Pivoting Tables --------------- Use this function to pivot tables. This is performed by designating three columns: index, column, and value. Index is the column where pivoted rows should be aligned on; column represents the column to pivot; and value specifies the values of the pivoted table. For cases with multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame. **Notes**: - All rows of a single index value must fit on one node. - The maximum rows for a single index value and column label is ``Chunk size * Chunk size``. .. tabs:: .. code-tab:: r R library(h2o) h2o.init() # Create a simple data frame by inputting values data <- data.frame(colorID = c('1', '2', '3', '3', '1', '4'), value = c('red', 'orange', 'yellow', 'yellow', 'red', 'blue'), amount = c('4', '2', '4', '3', '6', '3')) df <- as.h2o(data) # View the dataset df colorID value amount 1 1 red 4 2 2 orange 2 3 3 yellow 4 4 3 yellow 3 5 1 red 6 6 4 blue 3 [6 rows x 3 columns] # Pivot the table on the colorID column and aligned on the amount column df2 <- h2o.pivot(df, index = "amount", column = "colorID", value = "value") df2 amount 1 2 3 4 1 2 NaN 1 NaN NaN 2 3 NaN NaN 3 0 3 4 2 NaN 3 NaN 4 6 2 NaN NaN NaN [4 rows x 5 columns] .. code-tab:: python import h2o h2o.init() # Create a simple data frame by inputting values df = h2o.H2OFrame({'colorID': ['1','2','3','3','1','4'], 'value': ['red','orange','yellow','yellow','red','blue'], 'amount': ['4','2','4','3','6','3']}) # View the dataset df colorID amount value --------- -------- ------- 1 4 red 2 2 orange 3 4 yellow 3 3 yellow 1 6 red 4 3 blue [6 rows x 3 columns] # Pivot the table on the colorID column and aligned on the amount column df2 = df.pivot(index="amount",column="colorID",value="value") df2 amount 1 2 3 4 -------- --- --- --- --- 2 nan 1 nan nan 3 nan nan 3 0 4 2 nan 3 nan 6 2 nan nan nan [4 rows x 5 columns]