Daniel Blake dmbdesignpdx

## 2024.css
/*
 * Scroll-based animations
 */

@supports (animation-timeline: view()) { ... }
@media (prefers-reduced-motion: no-preference) { ... }

@keyframes appear {
  from {
    opacity: 0;

## update_sequence.sql
-- Example with a table called "roles"

-- Get the sequence name
select pg_get_serial_sequence('roles', 'role_id');

-- Result: roles_role_id_seq

-- Set the new value
select setval('roles_role_id_seq', <NEW_VALUE>, true);

## morph_columns.py
def morph_columns(df: DataFrame, char: str = '_') -> DataFrame:
    """ Standardize and remove spaces of column names of a given dataframe
    """

    def mapper(item: str) -> str:
        """ Set item to lowercase and replace spaces with desired character
        """
        if ' ' in item:
          return item.replace(' ', char).lower()

## clean_dataframe.py
""" Note:
Why not just use .drop_duplicates()?
Well, there is a known issue with that method increasing memory usage
"""

def clean_dataframe(df: DataFrame) -> DataFrame:
    """ Removes duplicate and null rows and returns the resulting dataframe
    """
    duplicate_rows = df[df.duplicated()]
    indices = [row[0] for row in duplicate_rows.itertuples()]

## semantic.html
<html>

  <head>

    <title>Semantic Structure</title>

  </head>

  <body>
	/*
	* Scroll-based animations
	*/

	@supports (animation-timeline: view()) { ... }
	@media (prefers-reduced-motion: no-preference) { ... }

	@keyframes appear {
	from {
	opacity: 0;
	-- Example with a table called "roles"

	-- Get the sequence name
	select pg_get_serial_sequence('roles', 'role_id');

	-- Result: roles_role_id_seq

	-- Set the new value
	select setval('roles_role_id_seq', <NEW_VALUE>, true);
	def morph_columns(df: DataFrame, char: str = '_') -> DataFrame:
	""" Standardize and remove spaces of column names of a given dataframe
	"""

	def mapper(item: str) -> str:
	""" Set item to lowercase and replace spaces with desired character
	"""
	if ' ' in item:
	return item.replace(' ', char).lower()
	""" Note:
	Why not just use .drop_duplicates()?
	Well, there is a known issue with that method increasing memory usage
	"""

	def clean_dataframe(df: DataFrame) -> DataFrame:
	""" Removes duplicate and null rows and returns the resulting dataframe
	"""
	duplicate_rows = df[df.duplicated()]
	indices = [row[0] for row in duplicate_rows.itertuples()]