Automated commit of generated code

Author: github-actions[bot]

core/generated-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/api/DataColumnType.kt

@@ -67,7 +67,7 @@ public fun AnyCol.isPrimitiveOrMixedNumber(): Boolean = type().isPrimitiveOrMixe
 
 public fun AnyCol.isList(): Boolean = typeClass == List::class
 
-/** Returns `true` if [this] column is intra-comparable, i.e.
+/** Returns `true` if [this] column is intra-comparable (mutually comparable), i.e.,
  * its values can be compared with each other and thus ordered.
  *
  * If true, operations like [`min()`][AnyCol.min], [`max()`][AnyCol.max], [`median()`][AnyCol.median], etc.
@@ -82,7 +82,7 @@ public fun AnyCol.isList(): Boolean = typeClass == List::class
 public fun AnyCol.isComparable(): Boolean = valuesAreComparable()
 
 /**
- * Returns `true` if [this] column is intra-comparable, i.e.
+ * Returns `true` if [this] column is intra-comparable (mutually comparable), i.e.,
  * its values can be compared with each other and thus ordered.
  *
  * If true, operations like [`min()`][AnyCol.min], [`max()`][AnyCol.max], [`median()`][AnyCol.median], etc.

core/generated-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/impl/TypeUtils.kt

@@ -696,7 +696,7 @@ internal fun Iterable<Any?>.types(): Set<KType> =
     }
 
 /**
- * Checks whether this KType adheres to `T : Comparable<T & Any>?`, aka, it is comparable with itself.
+ * Checks whether this KType adheres to `T : Comparable<T & Any>?`, aka, it is mutually comparable with itself.
  */
 internal fun KType.isIntraComparable(): Boolean =
     this.isSubtypeOf(

core/generated-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/impl/aggregation/getColumns.kt

@@ -18,7 +18,7 @@ internal inline fun <T> Aggregatable<T>.remainingColumns(
 ): ColumnsSelector<T, Any?> = remainingColumnsSelector().filter { predicate(it.data) }
 
 /**
- * Emulates selecting all columns whose values are comparable to each other.
+ * Emulates selecting all columns whose values are mutually comparable to each other.
  * These are columns of type `R` where `R : Comparable<R>`.
  *
  * There is no way to denote this generically in types, however,

core/generated-sources/src/test/kotlin/org/jetbrains/kotlinx/dataframe/samples/api/Analyze.kt

@@ -179,7 +179,7 @@ class Analyze : TestBase() {
     @TransformDataFrameExpressions
     fun minmaxModes() {
         // SampleStart
-        df.min() // min of values per every comparable column
+        df.min() // min of values for every comparable column with mutually comparable values
         df.min { age and weight } // min of all values in `age` and `weight`
         df.minFor(skipNaN = true) { age and name.firstName } // min of values per `age` and `firstName` separately
         df.minOf { (weight ?: 0) / age } // min of expression evaluated for every row
@@ -203,7 +203,7 @@ class Analyze : TestBase() {
     @TransformDataFrameExpressions
     fun medianModes() {
         // SampleStart
-        df.median() // median of values per every comparable column
+        df.median() // median of values for every column with mutually comparable values
         df.median { age and weight } // median of all values in `age` and `weight`
         df.medianFor(skipNaN = true) { age and name.firstName } // median of values per `age` and `firstName` separately
         df.medianOf { (weight ?: 0) / age } // median of expression evaluated for every row
@@ -227,7 +227,7 @@ class Analyze : TestBase() {
     @TransformDataFrameExpressions
     fun percentileModes() {
         // SampleStart
-        df.percentile(25.0) // 25th percentile of values per every comparable column
+        df.percentile(25.0) // 25th percentile of values for every column with mutually comparable values
         df.percentile(75.0) { age and weight } // 75th percentile of all values in `age` and `weight`
         df.percentileFor(50.0, skipNaN = true) { age and name.firstName } // 50th percentile of values per `age` and `firstName` separately
         df.percentileOf(75.0) { (weight ?: 0) / age } // 75th percentile of expression evaluated for every row
@@ -638,7 +638,7 @@ class Analyze : TestBase() {
     @TransformDataFrameExpressions
     fun groupByDirectAggregations_properties() {
         // SampleStart
-        df.groupBy { city }.max() // max for every comparable column
+        df.groupBy { city }.max() // max for every column with mutually comparable values
         df.groupBy { city }.mean() // mean for every numeric column
         df.groupBy { city }.max { age } // max age into column "age"
         df.groupBy { city }.sum("total weight") { weight } // sum of weights into column "total weight"
@@ -657,7 +657,7 @@ class Analyze : TestBase() {
     @TransformDataFrameExpressions
     fun groupByDirectAggregations_strings() {
         // SampleStart
-        df.groupBy("city").max() // max for every comparable column
+        df.groupBy("city").max() // max for every column with mutually comparable values
         df.groupBy("city").mean() // mean for every numeric column
         df.groupBy("city").max("age") // max age into column "age"
         df.groupBy("city").sum("weight", name = "total weight") // sum of weights into column "total weight"

docs/StardustDocs/topics/groupBy.md

@@ -215,7 +215,7 @@ Most common aggregation functions can be computed directly at [`GroupBy DataFram
 <tab title="Properties">
 
 ```kotlin
-df.groupBy { city }.max() // max for every comparable column
+df.groupBy { city }.max() // max for every column with mutually comparable values
 df.groupBy { city }.mean() // mean for every numeric column
 df.groupBy { city }.max { age } // max age into column "age"
 df.groupBy { city }.sum("total weight") { weight } // sum of weights into column "total weight"
@@ -233,7 +233,7 @@ df.groupBy { city }.meanOf("mean ratio") { weight?.div(age) } // mean of weight/
 <tab title="Strings">
 
 ```kotlin
-df.groupBy("city").max() // max for every comparable column
+df.groupBy("city").max() // max for every column with mutually comparable values
 df.groupBy("city").mean() // mean for every numeric column
 df.groupBy("city").max("age") // max age into column "age"
 df.groupBy("city").sum("weight", name = "total weight") // sum of weights into column "total weight"

docs/StardustDocs/topics/median.md

@@ -30,7 +30,7 @@ When it's set to `true`, `NaN` values are ignored.
 <!---FUN medianModes-->
 
 ```kotlin
-df.median() // median of values per every comparable column
+df.median() // median of values for every column with mutually comparable values
 df.median { age and weight } // median of all values in `age` and `weight`
 df.medianFor(skipNaN = true) { age and name.firstName } // median of values per `age` and `firstName` separately
 df.medianOf { (weight ?: 0) / age } // median of expression evaluated for every row

docs/StardustDocs/topics/minmax.md

@@ -19,7 +19,7 @@ When it's set to `true`, `NaN` values are ignored.
 <!---FUN minmaxModes-->
 
 ```kotlin
-df.min() // min of values per every comparable column
+df.min() // min of values for every comparable column with mutually comparable values
 df.min { age and weight } // min of all values in `age` and `weight`
 df.minFor(skipNaN = true) { age and name.firstName } // min of values per `age` and `firstName` separately
 df.minOf { (weight ?: 0) / age } // min of expression evaluated for every row

docs/StardustDocs/topics/percentile.md

@@ -52,7 +52,7 @@ In the future we might add an option to change the quantile estimation method.
 <!---FUN percentileModes-->
 
 ```kotlin
-df.percentile(25.0) // 25th percentile of values per every comparable column
+df.percentile(25.0) // 25th percentile of values for every column with mutually comparable values
 df.percentile(75.0) { age and weight } // 75th percentile of all values in `age` and `weight`
 df.percentileFor(50.0, skipNaN = true) { age and name.firstName } // 50th percentile of values per `age` and `firstName` separately
 df.percentileOf(75.0) { (weight ?: 0) / age } // 75th percentile of expression evaluated for every row