taxas sem padrao de projeto

script/analysis/course_analysis.py

+# -*- coding: utf-8 -*-
+
+import pandas as pd 
+import numpy as np
+
+def print_analise(d):
+	with pd.option_context('display.max_rows', None, 'display.max_columns', 27):
+		print(d)
+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']).apply(lambda x: x['counts'] / (c[x['COD_ATIV_CURRIC']].values[0])).reset_index(name='taxas')
+	print_analise(i)
+analise(df)
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+#
+##f = lambda x: x / c[x]
+## p = df.groupby(['COD_ATIV_CURRIC','SIGLA']).size().apply(lambda x: (x /c['CI055'])*100)
+#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()
+## ''' 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','
+#p
+#df = pd.read_excel("../base/historico.xls")