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# Styling and Themes
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Configure plot aesthetics, themes, color palettes, and plotting contexts with extensive customization options for publication-quality graphics. These functions provide comprehensive control over the visual appearance of seaborn and matplotlib plots.
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## Capabilities
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### Theme Configuration
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Set overall aesthetic parameters and make seaborn the default matplotlib style.
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```python { .api }
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def set_theme(
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    context="notebook",
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    style="darkgrid",
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    palette="deep",
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    font="sans-serif",
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    font_scale=1,
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    color_codes=True,
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    rc=None
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):
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    """
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    Set multiple theme parameters in one step.
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    Parameters:
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    - context: str, plotting context ("paper", "notebook", "talk", "poster")
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    - style: str, axes style ("darkgrid", "whitegrid", "dark", "white", "ticks")
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    - palette: str or list, color palette
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    - font: str, font family
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    - font_scale: float, scaling factor for font sizes
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    - color_codes: bool, use seaborn color codes
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    - rc: dict, matplotlib rcParams to override
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    """
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def set(
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    context="notebook",
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    style="darkgrid", 
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    palette="deep",
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    font="sans-serif",
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    font_scale=1,
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    color_codes=True,
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    rc=None
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):
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    """
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    Alias for set_theme() - sets aesthetic parameters in one step.
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    """
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```
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### Style Management
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Control the appearance of plot axes and backgrounds.
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```python { .api }
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def axes_style(style=None, rc=None):
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    """
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    Get the parameters that control the general style of the plots.
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    Parameters:
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    - style: str, style name ("darkgrid", "whitegrid", "dark", "white", "ticks")
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    - rc: dict, parameter overrides
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    Returns:
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    dict of matplotlib rcParams
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    Can be used as context manager:
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    with sns.axes_style("white"):
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        # plots with white background
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    """
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def set_style(style, rc=None):
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    """
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    Set the aesthetic style of the plots.
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    Parameters:
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    - style: str, style name or dict of parameters
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    - rc: dict, parameter overrides
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    """
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```
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### Plotting Context
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Control the scale of plot elements for different contexts.
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```python { .api }
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def plotting_context(context=None, font_scale=1, rc=None):
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    """
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    Get the parameters that control the scaling of plot elements.
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    Parameters:
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    - context: str, context name ("paper", "notebook", "talk", "poster")
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    - font_scale: float, scaling factor for font sizes
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    - rc: dict, parameter overrides
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    Returns:
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    dict of matplotlib rcParams
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    Can be used as context manager:
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    with sns.plotting_context("poster"):
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        # plots scaled for poster presentation
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    """
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def set_context(context, font_scale=1, rc=None):
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    """
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    Set the plotting context parameters.
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    Parameters:
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    - context: str, context name or dict of parameters
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    - font_scale: float, scaling factor for font sizes
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    - rc: dict, parameter overrides
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    """
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```
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### Palette Management
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Set and retrieve color palettes for consistent color schemes.
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```python { .api }
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def set_palette(palette, n_colors=None, desat=None, color_codes=False):
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    """
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    Set the matplotlib color cycle using a seaborn palette.
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    Parameters:
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    - palette: str, list, or dict, palette specification
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    - n_colors: int, number of colors in cycle
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    - desat: float, desaturation factor (0-1)
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    - color_codes: bool, use seaborn color codes
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    """
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def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
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    """
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    Return a list of colors or continuous colormap defining a palette.
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    Parameters:
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    - palette: str, list, or None, palette specification
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    - n_colors: int, number of colors to return
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    - desat: float, desaturation factor (0-1)
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    - as_cmap: bool, return matplotlib Colormap object
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    Returns:
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    list of RGB tuples or matplotlib Colormap
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    """
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```
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### Reset Functions
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Restore default settings.
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```python { .api }
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def reset_defaults():
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    """Restore all seaborn style settings to default values."""
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def reset_orig():
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    """Restore all matplotlib rcParams to original settings before seaborn import."""
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```
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### Color Code Configuration
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Control interpretation of matplotlib color abbreviations.
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```python { .api }
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def set_color_codes(palette="deep"):
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    """
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    Change how matplotlib color shorthands are interpreted.
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    Parameters:
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    - palette: str, palette name for color codes
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    Calling this changes the meaning of abbreviations like 'b', 'g', 'r', etc.
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    """
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```
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## Available Styles
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### Axes Styles
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- **"darkgrid"**: Dark background with white grid lines (default)
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- **"whitegrid"**: White background with gray grid lines  
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- **"dark"**: Dark background without grid
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- **"white"**: White background without grid
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- **"ticks"**: White background with ticks but no grid
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### Plotting Contexts
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- **"paper"**: Smallest scale, for journal figures
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- **"notebook"**: Medium scale, for Jupyter notebooks (default)
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- **"talk"**: Larger scale, for presentations
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- **"poster"**: Largest scale, for posters
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## Usage Examples
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### Basic Theme Setup
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```python
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import seaborn as sns
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import matplotlib.pyplot as plt
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# Set overall theme
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sns.set_theme(style="whitegrid", palette="pastel")
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# Create a plot - will use theme settings
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tips = sns.load_dataset("tips")
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sns.boxplot(data=tips, x="day", y="total_bill")
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plt.show()
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```
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### Style Context Manager
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```python
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# Temporarily change style
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with sns.axes_style("white"):
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    sns.scatterplot(data=tips, x="total_bill", y="tip")
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    plt.show()
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# Back to default style
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sns.boxplot(data=tips, x="day", y="total_bill")
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plt.show()
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```
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### Context Scaling
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```python
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# Scale for poster presentation
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sns.set_context("poster", font_scale=1.2)
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sns.barplot(data=tips, x="day", y="total_bill")
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plt.show()
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# Reset to notebook context
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sns.set_context("notebook")
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```
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### Custom Palette
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```python
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# Set custom color palette
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custom_colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#FFA07A"]
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sns.set_palette(custom_colors)
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# Plots will use custom colors
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sns.countplot(data=tips, x="day")
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plt.show()
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```
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### Color Codes
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```python
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# Enable seaborn color codes
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sns.set_color_codes("muted")
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# Now 'b', 'g', 'r' use seaborn colors
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plt.plot([1, 2, 3], [1, 4, 2], 'b-')  # Uses seaborn blue
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plt.show()
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```
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### Context Manager for Scaling
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```python
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# Temporarily scale for presentation
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with sns.plotting_context("talk", font_scale=1.5):
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    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
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    sns.scatterplot(data=tips, x="total_bill", y="tip", ax=axes[0,0])
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    sns.boxplot(data=tips, x="day", y="total_bill", ax=axes[0,1])
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    sns.histplot(data=tips, x="total_bill", ax=axes[1,0])
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    sns.barplot(data=tips, x="day", y="total_bill", ax=axes[1,1])
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    plt.tight_layout()
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    plt.show()
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```
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### Reset to Defaults
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```python
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# Reset all seaborn settings
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sns.reset_defaults()
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# Or reset to pre-seaborn matplotlib settings
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sns.reset_orig()
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```
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### Fine-tuned Theme
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```python
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# Detailed theme customization
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sns.set_theme(
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    context="notebook",
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    style="ticks",
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    palette="Set2",
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    font="serif",
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    font_scale=1.1,
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    rc={
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        "axes.spines.right": False,
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        "axes.spines.top": False,
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        "axes.linewidth": 1.2,
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        "grid.alpha": 0.4
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    }
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)
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sns.scatterplot(data=tips, x="total_bill", y="tip", hue="smoker")
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plt.show()
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```
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## Types
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```python { .api }
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# Style options
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AxesStyle = Literal["darkgrid", "whitegrid", "dark", "white", "ticks"]
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PlottingContext = Literal["paper", "notebook", "talk", "poster"]
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# Palette specifications
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PaletteSpec = str | list | dict | None
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# RC parameters
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RCParams = dict[str, Any]  # matplotlib rcParams dictionary
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```