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with pm.Model() as model_30: | |
#priori | |
p_30 = pm.Uniform('p_30',lower=0, upper=1) | |
#likelihood | |
obs_30 = pm.Bernoulli('obs_30', p_30, observed=g30_1) | |
#MCMC | |
step = pm.Metropolis() | |
trace = pm.sample(2000, step = step) |
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#retenção de 1 dia | |
g30_1 = df_game.query("version == 'gate_30'")['retention_1'].values.astype(int) | |
g40_1 = df_game.query("version == 'gate_40'")['retention_1'].values.astype(int) |
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# To add a new cell, type '# %%' | |
# To add a new markdown cell, type '# %% [markdown]' | |
# %% | |
import pandas as pd | |
import seaborn as sns | |
import plotly.express as px | |
# %% | |
#importando dataset |
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$ heroku create cartola-dash # change my-dash-app to a unique name | |
$ heroku git:remote -a cartola-dash | |
$ git add . # add all files to git | |
$ git commit -m 'Deploy Heroku' | |
$ git push heroku master # deploy code to heroku | |
$ heroku ps:scale web=1 # run the app with a 1 heroku "dyno" |
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from dash.dependencies import Input, Output, State | |
#import helper.coleta_dados as cd | |
from app import app, cache | |
import plotly.express as px | |
import pandas as pd | |
import numpy as np | |
TIMEOUT = 60 |
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#Callback para a exibição do gráfico com o histórico do jogador | |
@app.callback( | |
Output('tab-01-scatter-pontos','figure'), | |
[Input('drop-clube-01','value'), | |
Input('drop-posicao-01','value'), | |
Input('drop-jogador-01','value')] | |
) | |
def update_scatter_jogador(clube,posicao,jogador): | |
#Filtragem do dataframe | |
df_filtered = df[(df['atletas.clube.id.full.name']==clube) & \ |
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#filtrando os jogadores pela posição e o clube | |
@app.callback( | |
#Colocamos com Output o próprio Dropdown de jogadores | |
Output('drop-jogador-01', 'options'), | |
#Lista de inputs (clubes e posições) | |
[Input('drop-clube-01', 'value'),Input('drop-posicao-01', 'value')]) | |
def set_jogador_values(clube,posicao): | |
#Criação de um dataframe somente com os dados filtrados | |
df_filtered = df[(df['atletas.clube.id.full.name']==clube) & (df['atletas.posicao_id']==posicao)] | |
#retorna a lista de jogadores com o filtro aplicado |
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#criando dataframe com os dados das rodadas | |
df = fetch_github() | |
#criando a lista para o menu Dropdown com todos os clubes do campeonato | |
@app.callback( | |
Output('drop-clube-01', 'options'), | |
[Input('drop-clube-01', 'search_value')]) | |
def set_clube_values(search_value): | |
return [{'label': i, 'value': i} for i in df['atletas.clube.id.full.name'].unique()] |
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def fetch_github(): | |
#criando as urls para 7 rodadas | |
urls = [] | |
for i in range(7): | |
urls.append(f'https://raw.githubusercontent.com/henriquepgomide/caRtola/master/data/2020/rodada-{i+1}.csv') | |
#criando lista de dataframes das rodadas | |
rodadas = [] | |
for url in urls: | |
rodadas.append(pd.read_csv(url)) |
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import dash_core_components as dcc | |
import dash_bootstrap_components as dbc | |
import dash_html_components as html | |
import construtores.layout as cl | |
#inserindo a navbar | |
navbar = dbc.Navbar( | |
dbc.Container( | |
[ | |
html.A( |
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