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Creating Things & Solving Problems

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Creating Things & Solving Problems
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rodrigols89 / .md
Last active July 30, 2024 19:22
[ENG > PT-BR] - List of Translations
English Portuguese (BR)
Provide Provê
Enhancement Aprimoramento
Enforce Força
@rodrigols89
rodrigols89 / .md
Last active July 7, 2024 15:38
Challenge

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.

Requisitos do Sistema

# 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"
@rodrigols89
rodrigols89 / ANSI.md
Created July 11, 2023 17:52 — forked from fnky/ANSI.md
ANSI Escape Codes

ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

  • Ctrl-Key: ^[
  • Octal: \033
  • Unicode: \u001b
  • Hexadecimal: \x1B
  • Decimal: 27
@rodrigols89
rodrigols89 / .md
Last active May 11, 2024 16:59
The Checklist (Good practices for good programmers)
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
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)
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())
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))
import pandas as pd
pd.set_option('display.max_columns', 18)
data = pd.read_csv('../datasets/athlete_events.csv')
data['Medal'] = data['Medal'].fillna('Nenhuma')
print(data['Medal'].head(10))