diff --git a/all in one file.py b/all in one file.py
new file mode 100644
index 0000000..83961f2
--- /dev/null
+++ b/all in one file.py	
@@ -0,0 +1,136 @@
+""" 
+1.copy the current code into the code editor of your choice to run python. 
+2.In line 122 You insert the url into the inverted commas and run the program with internet and the files will be created in yor folder 
+
+"""
+Shopify scraper
+Description: Scrapes products from a Shopify store by parsing products.json and converting it to a pandas DataFrame.
+Author: Matt Clarke
+"""
+
+import json
+import pandas as pd
+import requests
+
+def get_json(url: str, page: int) -> str:
+    """
+    Get Shopify products.json from a store URL.
+
+    Args:
+        url (str): URL of the store.
+        page (int): Page number of the products.json.
+    Returns:
+        products_json: Products.json from the store.
+    """
+
+    response = requests.get(f'{url}/products.json?limit=250&page={page}', timeout=5)
+    products_json = response.text
+    response.raise_for_status()
+    return products_json
+
+def _products_json_to_df(products_json: str) -> pd.DataFrame:
+    """
+    Convert products.json to a pandas DataFrame.
+
+    Args:
+        products_json (json): Products.json from the store.
+    Returns:
+        df: Pandas DataFrame of the products.json.
+    """
+
+    products_dict = json.loads(products_json)
+    df = pd.DataFrame.from_dict(products_dict['products'])
+    return df
+
+def get_products(url: str) -> pd.DataFrame:
+    """
+    Get all products from a store.
+
+    Returns:
+        df: Pandas DataFrame of the products.json.
+    """
+
+    results = True
+    page = 1
+    df = pd.DataFrame()
+
+    while results:
+        products_json = get_json(url, page)
+        products_dict = _products_json_to_df(products_json)
+
+        if len(products_dict) == 0:
+            break
+        else:
+            df = pd.concat([df, products_dict], ignore_index=True)
+            page += 1
+
+    df['url'] = f"{url}/products/" + df['handle']
+    return df
+
+def get_variants(products: pd.DataFrame) -> pd.DataFrame:
+    """Get variants from a table of products.
+
+    Args:
+        products (pd.DataFrame): Pandas dataframe of products from get_products()
+
+    Returns:
+        variants (pd.DataFrame): Pandas dataframe of variants
+    """
+
+    products['id'].astype(int)
+    df_variants = pd.DataFrame()
+
+    for row in products.itertuples(index='True'):
+        for variant in getattr(row, 'variants'):
+            df_variants = pd.concat([df_variants, pd.DataFrame.from_records(variant, index=[0])])
+
+    df_variants['id'].astype(int)
+    df_variants['product_id'].astype(int)
+    df_product_data = products[['id', 'title', 'vendor']]
+    df_product_data = df_product_data.rename(columns={'title': 'product_title', 'id': 'product_id', 'vendor': 'product_vendor'})
+    df_variants = df_variants.merge(df_product_data, left_on='product_id', right_on='product_id', validate='m:1')
+    return df_variants
+
+def _flatten_column_to_dataframe(df: pd.DataFrame, col: str) -> pd.DataFrame:
+    """Return a Pandas dataframe based on a column that contains a list of JSON objects.
+
+    Args:
+        df (Pandas dataframe): The dataframe to be flattened.
+        col (str): The name of the column that contains the JSON objects.
+
+    Returns:
+        Pandas dataframe: A new dataframe with the JSON objects expanded into columns.
+    """
+
+    rows = [item for row in df[col] for item in row]
+    return pd.DataFrame(rows)
+
+def get_images(df_products: pd.DataFrame) -> pd.DataFrame:
+    """Get images from a list of products.
+
+    Args:
+        df_products (pd.DataFrame): Pandas dataframe of products from get_products()
+
+    Returns:
+        images (pd.DataFrame): Pandas dataframe of images
+    """
+
+    return _flatten_column_to_dataframe(df_products, 'images')
+
+
+
+url = "https://yourshopifydomain.com"
+
+products = get_products(url)
+products.to_csv('products.csv', index=False)
+print('Products: count=', len(products))
+
+
+variants = get_variants(products)
+variants.to_csv('variants.csv', index=False)
+print('Variants: count=', len(variants))
+
+
+images = get_images(products)
+images.to_csv('images.csv', index=False)
+print('Images: count=', len(images))
diff --git a/shopify_scraper/scraper.py b/shopify_scraper/scraper.py
index a2973ed..c3408ca 100644
--- a/shopify_scraper/scraper.py
+++ b/shopify_scraper/scraper.py
@@ -121,7 +121,7 @@ def json_list_to_df(df, col):
     """
 
     rows = []
-    for index, row in df[col].iteritems():
+    for index, row in df[col].items():
         for item in row:
             rows.append(item)
     df = pd.DataFrame(rows)