add dependency:
+
+```kotlin
+implementation("org.jetbrains.kotlinx:dataframe-excel:$dataframe_version")
+```
+
+Right now DataFrame supports reading Excel spreadsheet formats: xls, xlsx.
 
 You can read from file or URL.
 
 Cells representing dates will be read as `kotlinx.datetime.LocalDateTime`.
 Cells with number values, including whole numbers such as "100", or calculated formulas will be read as `Double` 
 
 Sometimes cells can have wrong format in Excel file, for example you expect to read column of String:
-```
+
+```text
 IDS
 100 <-- Intended to be String, but has wrong cell format in original .xlsx file
 A100
@@ -173,3 +180,23 @@ df1["IDS"].type() shouldBe typeOf<String>()
 ```
 
 <!---END-->
+
+### Reading Apache Arrow formats
+
+Add dependency:
+
+```kotlin
+implementation("org.jetbrains.kotlinx:dataframe-arrow:$dataframe_version")
+```
+
+Dataframe supports reading from [Arrow interprocess streaming format](https://arrow.apache.org/docs/java/ipc.html#writing-and-reading-streaming-format) and [Arrow random access format](https://arrow.apache.org/docs/java/ipc.html#writing-and-reading-random-access-files)
+
+<!---FUN readArrowFeather-->
+
+```kotlin
+val df = DataFrame.readArrowFeather(file)
+```
+
+<!---END-->
+
+

docs/StardustDocs/topics/write.md

@@ -51,7 +51,13 @@ val jsonStr = df.toJson(prettyPrint = true)
 
 ### Writing spreadsheets
 
-You can write your dataframe in XLS, XLSX format to a file or `OutputStream`
+Add dependency:
+
+```kotlin
+implementation("org.jetbrains.kotlinx:dataframe-excel:$dataframe_version")
+```
+
+You can write your dataframe in XLS, XLSX format to a file, `OutputStream` or Workbook object.
 
 <!---FUN writeXls-->
 

tests/build.gradle.kts

@@ -16,6 +16,7 @@ repositories {
 dependencies {
     implementation(project(":"))
     implementation(project(":dataframe-excel"))
+    implementation(project(":dataframe-arrow"))
     testImplementation(libs.junit)
     testImplementation(libs.kotestAssertions) {
         exclude("org.jetbrains.kotlin", "kotlin-stdlib-jdk8")

tests/src/test/kotlin/org/jetbrains/kotlinx/dataframe/samples/api/Read.kt

@@ -10,8 +10,10 @@ import org.jetbrains.kotlinx.dataframe.api.columnTypes
 import org.jetbrains.kotlinx.dataframe.api.convert
 import org.jetbrains.kotlinx.dataframe.api.dataFrameOf
 import org.jetbrains.kotlinx.dataframe.api.with
+import org.jetbrains.kotlinx.dataframe.io.readArrowFeather
 import org.jetbrains.kotlinx.dataframe.io.readCSV
 import org.jetbrains.kotlinx.dataframe.io.readJson
+import org.jetbrains.kotlinx.dataframe.testArrowFeather
 import org.jetbrains.kotlinx.dataframe.testCsv
 import org.jetbrains.kotlinx.dataframe.testJson
 import org.junit.Test
@@ -72,4 +74,14 @@ class Read : TestBase() {
         df1["IDS"].type() shouldBe typeOf<String>()
         // SampleEnd
     }
+
+    @Test
+    fun readArrowFeather() {
+        val file = testArrowFeather("data-arrow_2.0.0_uncompressed")
+        // SampleStart
+        val df = DataFrame.readArrowFeather(file)
+        // SampleEnd
+        df.rowsCount() shouldBe 1
+        df.columnsCount() shouldBe 4
+    }
 }