Expressions¶

Vortex expressions represent simple filtering conditions on the rows of a Vortex array. For example, the following expression represents the set of rows for which the age column lies between 23 and 55:

>>> import vortex.expr
>>> age = vortex.expr.column("age")
>>> (23 > age) & (age < 55)  

`column`	Create an expression that refers to a column by its name.
`Expr`	An expression describes how to filter rows when reading an array from a file.

vortex.expr.column(name)¶

Create an expression that refers to a column by its name.

Parameters:: name (str) – The name of the column.
Return type:: vortex.Expr

Examples

>>> import vortex.expr as ve
>>> ve.column("age")
<vortex.Expr object at ...>

class vortex.expr.Expr¶

An expression describes how to filter rows when reading an array from a file.

See also

column()

Examples

All the examples read the following file.

>>> import vortex as vx
>>> a = vx.array([
...     {'name': 'Joseph', 'age': 25},
...     {'name': None, 'age': 31},
...     {'name': 'Angela', 'age': None},
...     {'name': 'Mikhail', 'age': 57},
...     {'name': None, 'age': None},
... ])
>>> vx.io.write_path(a, "a.vortex")

Read only those rows whose age column is greater than 35:

>>> e = vx.io.read_path("a.vortex", row_filter = vx.expr.column("age") > 35)
>>> e.to_arrow_array()
<pyarrow.lib.StructArray object at ...>
-- is_valid: all not null
-- child 0 type: int64
  [
    57
  ]
-- child 1 type: string_view
  [
    "Mikhail"
  ]

Read only those rows whose age column lies in (21, 33]. Notice that we must use parentheses because of the Python precedence rules for &:

>>> age = vx.expr.column("age")
>>> e = vx.io.read_path("a.vortex", row_filter = (age > 21) & (age <= 33))
>>> e.to_arrow_array()
<pyarrow.lib.StructArray object at ...>
-- is_valid: all not null
-- child 0 type: int64
  [
    25,
    31
  ]
-- child 1 type: string_view
  [
    "Joseph",
    null
  ]

Read only those rows whose name is Joseph:

>>> name = vx.expr.column("name")
>>> e = vx.io.read_path("a.vortex", row_filter = name == "Joseph")
>>> e.to_arrow_array()
<pyarrow.lib.StructArray object at ...>
-- is_valid: all not null
-- child 0 type: int64
  [
    25
  ]
-- child 1 type: string_view
  [
    "Joseph"
  ]

Read all the rows whose name is _not_ Joseph

>>> name = vx.expr.column("name")
>>> e = vx.io.read_path("a.vortex", row_filter = name != "Joseph")
>>> e.to_arrow_array()
<pyarrow.lib.StructArray object at ...>
-- is_valid: all not null
-- child 0 type: int64
  [
    null,
    57
  ]
-- child 1 type: string_view
  [
    "Angela",
    "Mikhail"
  ]

Read rows whose name is Angela or whose age is between 20 and 30, inclusive. Notice that the Angela row is included even though its age is null. Under SQL / Kleene semantics, true or null is true.

>>> name = vx.expr.column("name")
>>> e = vx.io.read_path("a.vortex", row_filter = (name == "Angela") | ((age >= 20) & (age <= 30)))
>>> e.to_arrow_array()
<pyarrow.lib.StructArray object at ...>
-- is_valid: all not null
-- child 0 type: int64
  [
    25,
    null
  ]
-- child 1 type: string_view
  [
    "Joseph",
    "Angela"
  ]