Aesthetic Mappings

def aes(x=None, y=None, **kwargs):
    """
    Create aesthetic mappings between data variables and visual properties.
    
    Common aesthetic parameters:
    - x, y: str, column names for position aesthetics
    - color/colour: str, column name for color aesthetic
    - fill: str, column name for fill color aesthetic  
    - size: str, column name for size aesthetic
    - alpha: str, column name for transparency aesthetic
    - shape: str, column name for point shape aesthetic
    - linetype: str, column name for line type aesthetic
    - stroke: str, column name for stroke width aesthetic
    - group: str, column name for grouping aesthetic
    
    Returns:
    aes object (extends dict) containing aesthetic mappings
    """

from plotnine import ggplot, aes, geom_point
import pandas as pd

data = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [2, 5, 3, 8, 7], 
    'color_var': ['A', 'B', 'A', 'B', 'A'],
    'size_var': [1, 3, 2, 4, 2]
})

# Basic position mapping
ggplot(data, aes(x='x', y='y'))

# Multiple aesthetic mappings
ggplot(data, aes(x='x', y='y', color='color_var', size='size_var'))

def after_stat(x):
    """
    Reference values computed by statistical transformations.
    
    Parameters:
    - x: str, name of computed statistic
    
    Common computed statistics:
    - count: number of observations
    - density: density values
    - prop: proportions  
    - level: contour levels
    - quantile: quantile values
    
    Returns:
    after_stat object for use in aesthetic mappings
    """

def after_scale(x):
    """
    Reference values after scale transformation.
    
    Parameters:
    - x: str, name of aesthetic after scaling
    
    Returns:
    after_scale object for use in aesthetic mappings
    """

def stage(start=None, after_stat=None, after_scale=None):
    """
    Control evaluation timing of aesthetic mappings.
    
    Parameters:
    - start: expression evaluated on original data
    - after_stat: expression evaluated after statistical transformation
    - after_scale: expression evaluated after scale transformation
    
    Returns:
    stage object that evaluates expressions at appropriate times
    """

# Map fill to computed count in histogram
ggplot(data, aes(x='value')) + geom_histogram(aes(fill=after_stat('count')))

# Map color to density values
ggplot(data, aes(x='x', y='y')) + geom_density_2d(aes(color=after_stat('level')))

# Access scaled values
ggplot(data, aes(x='x', y='y')) + geom_point(aes(size=after_scale('x')))

# Stage different computations
ggplot(data, aes(x='value')) + geom_histogram(
    aes(fill=stage(start='group', after_stat='count'))
)

# Primary position
aes(x='column_name', y='column_name')

# Secondary position (for range geoms)
aes(xmin='min_col', xmax='max_col', ymin='min_col', ymax='max_col')

# Positioning for specific geoms
aes(xend='end_x', yend='end_y')  # For segments
aes(slope='slope_col', intercept='intercept_col')  # For ablines

# Color (for points, lines, text)
aes(color='group_column')
aes(colour='group_column')  # British spelling also supported

# Fill (for areas, bars, polygons)
aes(fill='category_column')

# Both color and fill
aes(color='group', fill='category')

# Size (for points and lines)
aes(size='numeric_column')

# Shape (for points)
aes(shape='category_column')

# Stroke width
aes(stroke='width_column')

# Alpha (transparency)
aes(alpha='transparency_column')

# Line type
aes(linetype='line_category')

# Grouping (important for line plots)
aes(group='grouping_column')

# Global aesthetics (apply to all layers)
ggplot(data, aes(x='x', y='y', color='group')) + \
    geom_point() + \
    geom_line()

# Local aesthetics (apply to specific layer)
ggplot(data, aes(x='x', y='y')) + \
    geom_point(aes(color='group')) + \
    geom_line(color='blue')  # Fixed value, not mapped

# Mapping variable to aesthetic (inside aes())
ggplot(data, aes(x='x', y='y')) + geom_point(aes(color='group'))

# Setting fixed value (outside aes())
ggplot(data, aes(x='x', y='y')) + geom_point(color='blue', size=3)

# Mixed approach
ggplot(data, aes(x='x', y='y')) + geom_point(aes(color='group'), size=3)

# Histogram with fill mapped to computed count
ggplot(data, aes(x='value')) + \
    geom_histogram(aes(fill=after_stat('count')), bins=20)

# Bar plot with proportion instead of count
ggplot(data, aes(x='category')) + \
    geom_bar(aes(y=after_stat('prop')))

# Density plot with color mapped to computed density
ggplot(data, aes(x='value')) + \
    geom_density(aes(color=after_stat('density')))

# Transform data in aesthetic mapping
ggplot(data, aes(x='x', y='y**2'))  # Square the y values

# Conditional mapping
ggplot(data, aes(x='x', y='y', color='value > 5'))

# String concatenation
ggplot(data, aes(x='x', y='y', label='name + ": " + str(value)'))