import warnings
from statsmodels.tools.sm_exceptions import ConvergenceWarning, ValueWarning
warnings.filterwarnings("ignore", category=ValueWarning)
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)

# Import basic libraries for the whole notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(42)

# Simulate a fair coin flip 10 times
np.random.seed(1)
outcomes = np.random.choice(["Heads", "Tails"], size=10)
print("Coin flips:", outcomes)

# Count how many times we got heads
heads = np.sum(outcomes == "Heads")
tails = np.sum(outcomes == "Tails")
print("Heads:", heads, ", Tails:", tails)

# Simulate rolling a die 12 times
np.random.seed(2)
die_rolls = np.random.randint(1, 7, size=12)
print("Die rolls:", die_rolls)

# Count frequency of each face
values, counts = np.unique(die_rolls, return_counts=True)
for val, c in zip(values, counts):
    print(f"Number {val}: {c} times")
    

# Calculate mean and standard deviation
mean_value = np.mean(die_rolls)
std_value = np.std(die_rolls)
print("Mean:", mean_value)
print("Standard deviation:", std_value)

# Create data with a missing value
data = pd.Series([5, 3, np.nan, 7, 6, np.nan])
print("Data:")
print(data)

# Find missing entries
missing = data.isnull()
print("Missing values:")
print(missing)

# Fill missing data with the mean
filled = data.fillna(data.mean())
print("After filling missing values:")
print(filled)

from scipy.stats import bernoulli, binom, poisson

# Bernoulli: Single coin flip (success/failure)
probs = bernoulli.pmf([0, 1], p=0.5)
print("Bernoulli PMF (p=0.5):", probs)

# Binomial: 10 trials, p=0.5, probability of 6 successes
binom_p = binom.pmf(6, n=10, p=0.5)
print("Binomial (n=10, p=0.5) probability of 6 successes:", binom_p)

# Poisson: Probability of 3 arrivals, average rate 2
poisson_p = poisson.pmf(3, mu=2)
print("Poisson (mu=2) probability of 3 events:", poisson_p)

from scipy.stats import norm, expon

# Normal distribution (bell curve)
x = np.linspace(-3, 3, 100)
y = norm.pdf(x, loc=0, scale=1)
plt.figure(figsize=(6, 2))
plt.plot(x, y, label="Normal PDF")
plt.title("Normal Distribution")
plt.legend()
plt.show()

# Exponential: Models waiting times
x2 = np.linspace(0, 6, 100)
y2 = expon.pdf(x2, scale=2)
plt.figure(figsize=(6, 2))
plt.plot(x2, y2, color="orange", label="Exponential PDF")
plt.title("Exponential Distribution")
plt.legend()
plt.show()

# Data setup: Load Titanic dataset, use seaborn version or fallback dataset if not available
try:
    titanic = sns.load_dataset('titanic')
    print('Shape:', titanic.shape)
    print(titanic.head())
except Exception:
    # Fallback: Use penguins if Titanic is not available
    titanic = sns.load_dataset('penguins')
    print('Titanic dataset not found. Fallback to penguins dataset.')
    print('Shape:', titanic.shape)
    print(titanic.head())