Feature docs frameworks (#234)

* Added version for documents

* Working example documentation

cuallee/__init__.py

@@ -284,7 +284,7 @@ def add_rule(self, method: str, *arg):
         
         Args:
             method (str): Check name
-            args (list): Parameters of the check
+            arg (list): Parameters of the check
         """
         return operator.methodcaller(method, *arg)(self)
 
@@ -534,14 +534,14 @@ def is_legit(self, column: str, pct: float = 1.0):
         Validation for string columns giving wrong signal about completeness due to empty strings. 
         
         Useful for reading CSV files and preventing empty strings being reported as valid records.
-        This is an `alias` implementation of the `has_pattern` rule using `^\S+$` as the pattern 
+        This is an `alias` implementation of the `has_pattern` rule using `not black space` as the pattern 
         Which validates the presence of non-empty characters between the begining and end of a string.
         
         Args:
             column (str): Column name in dataframe
             pct (float): The threshold percentage required to pass
         """
-        Rule("has_pattern", column, "^\S+$", CheckDataType.STRING, pct) >> self._rule
+        Rule("has_pattern", column, r"^\S+$", CheckDataType.STRING, pct) >> self._rule
         return self
 
     def has_min(self, column: str, value: float):

docs/bigquery/index.md

@@ -0,0 +1,120 @@
+# :simple-googlebigquery: BigQuery
+In order to follow this examples, make sure your installation is all set for `bigquery`
+
+!!! success "Install"
+    ``` sh
+    pip install cuallee
+    pip install cuallee[bigquery]
+    ```
+
+## Pre-Requisites
+
+You will need a Google Cloud account active, with the BigQuery API enabled to proceed with this examples.<br/>
+Once your account is enabled with BigQuery, you will have to export a `service account` credential file in `json` format.
+
+`cuallee` will read the environment variable `GOOGLE_APPLICATION_CREDENTIALS` expecting the name of the file that contains your `service account credentials`
+
+!!! warning "Cost Associated"
+    Be aware that running `cuallee` checks in `bigquery` incurs into cloud costs. 
+
+
+The process inside `cuallee` to handle the credentials is as follows:
+
+
+!!! tip "Credentials Handling"
+    ``` py
+    import os
+    from google.cloud import bigquery
+
+    credentials = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')
+    client = bigquery.Client(project="GOOGLE_CLOUD_PROJECT_IDENTIFIER", credentials=credentials)
+
+    ```
+
+
+### is_complete
+
+It validates the _completeness_ attribute of a data set. It confirms that a column does not contain `null` values   .
+
+
+???+ example "is_complete"
+
+    === ":material-checkbox-marked-circle:{ .ok } PASS"
+
+        In this example, we validate that the column `id` does not have any missing values.
+
+        ``` py
+        from google.cloud import bigquery
+        from cuallee import Check
+        
+        # Public dataset in BigQuery
+        df = bigquery.dataset.Table("bigquery-public-data.chicago_taxi_trips.taxi_trips")
+        check = Check()
+        check.is_complete("taxi_id")
+
+        # Validate
+        check.validate(df)
+        ```
+
+        :material-export: __output:__ 
+
+        ``` markdown
+                    timestamp          check    level   column         rule value       rows  violations  pass_rate  pass_threshold status
+        id                                                                                                                                  
+        1   2024-05-18 21:24:15  cuallee.check  WARNING  taxi_id  is_complete   N/A  102589284           0        1.0             1.0   PASS
+        ```
+
+    === ":material-close-circle:{ .ko } FAIL"
+
+        In this example, we intentionally place 2 `null` values in the dataframe and that produces a `FAIL` check as result.
+
+        ``` py
+        from google.cloud import bigquery
+        from cuallee import Check
+        
+        # Public dataset in BigQuery
+        df = bigquery.dataset.Table("bigquery-public-data.chicago_taxi_trips.taxi_trips")
+        check = Check()
+        check.is_complete("trip_end_timestamp")
+
+        # Validate
+        check.validate(df)
+        ```
+
+        :material-export: __output:__ 
+
+        ``` markdown
+                    timestamp          check    level              column         rule value       rows  violations  pass_rate  pass_threshold status
+        id                                                                                                                                             
+        1   2024-05-18 21:24:15  cuallee.check  WARNING  trip_end_timestamp  is_complete   N/A  102589284        1589   0.999985             1.0   FAIL
+        ```
+
+        
+        
+    === ":material-alert-circle:{ .kk } THRESHOLD"
+
+        In this example, we validate reuse the data frame with empty values from the previous example, however we set our tolerance via the `pct` parameter on the rule `is_complete` to `0.6`. Producing now a `PASS` result on the check, regardless of the `2` present `null` values.
+
+        ``` py
+        from google.cloud import bigquery
+        from cuallee import Check
+        
+        # Public dataset in BigQuery
+        df = bigquery.dataset.Table("bigquery-public-data.chicago_taxi_trips.taxi_trips")
+        check = Check()
+        check.is_complete("trip_end_timestamp", pct=0.9)
+
+        # Validate
+        check.validate(df)
+        ```
+
+        :material-export: __output:__ 
+
+        ``` markdown
+                    timestamp          check    level              column         rule value       rows  violations  pass_rate  pass_threshold status
+        id                                                                                                                                             
+        1   2024-05-18 21:24:15  cuallee.check  WARNING  trip_end_timestamp  is_complete   N/A  102589284        1589   0.999985             0.9   PASS
+        ```
+
+        
+