Commit 7a2784b4 authored by Odair Mario's avatar Odair Mario
Browse files

Merge remote-tracking branch 'origin/degree'

parents d17e6f05 e611ae02
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
......@@ -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):
......
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