# Suppress warnings for cleaner output
import warnings
import numpy as np
np.random.seed(42)
warnings.filterwarnings("ignore")
 
 

# Data setup (Restaurant Tips Dataset)
import seaborn as sns
df = sns.load_dataset('tips')
print(df.shape)
print(df.head(3))

# A first plot: histogram of total_bill column
df['total_bill'].plot(kind='hist', bins=20, color='skyblue', edgecolor='black', figsize=(8,4), title='Distribution of Total Bill')

# Bar plot: average tip by day of week
df.groupby('day')['tip'].mean().plot(kind='bar', color='coral', figsize=(6,4), title='Average Tip by Day')

# Scatter plot: total bill vs tip
df.plot(kind='scatter', x='total_bill', y='tip', color='purple', alpha=0.7, figsize=(7,5), title='Total Bill vs Tip Amount')

# Box plot: distribution of tips by gender
df.boxplot(column='tip', by='sex', grid=False, figsize=(6,4))

# Setting axis labels and chart title
ax = df['total_bill'].plot(kind='hist', bins=15, color='teal', alpha=0.8, figsize=(7,4))
ax.set_xlabel('Total Bill (USD)')
ax.set_ylabel('Number of Customers')
ax.set_title('Restaurant Bills')

# Plotting multiple columns on the same figure
df[['total_bill', 'tip']].plot(kind='line', figsize=(10,4), title='Total Bill and Tip (First 30 Rows)')

# Pie chart: proportion of male and female customers
df['sex'].value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=['#66b3ff','#ff9999'], figsize=(5,5), ylabel='', title='Customer Gender Split')

# Horizontal bar plot: total customers per time of day
df['time'].value_counts().plot(kind='barh', color='goldenrod', figsize=(6,3), title='Lunch vs Dinner Counts')

# Grouped bar plot: tips by day and gender
pivot = df.pivot_table(values='tip', index='day', columns='sex', aggfunc='mean')
pivot.plot(kind='bar', figsize=(8,5), title='Mean Tip by Day and Gender', colormap='Set2')

# Exploring relationships: hexbin plot (advanced scatter density)
df.plot(x='total_bill', y='tip', kind='hexbin', gridsize=20, cmap='Blues', sharex=False, figsize=(7,5), title='Tip Density by Bill Size')

# Time-based plotting: fake time series with sample data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
date_range = pd.date_range(start='2023-01-01', periods=244, freq='D')
tips_by_date = pd.DataFrame({'date': date_range, 'total_bill': np.random.choice(df['total_bill'], size=244)})
tips_by_date.set_index('date')['total_bill'].plot(title='Simulated Daily Total Bill', figsize=(10,3))
plt.xlabel('Date')
plt.ylabel('Total Bill (USD)')

# Boxplot: tips by lunch vs dinner
df.boxplot(column='tip', by='time', grid=False, figsize=(7,4))

# Violin plot (extra with seaborn): smooth tip distribution by time
import seaborn as sns
sns.violinplot(x='time', y='tip', data=df, palette='pastel')

# Troubleshooting: What if plt.show() is needed?
import matplotlib.pyplot as plt
ax = df['tip'].plot(kind='hist', bins=10)
plt.show()  # Just in case charts do not appear automatically