from math import e
import panel as pn 
import pandas as pd
import hvplot.pandas

# A class to process the dataframe and create visualizations according to the shape, nature, and distribution of the data.
class Visualizations:
    def __init__(self, df, pn: pn):
        self.df = df
        self.pn = pn

    def high_level_visualization(self):
        # Create a high-level visualization of the dataframe
        # Check the number of rows and columns in the dataframe
        rows, columns = self.df.shape
        
        if columns > 7:
            widget = self.pn.Column(
            self.pn.pane.Markdown(
                """
                # Data Preview

                ### Here is a preview of the dataset.
                """
            ),
            self.pn.Column(
                self.pn.Column(
                    self.pn.pane.Markdown("## Head"),
                    self.pn.pane.DataFrame(self.df.head(5)),
                ),
                self.pn.Column(
                    self.pn.pane.Markdown("## Describe"),
                    self.pn.pane.DataFrame(self.df.describe(),)
                ),
            ),
            
            )
            return widget
        else:
            widget = self.pn.Column(
            self.pn.pane.Markdown(
                """
                # Data Preview

                ### Here is a preview of the dataset.
                """
            ),
            self.pn.Row(
                self.pn.Column(
                    self.pn.pane.Markdown("## Head"),
                    self.pn.pane.DataFrame(self.df.head(5)),
                ),
                self.pn.Column(
                    self.pn.pane.Markdown("## Describe"),
                    self.pn.pane.DataFrame(self.df.describe(),)
                ),
            ),
            
            )
            return widget
    
    def data_shape_visualization(self):
        # Create a visualization of the shape of the dataframe
        widget = self.pn.Column(
            self.pn.pane.Markdown(
                """
                # Data Shape

                ### Here is the shape of the dataset.
                """
            ),
            self.pn.Column(
                self.pn.Column(
                    self.pn.pane.Markdown("## Shape"),
                    self.pn.pane.DataFrame(pd.DataFrame({'Rows': [self.df.shape[0]], 'Columns': [self.df.shape[1]]}),   
                )),
            ),
        )
        return widget
    
    def nature_visualization(self):
        # Create a visualization of the nature of the dataframe
        widget = self.pn.Column(
            self.pn.pane.Markdown(
                """
                # Data Nature

                ### Here is the nature of the dataset.
                """
            ),
            self.pn.Row(
                self.pn.Column(
                    self.pn.pane.Markdown("## Data Types"),
                    self.pn.pane.DataFrame(self.df.dtypes.to_frame(),)
                ),
                self.pn.Column(
                    self.pn.pane.Markdown("## Null Values"),
                    self.pn.pane.DataFrame(self.df.isnull().sum().to_frame(),)
                ),
            ),
        )
        return widget
    
    def distribution_visualization(self):
        # Create a visualization of the distribution of the dataframe
        widget = self.pn.Column(
            self.pn.pane.Markdown(
                """
                # Data Distribution

                ### Here is the distribution of the dataset.
                """
            ),
            self.pn.Column(
                self.pn.Column(
                    self.pn.pane.Markdown("## Distribution"),
                    self.df.hvplot.density()
                ),
            ),
        )
        return widget