summarease.summarize_numeric
============================

.. py:module:: summarease.summarize_numeric


Functions
---------

.. autoapisummary::

   summarease.summarize_numeric.plot_numeric_density
   summarease.summarize_numeric.plot_correlation_heatmap
   summarease.summarize_numeric.summarize_numeric


Module Contents
---------------

.. py:function:: plot_numeric_density(dataset_numeric: pandas.DataFrame)

   Generate density plots for each numeric column in the provided dataset. Each plot represents the
   distribution of values in a numeric column using a density estimate.

   Parameters:
   ----------
   dataset_numeric : pd.DataFrame
       A pandas DataFrame containing numeric columns. The function will generate a density plot for
       each numeric column in the dataset.

   Returns:
   -------
   alt.Chart
       An Altair chart object representing a vertical concatenation of density plots for each numeric column.
       The plots are grouped into rows with a maximum of 4 plots per row.

   Example:
   -------
   >>> plot_numeric_density(dataset_numeric=df)


.. py:function:: plot_correlation_heatmap(dataset_numeric: pandas.DataFrame)

   Generate and save a correlation heatmap for the specified numeric columns in a dataset.

   Parameters:
   ----------
   dataset : pd.DataFrame
       The input dataset containing the data for the heatmap.

   numeric_columns : list of str, optional
       A list of column names to include in the correlation heatmap. If None, all numeric columns in the dataset will be used.

   save_path : str, optional
       File path to save the generated heatmap. If None, the plot will not be saved.

   Returns:
   -------
   alt.Chart
       The Altair chart visualizing the correlation heatmap.

   Example:
   -------
   >>> plot_correlation_heatmap(dataset=df, numeric_columns=["col1", "col2", "col3"], save_path="heatmap.png")


.. py:function:: summarize_numeric(dataset: pandas.DataFrame, summarize_by: str = 'table')

   Summarize the numeric variables in the dataset by providing the summary statistics (e.g., mean,
   standard deviation, min, max, etc.) for each numeric column or plotting the correlation heatmap
   to visualize the relationships between numeric variables. The summary type provided is
   requested based on the `summarize_by` argument.

   Parameters:
   ----------
       dataset : pd.DataFrame
           The dataset to analyze.
       summarize_by (str):
           The format for summarizing the numeric variables.
                           Options are "table" (default) or "plot". If "table", a summary table is
                           generated with statistics for each numeric column. If "plot", a correlation
                           heatmap is displayed to visualize the correlation between numeric variables.

   Returns:
   -------
       A table of summary statistics or a plot (correlation heatmap), depending on the
             `summarize_by` argument.

   Notes:
   ------
       - The correlation heatmap is only applicable if there are two or more numeric columns in the dataset.
       - The summary statistics for numeric columns are computed using `df.describe()`, and additional details
         (such as count, mean, standard deviation, min, max, etc.) will be included.

   Example:
   -------
   >>> summarize_numeric(dataset=df, summarize_by="table")