updated doc examples for xxFor {} statistics to show types can now be mixed. Also tests compilation

Author: Jolanrensen

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

@@ -181,7 +181,7 @@ class Analyze : TestBase() {
         // SampleStart
         df.min() // min of values per every comparable column
         df.min { age and weight } // min of all values in `age` and `weight`
-        df.minFor(skipNaN = true) { age and weight } // min of values per `age` and `weight` separately
+        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
         df.minBy { age } // DataRow with minimal `age`
         // SampleEnd
@@ -205,7 +205,7 @@ class Analyze : TestBase() {
         // SampleStart
         df.median() // median of values per every comparable column
         df.median { age and weight } // median of all values in `age` and `weight`
-        df.medianFor(skipNaN = true) { age and weight } // median of values per `age` and `weight` separately
+        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
         df.medianBy { age } // DataRow where the median age lies (lower-median for an even number of values)
         // SampleEnd
@@ -229,7 +229,7 @@ class Analyze : TestBase() {
         // SampleStart
         df.percentile(25.0) // 25th percentile of values per every comparable column
         df.percentile(75.0) { age and weight } // 75th percentile of all values in `age` and `weight`
-        df.percentileFor(50.0, skipNaN = true) { age and weight } // 50th percentile of values per `age` and `weight` separately
+        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
         df.percentileBy(25.0) { age } // DataRow where the 25th percentile of `age` lies (index rounded using R3)
         // SampleEnd
@@ -438,7 +438,7 @@ class Analyze : TestBase() {
     fun columnsFor_properties() {
         // SampleStart
         df.minFor { colsOf<Int>() }
-        df.maxFor { name.firstName and name.lastName }
+        df.maxFor { name.firstName and age }
         df.sumFor { age and weight }
         df.meanFor { cols(1, 3).asNumbers() }
         df.medianFor { name.allCols().asComparable() }
@@ -457,7 +457,7 @@ class Analyze : TestBase() {
 
         df.minFor { colsOf<Int>() }
 
-        df.maxFor { firstName and lastName }
+        df.maxFor { firstName and age }
         // or
         df.maxFor(firstName, lastName)
 
@@ -475,7 +475,7 @@ class Analyze : TestBase() {
     fun columnsFor_strings() {
         // SampleStart
         df.minFor { colsOf<Int>() }
-        df.maxFor { "name"["firstName"].asComparable() and "name"["lastName"].asComparable() }
+        df.maxFor { "name"["firstName"].asComparable() and "age"<Int>() }
 
         df.sumFor("age", "weight")
         // or

docs/StardustDocs/resources/snippets/org.jetbrains.kotlinx.dataframe.samples.api.Analyze.columnsFor.html

@@ -194,7 +194,7 @@
                                 </details>
                                 <br>
                                 <details>
-                                <summary>df.maxFor { name.firstName and name.lastName }</summary>
+                                <summary>df.maxFor { name.firstName and age }</summary>
                                                                 <details>
                                 <summary>Input DataFrame: rowsCount = 7, columnsCount = 5</summary>
                                  <table class="dataframe" id="df_2"></table>
@@ -668,7 +668,7 @@
 
 /*<!--*/
 call_DataFrame(function() { DataFrame.addTable({ cols: [{ name: "<span title=\"firstName: String\">firstName</span>", children: [], rightAlign: false, values: ["Charlie"] }, 
-{ name: "<span title=\"lastName: String\">lastName</span>", children: [], rightAlign: false, values: ["Wolf"] }, 
+{ name: "<span title=\"age: Int\">age</span>", children: [], rightAlign: true, values: ["<span class=\"formatted\" title=\"\"><span class=\"numbers\">45</span></span>"] }, 
 ], id: 3, rootId: 3, totalRows: 1 } ) });
 /*-->*/
 

docs/StardustDocs/topics/median.md

@@ -30,7 +30,7 @@ When it's set to `true`, `NaN` values are ignored.
 ```kotlin
 df.median() // median of values per every comparable column
 df.median { age and weight } // median of all values in `age` and `weight`
-df.medianFor(skipNaN = true) { age and weight } // median of values per `age` and `weight` separately
+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
 df.medianBy { age } // DataRow where the median age lies (lower-median for an even number of values)
 ```

docs/StardustDocs/topics/minmax.md

@@ -21,7 +21,7 @@ When it's set to `true`, `NaN` values are ignored.
 ```kotlin
 df.min() // min of values per every comparable column
 df.min { age and weight } // min of all values in `age` and `weight`
-df.minFor(skipNaN = true) { age and weight } // min of values per `age` and `weight` separately
+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
 df.minBy { age } // DataRow with minimal `age`
 ```

docs/StardustDocs/topics/percentile.md

@@ -52,7 +52,7 @@ In the future we might add an option to change the quantile estimation method.
 ```kotlin
 df.percentile(25.0) // 25th percentile of values per every comparable column
 df.percentile(75.0) { age and weight } // 75th percentile of all values in `age` and `weight`
-df.percentileFor(50.0, skipNaN = true) { age and weight } // 50th percentile of values per `age` and `weight` separately
+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
 df.percentileBy(25.0) { age } // DataRow where the 25th percentile of `age` lies (index rounded using R3)
 ```