df-research/dataframe/docs/StardustDocs/topics/modify.md

DataFrame object is immutable and all operations return a new instance of DataFrame.

Naming conventions

DataFrame is a columnar data structure and is more oriented to column-wise operations. Most transformation operations start with column selector that selects target columns for the operation. Syntax of most column operations assumes that they are applied to columns, so they don't include word column in their naming.

On the other hand, the Kotlin DataFrame library follows Koltin Collections naming for row-wise operations as DataFrame can be interpreted as a Collection of rows. The slight naming difference with Kotlin Collection is that all operations are named in imperative way: sortBy, shuffle etc.

Pairs of column/row operations:

• add columns / append rows
• remove columns / drop rows
• select columns / filter rows
• group columns / groupBy for rows
• reorder columns / sortBy for rows
• join to unite columns / concat to unite rows

Horizontal (column) operations:

• add — add columns
• addId — add id column
• flatten — remove column groupings recursively
• group — group columns into ColumnGroup
• insert — insert column
• map — map columns into new DataFrame or DataColumn
• merge — merge several columns into one
• move — move columns or change column groupings
• remove — remove columns
• rename — rename columns
• reorder — reorder columns
• replace — replace columns
• select — select subset of columns
• split — split values into new columns
• ungroup — remove column grouping

Vertical (row) operations:

• append — add rows
• concat — union rows from several DataFrame objects
• distinct / distinctBy — remove duplicated rows
• drop / dropLast / dropWhile / dropNulls / dropNA — remove rows by condition
• duplicate — duplicate rows
• explode — spread lists and DataFrame objects vertically into new rows
• filter / filterBy — filter rows
• implode — merge column values into lists grouping by other columns
• reverse — reverse rows
• shuffle — reorder rows randomly
• sortBy / sortByDesc / sortWith — sort rows
• split — split values into new rows
• take / takeLast / takeWhile — get first/last rows

Value modification:

• convert — convert values into new types
• parse — try to convert String values into appropriate types
• unfold — convert / "unfold" objects to ColumnGroup
• update — update values preserving column types
• fillNulls / fillNaNs / fillNA — replace missing values

Reshaping:

• pivot / pivotCounts / pivotMatches — convert values into new columns
• gather — convert pairs of column names and values into key and value columns

Learn how to:

• Slice rows
• Select columns
• Filter rows
• Reorder rows
• Group/concat rows
• Update/convert values
• Split/merge values
• Add/map/remove columns
• Move/rename columns
• Group/ungroup/flatten columns
• Insert/replace columns
• Explode/implode columns
• Pivot/gather columns
• Append values
• Adjust schema