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The finance team sends a monthly spreadsheet, and the header row is written by hand. It has spaces, punctuation, inconsistent casing, a column whose name starts with a year, and — because three people maintain it — two headers that mean the same thing spelled two ways.
Write clean_column_names(names). Return one safe column name per header, in the same order.
Function to write
clean_column_names(names: list) -> list[str]One cleaned, unique snake_case name per header, in the original order.
How to approach it
Clean one name in a helper; the uniqueness rule needs a count of what you have already emitted.
Sample cases
+ 2 held back until you submit
a spreadsheet export
Spaces, padding, punctuation, a leading digit, three headers that collide, and one that is only punctuation.
Input
Argument 1
[
'Order ID',
' Customer Name ',
'Order-ID',
'2024 Total',
'Ship To / Country',
'order id',
'%'
]Returns
[
'order_id',
'customer_name',
'order_id_2',
'col_2024_total',
'ship_to_country',
'order_id_3',
'column'
]no headers
An empty header row produces an empty list rather than a list holding one empty name.
Input
Argument 1
[] (empty list)
Returns
[] (empty list)
already clean
Names that are already valid are returned untouched, with no suffix added.
Input
Argument 1
[
'order_id',
'customer_name'
]Returns
[
'order_id',
'customer_name'
]Constraints
col_.column.total becomes total_2, the third total_3.Worked example
Order ID, Order-ID and order id are three different headers that all clean down to order_id. They come back as order_id, order_id_2 and order_id_3 — renaming rather than dropping, because a dropped column shifts every column after it and the file silently loads into the wrong fields.
2024 Total becomes col_2024_total, and % cleans down to nothing at all and becomes column. Neither is a good column name and both are better than the alternative, which is a load that fails at three in the morning on a file that looked fine in the spreadsheet.
What this tests
String normalisation with a rule you can state, and the discipline of keeping position stable. Uniqueness is the part people skip, and it is the part that turns a clean-looking header row into data in the wrong columns.
clean_column_names(names: list) -> list[str]Submit for review to find out what your query gets right, what it gets wrong, and how it compares with the best working query for this exercise.
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