# Import essential libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd

# Data setup: Let's quickly load a dataset for fun examples
url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
titanic = pd.read_csv(url)
print("Titanic shape:", titanic.shape)
titanic.head()

# Generating a single random number between 0 and 1
rand = np.random.rand()
print("Random number:", rand)

# Generating several random numbers at once
random_numbers = np.random.rand(5)
print("Random numbers:", random_numbers)

# Getting reproducible results with 'random seed'
np.random.seed(42)
print(np.random.rand(3))

# Simulate a coin flip: heads (1) or tails (0)
coin = np.random.randint(0, 2)
print("Heads" if coin == 1 else "Tails")

# Simulate rolling a fair six-sided die
die = np.random.randint(1, 7)
print("Rolled:", die)

# Generate random integers: useful for games and sampling
sample = np.random.randint(10, 20, size=5)
print("Random numbers from 10 to 19:", sample)

# Generate a list of 100 random numbers from 0 to 100
data = np.random.randint(0, 101, size=100)
mean = np.mean(data)
std = np.std(data)
print("Sample mean:", mean)
print("Sample standard deviation:", std)

# What if some data is missing? Handle it safely
data_with_nan = data.astype(float)
data_with_nan[5] = np.nan
mean_nan = np.nanmean(data_with_nan)
print("Mean without nan:", mean_nan)

# Bernoulli distribution: Like flipping a coin many times
flips = np.random.binomial(1, 0.5, size=20)
print("Coin flips (1=heads, 0=tails):", flips)

# Binomial distribution: Counting heads in several coin games
heads = np.random.binomial(n=10, p=0.5, size=5)
print("Number of heads out of 10 flips, 5 experiments:", heads)

# Poisson distribution: Counting rare events per period
calls = np.random.poisson(lam=2, size=10)
print("Random number of daily calls (average 2):", calls)

# Normal distribution: Most values near the mean
normal_data = np.random.normal(loc=0, scale=1, size=1000)
print("First 10 random values:", normal_data[:10])

# Exponential distribution: Time until something happens
exp_data = np.random.exponential(scale=5, size=10)
print("Random waiting times:", exp_data)

# Mini-project: Simulate random survivors from Titanic
n_trials = 500
prob_survive = titanic['Survived'].mean()
experiments = np.random.binomial(1, prob_survive, size=n_trials)
simulated_survived = np.sum(experiments)
print("Predicted survivors in 500 random draws:", simulated_survived)

# Mini-project: Shuffle the dataset rows with numpy
shuffled = titanic.sample(frac=1, random_state=42).reset_index(drop=True)
print("First 5 shuffled passenger names:")
print(shuffled['Name'].head())

# Try it: Input your favorite number and get that many random values
n = int(input("How many random numbers would you like? "))
your_randoms = np.random.rand(n)
print("Here they are:", your_randoms)