mds_2025_helper_functions.eda
=============================

.. py:module:: mds_2025_helper_functions.eda


Functions
---------

.. autoapisummary::

   mds_2025_helper_functions.eda.perform_eda


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

.. py:function:: perform_eda(dataframe, rows=5, cols=2)

   A universal EDA function to generate data summaries and visualize features.

   :param dataframe: The input dataset for EDA.
   :type dataframe: pd.DataFrame
   :param rows: Number of rows in the grid layout for visualizations.
   :type rows: int
   :param cols: Number of columns in the grid layout for visualizations.
   :type cols: int

   :returns: None

   .. rubric:: Example

   >>> import pandas as pd
   >>> from mds_2025_helper_functions.eda import perform_eda
   >>> data = {
   ...     'Age': [25, 32, 47, 51, 62],
   ...     'Salary': [50000, 60000, 120000, 90000, 85000],
   ...     'Department': ['HR', 'Finance', 'IT', 'Finance', 'HR'],
   ...     'JoiningDate': pd.to_datetime(['2015-01-01', '2016-07-15', '2017-03-12', '2018-06-01', '2019-08-19']),
   ...     'Bonus': [0, 5000, 12000, 7500, 7000]
   ... }
   >>> df = pd.DataFrame(data)

   >>> # Use the function to perform EDA
   >>> perform_eda(df, rows=2, cols=2)

   # The above call will generate the following:
   # 1. A dataset overview
   # 2. Basic statistics for all columns
   # 3. A missing values report and heatmap (if applicable)
   # 4. Correlation heatmap for numeric columns
   # 5. Feature distribution/count plots
   # 6. Scatterplots for numeric feature pairs (if applicable)
   # 7. Outlier detection report for numeric features

   # Note: Visualizations will be shown as matplotlib and seaborn plots.