chore: update files

Author: rajacsp

.ant

@@ -1,6 +1,4 @@
-<<<<<<< HEAD
 source:https://github.com/nandita2000/Alteryx-workflows
-=======
 
 
 ## Admin notes
@@ -430,4 +428,3 @@ source:https://github.com/nandita2000/Alteryx-workflows
 
 
 --------------------------------------------------------------------------------
->>>>>>> 8cab3f6 (chore: update files)

Accident Workflow.yxmd Accident_Workflow.yxmdAccident Workflow.yxmd renamed to Accident_Workflow.yxmd

@@ -11,6 +11,37 @@ In this repository, you will find:
 - **Documentation**: Guides, tutorials, and explanations on how to use, customize, and deploy the workflows.
 - **Scripts**: Any additional scripts or tools used alongside Alteryx workflows.
 
+## Airflow DAG Conversion
+
+This repository includes a tool to convert Alteryx workflows to Apache Airflow DAGs:
+
+- **`alterxy2airflow.py`**: Python script that converts `.yxmd` files to Airflow DAG Python files
+
+### Usage
+
+```bash
+# Convert a single workflow
+python alterxy2airflow.py "Accident Workflow.yxmd" "accident_dag.py"
+
+# Convert all workflows in a directory
+python alterxy2airflow.py --all "path/to/workflows" "output/dir"
+```
+
+### Supported Tools
+
+The converter supports the following Alteryx tool types:
+- File Input/Output (CSV, Excel)
+- Formula
+- Filter
+- Sort
+- Sample
+- Summarize/Aggregate
+- Browse
+- Multi-Row Formula
+- Table Composer
+- Charts
+- Macro Input/Output
+
 ## Usage
 
 To utilize the Alteryx workflows in this repository:

README.md

@@ -0,0 +1,175 @@
+#!/usr/bin/env python3
+"""
+Airflow DAG generated from Alteryx workflow: Accident_Workflow
+Source: Accident_Workflow.yxmd
+Generated: 2026-02-23 18:32:36
+"""
+
+from datetime import datetime, timedelta
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+import pandas as pd
+import os
+
+default_args = {
+    'owner': 'airflow',
+    'depends_on_past': False,
+    'email_on_failure': False,
+    'email_on_retry': False,
+    'retries': 1,
+    'retry_delay': timedelta(minutes=5),
+}
+
+dag = DAG(
+    'accident_workflow',
+    default_args=default_args,
+    description='DAG generated from Alteryx workflow: Accident_Workflow',
+    schedule_interval='@daily',
+    start_date=datetime(2024, 1, 1),
+    catchup=False,
+    tags=['alteryx', 'generated'],
+)
+
+    # Read CSV file
+    def read_1(context):
+        import pandas as pd
+        ti = context['ti']
+        df = pd.read_csv('C:\Users\ash_s\Downloads\archive (8)\US_Accidents_March23.csv')
+        ti.xcom_push(key='data', value=df.to_json())
+        return df.shape[0]
+    
+    task_read_1 = PythonOperator(
+        task_id='read_1',
+        python_callable=read_1,
+        dag=dag,
+    )
+
+    # Aggregate data
+    def aggregate_data_2(context):
+        import pandas as pd
+        ti = context['ti']
+        json_data = ti.xcom_pull(key='data', task_ids='read_1')
+        df = pd.read_json(json_data)
+        
+        # Group by: []
+        # Aggregations: []
+pass
+        
+        ti.xcom_push(key='data', value=df.to_json())
+        return df.shape[0]
+    
+    task_aggregate_data_2 = PythonOperator(
+        task_id='aggregate_data_2',
+        python_callable=aggregate_data_2,
+        dag=dag,
+    )
+
+    # Browse/Print data
+    def browse_3(context):
+        import pandas as pd
+        ti = context['ti']
+        json_data = ti.xcom_pull(key='data', task_ids='read_2')
+        df = pd.read_json(json_data)
+        
+        print(f"Browse 3 - Shape: {df.shape}")
+        print(df.head())
+        return df.shape[0]
+    
+    task_browse_3 = PythonOperator(
+        task_id='browse_3',
+        python_callable=browse_3,
+        dag=dag,
+    )
+
+    # Browse/Print data
+    def browse_4(context):
+        import pandas as pd
+        ti = context['ti']
+        json_data = ti.xcom_pull(key='data', task_ids='read_1')
+        df = pd.read_json(json_data)
+        
+        print(f"Browse 4 - Shape: {df.shape}")
+        print(df.head())
+        return df.shape[0]
+    
+    task_browse_4 = PythonOperator(
+        task_id='browse_4',
+        python_callable=browse_4,
+        dag=dag,
+    )
+
+    # Filter data
+    def filter_data_5(context):
+        import pandas as pd
+        ti = context['ti']
+        json_data = ti.xcom_pull(key='data', task_ids='read_2')
+        df = pd.read_json(json_data)
+        
+        # Apply filter: Timezone IsNotNull Serious
+        try:
+            df = df.query('Timezone IsNotNull Serious')
+        except:
+            # Fallback for complex expressions
+            pass
+        
+        ti.xcom_push(key='data', value=df.to_json())
+        return df.shape[0]
+    
+    task_filter_data_5 = PythonOperator(
+        task_id='filter_data_5',
+        python_callable=filter_data_5,
+        dag=dag,
+    )
+
+    # Browse/Print data
+    def browse_7(context):
+        import pandas as pd
+        ti = context['ti']
+        json_data = ti.xcom_pull(key='data', task_ids='read_17')
+        df = pd.read_json(json_data)
+        
+        print(f"Browse 7 - Shape: {df.shape}")
+        print(df.head())
+        return df.shape[0]
+    
+    task_browse_7 = PythonOperator(
+        task_id='browse_7',
+        python_callable=browse_7,
+        dag=dag,
+    )
+
+    # Apply formulas
+    def apply_formulas_16(context):
+        import pandas as pd
+        import re
+        ti = context['ti']
+        json_data = ti.xcom_pull(key='data', task_ids='read_5')
+        df = pd.read_json(json_data)
+        
+        # Apply formulas
+
+        
+        ti.xcom_push(key='data', value=df.to_json())
+        return df.shape[0]
+    
+    task_apply_formulas_16 = PythonOperator(
+        task_id='apply_formulas_16',
+        python_callable=apply_formulas_16,
+        dag=dag,
+    )
+
+
+
+# Define task dependencies
+task_1 >> task_2
+task_1 >> task_4
+task_2 >> task_3
+task_2 >> task_5
+task_5 >> task_16
+task_16 >> task_17
+task_17 >> task_7
+task_17 >> task_22
+task_17 >> task_22
+
+if __name__ == "__main__":
+    dag.test()