Added samples with String API

examples/idea-examples/movies/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/movies/moviesWithInterfaceStringAPI.kt

@@ -0,0 +1,55 @@
+package org.jetbrains.kotlinx.dataframe.examples.movies
+
+import org.jetbrains.kotlinx.dataframe.DataFrame
+import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
+import org.jetbrains.kotlinx.dataframe.api.*
+import org.jetbrains.kotlinx.dataframe.io.*
+
+
+private const val pathToCsv = "examples/idea-examples/movies/src/main/resources/movies.csv"
+// Uncomment this line if you want to copy-paste and run the code in your project without downloading the file
+//private const val pathToCsv = "https://raw.githubusercontent.com/Kotlin/dataframe/master/examples/idea-examples/movies/src/main/resources/movies.csv"
+
+fun main() {
+    // This example shows how to the use extension properties API to address columns in different operations
+    // https://kotlin.github.io/dataframe/apilevels.html
+
+    // Add the Gradle plugin and run `assemble`
+    // check the README https://github.com/Kotlin/dataframe?tab=readme-ov-file#setup
+    val step1 = DataFrame
+        .read(pathToCsv).convertTo<Movie>()
+        .split("genres").by("|").inplace()
+        .split { "title"<String>() }.by {
+            listOf(
+                """\s*\(\d{4}\)\s*$""".toRegex().replace(it, ""),
+                "\\d{4}".toRegex().findAll(it).lastOrNull()?.value?.toIntOrNull() ?: -1
+            )
+        }.into("title", "year")
+        .explode("genres")
+    step1.print()
+
+    /**
+     * Data is parsed and prepared for aggregation
+     *                             movieId                                    title year      genres
+     *   0 9b30aff7943f44579e92c261f3adc193                           Women in Black 1997     Fantasy
+     *   1 9b30aff7943f44579e92c261f3adc193                           Women in Black 1997 Suspenseful
+     *   2 9b30aff7943f44579e92c261f3adc193                           Women in Black 1997      Comedy
+     *   3 2a1ba1fc5caf492a80188e032995843e                          Bumblebee Movie 2007      Comedy
+     *   4 2a1ba1fc5caf492a80188e032995843e                          Bumblebee Movie 2007        Jazz
+     *   5 2a1ba1fc5caf492a80188e032995843e                          Bumblebee Movie 2007      Family
+     *   6 2a1ba1fc5caf492a80188e032995843e                          Bumblebee Movie 2007   Animation
+     */
+    val step2 = step1
+        .filter { "year"<Int>() >= 0 && "genres"<String>() != "(no genres listed)" }
+        .groupBy("year")
+        .sortBy("year")
+        .pivot("genres", inward = false)
+        .aggregate {
+            count() into "count"
+            mean() into "mean"
+        }
+
+    step2.print(10)
+//    Discover the final reshaped data in an interactive HTML table
+//    step2.toStandaloneHTML().openInBrowser()
+}

examples/kotlin-dataframe-plugin-example/build.gradle.kts

@@ -6,11 +6,16 @@ plugins {
     val kotlinVersion = "2.2.20"
     kotlin("jvm") version kotlinVersion
     kotlin("plugin.dataframe") version kotlinVersion
+    application
 }
 
 group = "org.example"
 version = "1.0-SNAPSHOT"
 
+application {
+    mainClass.set("org.jetbrains.kotlinx.dataframe.examples.plugin.MainKt")
+}
+
 repositories {
     maven("https://packages.jetbrains.team/maven/p/kt/dev/")
     mavenCentral()

examples/kotlin-dataframe-plugin-example/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/plugin/MainStringAPI.kt

@@ -0,0 +1,65 @@
+package org.jetbrains.kotlinx.dataframe.examples.plugin
+
+import org.jetbrains.kotlinx.dataframe.DataFrame
+import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
+import org.jetbrains.kotlinx.dataframe.api.add
+import org.jetbrains.kotlinx.dataframe.api.convert
+import org.jetbrains.kotlinx.dataframe.api.convertTo
+import org.jetbrains.kotlinx.dataframe.api.filter
+import org.jetbrains.kotlinx.dataframe.api.into
+import org.jetbrains.kotlinx.dataframe.api.rename
+import org.jetbrains.kotlinx.dataframe.api.renameToCamelCase
+import org.jetbrains.kotlinx.dataframe.api.with
+import org.jetbrains.kotlinx.dataframe.io.readCsv
+import org.jetbrains.kotlinx.dataframe.io.writeCsv
+import java.net.URL
+
+fun main() {
+    val repos = DataFrame
+        // Read DataFrame from the CSV file.
+        .readCsv("https://raw.githubusercontent.com/Kotlin/dataframe/master/data/jetbrains_repositories.csv")
+        // And convert it to match the `Repositories` schema.
+        .convertTo<Repositories>()
+
+    // With Compiler Plugin, the DataFrame schema changes immediately after each operation:
+    // For example, if a new column is added or the old one is renamed (or its type is changed)
+    // during the operation, you can use the new name immediately in the following operations:
+    repos
+        // Add a new "name" column...
+        .add("name") { "full_name"<String>().substringAfterLast("/") }
+        // ... and now we can use "name" extension in DataFrame operations, such as `filter`.
+        .filter { name.lowercase().contains("kotlin") }
+
+    // Let's update the DataFrame with some operations using these features.
+    val reposUpdated = repos
+        // Rename columns to CamelCase.
+        // Note that after that, in the following operations, extension properties will have
+        // new names corresponding to the column names.
+        .renameToCamelCase()
+        // Rename "stargazersCount" column to "stars".
+        .rename { "stargazersCount"<String>() }.into("stars")
+        // And we can immediately use the updated name in the filtering.
+        .filter { "stars"<Int>() > 50 }
+        // Convert values in the "topic" column (which were `String` initially)
+        // to the list of topics.
+        .convert { "topics"<String>() }.with {
+            val inner = it.removeSurrounding("[", "]")
+            if (inner.isEmpty()) emptyList() else inner.split(',').map(String::trim)
+        }
+        // Now "topics" is a `List<String>` column.
+        // Add a new column with the number of topics.
+        .add("topicCount") { "topics"<List<String>>().size }
+        // Add a new column with the kind of repository.
+        .add("kind") { getKind("full_name"(), "topics"()) }
+
+    // Write the updated DataFrame to a CSV file.
+    reposUpdated.writeCsv("jetbrains_repositories_new.csv")
+
+    // TODO: Add Kandy Plot
+    //  reposUpdated.groupBy { kind }.max { stargazersCount  }.plot {
+    //      bars {
+    //          x(kind)
+    //          y(stargazersCount)
+    //      }
+    //  }
+}