From ee8c311c79a1e9cd908ed853d9d109b28391261e Mon Sep 17 00:00:00 2001
From: odair mario <badwolves123@gmail.com>
Date: Sun, 1 Oct 2017 20:59:35 -0300
Subject: [PATCH] qnt vez cursadas ate aprov, sem media
---
script/analysis/course_analysis.py | 64 +++++++++++++++---------------
1 file changed, 31 insertions(+), 33 deletions(-)
diff --git a/script/analysis/course_analysis.py b/script/analysis/course_analysis.py
index a9f9ce6..dd6591d 100644
--- a/script/analysis/course_analysis.py
+++ b/script/analysis/course_analysis.py
@@ -1,51 +1,49 @@
# -*- coding: utf-8 -*-
-
import pandas as pd
import numpy as np
+# import utils.situations
+
df = pd.read_excel("../base/historico.xls")
# imprime completamente um dataframe
+
def print_analise(d):
- with pd.option_context('display.max_rows', None, 'display.max_columns', 27):
- print(d)
+ with pd.option_context('display.max_rows', None, 'display.max_columns', 27):
+ print(d)
# calcula as taxas
-def func(x,matr):
- c = matr[x['COD_ATIV_CURRIC']].values[0]
- return (x['counts'] / c)
-
-#quantidade de matriculas
-def qnt_matr(df):
- return df.groupby(['COD_ATIV_CURRIC']).size()
-
-
-def analise(df):
- c = df.groupby(['COD_ATIV_CURRIC']).size()
- diciplinas = df.groupby(['COD_ATIV_CURRIC','SIGLA']).size().reset_index(name='counts')
- i=diciplinas.groupby(['COD_ATIV_CURRIC','SIGLA','counts']).apply(lambda x: func(x,matr)).reset_index(name='taxas gerais')
- print_analise(i)
-
-matr = qnt_matr(df)
-analise(df)
-
-
-
-
-
-
-
-
-
-
+def func(x, matr):
+ c = matr[x['COD_ATIV_CURRIC']].values[0]
+ return (x['counts'] / c)
+# quantidade de matriculas
+def counts_matr(df):
+ return df.groupby(['COD_ATIV_CURRIC']).size()
+def analysis(df):
+ qnt_matr = counts_matr(df) # quantidade de matriculas disciplina
+ # conta quantas vezes os valores de 'SIGLA' se repete para cada disciplina
+ disciplinas = df.groupby(['COD_ATIV_CURRIC', 'SIGLA']
+ ).size().reset_index(name='counts')
+ # adiciona mais uma coluna ao df disciplina com as taxas de cada valor de 'SIGLA'
+ disciplina = disciplinas.groupby(['COD_ATIV_CURRIC', 'SIGLA', 'counts']).apply(
+ lambda x: func(x, matr)).reset_index(name='taxas gerais')
+ return disciplina
+# quantidade de vezes cursadas ate obter a aprovacao
+def qnt_aprov(df):
+ qnt = df.groupby(['MATR_ALUNO','COD_ATIV_CURRIC']).size().reset_index(name='quantida aprov')
+ return qnt
+ # print(qnt)
+matr = counts_matr(df)
+analysis(df)
+qnt_aprov(df)
#
##f = lambda x: x / c[x]
@@ -53,9 +51,9 @@ analise(df)
#k = (df.sort(['ANO','PERIODO']))
##(p.apply(lambda x: print(p['COD_ATIV_CURRIC'])))
#
-## # .size().reset_index(name = "count");
-## # c = p.groupby(['count','SIGLA']).size()
+# .size().reset_index(name = "count");
+# c = p.groupby(['count','SIGLA']).size()
## ''' percorre mais uma vez a serie para aplicar a funcao lambida, se a '''
## c = lambda x: x+1
## curses = df['COD_ATIV_CURRIC'].drop_duplicates()
-## 'MATR_ALUNO','
+# 'MATR_ALUNO','
--
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