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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import
math
from
datetime
import
datetime
,
timezone
import
numpy
as
np
import
pyarrow
as
pa
import
pytest
from
datafusion
import
SessionContext
,
column
,
literal
,
string_literal
from
datafusion
import
functions
as
f
np
.
seterr
(
invalid
=
"ignore"
)
DEFAULT_TZ
=
timezone
.
utc
@
pytest
.
fixture
def
df
():
ctx
=
SessionContext
()
# create a RecordBatch and a new DataFrame from it
batch
=
pa
.
RecordBatch
.
from_arrays
(
[
pa
.
array
([
"Hello"
,
"World"
,
"!"
],
type
=
pa
.
string_view
()),
pa
.
array
([
4
,
5
,
6
]),
pa
.
array
([
"hello "
,
" world "
,
" !"
],
type
=
pa
.
string_view
()),
pa
.
array
(
[
datetime
(
2022
,
12
,
31
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2027
,
6
,
26
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2020
,
7
,
2
,
tzinfo
=
DEFAULT_TZ
),
]
),
pa
.
array
([
False
,
True
,
True
]),
],
names
=
[
"a"
,
"b"
,
"c"
,
"d"
,
"e"
],
)
return
ctx
.
create_dataframe
([[
batch
]])
def
test_named_struct
(
df
):
df
=
df
.
with_column
(
"d"
,
f
.
named_struct
(
[
(
"a"
,
column
(
"a"
)),
(
"b"
,
column
(
"b"
)),
(
"c"
,
column
(
"c"
)),
]
),
)
expected
=
"""DataFrame()
+-------+---+---------+------------------------------+-------+
| a | b | c | d | e |
+-------+---+---------+------------------------------+-------+
| Hello | 4 | hello | {a: Hello, b: 4, c: hello } | false |
| World | 5 | world | {a: World, b: 5, c: world } | true |
| ! | 6 | ! | {a: !, b: 6, c: !} | true |
+-------+---+---------+------------------------------+-------+
"""
.
strip
()
assert
str
(
df
)
==
expected
def
test_literal
(
df
):
df
=
df
.
select
(
literal
(
1
),
literal
(
"1"
),
literal
(
"OK"
),
literal
(
3.14
),
literal
(
True
),
literal
(
b"hello world"
),
)
result
=
df
.
collect
()
assert
len
(
result
)
==
1
result
=
result
[
0
]
assert
result
.
column
(
0
)
==
pa
.
array
([
1
]
*
3
)
assert
result
.
column
(
1
)
==
pa
.
array
([
"1"
]
*
3
,
type
=
pa
.
string_view
())
assert
result
.
column
(
2
)
==
pa
.
array
([
"OK"
]
*
3
,
type
=
pa
.
string_view
())
assert
result
.
column
(
3
)
==
pa
.
array
([
3.14
]
*
3
)
assert
result
.
column
(
4
)
==
pa
.
array
([
True
]
*
3
)
assert
result
.
column
(
5
)
==
pa
.
array
([
b"hello world"
]
*
3
)
def
test_lit_arith
(
df
):
"""Test literals with arithmetic operations"""
df
=
df
.
select
(
literal
(
1
)
+
column
(
"b"
),
f
.
concat
(
column
(
"a"
).
cast
(
pa
.
string
()),
literal
(
"!"
))
)
result
=
df
.
collect
()
assert
len
(
result
)
==
1
result
=
result
[
0
]
assert
result
.
column
(
0
)
==
pa
.
array
([
5
,
6
,
7
])
assert
result
.
column
(
1
)
==
pa
.
array
(
[
"Hello!"
,
"World!"
,
"!!"
],
type
=
pa
.
string_view
()
)
def
test_math_functions
():
ctx
=
SessionContext
()
# create a RecordBatch and a new DataFrame from it
batch
=
pa
.
RecordBatch
.
from_arrays
(
[
pa
.
array
([
0.1
,
-
0.7
,
0.55
]),
pa
.
array
([
float
(
"nan"
),
0
,
2.0
])],
names
=
[
"value"
,
"na_value"
],
)
df
=
ctx
.
create_dataframe
([[
batch
]])
values
=
np
.
array
([
0.1
,
-
0.7
,
0.55
])
na_values
=
np
.
array
([
np
.
nan
,
0
,
2.0
])
col_v
=
column
(
"value"
)
col_nav
=
column
(
"na_value"
)
df
=
df
.
select
(
f
.
abs
(
col_v
),
f
.
sin
(
col_v
),
f
.
cos
(
col_v
),
f
.
tan
(
col_v
),
f
.
asin
(
col_v
),
f
.
acos
(
col_v
),
f
.
exp
(
col_v
),
f
.
ln
(
col_v
+
literal
(
pa
.
scalar
(
1
))),
f
.
log2
(
col_v
+
literal
(
pa
.
scalar
(
1
))),
f
.
log10
(
col_v
+
literal
(
pa
.
scalar
(
1
))),
f
.
random
(),
f
.
atan
(
col_v
),
f
.
atan2
(
col_v
,
literal
(
pa
.
scalar
(
1.1
))),
f
.
ceil
(
col_v
),
f
.
floor
(
col_v
),
f
.
power
(
col_v
,
literal
(
pa
.
scalar
(
3
))),
f
.
pow
(
col_v
,
literal
(
pa
.
scalar
(
4
))),
f
.
round
(
col_v
),
f
.
round
(
col_v
,
literal
(
pa
.
scalar
(
3
))),
f
.
sqrt
(
col_v
),
f
.
signum
(
col_v
),
f
.
trunc
(
col_v
),
f
.
asinh
(
col_v
),
f
.
acosh
(
col_v
),
f
.
atanh
(
col_v
),
f
.
cbrt
(
col_v
),
f
.
cosh
(
col_v
),
f
.
degrees
(
col_v
),
f
.
gcd
(
literal
(
9
),
literal
(
3
)),
f
.
lcm
(
literal
(
6
),
literal
(
4
)),
f
.
nanvl
(
col_nav
,
literal
(
5
)),
f
.
pi
(),
f
.
radians
(
col_v
),
f
.
sinh
(
col_v
),
f
.
tanh
(
col_v
),
f
.
factorial
(
literal
(
6
)),
f
.
isnan
(
col_nav
),
f
.
iszero
(
col_nav
),
f
.
log
(
literal
(
3
),
col_v
+
literal
(
pa
.
scalar
(
1
))),
)
batches
=
df
.
collect
()
assert
len
(
batches
)
==
1
result
=
batches
[
0
]
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
0
),
np
.
abs
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
1
),
np
.
sin
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
2
),
np
.
cos
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
3
),
np
.
tan
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
4
),
np
.
arcsin
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
5
),
np
.
arccos
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
6
),
np
.
exp
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
7
),
np
.
log
(
values
+
1.0
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
8
),
np
.
log2
(
values
+
1.0
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
9
),
np
.
log10
(
values
+
1.0
))
np
.
testing
.
assert_array_less
(
result
.
column
(
10
),
np
.
ones_like
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
11
),
np
.
arctan
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
12
),
np
.
arctan2
(
values
,
1.1
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
13
),
np
.
ceil
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
14
),
np
.
floor
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
15
),
np
.
power
(
values
,
3
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
16
),
np
.
power
(
values
,
4
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
17
),
np
.
round
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
18
),
np
.
round
(
values
,
3
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
19
),
np
.
sqrt
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
20
),
np
.
sign
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
21
),
np
.
trunc
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
22
),
np
.
arcsinh
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
23
),
np
.
arccosh
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
24
),
np
.
arctanh
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
25
),
np
.
cbrt
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
26
),
np
.
cosh
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
27
),
np
.
degrees
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
28
),
np
.
gcd
(
9
,
3
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
29
),
np
.
lcm
(
6
,
4
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
30
),
np
.
where
(
np
.
isnan
(
na_values
),
5
,
na_values
)
)
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
31
),
np
.
pi
)
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
32
),
np
.
radians
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
33
),
np
.
sinh
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
34
),
np
.
tanh
(
values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
35
),
math
.
factorial
(
6
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
36
),
np
.
isnan
(
na_values
))
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
37
),
na_values
==
0
)
np
.
testing
.
assert_array_almost_equal
(
result
.
column
(
38
),
np
.
emath
.
logn
(
3
,
values
+
1.0
)
)
def
py_indexof
(
arr
,
v
):
try
:
return
arr
.
index
(
v
)
+
1
except
ValueError
:
return
np
.
nan
def
py_arr_remove
(
arr
,
v
,
n
=
None
):
new_arr
=
arr
[:]
found
=
0
try
:
while
found
!=
n
:
new_arr
.
remove
(
v
)
found
+=
1
except
ValueError
:
pass
return
new_arr
def
py_arr_replace
(
arr
,
from_
,
to
,
n
=
None
):
new_arr
=
arr
[:]
found
=
0
try
:
while
found
!=
n
:
idx
=
new_arr
.
index
(
from_
)
new_arr
[
idx
]
=
to
found
+=
1
except
ValueError
:
pass
return
new_arr
def
py_arr_resize
(
arr
,
size
,
value
):
arr
=
np
.
asarray
(
arr
)
return
np
.
pad
(
arr
,
[(
0
,
size
-
arr
.
shape
[
0
])],
"constant"
,
constant_values
=
value
,
)
def
py_flatten
(
arr
):
result
=
[]
for
elem
in
arr
:
if
isinstance
(
elem
,
list
):
result
.
extend
(
py_flatten
(
elem
))
else
:
result
.
append
(
elem
)
return
result
@
pytest
.
mark
.
parametrize
(
(
"stmt"
,
"py_expr"
),
[
(
lambda
col
:
f
.
array_append
(
col
,
literal
(
99.0
)),
lambda
data
: [
np
.
append
(
arr
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_push_back
(
col
,
literal
(
99.0
)),
lambda
data
: [
np
.
append
(
arr
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_append
(
col
,
literal
(
99.0
)),
lambda
data
: [
np
.
append
(
arr
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_push_back
(
col
,
literal
(
99.0
)),
lambda
data
: [
np
.
append
(
arr
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_concat
(
col
,
col
),
lambda
data
: [
np
.
concatenate
([
arr
,
arr
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_cat
(
col
,
col
),
lambda
data
: [
np
.
concatenate
([
arr
,
arr
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_cat
(
col
,
col
),
lambda
data
: [
np
.
concatenate
([
arr
,
arr
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_concat
(
col
,
col
),
lambda
data
: [
np
.
concatenate
([
arr
,
arr
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_dims
(
col
),
lambda
data
: [[
len
(
r
)]
for
r
in
data
],
),
(
lambda
col
:
f
.
array_distinct
(
col
),
lambda
data
: [
list
(
set
(
r
))
for
r
in
data
],
),
(
lambda
col
:
f
.
list_distinct
(
col
),
lambda
data
: [
list
(
set
(
r
))
for
r
in
data
],
),
(
lambda
col
:
f
.
list_dims
(
col
),
lambda
data
: [[
len
(
r
)]
for
r
in
data
],
),
(
lambda
col
:
f
.
array_element
(
col
,
literal
(
1
)),
lambda
data
: [
r
[
0
]
for
r
in
data
],
),
(
lambda
col
:
f
.
array_empty
(
col
),
lambda
data
: [
len
(
r
)
==
0
for
r
in
data
],
),
(
lambda
col
:
f
.
empty
(
col
),
lambda
data
: [
len
(
r
)
==
0
for
r
in
data
],
),
(
lambda
col
:
f
.
array_extract
(
col
,
literal
(
1
)),
lambda
data
: [
r
[
0
]
for
r
in
data
],
),
(
lambda
col
:
f
.
list_element
(
col
,
literal
(
1
)),
lambda
data
: [
r
[
0
]
for
r
in
data
],
),
(
lambda
col
:
f
.
list_extract
(
col
,
literal
(
1
)),
lambda
data
: [
r
[
0
]
for
r
in
data
],
),
(
lambda
col
:
f
.
array_length
(
col
),
lambda
data
: [
len
(
r
)
for
r
in
data
],
),
(
lambda
col
:
f
.
list_length
(
col
),
lambda
data
: [
len
(
r
)
for
r
in
data
],
),
(
lambda
col
:
f
.
array_has
(
col
,
literal
(
1.0
)),
lambda
data
: [
1.0
in
r
for
r
in
data
],
),
(
lambda
col
:
f
.
array_has_all
(
col
,
f
.
make_array
(
*
[
literal
(
v
)
for
v
in
[
1.0
,
3.0
,
5.0
]])
),
lambda
data
: [
np
.
all
([
v
in
r
for
v
in
[
1.0
,
3.0
,
5.0
]])
for
r
in
data
],
),
(
lambda
col
:
f
.
array_has_any
(
col
,
f
.
make_array
(
*
[
literal
(
v
)
for
v
in
[
1.0
,
3.0
,
5.0
]])
),
lambda
data
: [
np
.
any
([
v
in
r
for
v
in
[
1.0
,
3.0
,
5.0
]])
for
r
in
data
],
),
(
lambda
col
:
f
.
array_position
(
col
,
literal
(
1.0
)),
lambda
data
: [
py_indexof
(
r
,
1.0
)
for
r
in
data
],
),
(
lambda
col
:
f
.
array_indexof
(
col
,
literal
(
1.0
)),
lambda
data
: [
py_indexof
(
r
,
1.0
)
for
r
in
data
],
),
(
lambda
col
:
f
.
list_position
(
col
,
literal
(
1.0
)),
lambda
data
: [
py_indexof
(
r
,
1.0
)
for
r
in
data
],
),
(
lambda
col
:
f
.
list_indexof
(
col
,
literal
(
1.0
)),
lambda
data
: [
py_indexof
(
r
,
1.0
)
for
r
in
data
],
),
(
lambda
col
:
f
.
array_positions
(
col
,
literal
(
1.0
)),
lambda
data
: [[
i
+
1
for
i
,
_v
in
enumerate
(
r
)
if
_v
==
1.0
]
for
r
in
data
],
),
(
lambda
col
:
f
.
list_positions
(
col
,
literal
(
1.0
)),
lambda
data
: [[
i
+
1
for
i
,
_v
in
enumerate
(
r
)
if
_v
==
1.0
]
for
r
in
data
],
),
(
lambda
col
:
f
.
array_ndims
(
col
),
lambda
data
: [
np
.
array
(
r
).
ndim
for
r
in
data
],
),
(
lambda
col
:
f
.
list_ndims
(
col
),
lambda
data
: [
np
.
array
(
r
).
ndim
for
r
in
data
],
),
(
lambda
col
:
f
.
array_prepend
(
literal
(
99.0
),
col
),
lambda
data
: [
np
.
insert
(
arr
,
0
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_push_front
(
literal
(
99.0
),
col
),
lambda
data
: [
np
.
insert
(
arr
,
0
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_prepend
(
literal
(
99.0
),
col
),
lambda
data
: [
np
.
insert
(
arr
,
0
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_push_front
(
literal
(
99.0
),
col
),
lambda
data
: [
np
.
insert
(
arr
,
0
,
99.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_pop_back
(
col
),
lambda
data
: [
arr
[:
-
1
]
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_pop_front
(
col
),
lambda
data
: [
arr
[
1
:]
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_remove
(
col
,
literal
(
3.0
)),
lambda
data
: [
py_arr_remove
(
arr
,
3.0
,
1
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_remove
(
col
,
literal
(
3.0
)),
lambda
data
: [
py_arr_remove
(
arr
,
3.0
,
1
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_remove_n
(
col
,
literal
(
3.0
),
literal
(
2
)),
lambda
data
: [
py_arr_remove
(
arr
,
3.0
,
2
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_remove_n
(
col
,
literal
(
3.0
),
literal
(
2
)),
lambda
data
: [
py_arr_remove
(
arr
,
3.0
,
2
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_remove_all
(
col
,
literal
(
3.0
)),
lambda
data
: [
py_arr_remove
(
arr
,
3.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_remove_all
(
col
,
literal
(
3.0
)),
lambda
data
: [
py_arr_remove
(
arr
,
3.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_repeat
(
col
,
literal
(
2
)),
lambda
data
: [[
arr
]
*
2
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_repeat
(
col
,
literal
(
2
)),
lambda
data
: [[
arr
]
*
2
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_replace
(
col
,
literal
(
3.0
),
literal
(
4.0
)),
lambda
data
: [
py_arr_replace
(
arr
,
3.0
,
4.0
,
1
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_replace
(
col
,
literal
(
3.0
),
literal
(
4.0
)),
lambda
data
: [
py_arr_replace
(
arr
,
3.0
,
4.0
,
1
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_replace_n
(
col
,
literal
(
3.0
),
literal
(
4.0
),
literal
(
1
)),
lambda
data
: [
py_arr_replace
(
arr
,
3.0
,
4.0
,
1
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_replace_n
(
col
,
literal
(
3.0
),
literal
(
4.0
),
literal
(
2
)),
lambda
data
: [
py_arr_replace
(
arr
,
3.0
,
4.0
,
2
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_replace_all
(
col
,
literal
(
3.0
),
literal
(
4.0
)),
lambda
data
: [
py_arr_replace
(
arr
,
3.0
,
4.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_replace_all
(
col
,
literal
(
3.0
),
literal
(
4.0
)),
lambda
data
: [
py_arr_replace
(
arr
,
3.0
,
4.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_sort
(
col
,
descending
=
True
,
null_first
=
True
),
lambda
data
: [
np
.
sort
(
arr
)[::
-
1
]
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_sort
(
col
,
descending
=
False
,
null_first
=
False
),
lambda
data
: [
np
.
sort
(
arr
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_slice
(
col
,
literal
(
2
),
literal
(
4
)),
lambda
data
: [
arr
[
1
:
4
]
for
arr
in
data
],
),
pytest
.
param
(
lambda
col
:
f
.
list_slice
(
col
,
literal
(
-
1
),
literal
(
2
)),
lambda
data
: [
arr
[
-
1
:
2
]
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_intersect
(
col
,
literal
([
3.0
,
4.0
])),
lambda
data
: [
np
.
intersect1d
(
arr
, [
3.0
,
4.0
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_intersect
(
col
,
literal
([
3.0
,
4.0
])),
lambda
data
: [
np
.
intersect1d
(
arr
, [
3.0
,
4.0
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_union
(
col
,
literal
([
12.0
,
999.0
])),
lambda
data
: [
np
.
union1d
(
arr
, [
12.0
,
999.0
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_union
(
col
,
literal
([
12.0
,
999.0
])),
lambda
data
: [
np
.
union1d
(
arr
, [
12.0
,
999.0
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_except
(
col
,
literal
([
3.0
])),
lambda
data
: [
np
.
setdiff1d
(
arr
, [
3.0
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_except
(
col
,
literal
([
3.0
])),
lambda
data
: [
np
.
setdiff1d
(
arr
, [
3.0
])
for
arr
in
data
],
),
(
lambda
col
:
f
.
array_resize
(
col
,
literal
(
10
),
literal
(
0.0
)),
lambda
data
: [
py_arr_resize
(
arr
,
10
,
0.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
list_resize
(
col
,
literal
(
10
),
literal
(
0.0
)),
lambda
data
: [
py_arr_resize
(
arr
,
10
,
0.0
)
for
arr
in
data
],
),
(
lambda
col
:
f
.
range
(
literal
(
1
),
literal
(
5
),
literal
(
2
)),
lambda
data
: [
np
.
arange
(
1
,
5
,
2
)],
),
],
)
def
test_array_functions
(
stmt
,
py_expr
):
data
=
[[
1.0
,
2.0
,
3.0
,
3.0
], [
4.0
,
5.0
,
3.0
], [
6.0
]]
ctx
=
SessionContext
()
batch
=
pa
.
RecordBatch
.
from_arrays
([
np
.
array
(
data
,
dtype
=
object
)],
names
=
[
"arr"
])
df
=
ctx
.
create_dataframe
([[
batch
]])
col
=
column
(
"arr"
)
query_result
=
df
.
select
(
stmt
(
col
)).
collect
()[
0
].
column
(
0
)
for
a
,
b
in
zip
(
query_result
,
py_expr
(
data
)):
np
.
testing
.
assert_array_almost_equal
(
np
.
array
(
a
.
as_py
(),
dtype
=
float
),
np
.
array
(
b
,
dtype
=
float
)
)
def
test_array_function_flatten
():
data
=
[[
1.0
,
2.0
,
3.0
,
3.0
], [
4.0
,
5.0
,
3.0
], [
6.0
]]
ctx
=
SessionContext
()
batch
=
pa
.
RecordBatch
.
from_arrays
([
np
.
array
(
data
,
dtype
=
object
)],
names
=
[
"arr"
])
df
=
ctx
.
create_dataframe
([[
batch
]])
stmt
=
f
.
flatten
(
literal
(
data
))
py_expr
=
[
py_flatten
(
data
)]
query_result
=
df
.
select
(
stmt
).
collect
()[
0
].
column
(
0
)
for
a
,
b
in
zip
(
query_result
,
py_expr
):
np
.
testing
.
assert_array_almost_equal
(
np
.
array
(
a
.
as_py
(),
dtype
=
float
),
np
.
array
(
b
,
dtype
=
float
)
)
def
test_array_function_cardinality
():
data
=
[[
1
,
2
,
3
], [
4
,
4
,
5
,
6
]]
ctx
=
SessionContext
()
batch
=
pa
.
RecordBatch
.
from_arrays
([
np
.
array
(
data
,
dtype
=
object
)],
names
=
[
"arr"
])
df
=
ctx
.
create_dataframe
([[
batch
]])
stmt
=
f
.
cardinality
(
column
(
"arr"
))
py_expr
=
[
len
(
arr
)
for
arr
in
data
]
# Expected lengths: [3, 3]
# assert py_expr lengths
query_result
=
df
.
select
(
stmt
).
collect
()[
0
].
column
(
0
)
for
a
,
b
in
zip
(
query_result
,
py_expr
):
np
.
testing
.
assert_array_equal
(
np
.
array
([
a
.
as_py
()],
dtype
=
int
),
np
.
array
([
b
],
dtype
=
int
)
)
@
pytest
.
mark
.
parametrize
(
"make_func"
, [
f
.
make_array
,
f
.
make_list
])
def
test_make_array_functions
(
make_func
):
ctx
=
SessionContext
()
batch
=
pa
.
RecordBatch
.
from_arrays
(
[
pa
.
array
([
"Hello"
,
"World"
,
"!"
],
type
=
pa
.
string
()),
pa
.
array
([
4
,
5
,
6
]),
pa
.
array
([
"hello "
,
" world "
,
" !"
],
type
=
pa
.
string
()),
],
names
=
[
"a"
,
"b"
,
"c"
],
)
df
=
ctx
.
create_dataframe
([[
batch
]])
stmt
=
make_func
(
column
(
"a"
).
cast
(
pa
.
string
()),
column
(
"b"
).
cast
(
pa
.
string
()),
column
(
"c"
).
cast
(
pa
.
string
()),
)
py_expr
=
[
[
"Hello"
,
"4"
,
"hello "
],
[
"World"
,
"5"
,
" world "
],
[
"!"
,
"6"
,
" !"
],
]
query_result
=
df
.
select
(
stmt
).
collect
()[
0
].
column
(
0
)
for
a
,
b
in
zip
(
query_result
,
py_expr
):
np
.
testing
.
assert_array_equal
(
np
.
array
(
a
.
as_py
(),
dtype
=
str
),
np
.
array
(
b
,
dtype
=
str
)
)
@
pytest
.
mark
.
parametrize
(
(
"stmt"
,
"py_expr"
),
[
(
f
.
array_to_string
(
column
(
"arr"
),
literal
(
","
)),
lambda
data
: [
","
.
join
([
str
(
int
(
v
))
for
v
in
r
])
for
r
in
data
],
),
(
f
.
array_join
(
column
(
"arr"
),
literal
(
","
)),
lambda
data
: [
","
.
join
([
str
(
int
(
v
))
for
v
in
r
])
for
r
in
data
],
),
(
f
.
list_to_string
(
column
(
"arr"
),
literal
(
","
)),
lambda
data
: [
","
.
join
([
str
(
int
(
v
))
for
v
in
r
])
for
r
in
data
],
),
(
f
.
list_join
(
column
(
"arr"
),
literal
(
","
)),
lambda
data
: [
","
.
join
([
str
(
int
(
v
))
for
v
in
r
])
for
r
in
data
],
),
],
)
def
test_array_function_obj_tests
(
stmt
,
py_expr
):
data
=
[[
1.0
,
2.0
,
3.0
,
3.0
], [
4.0
,
5.0
,
3.0
], [
6.0
]]
ctx
=
SessionContext
()
batch
=
pa
.
RecordBatch
.
from_arrays
([
np
.
array
(
data
,
dtype
=
object
)],
names
=
[
"arr"
])
df
=
ctx
.
create_dataframe
([[
batch
]])
query_result
=
np
.
array
(
df
.
select
(
stmt
).
collect
()[
0
].
column
(
0
))
for
a
,
b
in
zip
(
query_result
,
py_expr
(
data
)):
assert
a
==
b
@
pytest
.
mark
.
parametrize
(
(
"function"
,
"expected_result"
),
[
(
f
.
ascii
(
column
(
"a"
)),
pa
.
array
([
72
,
87
,
33
],
type
=
pa
.
int32
()),
),
# H = 72; W = 87; ! = 33
(
f
.
bit_length
(
column
(
"a"
).
cast
(
pa
.
string
())),
pa
.
array
([
40
,
40
,
8
],
type
=
pa
.
int32
()),
),
(
f
.
btrim
(
literal
(
" World "
)),
pa
.
array
([
"World"
,
"World"
,
"World"
],
type
=
pa
.
string_view
()),
),
(
f
.
character_length
(
column
(
"a"
)),
pa
.
array
([
5
,
5
,
1
],
type
=
pa
.
int32
())),
(
f
.
chr
(
literal
(
68
)),
pa
.
array
([
"D"
,
"D"
,
"D"
])),
(
f
.
concat_ws
(
"-"
,
column
(
"a"
),
literal
(
"test"
)),
pa
.
array
([
"Hello-test"
,
"World-test"
,
"!-test"
]),
),
(
f
.
concat
(
column
(
"a"
).
cast
(
pa
.
string
()),
literal
(
"?"
)),
pa
.
array
([
"Hello?"
,
"World?"
,
"!?"
],
type
=
pa
.
string_view
()),
),
(
f
.
initcap
(
column
(
"c"
)),
pa
.
array
([
"Hello "
,
" World "
,
" !"
],
type
=
pa
.
string_view
()),
),
(
f
.
left
(
column
(
"a"
),
literal
(
3
)),
pa
.
array
([
"Hel"
,
"Wor"
,
"!"
])),
(
f
.
length
(
column
(
"c"
)),
pa
.
array
([
6
,
7
,
2
],
type
=
pa
.
int32
())),
(
f
.
lower
(
column
(
"a"
)),
pa
.
array
([
"hello"
,
"world"
,
"!"
])),
(
f
.
lpad
(
column
(
"a"
),
literal
(
7
)),
pa
.
array
([
" Hello"
,
" World"
,
" !"
])),
(
f
.
ltrim
(
column
(
"c"
)),
pa
.
array
([
"hello "
,
"world "
,
"!"
],
type
=
pa
.
string_view
()),
),
(
f
.
md5
(
column
(
"a"
)),
pa
.
array
(
[
"8b1a9953c4611296a827abf8c47804d7"
,
"f5a7924e621e84c9280a9a27e1bcb7f6"
,
"9033e0e305f247c0c3c80d0c7848c8b3"
,
]
),
),
(
f
.
octet_length
(
column
(
"a"
)),
pa
.
array
([
5
,
5
,
1
],
type
=
pa
.
int32
())),
(
f
.
repeat
(
column
(
"a"
),
literal
(
2
)),
pa
.
array
([
"HelloHello"
,
"WorldWorld"
,
"!!"
]),
),
(
f
.
replace
(
column
(
"a"
),
literal
(
"l"
),
literal
(
"?"
)),
pa
.
array
([
"He??o"
,
"Wor?d"
,
"!"
]),
),
(
f
.
reverse
(
column
(
"a"
)),
pa
.
array
([
"olleH"
,
"dlroW"
,
"!"
])),
(
f
.
right
(
column
(
"a"
),
literal
(
4
)),
pa
.
array
([
"ello"
,
"orld"
,
"!"
])),
(
f
.
rpad
(
column
(
"a"
),
literal
(
8
)),
pa
.
array
([
"Hello "
,
"World "
,
"! "
]),
),
(
f
.
rtrim
(
column
(
"c"
)),
pa
.
array
([
"hello"
,
" world"
,
" !"
],
type
=
pa
.
string_view
()),
),
(
f
.
split_part
(
column
(
"a"
),
literal
(
"l"
),
literal
(
1
)),
pa
.
array
([
"He"
,
"Wor"
,
"!"
]),
),
(
f
.
starts_with
(
column
(
"a"
),
literal
(
"Wor"
)),
pa
.
array
([
False
,
True
,
False
])),
(
f
.
strpos
(
column
(
"a"
),
literal
(
"o"
)),
pa
.
array
([
5
,
2
,
0
],
type
=
pa
.
int32
())),
(
f
.
substr
(
column
(
"a"
),
literal
(
3
)),
pa
.
array
([
"llo"
,
"rld"
,
""
],
type
=
pa
.
string_view
()),
),
(
f
.
translate
(
column
(
"a"
),
literal
(
"or"
),
literal
(
"ld"
)),
pa
.
array
([
"Helll"
,
"Wldld"
,
"!"
]),
),
(
f
.
trim
(
column
(
"c"
)),
pa
.
array
([
"hello"
,
"world"
,
"!"
],
type
=
pa
.
string_view
())),
(
f
.
upper
(
column
(
"c"
)),
pa
.
array
([
"HELLO "
,
" WORLD "
,
" !"
])),
(
f
.
ends_with
(
column
(
"a"
),
literal
(
"llo"
)),
pa
.
array
([
True
,
False
,
False
])),
(
f
.
overlay
(
column
(
"a"
),
literal
(
"--"
),
literal
(
2
)),
pa
.
array
([
"H--lo"
,
"W--ld"
,
"--"
]),
),
(
f
.
regexp_like
(
column
(
"a"
),
literal
(
"(ell|orl)"
)),
pa
.
array
([
True
,
True
,
False
]),
),
(
f
.
regexp_match
(
column
(
"a"
),
literal
(
"(ell|orl)"
)),
pa
.
array
([[
"ell"
], [
"orl"
],
None
],
type
=
pa
.
list_
(
pa
.
string_view
())),
),
(
f
.
regexp_replace
(
column
(
"a"
),
literal
(
"(ell|orl)"
),
literal
(
"-"
)),
pa
.
array
([
"H-o"
,
"W-d"
,
"!"
]),
),
(
f
.
regexp_count
(
column
(
"a"
),
literal
(
"(ell|orl)"
),
literal
(
1
)),
pa
.
array
([
1
,
1
,
0
],
type
=
pa
.
int64
()),
),
],
)
def
test_string_functions
(
df
,
function
,
expected_result
):
df
=
df
.
select
(
function
)
result
=
df
.
collect
()
assert
len
(
result
)
==
1
result
=
result
[
0
]
assert
result
.
column
(
0
)
==
expected_result
def
test_hash_functions
(
df
):
exprs
=
[
f
.
digest
(
column
(
"a"
),
literal
(
m
))
for
m
in
(
"md5"
,
"sha224"
,
"sha256"
,
"sha384"
,
"sha512"
,
"blake2s"
,
"blake3"
,
)
]
df
=
df
.
select
(
*
exprs
,
f
.
sha224
(
column
(
"a"
)),
f
.
sha256
(
column
(
"a"
)),
f
.
sha384
(
column
(
"a"
)),
f
.
sha512
(
column
(
"a"
)),
)
result
=
df
.
collect
()
assert
len
(
result
)
==
1
result
=
result
[
0
]
b
=
bytearray
.
fromhex
assert
result
.
column
(
0
)
==
pa
.
array
(
[
b
(
"8B1A9953C4611296A827ABF8C47804D7"
),
b
(
"F5A7924E621E84C9280A9A27E1BCB7F6"
),
b
(
"9033E0E305F247C0C3C80D0C7848C8B3"
),
]
)
assert
result
.
column
(
1
)
==
pa
.
array
(
[
b
(
"4149DA18AA8BFC2B1E382C6C26556D01A92C261B6436DAD5E3BE3FCC"
),
b
(
"12972632B6D3B6AA52BD6434552F08C1303D56B817119406466E9236"
),
b
(
"6641A7E8278BCD49E476E7ACAE158F4105B2952D22AEB2E0B9A231A0"
),
]
)
assert
result
.
column
(
2
)
==
pa
.
array
(
[
b
(
"185F8DB32271FE25F561A6FC938B2E264306EC304EDA518007D1764826381969"
),
b
(
"78AE647DC5544D227130A0682A51E30BC7777FBB6D8A8F17007463A3ECD1D524"
),
b
(
"BB7208BC9B5D7C04F1236A82A0093A5E33F40423D5BA8D4266F7092C3BA43B62"
),
]
)
assert
result
.
column
(
3
)
==
pa
.
array
(
[
b
(
"3519FE5AD2C596EFE3E276A6F351B8FC"
"0B03DB861782490D45F7598EBD0AB5FD"
"5520ED102F38C4A5EC834E98668035FC"
),
b
(
"ED7CED84875773603AF90402E42C65F3"
"B48A5E77F84ADC7A19E8F3E8D3101010"
"22F552AEC70E9E1087B225930C1D260A"
),
b
(
"1D0EC8C84EE9521E21F06774DE232367"
"B64DE628474CB5B2E372B699A1F55AE3"
"35CC37193EF823E33324DFD9A70738A6"
),
]
)
assert
result
.
column
(
4
)
==
pa
.
array
(
[
b
(
"3615F80C9D293ED7402687F94B22D58E"
"529B8CC7916F8FAC7FDDF7FBD5AF4CF7"
"77D3D795A7A00A16BF7E7F3FB9561EE9"
"BAAE480DA9FE7A18769E71886B03F315"
),
b
(
"8EA77393A42AB8FA92500FB077A9509C"
"C32BC95E72712EFA116EDAF2EDFAE34F"
"BB682EFDD6C5DD13C117E08BD4AAEF71"
"291D8AACE2F890273081D0677C16DF0F"
),
b
(
"3831A6A6155E509DEE59A7F451EB3532"
"4D8F8F2DF6E3708894740F98FDEE2388"
"9F4DE5ADB0C5010DFB555CDA77C8AB5D"
"C902094C52DE3278F35A75EBC25F093A"
),
]
)
assert
result
.
column
(
5
)
==
pa
.
array
(
[
b
(
"F73A5FBF881F89B814871F46E26AD3FA37CB2921C5E8561618639015B3CCBB71"
),
b
(
"B792A0383FB9E7A189EC150686579532854E44B71AC394831DAED169BA85CCC5"
),
b
(
"27988A0E51812297C77A433F635233346AEE29A829DCF4F46E0F58F402C6CFCB"
),
]
)
assert
result
.
column
(
6
)
==
pa
.
array
(
[
b
(
"FBC2B0516EE8744D293B980779178A3508850FDCFE965985782C39601B65794F"
),
b
(
"BF73D18575A736E4037D45F9E316085B86C19BE6363DE6AA789E13DEAACC1C4E"
),
b
(
"C8D11B9F7237E4034ADBCD2005735F9BC4C597C75AD89F4492BEC8F77D15F7EB"
),
]
)
assert
result
.
column
(
7
)
==
result
.
column
(
1
)
# SHA-224
assert
result
.
column
(
8
)
==
result
.
column
(
2
)
# SHA-256
assert
result
.
column
(
9
)
==
result
.
column
(
3
)
# SHA-384
assert
result
.
column
(
10
)
==
result
.
column
(
4
)
# SHA-512
def
test_temporal_functions
(
df
):
df
=
df
.
select
(
f
.
date_part
(
literal
(
"month"
),
column
(
"d"
)),
f
.
datepart
(
literal
(
"year"
),
column
(
"d"
)),
f
.
date_trunc
(
literal
(
"month"
),
column
(
"d"
)),
f
.
datetrunc
(
literal
(
"day"
),
column
(
"d"
)),
f
.
date_bin
(
literal
(
"15 minutes"
).
cast
(
pa
.
string
()),
column
(
"d"
),
literal
(
"2001-01-01 00:02:30"
).
cast
(
pa
.
string
()),
),
f
.
from_unixtime
(
literal
(
1673383974
)),
f
.
to_timestamp
(
literal
(
"2023-09-07 05:06:14.523952"
)),
f
.
to_timestamp_seconds
(
literal
(
"2023-09-07 05:06:14.523952"
)),
f
.
to_timestamp_millis
(
literal
(
"2023-09-07 05:06:14.523952"
)),
f
.
to_timestamp_micros
(
literal
(
"2023-09-07 05:06:14.523952"
)),
f
.
extract
(
literal
(
"day"
),
column
(
"d"
)),
f
.
to_timestamp
(
literal
(
"2023-09-07 05:06:14.523952000"
),
literal
(
"%Y-%m-%d %H:%M:%S.%f"
)
),
f
.
to_timestamp_seconds
(
literal
(
"2023-09-07 05:06:14.523952000"
),
literal
(
"%Y-%m-%d %H:%M:%S.%f"
)
),
f
.
to_timestamp_millis
(
literal
(
"2023-09-07 05:06:14.523952000"
),
literal
(
"%Y-%m-%d %H:%M:%S.%f"
)
),
f
.
to_timestamp_micros
(
literal
(
"2023-09-07 05:06:14.523952000"
),
literal
(
"%Y-%m-%d %H:%M:%S.%f"
)
),
f
.
to_timestamp_nanos
(
literal
(
"2023-09-07 05:06:14.523952"
)),
f
.
to_timestamp_nanos
(
literal
(
"2023-09-07 05:06:14.523952000"
),
literal
(
"%Y-%m-%d %H:%M:%S.%f"
)
),
)
result
=
df
.
collect
()
assert
len
(
result
)
==
1
result
=
result
[
0
]
assert
result
.
column
(
0
)
==
pa
.
array
([
12
,
6
,
7
],
type
=
pa
.
int32
())
assert
result
.
column
(
1
)
==
pa
.
array
([
2022
,
2027
,
2020
],
type
=
pa
.
int32
())
assert
result
.
column
(
2
)
==
pa
.
array
(
[
datetime
(
2022
,
12
,
1
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2027
,
6
,
1
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2020
,
7
,
1
,
tzinfo
=
DEFAULT_TZ
),
],
type
=
pa
.
timestamp
(
"ns"
,
tz
=
DEFAULT_TZ
),
)
assert
result
.
column
(
3
)
==
pa
.
array
(
[
datetime
(
2022
,
12
,
31
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2027
,
6
,
26
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2020
,
7
,
2
,
tzinfo
=
DEFAULT_TZ
),
],
type
=
pa
.
timestamp
(
"ns"
,
tz
=
DEFAULT_TZ
),
)
assert
result
.
column
(
4
)
==
pa
.
array
(
[
datetime
(
2022
,
12
,
30
,
23
,
47
,
30
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2027
,
6
,
25
,
23
,
47
,
30
,
tzinfo
=
DEFAULT_TZ
),
datetime
(
2020
,
7
,
1
,
23
,
47
,
30
,
tzinfo
=
DEFAULT_TZ
),
],
type
=
pa
.
timestamp
(
"ns"
,
tz
=
DEFAULT_TZ
),
)
assert
result
.
column
(
5
)
==
pa
.
array
(
[
datetime
(
2023
,
1
,
10
,
20
,
52
,
54
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"s"
),
)
assert
result
.
column
(
6
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523952
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"ns"
),
)
assert
result
.
column
(
7
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"s"
)
)
assert
result
.
column
(
8
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523000
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"ms"
),
)
assert
result
.
column
(
9
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523952
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"us"
),
)
assert
result
.
column
(
10
)
==
pa
.
array
([
31
,
26
,
2
],
type
=
pa
.
int32
())
assert
result
.
column
(
11
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523952
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"ns"
),
)
assert
result
.
column
(
12
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"s"
),
)
assert
result
.
column
(
13
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523000
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"ms"
),
)
assert
result
.
column
(
14
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523952
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"us"
),
)
assert
result
.
column
(
15
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523952
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"ns"
),
)
assert
result
.
column
(
16
)
==
pa
.
array
(
[
datetime
(
2023
,
9
,
7
,
5
,
6
,
14
,
523952
,
tzinfo
=
DEFAULT_TZ
)]
*
3
,
type
=
pa
.
timestamp
(
"ns"
),
)
def
test_arrow_cast
(
df
):
df
=
df
.
select
(
# we use `string_literal` to return utf8 instead of `literal` which returns
# utf8view because datafusion.arrow_cast expects a utf8 instead of utf8view
# https://github.com/apache/datafusion/blob/86740bfd3d9831d6b7c1d0e1bf4a21d91598a0ac/datafusion/functions/src/core/arrow_cast.rs#L179
f
.
arrow_cast
(
column
(
"b"
),
string_literal
(
"Float64"
)).
alias
(
"b_as_float"
),
f
.
arrow_cast
(
column
(
"b"
),
string_literal
(
"Int32"
)).
alias
(
"b_as_int"
),
)
result
=
df
.
collect
()
assert
len
(
result
)
==
1
result
=
result
[
0
]
assert
result
.
column
(
0
)
==
pa
.
array
([
4.0
,
5.0
,
6.0
],
type
=
pa
.
float64
())
assert
result
.
column
(
1
)
==
pa
.
array
([
4
,
5
,
6
],
type
=
pa
.
int32
())
def
test_case
(
df
):
df
=
df
.
select
(
f
.
case
(
column
(
"b"
)).
when
(
literal
(
4
),
literal
(
10
)).
otherwise
(
literal
(
8
)),
f
.
case
(
column
(
"a"
))
.
when
(
literal
(
"Hello"
),
literal
(
"Hola"
))
.
when
(
literal
(
"World"
),
literal
(
"Mundo"
))
.
otherwise
(
literal
(
"!!"
)),
f
.
case
(
column
(
"a"
))
.
when
(
literal
(
"Hello"
),
literal
(
"Hola"
))
.
when
(
literal
(
"World"
),
literal
(
"Mundo"
))
.
end
(),
)
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