vastorbit.VastFrame.regexp¶

VastFrame.regexp(column: str, pattern: str, method: Literal['count', 'ilike', 'instr', 'like', 'not_ilike', 'not_like', 'replace', 'substr'] = 'substr', position: int = 1, occurrence: int = 1, replacement: str | None = None, return_position: int = 0, name: str | None = None) → VastFrame¶

Computes a new VastColumn based on regular expressions.

Parameters:

column (str) – Input VastColumn used to compute the regular expression.
pattern (str) – The regular expression.
method (str, optional) –
Method used to compute the regular expressions.
- count:
  Returns the number of times a regular expression matches each element of the input VastColumn.
- ilike:
  Returns True if the VastColumn element contains a match for the regular expression (case-insensitive).
- instr:
  Returns the starting position in a VastColumn element where a regular expression matches.
- like:
  Returns True if the VastColumn element matches the regular expression.
- not_ilike :
  Returns True if the VastColumn element does not match the case -insensitive regular expression.
- not_like:
  Returns True if the VastColumn element does not contain a match for the regular expression.
- replace:
  Replaces all occurrences of a substring that match a regular expression with another substring.
- substr:
  Returns the substring that matches a regular expression within a VastColumn.
position (int, optional) – The number of characters from the start of the string where the function should start searching for matches.
occurrence (int, optional) – Controls which occurrence of a pattern match in the string to return.
replacement (str, optional) – The string to replace matched substrings.
return_position (int, optional) – Sets the position within the string to return.
name (str, optional) – New feature name. If empty, a name is generated.

Returns:

self

Return type:

VastFrame

Examples

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s generate a small dataset using the following data:

data = vo.VastFrame(
    {
        "rollno": ['1', '2', '3', '4'],
        "subjects": [
            'English, Math',
            'English, Math, Computer',
            'Math, Computer, Science',
            'Math, Science',
        ],
    }
)

Let’s retrieve the second subject.

data.regexp(
    column = "subjects",
    pattern = "[^,]+",
    method = "substr",
    occurrence = 2,
    name = "subject_2").select(
        [
            "subjects",
            "subject_2",
        ]
    )

	Abc subjects Varchar(23)	Abc subject_2 Varchar(23)
1	Math, Computer, Science	Computer
2	Math, Science	Science
3	English, Math, Computer	Math
4	English, Math	Math

Rows: 1-4 | Columns: 2

Let’s count the number of subjects.

data.regexp(
    column = "subjects",
    pattern = ",",
    method = "count",
    name = "nb_subjects",
)
data["nb_subjects"].add(1)
data.select(["subjects", "nb_subjects"])

	Abc subjects Varchar(23)	123 nb_subjects Bigint
1	Math, Science	2
2	English, Math	2
3	English, Math, Computer	3
4	Math, Computer, Science	3

Rows: 1-4 | Columns: 2