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‎docs/StardustDocs/topics/concat.md

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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
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Returns [`DataFrame`](DataFrame.md) with the union of rows from several given [`DataFrames`](DataFrame.md).
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Returns a [`DataFrame`](DataFrame.md) with the union of rows from several given [`DataFrames`](DataFrame.md).
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`concat` is available for:
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<!---END-->
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If you want to union columns (not rows) from several [`DataFrames`](DataFrame.md), see [`add`](add.md).
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If you want to take the union of columns (not rows) from several [`DataFrames`](DataFrame.md), see [`add`](add.md).
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## Schema unification
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If input [`DataFrames`](DataFrame.md) have different schemas, every column in resulting [`DataFrames`](DataFrame.md) will have the most common type of the original columns with the same name.
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If input [`DataFrames`](DataFrame.md) have different schemas, every column in the resulting [`DataFrames`](DataFrame.md)
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will get the lowest common type of the original columns with the same name.
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For example, if one [`DataFrame`](DataFrame.md) has column `A: Int` and other [`DataFrame`](DataFrame.md) has column `A: Double`,
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the resulting ` DataFrame ` will have column `A: Number`.
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For example, if one [`DataFrame`](DataFrame.md) has a column `A: Int` and another [`DataFrame`](DataFrame.md) has a column `A: Double`,
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the resulting ` DataFrame ` will have a column `A: Number`.
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Missing columns in dataframes will be filled with `null`.

‎docs/StardustDocs/topics/convert.md

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<dataFrame src="org.jetbrains.kotlinx.dataframe.samples.api.Modify.convertTo.html"/>
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<!---END-->
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Automatic conversion from `String` into enum class is also supported:
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Automatic conversion from `String` to enum classes is also supported:
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```kotlin
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enum class Direction { NORTH, SOUTH, WEST, EAST }

‎docs/StardustDocs/topics/convertTo.md

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[//]: # (title: convertTo)
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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
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[Converts](convert.md) columns in [`DataFrame`](DataFrame.md) to match given schema [`Schema`](schema.md).
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[Converts](convert.md) columns in [`DataFrame`](DataFrame.md) to match a given schema [`Schema`](schema.md).
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```kotlin
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convertTo<Schema>(excessiveColumns = ExcessiveColumns.Keep)

‎docs/StardustDocs/topics/corr.md

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[//]: # (title: corr)
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Returns [`DataFrame`](DataFrame.md) with pairwise correlation between two sets of columns.
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Returns [`DataFrame`](DataFrame.md) with the pairwise correlation between two sets of columns.
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Computes Pearson correlation coefficient.
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It computes the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
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```kotlin
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corr { columns1 }
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.with { columns2 } | .withItself()
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```
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To compute pairwise correlation between all columns in [`DataFrame`](DataFrame.md) use `corr` without arguments:
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To compute pairwise correlation between all columns in the [`DataFrame`](DataFrame.md) use `corr` without arguments:
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```kotlin
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corr()
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```
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Available for numeric and `Boolean` columns. `Boolean` values are converted into `1` for `true` and `0` for `false`. All other columns are ignored.
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The function is available for numeric- and `Boolean` columns.
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`Boolean` values are converted into `1` for `true` and `0` for `false`.
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All other columns are ignored.
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If [`ColumnGroup`](DataColumn.md#columngroup) is passed as target column for correlation, it will be unpacked into suitable nested columns.
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If a [`ColumnGroup`](DataColumn.md#columngroup) instance is passed as target column for correlation,
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it will be unpacked into suitable nested columns.
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Resulting [`DataFrame`](DataFrame.md) will have `n1` rows and `n2+1` columns, where `n1` and `n2` are numbers of columns in `columns1` and `columns2` correspondingly.
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The resulting [`DataFrame`](DataFrame.md) will have `n1` rows and `n2+1` columns,
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where `n1` and `n2` are the number of columns in `columns1` and `columns2` correspondingly.
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The first column will have the name "column" and will contain names of columns in `column1`. Other columns will have the same names as in `columns2` and will contain computed correlation coefficients.
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The first column will have the name "column" and will contain names of columns in `column1`.
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Other columns will have the same names as in `columns2` and will contain the computed correlation coefficients.
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If exactly one [`ColumnGroup`](DataColumn.md#columngroup) is passed in `columns1`, the first column in output will have its name.
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If exactly one [`ColumnGroup`](DataColumn.md#columngroup) is passed in `columns1`,
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the first column in the output will have its name.

‎docs/StardustDocs/topics/count.md

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<!---END-->
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Pass [row condition](DataRow.md#row-conditions) to count the number of rows that satisfy that condition:
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Pass a [row condition](DataRow.md#row-conditions) to count only the number of rows that satisfy that condition:
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<!---FUN countCondition-->
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<!---END-->
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When `count` is used in [`groupBy`](groupBy.md#aggregation) or [`pivot`](pivot.md#aggregation) aggregations, it counts rows for every data group:
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When `count` is used in [`groupBy`](groupBy.md#aggregation) or [`pivot`](pivot.md#aggregation) aggregations,
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it counts rows for every data group:
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<!---FUN countAggregation-->
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‎docs/StardustDocs/topics/create.md

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<show-structure depth="3"/>
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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Create-->
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There are several ways to create [`dataframes`](DataFrame.md) from the data that is already loaded into memory:
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* [create columns with data](createColumn.md) and then [bundle them](createDataFrame.md) into [`DataFrame`](DataFrame.md)
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* create and initialize [`DataFrame`](DataFrame.md) directly from values using `vararg` variants of the [corresponding functions](createDataFrame.md) .
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There are several ways to create [`dataframes`](DataFrame.md) from data that is already loaded into memory:
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* [create columns with data](createColumn.md) and then [bundle them](createDataFrame.md) into a [`DataFrame`](DataFrame.md)
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* create and initialize [`DataFrame`](DataFrame.md) directly from values using `vararg` variants of the [corresponding functions](createDataFrame.md).
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* [convert Kotlin objects](createDataFrame.md#todataframe) into [`DataFrame`](DataFrame.md)
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To learn how to read dataframes from files and URLs, go to [the next section](read.md).
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To learn how to read [`dataframes`](DataFrame.md) from files and URLs, go to the [next section](read.md).

‎docs/StardustDocs/topics/createColumn.md

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[//]: # (title: Create DataColumn)
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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Create-->
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This section describes ways to create [`DataColumn`](DataColumn.md).
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This section describes ways to create a [`DataColumn`](DataColumn.md).
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### columnOf
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Returns new column with given elements.
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Column [`type`](DataColumn.md#properties) is deduced from a compile-time type of elements,
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column [`name`](DataColumn.md#properties) is taken from the name of the variable.
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Returns new column with the given elements.
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The column [`type`](DataColumn.md#properties) is deduced from the compile-time type of the elements inside.
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The column [`name`](DataColumn.md#properties) is taken from the name of the variable.
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<!---FUN createValueByColumnOf-->
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<!---END-->
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To assign column name explicitly, use `named` infix function and replace `by` with `=`.
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To assign column name explicitly, use the `named` infix function and replace `by` with `=`.
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When column elements are columns themselves, it returns [`ColumnGroup`](DataColumn.md#columngroup):
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When column elements are columns themselves, it returns a [`ColumnGroup`](DataColumn.md#columngroup):
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<!---FUN createColumnGroup-->
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<!---END-->
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When column elements are [`DataFrames`](DataFrame.md) it returns [`FrameColumn`](DataColumn.md#framecolumn):
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When column elements are [`DataFrames`](DataFrame.md) it returns a [`FrameColumn`](DataColumn.md#framecolumn):
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### toColumn
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Converts `Iterable` of values into column.
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Converts an `Iterable` of values into a column.
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To compute a column type at runtime by scanning through actual values, set `Infer.Type` option.
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To compute a column type at runtime by scanning through the actual values, enable the `Infer.Type` option.
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To inspect values only for a nullability set `Infer.Nulls` an option.
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To inspect values only for nullability, enable the `Infer.Nulls` option.
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### toColumnOf
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Converts [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of values into column of a given type
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Converts an [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/)
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of values into a column of a given type:
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‎docs/StardustDocs/topics/createDataFrame.md

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[//]: # (title: Create DataFrame)
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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Create-->
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This section describes ways to create [`DataFrame`](DataFrame.md).
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This section describes ways to create a [`DataFrame`](DataFrame.md) instance.
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### emptyDataFrame
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Returns [`DataFrame`](DataFrame.md) with no rows and no columns.
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Returns a [`DataFrame`](DataFrame.md) with no rows and no columns.
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Returns [`DataFrame`](DataFrame.md) with given column names and values.
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Returns a [`DataFrame`](DataFrame.md) with given column names and values.
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[`DataFrame`](DataFrame.md) from `Iterable<DataColumn>`:
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Creates a [`DataFrame`](DataFrame.md) from an `Iterable<DataColumn>`:
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[`DataFrame`](DataFrame.md) from [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of [basic types](https://kotlinlang.org/docs/basic-types.html) (except arrays):
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Creates a [`DataFrame`](DataFrame.md) from an [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of [basic types](https://kotlinlang.org/docs/basic-types.html) (except arrays):
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The return type of these overloads is a typed DataFrame. Its data schema defines column that can be used right after conversion for additional computations
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The return type of these overloads is a typed [`DataFrame`](DataFrame.md).
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Its data schema defines the column that can be used right after the conversion for additional computations.
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[`DataFrame`](DataFrame.md) from [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of objects:
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Creates a [`DataFrame`](DataFrame.md) from an [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of objects:
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Scans object properties using reflection and creates a [ValueColumn](DataColumn.md#valuecolumn) for every property.
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Scope of properties for scanning is defined at compile-time by formal types of objects in [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/), so properties of implementation classes will not be scanned.
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The scope of properties for scanning is defined at compile-time by the formal types of the objects in the [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/),
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Specify `depth` parameter to perform deep object graph traversal and convert nested objects into [ColumnGroups](DataColumn.md#columngroup) and [FrameColumns](DataColumn.md#framecolumn):
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Specify the `depth` parameter to perform deep object graph traversal
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For detailed control over object graph transformations, use the configuration DSL.
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It allows you to exclude particular properties or classes from the object graph traversal,
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‎docs/StardustDocs/topics/cumSum.md

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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Analyze-->
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Computes a cumulative sum of values in selected columns.
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Computes the cumulative sum of values in the selected columns.
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```text
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Returns a [`DataFrame`](DataFrame.md) or [`DataColumn`](DataColumn.md) containing the cumulative sum.
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**Parameters:**
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* `skipNA` — when `true`, ignores `NA` (`null` or `NaN`) values.
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When `false`, all values after first `NA` will be `NaN` (for `Double` and `Float` columns) or `null` (for integer columns).
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**Available for:**
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