Merge remote-tracking branch 'origin/degree'

script/analysis/degree_analysis.py

 import pandas as pd
 import math
+import json
 from utils.situations import Situation, EvasionForm
 
 
@@ -29,18 +30,59 @@ def general_failure(df):
     return (average, standard_deviation)
 
 
+def current_students_failure(df):
+    fixed = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)]
+    affect_ira = fixed[fixed.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
+    failures = affect_ira[affect_ira.SITUACAO.isin(Situation.SITUATION_FAIL)]
+
+    average = failures.shape[0] / affect_ira.shape[0]
+
+    student_courses = affect_ira.groupby(['MATR_ALUNO'], as_index=False)\
+                                .aggregate({'SITUACAO': 'count'})
+    student_failures = failures.groupby(['MATR_ALUNO'], as_index=False)\
+                               .aggregate({'SITUACAO': 'count'})
+
+    merged = pd.merge(student_courses, student_failures, on=['MATR_ALUNO'])
+    merged.columns = ['MART_ALUNO', 'FEITAS', 'REPROVADO']
+    variance = merged['REPROVADO'].div(merged['FEITAS']).sub(average)\
+                                      .pow(2).sum() / merged.shape[0]
+    standard_deviation = math.sqrt(variance)
+    return (average, standard_deviation)
+
 def general_ira(df):
     fixed = df[df.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
     fixed = fixed[fixed.MEDIA_FINAL <= 100]
     return (fixed.MEDIA_FINAL.mean(), fixed.MEDIA_FINAL.std())
 
-def total_evasion_rate(df):
+def current_ira(df):
+    ano_grade = int(df.loc[df['NUM_VERSAO'].idxmax()]['NUM_VERSAO'])
+    fixed = df.loc[(df['NUM_VERSAO'] == ano_grade)]
+    fixed = fixed[fixed.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
+    fixed = fixed[fixed.MEDIA_FINAL <= 100]
+    return (fixed.MEDIA_FINAL.mean(), fixed.MEDIA_FINAL.std())
+
+def current_students_ira(df):
+    fixed = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)]
+    fixed = fixed[fixed.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
+    fixed = fixed[fixed.MEDIA_FINAL <= 100]
+    return (fixed.MEDIA_FINAL.mean(), fixed.MEDIA_FINAL.std())
+
+def general_evasion_rate(df):
     students = df['MATR_ALUNO'].drop_duplicates()
     total_student = students.shape[0]
     total_evasion = students.loc[(df.FORMA_EVASAO != EvasionForm.EF_ATIVO) & (df.FORMA_EVASAO != EvasionForm.EF_FORMATURA) & (df.FORMA_EVASAO != EvasionForm.EF_REINTEGRACAO)].shape[0]
 
     return total_evasion / total_student
 
+def current_evasion_rate(df):
+    ano_grade = int(df.loc[df['NUM_VERSAO'].idxmax()]['NUM_VERSAO'])
+    students = df.loc[(df['NUM_VERSAO'] == ano_grade)]
+    students = students['MATR_ALUNO'].drop_duplicates()
+    total_student = students.shape[0]
+    total_evasion = students.loc[(df.FORMA_EVASAO != EvasionForm.EF_ATIVO) & (df.FORMA_EVASAO != EvasionForm.EF_FORMATURA) & (df.FORMA_EVASAO != EvasionForm.EF_REINTEGRACAO)].shape[0]
+
+    return total_evasion / total_student
+
 def average_graduation_time(df):
     graduates = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_FORMATURA)]
     total_graduate = graduates.shape[0]
@@ -61,4 +103,105 @@ def average_graduation_time(df):
     average_time /= total_graduate
     average_time /= 2
 
-    return average_time
\ No newline at end of file
+    return average_time
+
+def total_students(df):
+    return df.drop_duplicates('MATR_ALUNO').shape[0]
+
+def current_total_students(df):
+    return df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)].drop_duplicates('MATR_ALUNO').shape[0]
+
+def taxa_abandono(df):
+    students = df['MATR_ALUNO'].drop_duplicates()
+    total_student = students.shape[0]
+    total_abandono = students.loc[(df.FORMA_EVASAO == EvasionForm.EF_ABANDONO)].shape[0]
+
+    return total_abandono / total_student
+
+def average_ira_graph(df):
+    alunos = df.drop_duplicates('MATR_ALUNO')
+
+    dic = build_dict_ira_medio(alunos)
+
+    return dic
+
+def current_students_average_ira_graph(df):
+    alunos_se = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)]
+    alunos_se = alunos_se.drop_duplicates('MATR_ALUNO')
+
+    dic_se = build_dict_ira_medio(alunos_se)
+
+    return dic_se
+
+def graduates_average_ira_graph(df):
+    alunos_for = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_FORMATURA)]
+    alunos_for = alunos_for.drop_duplicates('MATR_ALUNO')
+
+    dic_for = build_dict_ira_medio(alunos_for)
+
+    return dic_for
+
+def period_evasion_graph(df):
+    di_qtd = {}
+    dic = {}
+    evasions_total = 0
+    year_start = int(df['ANO'].min())
+    year_end = int(df['ANO'].max()) + 1
+    students = df.drop_duplicates()
+    for year in range(year_start, year_end):
+        for semester in range(1, 3):
+            evasions = students.loc[(df['ANO_EVASAO'] == str(year)) & (df['SEMESTRE_EVASAO'] == str(semester))].shape[0]
+            date = str(year) + ' {}º Período'.format(semester)
+            di_qtd[date] = evasions
+            evasions_total += evasions
+    if evasions_total:
+        for di in di_qtd:
+            qtd = di_qtd[di]
+            dic[di] = {'qtd': qtd, 'taxa': (qtd/evasions_total)*100}
+
+    return dic
+
+def build_dict_ira_medio(alunos):
+    dic = {"00-4.9":0, "05-9.9":0, "10-14.9":0, "15-19.9":0, "20-24.9":0, "25-29.9":0, "30-34.9":0,
+           "35-39.9":0, "40-44.9":0, "45-49.9":0, "50-54.9":0, "55-59.9":0, "60-64.9":0, "65-69.9":0,
+           "70-74.9":0, "75-79.9":0, "80-84.9":0, "85-89.9":0, "90-94.9": 0,"95-100":0}
+
+    iras = []
+    for index, row in alunos.iterrows():
+        if(row['MEDIA_FINAL'] is not None):
+            iras.append(row['MEDIA_FINAL'])
+
+    for d in dic:
+        aux = d.split('-')
+        v1 = float(aux[0])
+        if (v1 == 0.0):
+            v1 += 0.01
+        v2 = float(aux[1])
+        dic[d] = sum((float(num) >= v1) and (float(num) < v2) for num in iras)
+
+    return dic
+
+def build_degree_json(df):
+    def merge_dicts(dict1, dict2, dict3):
+        dict_out = {}
+        for key, value in dict1.items():
+            v2 = dict2[key] if key in dict2 else None
+            v3 = dict3[key] if key in dict3 else None
+            dict_out[key] = {'ira_medio': value, 'sem_evasao': v2, 'formatura': v3}
+        return dict_out
+    dic = merge_dicts(average_ira_graph(df),current_students_average_ira_graph(df),graduates_average_ira_graph(df))
+    degree_json = {
+        "ira_medio_grafico": json.dumps(sorted(dic.items())),
+        "evasao_grafico": json.dumps(sorted(period_evasion_graph(df).items())),
+        "ira_atual": current_students_ira(df),
+        "ira_medio": general_ira(df),
+        "qtd_alunos": total_students(df),
+        "qtd_alunos_atuais": current_total_students(df),
+        "taxa_evasao": general_evasion_rate(df),
+        "taxa_formatura": average_graduation(df),
+        "taxa_reprovacao": general_failure(df),
+        "taxa_reprovacao_atual": current_students_failure(df),
+        "tempo_formatura": average_graduation_time(df),
+    }
+    with open("cache/curso/curso.json",'w') as f:
+        f.write(json.dumps(degree_json,indent=4))
\ No newline at end of file

script/base/dataframe_base.py

@@ -73,11 +73,19 @@ def fix_dataframes(dataframes):
 
 
 def clean_history(df):
+<<<<<<< HEAD
 	df.drop(['ID_NOTA', 'CONCEITO', 'ID_LOCAL_DISPENSA', 'SITUACAO_CURRICULO',
 			 'ID_CURSO_ALUNO', 'ID_VERSAO_CURSO', 'ID_CURRIC_ALUNO',
 			 'ID_ATIV_CURRIC', 'SITUACAO_ITEM', 'ID_ESTRUTURA_CUR'
 			], axis=1, inplace=True)
 	df['PERIODO'] = df['PERIODO'].str.split('o').str[0]
+=======
+    df.drop(['ID_NOTA', 'CONCEITO', 'ID_LOCAL_DISPENSA', 'SITUACAO_CURRICULO',
+             'ID_CURSO_ALUNO', 'ID_VERSAO_CURSO', 'ID_CURRIC_ALUNO',
+             'ID_ATIV_CURRIC', 'SITUACAO_ITEM', 'ID_ESTRUTURA_CUR', 'NUM_VERSAO'
+            ], axis=1, inplace=True)
+    df['PERIODO'] = df['PERIODO'].str.split('o').str[0]
+>>>>>>> origin/degree
 
 def clean_register(df):
 	df_split = df['PERIODO_INGRESSO'].str.split('/')
@@ -87,9 +95,15 @@ def clean_register(df):
 	df['ANO_EVASAO'] = df_split.str[0]
 	df['SEMESTRE_EVASAO'] = df_split.str[1].str.split('o').str[0]
 
+<<<<<<< HEAD
 	df.drop(['ID_PESSOA', 'NOME_PESSOA', 'DT_NASCIMENTO', 'NOME_UNIDADE',
 			 'COD_CURSO', 'NUM_VERSAO', 'PERIODO_INGRESSO', 'PERIODO_EVASAO',
 			],axis=1, inplace=True)
+=======
+    df.drop(['ID_PESSOA', 'NOME_PESSOA', 'DT_NASCIMENTO', 'NOME_UNIDADE',
+             'COD_CURSO', 'PERIODO_INGRESSO', 'PERIODO_EVASAO',
+            ],axis=1, inplace=True)
+>>>>>>> origin/degree
 
 
 def fix_situation(df):