diff --git a/docs/website/docs/general-usage/customising-pipelines/removing_columns.md b/docs/website/docs/general-usage/customising-pipelines/removing_columns.md
index 39f38579f4..fa936566d0 100644
--- a/docs/website/docs/general-usage/customising-pipelines/removing_columns.md
+++ b/docs/website/docs/general-usage/customising-pipelines/removing_columns.md
@@ -10,72 +10,82 @@ Removing columns before loading data into a database is a reliable method to eli
 unnecessary fields. For example, in the given scenario, a source is created with a "country_id" column,
 which is then excluded from the database before loading.
 
-```python
-import dlt
-
-@dlt.source
-def dummy_source(prefix: str = None):
-    #This function creates a dummy data source.
-    @dlt.resource(write_disposition='replace')
-    def dummy_data():
-        for _ in range(3):
-            yield {'id': _, 'name': f'Jane Washington {_}', 'country_id': 90 + _}
-    return dummy_data(),
-
-def remove_columns(doc, remove_columns=None):
-    """
-    Removes specified columns from a document (row of data).
-
-    This function is used to filter out columns from the data before loading it into a database,
-    which can be useful for excluding sensitive or unnecessary information.
-
-    :param doc: The document (row) from which columns will be removed.
-    :param remove_columns: List of column names to be removed, defaults to None.
-    :return: The document with specified columns removed.
-    """
-
-    if remove_columns is None:
-        remove_columns = []
-
-    # Iterating over the list of columns to be removed
-    for column_name in remove_columns:
-        # Removing the column if it exists in the document
-        if column_name in doc:
-            del doc[column_name]
-
-    return doc
-
-# Example usage:
-remove_columns_list = ["country_id"]
-
-# run it as it is
-for row in dummy_source().dummy_data.add_map(
-    lambda doc: remove_columns(doc, remove_columns_list)):
-    print(row)
-
-#{'id': 0, 'name': 'Jane Washington 0'}
-#{'id': 1, 'name': 'Jane Washington 1'}
-#{'id': 2, 'name': 'Jane Washington 2'}
-
-
-# Or create an instance of the data source, modify the resource and run the source.
-
-# 1. Create an instance of the source so you can edit it.
-data_source = dummy_source()
-# 2. Modify this source instance's resource
-data_source = (
-    data_source.dummy_data.add_map(
-        lambda doc: remove_columns(doc, remove_columns_list)
-    )
-)# 3. Inspect your result
-for row in data_source:
-    print(row)
-
-# Integrating with a DLT pipeline
-pipeline = dlt.pipeline(
-    pipeline_name='example',
-    destination='bigquery',
-    dataset_name='filtered_data'
-)
-load_info = pipeline.run(data_source)
-```
\ No newline at end of file
+Let's create a sample pipeline demonstrating the process of removing a column.
+
+1. Create a source function that creates dummy data as follows:
+
+   ```python
+   import dlt
+
+   @dlt.source
+   def dummy_source(prefix: str = None):
+       #This function creates a dummy data source.
+       @dlt.resource(write_disposition='replace')
+       def dummy_data():
+             for _ in range(3):
+               yield {'id': _, 'name': f'Jane Washington {_}', 'country_code': 40 + _}
+       return dummy_data()
+   ```
+   This function creates three columns `id`, `name` and `country_code`.
+
+1. Next, create a function to filter out columns from the data before loading it into a database as follows:
+
+   ```python
+   def remove_columns(doc, remove_columns=None):
+
+
+       if remove_columns is None:
+           remove_columns = []
+
+       # Iterating over the list of columns to be removed
+       for column_name in remove_columns:
+           # Removing the column if it exists in the document
+           if column_name in doc:
+               del doc[column_name]
+
+       return doc
+   ```
+   `doc`: The document (row) from which columns will be removed.
+
+   `remove_columns`: List of column names to be removed, defaults to None.
+
+1. Next, declare the columns to be removed from the table, and then modify the source as follows:
+
+   ```python
+   # Example columns to remove:
+   remove_columns_list = ["country_code"]
+
+   # 1. Create an instance of the source so you can edit it.
+   data_source = dummy_source()
+
+   # 2. Modify this source instance's resource
+   data_source = (
+       data_source.dummy_data.add_map(
+           lambda doc: remove_columns(doc, remove_columns_list)
+       )
+   )
+   ```
+1. You can optionally inspect the result:
+
+   ```python
+   for row in data_source:
+       print(row)
+   #{'id': 0, 'name': 'Jane Washington 0'}
+   #{'id': 1, 'name': 'Jane Washington 1'}
+   #{'id': 2, 'name': 'Jane Washington 2'}
+   ```
+
+1. At last, create a pipeline:
+
+   ```python
+   # Integrating with a DLT pipeline
+   pipeline = dlt.pipeline(
+       pipeline_name='example',
+       destination='bigquery',
+       dataset_name='filtered_data'
+   )
+
+   load_info = pipeline.run(data_source)
+   print(   load_info)
+   ```
+