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Diffstat (limited to 'backend/microservice/api/newmlservice.py')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 49 |
1 files changed, 41 insertions, 8 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 604e4d3c..f74f8386 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -1,6 +1,7 @@ from enum import unique from itertools import count import os +from sys import breakpointhook import pandas as pd from sklearn import datasets, multiclass import tensorflow as tf @@ -38,27 +39,38 @@ def returnColumnsInfo(dataset): unique=datafront[kolona].value_counts() uniquevalues=[] uniquevaluescount=[] + uniquevaluespercent=[] for val, count in unique.iteritems(): - uniquevalues.append(val) - uniquevaluescount.append(count) + if(val): + uniquevalues.append(val) + uniquevaluescount.append(count) + percent=count/rowCount + uniquevaluespercent.append(percent) #print(uniquevalues) #print(uniquevaluescount) mean=0 median=0 minimum=0 maximum=0 + q1=0 + q3=0 nullCount=datafront[kolona].isnull().sum() if(nullCount>0): allNullCols=allNullCols+1 - frontreturn={'columnName':kolona, + frontreturn={ + 'columnName':kolona, 'isNumber':False, 'uniqueValues':uniquevalues, 'uniqueValuesCount':uniquevaluescount, - 'median':float(mean), - 'mean':float(median), + 'uniqueValuesPercent':uniquevaluespercent, + 'mean':float(mean), + 'median':float(median), 'numNulls':int(nullCount), 'min':float(minimum), 'max':float(maximum), + 'q1':float(q1), + 'q3':float(q3), + } dict.append(frontreturn) else: @@ -66,18 +78,39 @@ def returnColumnsInfo(dataset): maximum=max(datafront[kolona]) mean=datafront[kolona].mean() median=s.median(datafront[kolona].copy().dropna()) + q1= np.percentile(datafront[kolona].copy().dropna(), 25) + q3= np.percentile(datafront[kolona].copy().dropna(), 75) nullCount=datafront[kolona].isnull().sum() if(nullCount>0): allNullCols=allNullCols+1 - frontreturn={'columnName':kolona, + + #pretvaranje u kategorijsku + datafront = datafront.astype({kolona: str}) + print(datafront.dtypes) + unique=datafront[kolona].value_counts() + uniquevaluesn=[] + uniquevaluescountn=[] + uniquevaluespercentn=[] + for val, count in unique.iteritems(): + if(val): + uniquevaluesn.append(val) + uniquevaluescountn.append(count) + percent=count/rowCount + uniquevaluespercentn.append(percent) + frontreturn={ + 'columnName':kolona, 'isNumber':1, - 'uniqueValues':[], - 'uniqueValuesCount':[], + 'uniqueValues':uniquevaluesn, + 'uniqueValuesCount':uniquevaluescountn, + 'uniqueValuesPercent':uniquevaluespercentn, 'mean':float(mean), 'median':float(median), 'numNulls':int(nullCount), 'min':float(minimum), 'max':float(maximum), + 'q1':float(q1), + 'q3':float(q3), + } dict.append(frontreturn) NullRows = datafront[datafront.isnull().any(axis=1)] |