Imputing Data¶
The impute function allows you to perform in-place imputation by filling missing values with aggregates computed on the “na.rm’d” vector. Additionally, you can also perform imputation based on groupings of columns from within the dataset. These columns can be passed by index or by column name to the by
parameter. Note that if a factor column is supplied, then the method must be mode
.
The impute
function accepts the following arguments:
dataset
: The dataset containing the column to imputecolumn
: A specific column to impute. The default of0
specifies to impute the entire frame.method
: The type of imputation to perform.mean
replaces NAs with the column mean;median
replaces NAs with the column median;mode
replaces with the most common factor (for factor columns only).combine_method
: If method ismedian
, then choose how to combine quantiles on even sample sizes. This parameter is ignored in all other cases. Available options forcombine_method
includeinterpolate
,average
,low
, andhigh
.by
: Group by columnsgroupByFrame
orgroup_by_frame
: Impute the column with this pre-computed grouped frame.values
: A vector of impute values (one per column). NaN indicates to skip the column.
- r
- python
#Upload the Airlines dataset
> filePath <- h2o:::.h2o.locate("smalldata/airlines/allyears2k_headers.zip")
> air <- h2o.importFile(filePath, "air")
> print(dim(air))
43978 31
#Show the number of rows with NA.
> print(numNAs <- sum(is.na(air$DepTime)))
[1] 1086
> DepTime_mean <- mean(air$DepTime, na.rm = TRUE)
> print(DepTime_mean)
[1] 1345.847
#Mean impute the DepTime column
> h2o.impute(air, "DepTime", method = "mean")
[1] NaN NaN NaN NaN 1345.847 NaN NaN NaN
[9] NaN NaN NaN NaN NaN NaN NaN NaN
[17] NaN NaN NaN NaN NaN NaN NaN NaN
[25] NaN NaN NaN NaN NaN NaN NaN
#Revert the imputations
> air <- h2o.importFile(filePath, "air")
#Impute the column using a grouping based on the Origin and Distance
#If the Origin and Distance produce groupings of NAs, then no imputation will be done (NAs will result).
> h2o.impute(air, "DepTime", method = "mean", by = c("Dest"))
Dest mean_DepTime
1 ABE 1671.795
2 ABQ 1308.074
3 ACY 1651.095
4 ALB 1405.412
5 AMA 1404.333
6 ANC 2022.000
[134 rows x 2 columns]
#Revert the imputations
> air <- h2o.importFile(filePath, "air")
#Impute a factor column by the most common factor in that column
> h2o.impute(air, "TailNum", method = "mode")
[1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3499 NaN NaN NaN NaN
[16] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[31] NaN
#Revert imputations
> air <- h2o.importFile(filePath, "air")
#Impute a factor column using a grouping based on the Month
> h2o.impute(air, "TailNum", method = "mode", by=c("Month"))
Month mode_TailNum
1 1 3499
2 10 3499