moved readme to sphinx

Author: ryanseq-gyg

.gitignore

@@ -94,11 +94,18 @@ ipython_config.py
 #   install all needed dependencies.
 # Pipfile.lock
 
-# UV
-#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-uv.lock
+# sphinx
+# Sphinx documentation build output
+build/
+
+# Sphinx cache and temporary files
+source/.doctrees/
+.doctrees/
+
+# Auto-generated API documentation (if using sphinx-apidoc)
+source/_autosummary/
+source/_generated/
+source/api/
 
 # poetry
 #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.

README.md

@@ -1,6 +1,7 @@
 Expectation Gallery
 ===================
 
+
 This page provides comprehensive documentation for all available DataFrame expectations.
 The expectations are automatically categorized and organized for easy browsing.
 

docs/source/adding_expectations.rst

@@ -0,0 +1,122 @@
+Getting Started
+===============
+
+Welcome to DataFrame Expectations! This guide will help you get up and running quickly with validating your Pandas and PySpark DataFrames.
+
+Installation
+------------
+
+Install DataFrame Expectations using pip:
+
+.. code-block:: bash
+
+   pip install dataframe-expectations
+
+Requirements
+~~~~~~~~~~~~
+
+* Python 3.10+
+* pandas >= 1.5.0
+* pyspark >= 3.3.0
+* tabulate >= 0.8.9
+
+Basic Usage
+-----------
+
+DataFrame Expectations provides a fluent API for building validation suites. Here's how to get started:
+
+Pandas Example
+~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+    import pandas as pd
+    from dataframe_expectations.expectations_suite import DataframeExpectationsSuite
+
+    # Create a sample DataFrame
+    df = pd.DataFrame({
+         "age": [25, 15, 45, 22],
+         "name": ["Alice", "Bob", "Charlie", "Diana"],
+         "salary": [50000, 60000, 80000, 45000]
+    })
+
+    # Build a validation suite
+    suite = (
+         DataframeExpectationsSuite()
+         .expect_min_rows(3)  # At least 3 rows
+         .expect_max_rows(10)  # At most 10 rows
+         .expect_value_greater_than("age", 18)  # All ages > 18
+         .expect_value_less_than("salary", 100000)  # All salaries < 100k
+         .expect_value_not_null("name")  # No null names
+    )
+
+    # Run validation
+     suite.run(df)
+
+
+PySpark Example
+~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+    from pyspark.sql import SparkSession
+    from dataframe_expectations.expectations_suite import DataframeExpectationsSuite
+
+    # Initialize Spark
+    spark = SparkSession.builder.appName("DataFrameExpectations").getOrCreate()
+
+    # Create a sample DataFrame
+    data = [
+         {"age": 25, "name": "Alice", "salary": 50000},
+         {"age": 15, "name": "Bob", "salary": 60000},
+         {"age": 45, "name": "Charlie", "salary": 80000},
+         {"age": 22, "name": "Diana", "salary": 45000}
+    ]
+    df = spark.createDataFrame(data)
+
+    # Build a validation suite (same API as Pandas!)
+    suite = (
+         DataframeExpectationsSuite()
+         .expect_min_rows(3)
+         .expect_max_rows(10)
+         .expect_value_greater_than("age", 18)
+         .expect_value_less_than("salary", 100000)
+         .expect_value_not_null("name")
+    )
+
+    # Run validation
+    suite.run(df)
+
+Example Output
+~~~~~~~~~~~~~~
+
+When validations fail, you'll see detailed output like this:
+
+.. code-block:: text
+
+    ========================== Running expectations suite ==========================
+    ExpectationMinRows (DataFrame contains at least 3 rows) ... OK
+    ExpectationMaxRows (DataFrame contains at most 10 rows) ... OK
+    ExpectationValueGreaterThan ('age' is greater than 18) ... FAIL
+    ExpectationValueLessThan ('salary' is less than 100000) ... OK
+    ExpectationValueNotNull ('name' is not null) ... OK
+    ============================ 4 success, 1 failures =============================
+
+    ExpectationSuiteFailure: (1/5) expectations failed.
+
+    ================================================================================
+    List of violations:
+    --------------------------------------------------------------------------------
+    [Failed 1/1] ExpectationValueGreaterThan ('age' is greater than 18): Found 1 row(s) where 'age' is not greater than 18.
+    Some examples of violations:
+    +-----+------+--------+
+    | age | name | salary |
+    +-----+------+--------+
+    | 15  | Bob  | 60000  |
+    +-----+------+--------+
+    ================================================================================
+
+How to contribute?
+------------------
+Contributions are welcome! You can enhance the library by adding new expectations, refining existing ones, or improving
+the testing framework or the documentation.

docs/source/expectations.rst

@@ -1,68 +1,22 @@
-DataFrame Expectations Documentation
-====================================
+DataFrame Expectations
+======================
 
-**DataFrameExpectations** is a Python library designed to validate **Pandas** and **PySpark**
-DataFrames using customizable, reusable expectations. It simplifies testing in data pipelines
-and end-to-end workflows by providing a standardized framework for DataFrame validation.
+**DataFrameExpectations** is a Python library designed to validate **Pandas** and **PySpark** DataFrames using
+customizable, reusable expectations. It simplifies testing in data pipelines and end-to-end workflows by providing a
+standardized framework for DataFrame validation.
+
+Instead of using different validation approaches for DataFrames, this library provides a standardized solution for this
+use case. As a result, any contributions made here, such as adding new expectations, can be leveraged by all users of
+the library.
+
+See the starter guide :doc:`here <getting_started>`.
+See the complete list of expectations :doc:`here <expectations>`.
 
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
    getting_started
+   adding_expectations
    expectations
    api_reference
-   contributing
-
-Quick Start
------------
-
-Install the package:
-
-.. code-block:: bash
-
-   pip install dataframe-expectations
-
-Basic usage with Pandas:
-
-.. code-block:: python
-
-   from dataframe_expectations.expectations_suite import DataframeExpectationsSuite
-   import pandas as pd
-
-   # Create a suite of expectations
-   suite = (
-       DataframeExpectationsSuite()
-       .expect_value_greater_than("age", 18)
-       .expect_value_less_than("age", 65)
-   )
-
-   # Create a DataFrame to validate
-   df = pd.DataFrame({"age": [25, 30, 45], "name": ["Alice", "Bob", "Charlie"]})
-
-   # Run the validation
-   suite.run(df)
-
-Basic usage with PySpark:
-
-.. code-block:: python
-
-   from dataframe_expectations.expectations_suite import DataframeExpectationsSuite
-   from pyspark.sql import SparkSession
-
-   # Initialize Spark session
-   spark = SparkSession.builder.appName("DataFrameExpectations").getOrCreate()
-
-   # Create a suite of expectations
-   suite = (
-       DataframeExpectationsSuite()
-       .expect_value_greater_than("age", 18)
-       .expect_value_less_than("age", 65)
-   )
-
-   # Create a PySpark DataFrame to validate
-   data = [{"age": 25, "name": "Alice"}, {"age": 30, "name": "Bob"}, {"age": 45, "name": "Charlie"}]
-   df = spark.createDataFrame(data)
-
-   # Run the validation
-   suite.run(df)