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-rw-r--r--backend/microservice/api/controller.py1
-rw-r--r--backend/microservice/api/newmlservice.py13
2 files changed, 12 insertions, 2 deletions
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 83741ce1..4d9f8f2a 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -55,6 +55,7 @@ def returnColumnsInfo():
#samo 10 jedinstvenih posto ih ima previse, bilo bi dobro da promenimo ovo da to budu 10 najzastupljenijih vrednosti
for col in preprocess["columnInfo"]:
col["uniqueValues"] = col["uniqueValues"][0:10]
+ col["uniqueValuesCount"] = col["uniqueValuesCount"][0:10]
dataset["columnInfo"] = preprocess["columnInfo"]
dataset["nullCols"] = preprocess["allNullColl"]
dataset["nullRows"] = preprocess["allNullRows"]
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index 77cc59d0..02ce2250 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -33,7 +33,14 @@ def returnColumnsInfo(dataset):
for kolona in svekolone:
if(kolona in kategorijskekolone):
- uniquevalues=datafront[kolona].unique()
+ unique=datafront[kolona].value_counts()
+ uniquevalues=[]
+ uniquevaluescount=[]
+ for val, count in unique.iteritems():
+ uniquevalues.append(val)
+ uniquevaluescount.append(count)
+ #print(uniquevalues)
+ #print(uniquevaluescount)
mean=0
median=0
minimum=0
@@ -43,7 +50,8 @@ def returnColumnsInfo(dataset):
allNullCols=allNullCols+1
frontreturn={'columnName':kolona,
'isNumber':False,
- 'uniqueValues':uniquevalues.tolist(),
+ 'uniqueValues':uniquevalues,
+ 'uniqueValuesCount':uniquevaluescount,
'median':float(mean),
'mean':float(median),
'numNulls':int(nullCount),
@@ -62,6 +70,7 @@ def returnColumnsInfo(dataset):
frontreturn={'columnName':kolona,
'isNumber':1,
'uniqueValues':[],
+ 'uniqueValuesCount':[],
'mean':float(mean),
'median':float(median),
'numNulls':int(nullCount),