Core Plot Construction

class ggplot:
    """
    Main plotting class that serves as the foundation for all plots.
    
    Parameters:
    - data: pandas.DataFrame, the data to be plotted
    - mapping: aes object, default aesthetic mappings for the plot
    
    Methods:
    - save(): Save plot to file
    - show(): Display plot  
    - draw(): Force drawing of the plot
    """
    def __init__(self, data=None, mapping=None): ...
    
    def save(self, filename, width=6.4, height=4.8, dpi=100, format=None, 
             units='in', limitsize=True, verbose=True, **kwargs): ...
    
    def show(self): ...
    
    def draw(self, return_ggplot=False): ...

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

data = pd.DataFrame({'x': [1, 2, 3], 'y': [1, 4, 9]})
plot = ggplot(data, aes(x='x', y='y')) + geom_point()
print(plot)  # Display the plot

def qplot(x=None, y=None, data=None, facets="", margins=False, geom="auto", 
          xlim=None, ylim=None, log=None, main=None, xlab=None, ylab=None, 
          asp=None, **kwargs):
    """
    Quick plot function for simple visualizations.
    
    Parameters:
    - x, y: str or array-like, variables to plot
    - data: pandas.DataFrame, data source
    - facets: str, faceting formula
    - margins: bool, whether to display marginal facets
    - geom: str or list, geometric object(s) to use
    - xlim, ylim: tuple, axis limits
    - log: str, which axes to log transform ('x', 'y', 'xy')
    - main: str, plot title
    - xlab, ylab: str, axis labels
    - asp: float, aspect ratio
    - **kwargs: additional aesthetic mappings and parameters
    
    Returns:
    ggplot object
    """

import pandas as pd
from plotnine import qplot

data = pd.DataFrame({'x': [1, 2, 3], 'y': [1, 4, 9]})
plot = qplot(x='x', y='y', data=data, geom='point')

def ggsave(filename, plot=None, width=6.4, height=4.8, dpi=100, format=None,
           units='in', limitsize=True, verbose=True, **kwargs):
    """
    Save plot to file (alias for ggplot.save method).
    
    Parameters:
    - filename: str, output filename
    - plot: ggplot object, plot to save (uses last plot if None)
    - width, height: float, dimensions in specified units
    - dpi: int, resolution in dots per inch
    - format: str, file format (inferred from filename if None)
    - units: str, units for width/height ('in', 'cm', 'mm')
    - limitsize: bool, whether to limit size to reasonable values
    - verbose: bool, whether to print save information
    - **kwargs: additional matplotlib savefig parameters
    """

def save_as_pdf_pages(plots, filename=None, **kwargs):
    """
    Save multiple plots to a single PDF file, one plot per page.
    
    Parameters:
    - plots: list of ggplot objects
    - filename: str, output PDF filename
    - **kwargs: additional parameters passed to PdfPages and savefig
    
    Returns:
    PdfPages object if filename is None, otherwise None
    """

# Save single plot
plot.save('my_plot.png', width=8, height=6, dpi=300)

# Using ggsave
ggsave('my_plot.pdf', plot, width=10, height=8)

# Save multiple plots to PDF
plots = [plot1, plot2, plot3]
save_as_pdf_pages(plots, 'multiple_plots.pdf')

# Display plot in Jupyter notebook or interactive environment
plot  # Simply evaluating the plot object displays it

# Force display
print(plot)

# In Jupyter notebooks, plots display automatically
plot  # Shows plot inline

# Start with data and aesthetic mappings
p = ggplot(data, aes(x='column1', y='column2'))

# Add geometric objects
p = p + geom_point()

# Add additional layers
p = p + geom_smooth(method='lm')

# Customize appearance  
p = p + theme_minimal() + labs(title='My Plot')

# Display or save
print(p)
p.save('plot.png')

plot = (ggplot(data, aes(x='x', y='y')) +
        geom_point(color='blue', size=3) +
        geom_smooth(method='lm', color='red') +
        theme_minimal() +
        labs(title='Scatter Plot with Trend Line',
             x='X Variable', y='Y Variable'))

# Quick scatter plot
qplot('x', 'y', data=df, geom='point')

# Quick histogram
qplot('x', data=df, geom='histogram', bins=30)

# Quick box plot by group
qplot('group', 'value', data=df, geom='boxplot')