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Pandas Data Visualization

Data visualization is essential for understanding patterns, relationships, and trends in your data. Pandas integrates seamlessly with Matplotlib and Seaborn to create powerful visualizations with minimal code.

1. Basic Plotting with Pandas

Pandas DataFrame and Series objects have built-in plotting methods that leverage Matplotlib.

Line plots: df.plot() or df.plot.line()
Bar plots: df.plot.bar() or df.plot.barh()
Histograms: df.plot.hist()
Box plots: df.plot.box()
Area plots: df.plot.area()

Plot Type	Best For	Pandas Method
Line Plot	Time series, trends over time	`.plot()` or `.plot.line()`
Bar Plot	Categorical comparisons	`.plot.bar()`
Histogram	Distribution of numerical data	`.plot.hist()`
Box Plot	Statistical distribution overview	`.plot.box()`
Scatter Plot	Relationship between two variables	`.plot.scatter()`

2. Advanced Pandas Plotting

Pandas offers specialized plotting functions for complex visualizations.

Scatter matrix: pd.plotting.scatter_matrix()
Autocorrelation: pd.plotting.autocorrelation_plot()
Bootstrap plot: pd.plotting.bootstrap_plot()
Lag plot: pd.plotting.lag_plot()

3. Seaborn Integration

Seaborn provides high-level interface for statistical visualizations.

Distribution plots: sns.histplot(), sns.kdeplot()
Categorical plots: sns.boxplot(), sns.violinplot()
Relational plots: sns.scatterplot(), sns.lineplot()
Matrix plots: sns.heatmap(), sns.clustermap()

4. Plot Customization

Customize plots for better presentation and clarity.

Styling: plt.style.use()
Colors: Color maps, custom palettes
Annotations: Text, arrows, labels
Layout: Subplots, figure size, spacing

Choosing the Right Visualization

📈 Trends Over Time

Line plots
Area plots
Stacked area

📊 Comparisons

Bar charts
Grouped bars
Radar charts

🔍 Distributions

Histograms
Box plots
Violin plots

🔗 Relationships

Scatter plots
Bubble charts
Correlation heatmaps

🍕 Proportions

Pie charts
Donut charts
Stacked bar charts

🌐 Composition

Stacked area
Tree maps
Sunburst charts

Example: Comprehensive Data Visualization

Data Visualization Examples

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

# Set style for better visualizations
plt.style.use('seaborn-v0_8')
sns.set_palette("husl")

# Create sample dataset for visualization
np.random.seed(42)
dates = pd.date_range('2023-01-01', periods=100, freq='D')

data = {
    'date': dates,
    'sales': 1000 + np.cumsum(np.random.normal(50, 20, 100)),
    'temperature': 20 + 10 * np.sin(2 * np.pi * np.arange(100) / 365) + np.random.normal(0, 3, 100),
    'category': np.random.choice(['Electronics', 'Clothing', 'Food', 'Books'], 100),
    'region': np.random.choice(['North', 'South', 'East', 'West'], 100),
    'customer_age': np.random.randint(18, 70, 100),
    'price': np.random.uniform(10, 500, 100),
    'quantity': np.random.randint(1, 10, 100)
}

df = pd.DataFrame(data)
df['revenue'] = df['price'] * df['quantity']
df['month'] = df['date'].dt.month_name()
df['day_of_week'] = df['date'].dt.day_name()

print("Dataset Overview:")
print(df.head())
print("\nDataset Info:")
print(df.info())

# 1. BASIC PLOTTING WITH PANDAS
print("\n=== BASIC PANDAS PLOTTING ===")

# Create a figure with subplots
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
fig.suptitle('Basic Pandas Visualization Examples', fontsize=16)

# Line plot - Sales over time
df.set_index('date')['sales'].plot(ax=axes[0,0], title='Sales Trend', color='blue', linewidth=2)
axes[0,0].set_ylabel('Sales')
axes[0,0].grid(True)

# Bar plot - Sales by category
df.groupby('category')['sales'].sum().plot(kind='bar', ax=axes[0,1], title='Total Sales by Category', color='green')
axes[0,1].set_ylabel('Total Sales')
axes[0,1].tick_params(axis='x', rotation=45)

# Histogram - Customer age distribution
df['customer_age'].plot(kind='hist', ax=axes[1,0], title='Customer Age Distribution', bins=15, color='orange', alpha=0.7)
axes[1,0].set_xlabel('Age')
axes[1,0].set_ylabel('Frequency')

# Box plot - Price by category
df.boxplot(column='price', by='category', ax=axes[1,1])
axes[1,1].set_title('Price Distribution by Category')
axes[1,1].set_ylabel('Price')

plt.tight_layout()
plt.show()

# 2. ADVANCED PANDAS PLOTTING
print("\n=== ADVANCED PANDAS PLOTTING ===")

# Area plot - Cumulative sales by region
plt.figure(figsize=(12, 6))
sales_pivot = df.pivot_table(values='sales', index='date', columns='region', aggfunc='sum')
sales_pivot.plot(kind='area', alpha=0.6, title='Cumulative Sales by Region')
plt.ylabel('Sales')
plt.legend(title='Region')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# Scatter plot matrix
numeric_cols = ['sales', 'temperature', 'customer_age', 'price']
plt.figure(figsize=(12, 8))
pd.plotting.scatter_matrix(df[numeric_cols], alpha=0.6, figsize=(12, 8), diagonal='hist')
plt.suptitle('Scatter Plot Matrix of Numeric Variables', fontsize=16)
plt.tight_layout()
plt.show()

# 3. SEABORN INTEGRATION
print("\n=== SEABORN VISUALIZATIONS ===")

# Create subplots for seaborn examples
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
fig.suptitle('Seaborn Visualization Examples', fontsize=16)

# Violin plot - Price distribution by category
sns.violinplot(data=df, x='category', y='price', ax=axes[0,0])
axes[0,0].set_title('Price Distribution by Category (Violin Plot)')
axes[0,0].tick_params(axis='x', rotation=45)

# Heatmap - Correlation matrix
correlation_matrix = df[numeric_cols].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, ax=axes[0,1])
axes[0,1].set_title('Correlation Heatmap')

# Count plot - Category distribution
sns.countplot(data=df, x='category', ax=axes[1,0], hue='region', dodge=False)
axes[1,0].set_title('Product Count by Category and Region')
axes[1,0].tick_params(axis='x', rotation=45)

# Regression plot
sns.regplot(data=df, x='temperature', y='sales', ax=axes[1,1], scatter_kws={'alpha':0.5})
axes[1,1].set_title('Sales vs Temperature (with Regression Line)')

plt.tight_layout()
plt.show()

# 4. TIME SERIES VISUALIZATION
print("\n=== TIME SERIES VISUALIZATION ===")

plt.figure(figsize=(14, 10))

# Subplot 1: Sales trend with rolling average
plt.subplot(2, 2, 1)
df.set_index('date')['sales'].plot(label='Daily Sales', alpha=0.7)
df.set_index('date')['sales'].rolling(window=7).mean().plot(label='7-Day Moving Avg', linewidth=2)
plt.title('Sales Trend with Moving Average')
plt.ylabel('Sales')
plt.legend()
plt.grid(True, alpha=0.3)

# Subplot 2: Seasonal decomposition (simplified)
plt.subplot(2, 2, 2)
monthly_sales = df.groupby('month')['sales'].mean()
monthly_sales.plot(kind='bar', color='lightblue')
plt.title('Average Sales by Month')
plt.ylabel('Average Sales')
plt.xticks(rotation=45)

# Subplot 3: Stacked area by category
plt.subplot(2, 2, 3)
category_sales = df.pivot_table(values='sales', index='date', columns='category', aggfunc='sum')
category_sales.plot(kind='area', alpha=0.7, stacked=True)
plt.title('Stacked Sales by Category Over Time')
plt.ylabel('Sales')
plt.legend(title='Category')

# Subplot 4: Dual y-axis plot
plt.subplot(2, 2, 4)
ax1 = plt.gca()
ax2 = ax1.twinx()

df.set_index('date')['sales'].plot(ax=ax1, color='blue', label='Sales')
df.set_index('date')['temperature'].plot(ax=ax2, color='red', label='Temperature')
ax1.set_ylabel('Sales', color='blue')
ax2.set_ylabel('Temperature', color='red')
plt.title('Sales vs Temperature (Dual Y-Axis)')

plt.tight_layout()
plt.show()

# 5. CUSTOMIZATION AND STYLING
print("\n=== PLOT CUSTOMIZATION ===")

# Custom styled plot
plt.figure(figsize=(12, 6))

# Create a more sophisticated plot
ax = df.groupby('day_of_week')['sales'].mean().reindex([
    'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'
]).plot(kind='bar', 
        color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57', '#FF9FF3', '#54A0FF'],
        edgecolor='black', 
        linewidth=0.5)

plt.title('Average Sales by Day of Week', fontsize=16, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Average Sales', fontsize=12)
plt.xticks(rotation=45)

# Add value labels on bars
for i, v in enumerate(df.groupby('day_of_week')['sales'].mean().reindex([
    'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'
])):
    ax.text(i, v + 10, f'{v:.0f}', ha='center', va='bottom', fontweight='bold')

plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

# 6. SAVING PLOTS
print("\n=== SAVING AND EXPORTING ===")

# Create and save a high-quality plot
plt.figure(figsize=(10, 6))
df.groupby('region')['revenue'].sum().plot(
    kind='pie', 
    autopct='%1.1f%%', 
    colors=['#FF9999', '#66B2FF', '#99FF99', '#FFD700'],
    startangle=90
)
plt.title('Revenue Distribution by Region')
plt.ylabel('')  # Remove y-axis label for pie chart

# Save the plot
plt.savefig('revenue_by_region.png', dpi=300, bbox_inches='tight')
plt.savefig('revenue_by_region.pdf', bbox_inches='tight')
print("Plots saved as 'revenue_by_region.png' and 'revenue_by_region.pdf'")

plt.show()

# 7. INTERACTIVE PLOT EXAMPLE (Commentated)
print("\n=== INTERACTIVE PLOTTING (Example) ===")
print("""
# For interactive plots, you can use:
# import plotly.express as px
# 
# fig = px.scatter(df, x='temperature', y='sales', color='category',
#                 size='quantity', hover_data=['region'],
#                 title='Interactive Sales Visualization')
# fig.show()
""")

print("\n=== VISUALIZATION SUMMARY ===")
print(f"Dataset shape: {df.shape}")
print(f"Number of visualizations created: 7 main figures with multiple subplots")
print(f"Plot types demonstrated: Line, Bar, Histogram, Box, Area, Scatter, Heatmap, Violin, Pie")

Essential Plotting Methods

.plot() - Basic plotting
.plot.kind() - Specific plot types
plt.subplots() - Multiple plots
sns.FacetGrid() - Multi-plot grids
plt.savefig() - Save plots

Customization Options

Colors and styles
Labels and titles
Legends and annotations
Figure size and DPI
Grids and axes

Best Practices:

Choose the right chart type for your data
Keep visualizations simple and clear
Use appropriate colors and labels
Consider your audience when designing plots
Always include titles and axis labels

Pro Tip: Use plt.tight_layout() to automatically adjust subplot parameters and avoid overlapping elements.

← Time Series Exporting Data →

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Pandas Tutorial

Pandas Data Visualization

1. Basic Plotting with Pandas

2. Advanced Pandas Plotting

3. Seaborn Integration

4. Plot Customization

Choosing the Right Visualization

📈 Trends Over Time

📊 Comparisons

🔍 Distributions

🔗 Relationships

🍕 Proportions

🌐 Composition

Example: Comprehensive Data Visualization

Data Visualization Examples

Essential Plotting Methods

Customization Options

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