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.
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]
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]