aluno_turmas

script/analysis/student_analysis.py

+import pandas as pd
+from utils.situations import *
+
+def average_ira(d):
+    temp = d.dropna(subset=['MEDIA_FINAL'])
+    temp = temp[temp['MEDIA_FINAL'] <= 100]
+    if not temp.empty:
+        #print(temp[['MEDIA_FINAL', 'CH_TOTAL']])
+        aux = np.sum(temp['MEDIA_FINAL']*temp['CH_TOTAL'])
+        ch_total = np.sum(temp['CH_TOTAL']) * 100
+        print(aux/ch_total)
+
+def aluno_turmas(df):
+	students = {}
+	df = df.dropna(subset=['MEDIA_FINAL'])
+	total_students = len(df["MATR_ALUNO"])
+	for i in range(total_students):
+		matr = (df["MATR_ALUNO"][i])
+		if(not (matr in students)):
+			students[matr] = []
+		
+		for s in Situation.SITUATIONS:
+			if(s[0] == df["SITUACAO"][i]):
+				situacao = s[1]
+				break
+		ano = (df["ANO"][i])
+		codigo = (df["COD_ATIV_CURRIC"][i])
+		nome = (df["NOME_ATIV_CURRIC"][i])
+		nota = (df["MEDIA_FINAL"][i])
+		semestre = (df["PERIODO"][i])
+		
+		students[matr].append({
+			"ano": ano,
+			"codigo": codigo,
+			"nome": nome,
+			"nota": nota,
+			"semestre": semestre,
+			"situacao": situacao
+		})
+	print(students)
+	#~ lines = (df[["MATR_ALUNO","ANO","COD_ATIV_CURRIC","NOME_ATIV_CURRIC","MEDIA_FINAL","PERIODO","SITUACAO"]])
+	#~ for st in (df.groupby("MATR_ALUNO")):
+		#~ print(st[1]["MATR_ALUNO"])
+		#~ print(st[1]["ANO"])
+		#~ print(st[1]["COD_ATIV_CURRIC"])
+		#~ print(st[1]["NOME_ATIV_CURRIC"])
+		#~ print(st[1]["MEDIA_FINAL"])
+		#~ print(st[1]["PERIODO"])
+		#~ print(st[1]["SITUACAO"])
+		#~ print("")
+	#~ total_student = df['MATR_ALUNO'].drop_duplicates()
+	#~ for st in total_student:
+		#~ students[st] = []
+		#~ hist = df[df["MATR_ALUNO"]==st]
+		#~ for matr in hist:
+			#~ print(hist["ANO"])
+			#~ print(hist[matr]["COD_ATIV_CURRIC"])
+			#~ print(hist[matr]["NOME_ATIV_CURRIC"])
+			#~ print(hist[matr]["MEDIA_FINAL"])
+			#~ print(hist[matr]["PERIODO"])
+			#~ print(hist[matr]["SITUACAO"])
+			#~ print(hist[matr])
+			#~ print("")

script/base/dataframe_base.py

@@ -8,15 +8,6 @@ from json import load as json_load
 from utils.situations import *
 
 
-def teste(d):
-    temp = d.dropna(subset=['MEDIA_FINAL'])
-    temp = temp[temp['MEDIA_FINAL'] <= 100]
-    if not temp.empty:
-        #print(temp[['MEDIA_FINAL', 'CH_TOTAL']])
-        aux = np.sum(temp['MEDIA_FINAL']*temp['CH_TOTAL'])
-        ch_total = np.sum(temp['CH_TOTAL']) * 100
-        print(aux/ch_total)
-
 class DataframeHolder:
     def __init__(self, dataframe):
         self.students = dataframe.groupby('MATR_ALUNO')
@@ -111,4 +102,4 @@ def fix_evasion(df):
                 #~ if(x != 0.0):
                     #~ print(x)
     #~ print(df.FORMA_EVASAO.str.contains(evasion[1]).fillna(5))
-    print(df[['MATR_ALUNO','FORMA_EVASAO']])
+    #~ print(df[['MATR_ALUNO','FORMA_EVASAO']])

script/build_cache.py

@@ -7,6 +7,7 @@ from datetime import timedelta
 from pathlib import Path
 from utils.utils import build_path
 from analysis.degree_analysis import *
+from analysis.student_analysis import *
 
 try:
     to_unicode = unicode
@@ -22,7 +23,7 @@ def build_cache(dataframe):
     build_path(path)
 
 #    generate_degree_data(path, dataframe)
-#    generate_student_data(path)
+    generate_student_data(path,dataframe)
 #    generate_student_list(path)
 #    generate_admission_data(path)
 #    generate_admission_list(path)
@@ -35,7 +36,8 @@ def generate_degree_data(path, dataframe):
     general_ira(dataframe)
     pass
 
-def generate_student_data(path):
+def generate_student_data(path,dataframe):
+    aluno_turmas(dataframe)
     pass
 
 def generate_student_list(path):