audhiaprilliant

# Data frame manipulation
import pandas as pd

# Matrices operation
import numpy as np

# Data visualization with plotnine
from plotnine import *
import plotnine

audhiaprilliant

audhiaprilliant

plotnine.options.figure_size = (10, 4.8)
(
    ggplot(
        data = df_general
    )+
    geom_line(
        aes(x = 'iter',
            y = 'value',
            group = 'status',
            color = 'status'),

audhiaprilliant

# Perform a empirical simulation
data_general = monty_hall(
    num_iter = 1000,
    switch = True
)

# Create a data rame
df_general = pd.DataFrame(
    data = data_general
)

audhiaprilliant

# Probability of winning the game (stick in the door)
plotnine.options.figure_size = (10, 4.8)
(
    ggplot(
        data = df_stick
    )+
    geom_line(
        aes(x = 'iter',
            y = 'win_rate',
            group = 1),

audhiaprilliant

# Perform a empirical simulation
data_stick = monty_hall(
    num_iter = 1000,
    switch = False
)

# Create a data rame
df_stick = pd.DataFrame(
    data = data_stick
)

audhiaprilliant

# Probability of winning the game (switch the door)
plotnine.options.figure_size = (10, 4.8)
(
    ggplot(
        data = df_switch
    )+
    geom_line(
        aes(x = 'iter',
            y = 'win_rate',
            group = 1),

audhiaprilliant

# Perform a empirical simulation
data_switch = monty_hall(
    num_iter = 1000,
    switch = True
)

# Create a data rame
df_switch = pd.DataFrame(
    data = data_switch
)

audhiaprilliant

# Monty Hall Problem
def monty_hall(
    num_iter: int,
    switch: bool
    ):
    # Data object
    obj = []
    
    for iteration in range(num_iter):
        # Possibilities in doors

audhiaprilliant

# Data frame manipulation
import pandas as pd

# Mathematical operations
import numpy as np

# Data visualization
import plotnine
from plotnine import *