Selecting certain dataframe columns with string processing

Question

I'm trying to select a set of columns from a dataset that meets a certain condition. So for example I have these columns in the dataframe:

['string_category_4', 'string_category_24', 'range_category_6',
  'range_category_17', 'int_numeric_21', 'string_category_15',
  'float_numeric_8', 'int_bool_19', 'int_bool_2']

Each of the trailing numbers in the column name is going to be a unique identifier, regardless of the prefix like category or numeric. I would like to select all the columns which are <=10 so it should return a list for me that is:

['string_category_4', 'range_category_6', 'float_numeric_8',
 'int_bool_2']

Is there a way with either string processing or even simpler way to do this?

Victor · Accepted Answer · 2020-03-31 22:12:22Z

2

Solution:

columns = [
    'category_4', 'category_24', 'category_6',
    'category_17', 'numeric_21', 'category_15',
    'numeric_8', 'bool_19', 'bool_2'
]
filtered_columns = [col for col in columns if int(col.rsplit('_')[-1]) <= 10]
print(filtered_columns)

Output:

> ['category_4', 'category_6', 'numeric_8', 'bool_2']

answered Mar 31, 2020 at 22:12

Victor

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2 Comments

mlenthusiast Over a year ago

This logic is sound, but how would you handle columns with names such as string_category_11 for example which have two underscores?

Victor Over a year ago

That's what the [-1] is for (selecting the last item). Split the string, then select the last item only. BTW, you could use .split("_") as well. Even better is .rsplit(''_", 1). "string_category_11".rsplit("_") yields ["string", "category", "11"]. By using [-1], you are selecting the last item in that list.

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