Don't miss your chance to take the Fabric Data Engineer (DP-700) exam on us!
Learn moreNext up in the FabCon + SQLCon recap series: The roadmap for Microsoft SQL and Maximizing Developer experiences in Fabric. All sessions are available on-demand after the live show. Register now
I have null values in my column identified as 999.9. I need to replace them with the average of the row above and the row below. In some circumstances like shown below, the row below is also a null value. In that case I need it to keep searching down until it comes to a value other than 999.9 and then use that to average with the row above. In this example:
1. I need a formula that for row 286 will average rows 285 and 289 getting 1.6.
2. Then the formula would averave rows 286 (which is now 1.6) and row 289 getting 2.4 for row 287.
3. Then the formula would average rows 287 (which is now 2.4) and row 289 getting 2.7 for row 288.
Final product would be:
Row 286 = 1.6
Row 287 = 2.4
Row 288 = 2.7
Any help would be much appreciated.
Solved! Go to Solution.
Hi @ven853,
To be honest, it's hard to achieve it using Power Query. I would suggest you leverage the power of Python and R. I created a demo solution with both Python and R. You can choose the best one that suits you. You can download it from the attachment.
# 'dataset' holds the input data for this script
def find_next(excluded, ds):
for item in ds:
if item != excluded:
return item
return 0
result = []
column1 = dataset.iloc[:, 0]
for index in range(len(column1)):
if column1[index] != 999.9:
result.append(column1[index])
else:
next = find_next(999.9, column1[index + 1:])
result.append((next + result[-1]) / 2)
dataset["new"] = result
# 'dataset' holds the input data for this script
find_next <- function(excluded, ds) {
for (item in ds[,1]) {
if (item != excluded) {
return(item)
}
}
return(0)
}
result <- c()
ds_length <- nrow(dataset)
for (index in 1: ds_length) {
if (dataset[index, 1] == 999.9) {
result[index] <- (tail(result, 1) + find_next(999.9, tail(dataset, -index))) / 2.0
}
else{
result[index] <- dataset[index, 1]
}
}
final <- data.frame(result)
Best Regards,
Dale
Hi @ven853,
To be honest, it's hard to achieve it using Power Query. I would suggest you leverage the power of Python and R. I created a demo solution with both Python and R. You can choose the best one that suits you. You can download it from the attachment.
# 'dataset' holds the input data for this script
def find_next(excluded, ds):
for item in ds:
if item != excluded:
return item
return 0
result = []
column1 = dataset.iloc[:, 0]
for index in range(len(column1)):
if column1[index] != 999.9:
result.append(column1[index])
else:
next = find_next(999.9, column1[index + 1:])
result.append((next + result[-1]) / 2)
dataset["new"] = result
# 'dataset' holds the input data for this script
find_next <- function(excluded, ds) {
for (item in ds[,1]) {
if (item != excluded) {
return(item)
}
}
return(0)
}
result <- c()
ds_length <- nrow(dataset)
for (index in 1: ds_length) {
if (dataset[index, 1] == 999.9) {
result[index] <- (tail(result, 1) + find_next(999.9, tail(dataset, -index))) / 2.0
}
else{
result[index] <- dataset[index, 1]
}
}
final <- data.frame(result)
Best Regards,
Dale
FYI, this will be very difficult if not impossible in DAX as it requires recursion.
Recursion is possible in the Power Query M Language though as seen here:
https://www.thebiccountant.com/2017/09/26/recursion-m-beginners/
If you have recently started exploring Fabric, we'd love to hear how it's going. Your feedback can help with product improvements.
A new Power BI DataViz World Championship is coming this June! Don't miss out on submitting your entry.
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
| User | Count |
|---|---|
| 53 | |
| 45 | |
| 44 | |
| 20 | |
| 19 |
| User | Count |
|---|---|
| 73 | |
| 71 | |
| 34 | |
| 33 | |
| 31 |