import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Create synthetic tips-like data
df = pd.DataFrame({
  'total_bill': np.random.uniform(10, 50, 100),
  'tip': np.random.uniform(1, 10, 100),
  'smoker': np.random.choice(['Yes', 'No'], 100),
  'day': np.random.choice(['Thur', 'Fri', 'Sat', 'Sun'], 100)
})

# Add a correlation
df['tip'] = df['total_bill'] * 0.15 + np.random.normal(0, 1, 100)

sns.set_theme(style="ticks")

# Plot relationship by smoker status and day
g = sns.relplot(
  data=df, x="total_bill", y="tip",
  col="day", hue="smoker", style="smoker",
  kind="scatter"
)

plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

data = pd.DataFrame({
  'Value': np.concatenate([np.random.normal(0, 1, 100), np.random.normal(2, 0.5, 100)]),
  'Group': ['A']*100 + ['B']*100
})

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Violin plot
sns.violinplot(data=data, x="Group", y="Value", ax=ax1, palette="muted")
ax1.set_title("Distribution by Group (Violin)")

# ECDF (Empirical Cumulative Distribution Function)
sns.ecdfplot(data=data, x="Value", hue="Group", ax=ax2)
ax2.set_title("CDF Comparison")

plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset("anscombe").query("dataset == 'I'")

sns.lmplot(x="x", y="y", data=df, ci=95, scatter_kws={"s": 80})
plt.title("Statistical Regression Analysis")
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Correlation matrix for synthetic features
data = np.random.rand(10, 10)
cols = [f'Feat_{i}' for i in range(10)]
df = pd.DataFrame(data, columns=cols)
corr = df.corr()

plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f")
plt.title("Feature Correlation Matrix")
plt.show()