otaviomguerra

from scipy import stats
k2, p = stats.normaltest(Alturas.ALTURA.values)
alpha = 0.05
print("p = {:g}".format(p))
if p < alpha:  # Hipótese nula: Os dados são de uma distribuição normal
    print("A hipótese nula pode ser rejeitada")
else:
    print("A hipótese nula não pode ser rejeitada")

otaviomguerra

#dataset name = data
# Define your investment
investment = 1000

# Calculate the daily returns here
returns = data.pct_change()

# Calculate the cumulative returns here
returns_plus_one = returns + 1
cumulative_return = returns_plus_one.cumprod()

otaviomguerra

# Import the module for estimating an ARMA model
from statsmodels.tsa.arima_model import ARMA

# Fit the data to an AR(p) for p = 0,...,6 , and save the BIC
BIC = np.zeros(7)
for p in range(7):
    mod = ARMA(simulated_data_2, order=(p,0))
    res = mod.fit()
# Save BIC for AR(p)    
    BIC[p] = res.bic

otaviomguerra

# Import the statsmodels module for regression and the adfuller function
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller

# Regress BTC on ETH
ETH = sm.add_constant(ETH)
result = sm.OLS(BTC,ETH).fit()

# Compute ADF
b = result.params[1]

otaviomguerra

variaveis_categoricas = [] #lista de variaveis categoricas
variaveis_continuas = []  #lista de variaveis continuas
for col in variaveis_categoricas:
    dataset[col] = dataset[col].astype('category')
    
    
for col in variaveis_continuas:
    dataset[col] = dataset[col].astype('float64') #pode ser int64

otaviomguerra

dataset_encoded = dataset_raw.apply(LabelEncoder().fit_transform)

otaviomguerra

import glob
import os
import pandas as pd

path = r'/path/to/Files'
all_files = glob.glob(os.path.join(path, "*.csv"))
df = pd.concat((pd.read_csv(f, sep = ';', header = None, encoding= 'ISO-8859-1') for f in all_files))
li = []

for filename in all_files:

otaviomguerra

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('postgresql+psycopg2://USER:PASSWORD@HOST/DATABASE')
query = "SELECT * FROM table"
df = pd.read_sql_query(query,engine)

otaviomguerra

campo1 = 'campo 1 do Json de request'
campo2 = 'campo 2 do Json de request'

# Dados de acesso da API
URL = 'https://link-da-api.app'
api_token = 'TOKEN DE ACESSO'

headers = {'Content-Type': 'application/json',
           'Authorization': 'Bearer {0}'.format(api_token),
          }

otaviomguerra

from sqlalchemy import create_engine
import io
import pandas as pd 

# Joga DataFrame diretamente no banco de dados
print("Carregando DataFrame no banco de dados...\n")

conn = engine.raw_connection()
cur = conn.cursor()
output = io.StringIO()