Commit 0daa8dc3 authored by João Denis Rodrigues's avatar João Denis Rodrigues
Browse files

Alguma coisa dos aluno

parent d5bcba9e
import re
import pandas as pd
import numpy as np
from utils.situations import *
def average_ira(df):
print(df)
new_df = df.dropna(subset=['MEDIA_FINAL'])
new_df = new_df[new_df['MEDIA_FINAL'] <= 100]
if not new_df.empty:
grade = np.sum(new_df['MEDIA_FINAL']*new_df['CH_TOTAL'])
total_ch = np.sum(new_df['CH_TOTAL']) * 100
return grade/total_ch
def pass_rate(dt):
new_dt = dt[dt['SITUACAO'].isin(Situation.SITATUION_PASS)]
......@@ -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')
......@@ -39,10 +30,11 @@ def load_dataframes(cwd='.'):
dataframes.append(dh)
dataframe = fix_dataframes(dataframes)
dh = DataframeHolder(dataframe)
dh.students.aggregate(teste)
# print(dh.students['MEDIA_FINAL'].aggregate(teste))
return dataframe
return dh
def read_excel(path, planilha='Planilha1'):
......
......@@ -6,8 +6,8 @@ import math
from datetime import timedelta
from pathlib import Path
from utils.utils import build_path
from analysis.degree_analysis import *
import analysis.degree_analysis as de_an
import analysis.student_analysis as st_an
try:
to_unicode = unicode
except NameError:
......@@ -21,6 +21,7 @@ def build_cache(dataframe):
path += "/curso"
build_path(path)
generate_student_data(path, dataframe)
# generate_degree_data(path, dataframe)
# generate_student_data(path)
# generate_student_list(path)
......@@ -30,12 +31,24 @@ def build_cache(dataframe):
# generate_course_general_data(path)
def generate_degree_data(path, dataframe):
average_graduation(dataframe)
general_failure(dataframe)
general_ira(dataframe)
de_an.average_graduation(dataframe)
de_an.general_failure(dataframe)
de_an.general_ira(dataframe)
pass
def generate_student_data(path):
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)
def generate_student_data(path, dataframe):
# student_df = dataframe.groupby('MATR_ALUNO')
dataframe.students.aggregate(teste)
pass
def generate_student_list(path):
......
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