Merge branch 'back-to-origins' into degree

script/analysis/student_analysis.py

+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)]
+    

script/base/dataframe_base.py

@@ -2,12 +2,19 @@ import re
 import os
 import sys
 import pandas as pd
-
+import numpy as np
 from glob import glob
 from json import load as json_load
 from utils.situations import *
 
 
+class DataframeHolder:
+    def __init__(self, dataframe):
+        self.students = dataframe.groupby('MATR_ALUNO')
+        self.courses = dataframe.groupby('COD_ATIV_CURRIC')
+        self.admission = dataframe.groupby(['ANO_INGRESSO', 'SEMESTRE_INGRESSO'])
+
+
 def load_dataframes(cwd='.'):
     dataframes = []
     for path, dirs, files in os.walk(cwd):       
@@ -23,7 +30,11 @@ def load_dataframes(cwd='.'):
                 dataframes.append(dh)
 
     dataframe = fix_dataframes(dataframes)
-    return dataframe
+
+    dh = DataframeHolder(dataframe)
+
+
+    return dh
 
 
 def read_excel(path, planilha='Planilha1'):

script/build_cache.py

@@ -6,13 +6,14 @@ 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:
     to_unicode = str
 
+
 def build_cache(dataframe):
 #    os.chdir("../src")
     path = "cache"
@@ -20,21 +21,34 @@ def build_cache(dataframe):
     path += "/curso"
     build_path(path)
 
-    generate_degree_data(path, dataframe)
-    generate_student_data(path)
-    generate_student_list(path)
-    generate_admission_data(path)
-    generate_admission_list(path)
-    generate_course_data(path)
-    generate_course_general_data(path)
+    generate_student_data(path, dataframe)
+#    generate_degree_data(path, dataframe)
+#    generate_student_data(path)
+#    generate_student_list(path)
+#    generate_admission_data(path)
+#    generate_admission_list(path)
+#    generate_course_data(path)
+#    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):