#121 Cleaning up & docker container integrating postgres+django

Dockerfile

 FROM python:3.5
-Run apt-get update -qq
-Run apt-get install -y \
+RUN apt-get update -qq
+RUN apt-get install -y \
 	python3-pip libpq-dev \ 
 	postgresql-client 
-Run mkdir /adega
+
+RUN mkdir /adega
 WORKDIR /adega
-ADD . /adega
-Run pip3 install -r requirements.txt
-run ./install.sh --configure
+
+# Not necessary (only do the build slow)
+# ADD . /adega
+
+ADD requirements.txt /adega/requirements.txt
+
+RUN pip3 install -r requirements.txt
+
+# Not necessary. The migrate can be done by makefile or in docker-compose.yml
+# Besides that, there is no database to migrate yet (while docker build)
+# RUN ./install.sh --configure

LICENSE

-The MIT License (MIT)
-
-Copyright (c) 2016 Simple is Better Than Complex
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

docker-compose.yml

@@ -8,11 +8,7 @@ services:
             - POSTGRES_DB=adega
     web:
         build: .
-        command: python3 ./src/manage.py makemigrations degree
-        command: python3 ./src/manage.py makemigrations uploads
-        command: python3 ./src/manage.py makemigrations educator
-        command: python3 ./src/manage.py migrate
-        command: python3 ./src/manage.py runserver 0.0.0.0:8000
+        command: bash "./docker_scripts/on_docker_init.sh"
         volumes:
             - .:/adega
         ports:
@@ -21,3 +17,8 @@ services:
             - db
         depends_on:
             - db
+        environment:
+            - POSTGRES_USER=adega
+            - POSTGRES_PASSWORD=adega
+            - POSTGRES_DB=adega
+            - POSTGRES_HOST=adega_db_1

docker_scripts/on_docker_init.sh

+bash ./docker_scripts/wait-for-postgres.sh
+python ./src/manage.py migrate
+python ./src/manage.py runserver 0.0.0.0:8000

docker_scripts/wait-for-postgres.sh

+#!/bin/bash
+# wait-for-postgres.sh
+
+set -e
+
+host="$1"
+
+cmd="$@"
+
+until PGPASSWORD=$POSTGRES_PASSWORD psql -h "$POSTGRES_HOST" -U "$POSTGRES_USER" -c '\q'; do
+  >&2 echo "Postgres is unavailable - sleeping"
+  sleep 1
+done
+
+>&2 echo "Postgres is up"
+#~ >&2 echo "Postgres is up - executing command"
+#~ exec $cmd

install.sh

@@ -4,8 +4,8 @@ verbose=0
 
 # ---------- functions ------------------
 configure() {
-	postgres psql < postgres/create.sql
-		
+	#~ postgres psql < postgres/create.sql
+	PGPASSWORD=adega psql -h adega_db_1 -U adega < postgres/create.sql
 	python3 src/manage.py makemigrations degree
 	python3 src/manage.py makemigrations uploads
 	python3 src/manage.py makemigrations educator

makefile

@@ -36,3 +36,13 @@ install-user:
 	pipenv install
 
 
+
+%:
+	@:
+args = `arg="$(filter-out $@,$(MAKECMDGOALS))" && echo $${arg:-${1}}`
+
+manage:
+	@echo $(call args,"")
+	sudo docker exec -it adega_web_1 python3 ./src/manage.py $(call args,"")
+	
+

postgres/create.sql

 CREATE DATABASE adega;
 
-CREATE USER adega WITH PASSWORD 'adega';
+-- ~ CREATE USER adega WITH PASSWORD 'adega';
 
-ALTER ROLE adega SET client_encoding TO 'utf8';
-ALTER ROLE adega SET default_transaction_isolation TO 'read committed';
-ALTER ROLE adega SET timezone TO 'UTC-3';
-GRANT ALL PRIVILEGES ON DATABASE adega TO adega;
+-- ~ ALTER ROLE adega SET client_encoding TO 'utf8';
+-- ~ ALTER ROLE adega SET default_transaction_isolation TO 'read committed';
+-- ~ ALTER ROLE adega SET timezone TO 'UTC-3';
+-- ~ GRANT ALL PRIVILEGES ON DATABASE adega TO adega;

script/.gitignore

-cache/*
-*.json

script/analysis/admission_analysis.py

-import numpy as np
-
-from utils.situations import *
-ANO_ATUAL = 2017
-SEMESTRE_ATUAL = 2
-
-
-def listagem_turma_ingresso(df):
-	#~ print(df.groupby(["ANO_INGRESSO", "SEMESTRE_INGRESSO"]).groups)
-	grupos = df.groupby(["ANO_INGRESSO", "SEMESTRE_INGRESSO"]).groups
-	for t in grupos:
-		print(t)
-		print("\n\n")
-		print(df["FORMA_INGRESSO"][grupos[t]].drop_duplicates())

script/analysis/degree_analysis.py

-import pandas as pd
-import math
-import ujson as json
-from utils.situations import Situation, EvasionForm
-
-
-def average_graduation(df):
-    total_student = df['MATR_ALUNO'].drop_duplicates().shape[0]
-    total_graduate = df[df.FORMA_EVASAO == EvasionForm.EF_FORMATURA].shape[0]
-
-    return total_graduate / total_student
-
-
-def general_failure(df):
-    affect_ira = df[df.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 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 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]
-    average_time = 0
-    year_end = int(df['ANO'].max())
-    semester_end = graduates['PERIODO'].max()
-    for index, row in graduates.iterrows():
-        if pd.notnull(row['ANO_EVASAO']):
-            year_end = int(row['ANO_EVASAO'])
-            try: 
-                semester_end = int(row['SEMESTRE_EVASAO'])
-            except ValueError:
-                semester_end = graduates['PERIODO'].max()
-        year = int(row['ANO_INGRESSO'])
-        semester = int(row['SEMESTRE_INGRESSO'])
-        difference = 2 * (year_end - year) + (semester_end - semester) + 1
-        average_time += difference
-    average_time /= total_graduate
-    average_time /= 2
-
-    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

script/analysis/student_analysis.py

-import numpy as np
-
-#~ TODO:
-#~ FAZER CACHE DE TUDO
-#~ AO CHAMAR A FUNCAO VERIFICAR SE TEM ALGO NA CACHE
-
-from utils.situations import *
-import pandas as pd
-
-ANO_ATUAL = 2017
-SEMESTRE_ATUAL = 2
-
-
-def listagem_alunos(df):
-	#~ ativos = df[["MATR_ALUNO", "NOME_PESSOA",]][df["FORMA_EVASAO"] == EvasionForm.EF_ATIVO].drop_duplicates()
-	situacoes = df.groupby(["MATR_ALUNO", "NOME_PESSOA", "FORMA_EVASAO"])
-	situacoes = list(pd.DataFrame({'count' : situacoes.size()}).reset_index().groupby(["FORMA_EVASAO"]))
-	#~ Cria lista de nome de listagens
-	retorno = {}
-	for s in situacoes:
-		#Busca a lista de alunos relacionados a um codigo
-		retorno[s[0]] = list(s[1]["MATR_ALUNO"])
-
-	return retorno
-
-def ira_alunos(df):
-	iras = ira_por_quantidade_disciplinas(df)
-	for i in iras:
-		ira_total = 0
-		carga_total = 0
-		for semestre in iras[i]:
-			ira_total += iras[i][semestre][0]*iras[i][semestre][2]
-			carga_total+=iras[i][semestre][2]
-		
-		if(carga_total != 0):
-			iras[i] = ira_total/carga_total
-		else:
-			iras[i] = 0
-	return iras
-	
-def taxa_aprovacao(df):
-	aprovacoes_semestres = indice_aprovacao_semestral(df)
-	
-	for aluno in aprovacoes_semestres:
-		total = sum([aprovacoes_semestres[aluno][s][1] for s in aprovacoes_semestres[aluno]])
-		aprovacoes = sum([aprovacoes_semestres[aluno][s][0] for s in aprovacoes_semestres[aluno]])
-		total = float(total)
-		aprovacoes = float(aprovacoes)
-		if(total != 0):
-			aprovacoes_semestres[aluno] = aprovacoes/total
-		else:
-			aprovacoes_semestres[aluno] = None
-		#~ for semestre in aprovacoes_semestres[aluno]:
-			#~ aprovacoes+=aprovacoes_semestres[aluno][semestre][0]
-			#~ total+=aprovacoes_semestres[semestre][1]
-			
-	return aprovacoes_semestres
-	
-	
-
-def posicao_turmaIngresso_semestral(df):
-	iras = ira_semestral(df)
-	iraMax = {}
-	for matr in iras:
-		for semestreAno in iras[matr]:
-			if not (semestreAno in iraMax):
-				iraMax[semestreAno] = iras[matr][semestreAno]
-			else:
-				if (iras[matr][semestreAno] > iraMax[semestreAno]):
-					iraMax[semestreAno] = iras[matr][semestreAno]
-	for matr in iras:
-		for semestreAno in iras[matr]:
-			iras[matr][semestreAno] /= iraMax[semestreAno]
-
-	return iras
-
-
-def periodo_real(df):
-	aux = df.groupby(["MATR_ALUNO"])
-	students = {}
-	for x in aux:
-		students[x[0]] = None
-	return students
-
-
-def periodo_pretendido(df):
-	aux = df.groupby(["MATR_ALUNO", "ANO_INGRESSO", "SEMESTRE_INGRESSO"])
-	students = {}
-	for x in aux:
-		students[x[0][0]] = (ANO_ATUAL - int(x[0][1])) * 2 + SEMESTRE_ATUAL - int(x[0][2]) + 1
-	return students
-
-
-def ira_semestral(df):
-	aux = ira_por_quantidade_disciplinas(df)
-	for matr in aux:
-		for periodo in aux[matr]:
-			aux[matr][periodo] = aux[matr][periodo][0]
-	return aux
-
-
-def ira_por_quantidade_disciplinas(df):
-	students = {}
-	df = df.dropna(subset=["MEDIA_FINAL"])
-
-	total_students = len(df["MATR_ALUNO"])
-	for i in range(total_students):
-		matr = df["MATR_ALUNO"][i]
-		if (not (matr in students)):
-			students[matr] = {}
-
-		ano = str(int(df["ANO"][i]))
-		semestre = str(df["PERIODO"][i])
-		situacao = int(df["SITUACAO"][i])
-		nota = float(df["MEDIA_FINAL"][i])
-		carga = float(df["CH_TOTAL"][i])
-		#media_credito = int(df["MEDIA_CREDITO"][i])
-		
-		
-		#if (situacao in Situation.SITUATION_AFFECT_IRA and media_credito != 0):
-		if (situacao in Situation.SITUATION_AFFECT_IRA):
-			if not (ano + "/" + semestre in students[matr]):
-				students[matr][ano + "/" + semestre] = [0, 0, 0]
-			
-			students[matr][ano + "/" + semestre][0] += nota*carga
-			students[matr][ano + "/" + semestre][1] += 1
-			students[matr][ano + "/" + semestre][2] += carga
-
-	for matr in students:
-		for periodo in students[matr]:
-			if (students[matr][periodo][2] != 0):
-				students[matr][periodo][0] /= students[matr][periodo][2] * 100
-	
-	return (students)
-
-
-def indice_aprovacao_semestral(df):
-	students = {}
-	df = df.dropna(subset=['MEDIA_FINAL'])
-	total_students = len(df["MATR_ALUNO"])
-	for i in range(total_students):
-		matr = (df["MATR_ALUNO"][i])
-		if (not (matr in students)):
-			students[matr] = {}
-
-		ano = str(int(df["ANO"][i]))
-		semestre = str(df["PERIODO"][i])
-		situacao = int(df["SITUACAO"][i])
-
-		if not (ano + "/" + semestre in students[matr]):
-			students[matr][ano + "/" + semestre] = [0, 0]
-
-		if situacao in Situation.SITUATION_PASS:
-			students[matr][ano + "/" + semestre][0] += 1
-			students[matr][ano + "/" + semestre][1] += 1
-		if situacao in Situation.SITUATION_FAIL:
-			students[matr][ano + "/" + semestre][1] += 1
-		
-	return (students)
-
-
-def aluno_turmas(df):
-	students = {}
-	df = df.dropna(subset=['MEDIA_FINAL'])
-
-	situations = dict(Situation.SITUATIONS)
-
-	for matr, hist in df.groupby('MATR_ALUNO'):
-		students[matr] = []
-
-		for _, row in hist.iterrows():
-			data = {
-				'ano': str(int(row["ANO"])),
-				'codigo': row["COD_ATIV_CURRIC"],
-				'nome': row["NOME_ATIV_CURRIC"],
-				'nota': row["MEDIA_FINAL"],
-				'semestre': row["PERIODO"],
-				'situacao': situations.get(row["SITUACAO"], Situation.SIT_OUTROS)
-			}
-
-			students[matr].append(data)
-
-	return students

script/base/dataframe_base.py

-
-import os
-import pandas as pd
-import numpy as np
-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):
-                for f in files:
-                        file_path = path + '/' + f
-                        dh = {'name': f, 'dataframe': None}
-                        if 'csv' in f:
-                                dh['dataframe'] = read_csv(file_path)
-                        if 'xls' in f:
-                                dh['dataframe'] = read_excel(file_path)
-
-                        if dh['dataframe'] is not None:
-                                dataframes.append(dh)
-
-        dataframe = fix_dataframes(dataframes)
-        dh = DataframeHolder(dataframe)
-        #~ dh.students.aggregate(teste)
-#       print(dh.students['MEDIA_FINAL'].aggregate(teste))
-        return dataframe
-
-
-def read_excel(path, planilha='Planilha1'):
-        return pd.read_excel(path)
-
-
-def read_csv(path):
-        return pd.read_csv(path)
-
-
-def fix_dataframes(dataframes):
-        for df in dataframes:
-                if df['name'] == 'historico.xls' or df['name'] == 'historico.csv':
-                        history = df['dataframe']
-                        history.rename(columns={'DESCR_SITUACAO': 'SITUACAO'}, inplace=True)
-                if df['name'] == 'matricula.xls'  or df['name'] == 'matricula.csv':
-                        register = df['dataframe']
-
-        #~ clean_history(history)
-        clean_register(register)
-        #~ df.dropna(axis=0, how='all')
-        history["MEDIA_FINAL"] = pd.to_numeric(history["MEDIA_FINAL"], errors='coerce')
-        history = history[np.isfinite(history['MEDIA_FINAL'])]
-
-
-        merged = pd.merge(history, register, how='outer', on=['MATR_ALUNO'])
-        merged = merged.rename(index=str, columns={"ANO_INGRESSO_x": "ANO_INGRESSO", "SEMESTRE_INGRESSO_x": "SEMESTRE_INGRESSO", "FORMA_INGRESSO_x": "FORMA_INGRESSO"})
-
-        fix_situation(merged)
-        fix_admission(merged)
-        fix_evasion(merged)
-        fix_carga(merged)
-

[Bruno Meyer]

@omdj17 @jcrodrigues Temos que começar a usar os envs pra ficar mais fácil de dar manutenção

[Bruno Meyer]

@omdj17 @jcrodrigues Coloquem aqui o que quiserem que aconteça quando o container é iniciado.

Recomendo NÃO colocar os makemigrations aqui, porque se não eventualmente pode acabar acontecendo de alterar um model sem vocês perceberem. Mas dai teria que ser pensado em uma maneira de fazer os makemigrations na primeira que rodar o docker pelo menos (não tem como fazer no build pois o DB não existe nesse momento).

[Bruno Meyer]

@jcrodrigues O docker-compose.yml já está criando o usuário. Ele só não cria o banco.