rodrigols89

Django

Contents

• Fundamentals:
  • Projects vs. apps
  • Arquitetura do Django (MVT | Model-View-Template)
  • Arquivos Estáticos vs. Média
  • Respostas HTTP
  • POST (CREATE) | GET (READ) | PUT (UPDATE) | DELETE (DELETE)

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English	Portuguese (BR)
Provide	Provê
Enhancement	Aprimoramento
Enforce	Força

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NOTAS

• O push final deverá ser realizado até às 23:59 do dia 15/07/24.
• A prova foi pensada para ser desenvolvida em um ambiente POSIX.
• O candidato deverá desenvolver a solução realizando commits na branch main do repositório clonado.

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Requisitos do Sistema

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# Alias:
alias cpython="cd Workspace/cpython/"
alias kali="sudo docker container start kali-container && sudo docker exec -it kali-container /bin/bash"
alias stopkali="sudo docker container stop kali-container"

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ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

• Ctrl-Key: ^[
• Octal: \033
• Unicode: \u001b
• Hexadecimal: \x1B
• Decimal: 27

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The Checklist (Good practices for good programmers)

Contents

• Software Engineering
• Software Development good practices
• A.I/+D.S Projects

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def ApplyesKFold(x_axis, y_axis):
  # Linear Models.
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import ElasticNet
  from sklearn.linear_model import Ridge
  from sklearn.linear_model import Lasso

  # Cross-Validation models.
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import KFold

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import matplotlib.pyplot as plt
import pandas as pd

from sklearn.model_selection import cross_val_score # Cross Validation Function.
from sklearn.model_selection import KFold # KFold Class.
from sklearn.linear_model import LinearRegression # Linear Regression class.

df = pd.read_csv("../datasets/Admission_Predict.csv")

df.drop('Serial No.', axis = 1, inplace = True)

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import pandas as pd
pd.set_option('display.max_columns', 42)

data = pd.read_csv('../datasets/2015-building-energy-benchmarking.csv')

# Exibe a média de cada coluna.
print((data.isnull().sum() / len(data['OSEBuildingID'])) * 100, '\n')

data['ENERGYSTARScore'] = data['ENERGYSTARScore'].fillna(data['ENERGYSTARScore'].median())

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import pandas as pd
pd.set_option('display.max_columns', 18)

data = pd.read_csv('../datasets/athlete_events.csv')
data['Height'] = data['Height'].fillna(data['Height'].mean())
data['Weight'] = data['Weight'].fillna(data['Weight'].mean())

print(data[['Height', 'Weight']].head(20))