def test_weighted_average(series):
assert eq(
c.aggregate(c.ReduceFuncs.Average(c.item(0),
c.item(1))).execute(series),
weighted_average(series),
)
result = (c.group_by(c.item(0) // 5).aggregate([
c.item(0) // 5,
c.ReduceFuncs.Average(c.item(1)),
c.ReduceFuncs.Average(c.item(1), where=c.item(0) > 10, default=-1),
]).execute(zip(range(10), range(10)), debug=False))
assert result == [
[0, 2, -1],
[1, 7, -1],
]
result = (c.group_by(c.item(0) // 5).aggregate([
c.item(0) // 5,
c.ReduceFuncs.Average(c.item(1), c.item(2)),
c.ReduceFuncs.Average(c.item(1),
c.item(2),
where=c.item(0) > 10,
default=-1),
]).execute(zip(range(10), range(10), cycle([1, 2])), debug=False))
assert result == [
[0, 2, -1],
[1, 7, -1],
]
def test_group_by_with_double_ended_pipes():
input_data = [
{
"value": 1
},
{
"value": 2
},
]
# fmt: off
conv = c.aggregate(
c.item("value").pipe(c.ReduceFuncs.Sum(c.this())).pipe(
c.this() * 2)).gen_converter()
# fmt: on
result = conv(input_data)
assert result == 6
input_data = [
{
"k": "A",
"v": 1
},
{
"k": "A",
"v": 2
},
]
reducer = c.ReduceFuncs.Sum(c.item("v"))
conv = (c.group_by(c.item("k")).aggregate({
"v1":
c.input_arg("test").pipe(reducer),
"v2":
reducer,
}).gen_converter())
assert conv(input_data, test={"v": 7}) == [{"v1": 14, "v2": 3}]
def test_multi_statement_reducers(dict_series):
output = (c.group_by(c.item("name")).aggregate((
c.item("name"),
SumReducer1(c.item("value")),
SumReducer2(c.item("value")),
SumReducer3(c.item("value")),
SumReducer4(c.item("value")),
SumReducer5(c.item("value"), initial=5),
)).execute(dict_series, debug=False))
assert output == [("Nick", 3, 3, 3, 3, 8), ("John", 63, 63, 63, 63, 68)]
with pytest.raises(AttributeError):
class SumReducer(MultiStatementReducer):
reduce = ("%(result)s = %(result)s + ({0} or 4)", )
default = 0
unconditional_init = True
# prepare_first is not specified
c.aggregate(SumReducer(c.item("value"))).gen_converter()
with pytest.raises(ValueError):
class SumReducer(MultiStatementReducer):
reduce = ("%(result)s = %(result)s + ({0} or 4)", )
unconditional_init = True
# default is not provided
SumReducer(c.item("value"))
def test_multi_statement_reducers(dict_series):
output = (c.group_by(c.item("name")).aggregate((
c.item("name"),
SumReducer1(c.item("value")),
SumReducer2(c.item("value")),
SumReducer3(c.item("value")),
SumReducer4(c.item("value")),
SumReducer5(c.item("value"), initial=5),
)).execute(dict_series, debug=False))
assert output == [("Nick", 3, 3, 3, 3, 8), ("John", 63, 63, 63, 63, 68)]
with pytest.raises(ValueError):
class SumReducer(MultiStatementReducer):
reduce = ("%(result)s = {0} + ({1} or 4)", )
default = 0
unconditional_init = True
SumReducer(c.item("value"))
with pytest.raises(ValueError):
class SumReducer(MultiStatementReducer):
reduce = ("%(result)s = {0} + ({1} or 4)", )
unconditional_init = True
SumReducer(c.item("value"))
def test_piped_group_by():
input_data = [
{
"a": 5,
"b": "foo",
"amount": 1
},
{
"a": 10,
"b": "bar",
"amount": 2
},
{
"a": 10,
"b": "bar",
"amount": 3
},
]
assert c.group_by(c.item("a"), c.item("b")).aggregate({
"a":
c.item("a"),
"b":
c.item("b"),
"amount":
c.ReduceFuncs.Sum(c.item("amount")),
}).pipe(
c.group_by(c.item("b")).aggregate({
"b":
c.item("b"),
"set_a":
c.ReduceFuncs.ArrayDistinct(c.item("a")),
"min_amount":
c.ReduceFuncs.Min(c.item("amount")),
})).execute(input_data) == [
{
"b": "foo",
"set_a": [5],
"min_amount": 1
},
{
"b": "bar",
"set_a": [10],
"min_amount": 5
},
]
def test_nested_group_by():
data = [
[0, [1, 2, 3]],
[0, [4, 5, 6]],
[1, [2, 3, 4]],
]
assert c.group_by(c.item(0)).aggregate(
(
c.item(0),
c.ReduceFuncs.Sum(
c.item(1).pipe(c.aggregate(c.ReduceFuncs.Sum(c.this())))
),
)
).execute(data, debug=False) == [
(0, 21),
(1, 9),
]
agg_conv = c.aggregate(c.ReduceFuncs.Sum(c.this()))
assert c.group_by(c.item(0)).aggregate(
(
c.item(0),
c.if_(c.item(1), c.item(1), c.item(1),).pipe(
c.if_(c.this(), c.this(), c.this(),).pipe(
c.ReduceFuncs.Sum(
c.if_(
c.this(),
c.this(),
c.this(),
)
.pipe((agg_conv, agg_conv))
.pipe(c.item(1))
).pipe(
c.if_(
c.this(),
c.this(),
c.this(),
)
),
)
),
)
).execute(data, debug=True) == [
(0, 21),
(1, 9),
]
def test_weighted_average_with_group_by(series):
assert eq(
c.group_by(c.item(0)).aggregate(
c.ReduceFuncs.Average(c.item(0), c.item(1))).execute(series),
[
weighted_average([x for x in series if x[0] == key])
for key in ordered_set(x[0] for x in series)
],
)
def test_median_with_group_by(series):
assert eq(
c.group_by(c.item(0)).aggregate(c.ReduceFuncs.Median(
c.item(1))).execute(series),
[
statistics.median(x[1] for x in series if x[0] == key)
for key in ordered_set(x[0] for x in series)
],
)
def test_top_k_with_group_by(series, k):
assert eq(
c.group_by(c.item(0)).aggregate(c.ReduceFuncs.TopK(
k, c.item(1))).execute(series),
[[
x[1]
for x in Counter(x[1]
for x in series if x[0] == key).most_common(k)
] for key in ordered_set(x[0] for x in series)],
)
def test_mode_with_groupby():
series = [(0, 1), (0, 1), (0, 2), (1, 1), (1, 2), (1, 2)]
assert eq(
(c.group_by(c.item(0)).aggregate(c.ReduceFuncs.Mode(
c.item(1))).execute(series)),
[
statistics.mode([x[1] for x in series if x[0] == key])
for key in ordered_set(x[0] for x in series)
],
)
def test_group_by_key_edge_case():
with pytest.raises(ValueError):
c.this.add_label("row").pipe(c.ReduceFuncs.Count())
with pytest.raises(ValueError):
(c.this.add_label("row") + 1).pipe(c.ReduceFuncs.Count() + 1)
with pytest.raises(ValueError):
c.this.pipe(c.ReduceFuncs.Count(), label_input="row")
data = [
(0, 1),
(1, 2),
]
assert c.group_by(c.item(0)).aggregate(
c.if_(c.item(1), c.item(1), c.item(1)).pipe(
(c.ReduceFuncs.Sum(c.this) /
c.ReduceFuncs.Count(c.this)).pipe(c.this + 10))).gen_converter(
debug=False)(data) == [11, 12]
assert c.group_by(c.item(0)).aggregate(
c.item(1).pipe(
c.ReduceFuncs.Sum(c.this),
label_output="count")).gen_converter(debug=False)(data) == [1, 2]
def test_reducers():
for config in reducers_in_out:
converter = (c.group_by(config["groupby"]).aggregate(
(config["groupby"],
config["reduce"])).gen_converter(debug=config.get("debug", True)))
if config["raises"]:
with pytest.raises(config["raises"]):
converter(config["data"])
else:
data = converter(config["data"])
assert data == config["output"]
def test_conditional_init_merges():
converter = (c.group_by(c.item(0)).aggregate([
c.ReduceFuncs.First(c.item(1)),
c.ReduceFuncs.First(c.item(2)),
c.ReduceFuncs.Min(c.item(1)),
c.ReduceFuncs.Max(c.item(1)),
c.ReduceFuncs.Min(c.item(2)),
c.ReduceFuncs.Max(c.item(2)),
c.ReduceFuncs.DictMin(c.item(0), c.item(1)),
c.ReduceFuncs.DictMin(c.item(0), c.item(2)),
c.ReduceFuncs.DictMax(c.item(0), c.item(1)),
c.ReduceFuncs.DictMax(c.item(0), c.item(2)),
c.ReduceFuncs.MaxRow(c.item(2)),
c.ReduceFuncs.MaxRow(c.item(2), where=c.item(0) == 1).item(-1),
]).gen_converter(debug=True))
# fmt: off
assert converter([
[1, 2, None],
[1, 1, 4],
[1, None, 3],
]) == [[2, None, 1, 2, 3, 4, {
1: 1
}, {
1: 3
}, {
1: 2
}, {
1: 4
}, [1, 1, 4], 4]]
# fmt: on
converter = (c.group_by(c.item(0)).aggregate([
c.ReduceFuncs.Min(c.item(1)),
c.ReduceFuncs.Min(c.item(1)) + 1,
]).gen_converter(debug=True))
assert (sum(
code_line.count("<") for code_line in next(
conf["code_str"]
for name, conf in converter._name_to_converter.items()
if name.startswith("group_by"))) == 1)
def test_manually_defined_reducers():
data = [
{
"name": "John",
"category": "Games",
"debit": 10,
"balance": 90
},
{
"name": "John",
"category": "Games",
"debit": 200,
"balance": -110
},
{
"name": "John",
"category": "Food",
"debit": 30,
"balance": -140
},
{
"name": "John",
"category": "Games",
"debit": 300,
"balance": 0
},
{
"name": "Nick",
"category": "Food",
"debit": 7,
"balance": 50
},
{
"name": "Nick",
"category": "Games",
"debit": 18,
"balance": 32
},
{
"name": "Bill",
"category": "Games",
"debit": 18,
"balance": 120
},
]
grouper = (c.group_by(c.item("name")).aggregate(
c.reduce(lambda a, b: a + b,
c.item(c.input_arg("group_key")),
initial=0)).filter(c.this() > 20).gen_converter(
signature="data_, group_key='debit'"))
assert grouper(data) == [540, 25]
assert grouper(data, group_key="balance") == [82, 120]
def test_group_by_percentile():
input_data = [{
"key": key,
"value": value
} for index, key in enumerate("abc")
for value in range(index + 90, -1, -1)]
c_round = c.call_func(round, c.this, 2)
result = (c.group_by(c.item("key")).aggregate({
"key":
c.item("key"),
"min":
c.ReduceFuncs.Percentile(0, c.item("value")).pipe(c_round),
"min":
c.ReduceFuncs.Percentile(0,
c.item("value"),
where=c.and_(default=True)).pipe(c_round),
"percentile_5":
c.ReduceFuncs.Percentile(5, c.item("value")).pipe(c_round),
"median":
c.ReduceFuncs.Percentile(50, c.item("value")).pipe(c_round),
"percentile_95":
c.ReduceFuncs.Percentile(95, c.item("value")).pipe(c_round),
"max":
c.ReduceFuncs.Percentile(100, c.item("value")).pipe(c_round),
}).execute(input_data))
assert result == [
{
"key": "a",
"max": 90,
"median": 45.0,
"min": 0.0,
"percentile_5": 4.5,
"percentile_95": 85.5,
},
{
"key": "b",
"max": 91,
"median": 45.5,
"min": 0.0,
"percentile_5": 4.55,
"percentile_95": 86.45,
},
{
"key": "c",
"max": 92,
"median": 46.0,
"min": 0.0,
"percentile_5": 4.6,
"percentile_95": 87.4,
},
]
def test_reducer_reuse(dict_series):
f = lambda a, b: a + b
reducer = c.reduce(f, c.item("value"), initial=0)
reducer2 = c.reduce(f, c.item("value"), initial=0)
output = (c.group_by(c.item("name")).aggregate((
c.item("name"),
reducer + 10,
reducer2 + 20,
)).execute(dict_series))
assert output == [
("Nick", 13, 23),
("John", 73, 83),
]
def test_group_by_key_edge_case():
with pytest.raises(ValueError):
c.this().add_label("row").pipe(c.ReduceFuncs.Count())
with pytest.raises(ValueError):
(c.this().add_label("row") + 1).pipe(c.ReduceFuncs.Count() + 1)
with pytest.raises(ValueError):
c.this().pipe(c.ReduceFuncs.Count(), label_input="row")
data = [
(0, 1),
(1, 2),
]
# TODO: try to test nested pipe (double overwrites)
# TODO: reducer + label then pipe to somewhere
assert c.group_by(c.item(0)).aggregate(
c.if_(c.item(1), c.item(1), c.item(1)).pipe(
(c.ReduceFuncs.Sum(c.this()) / c.ReduceFuncs.Count(c.this())).pipe(
c.this() + 10
)
)
).gen_converter(debug=False)(data) == [11, 12]
assert c.group_by(c.item(0)).aggregate(
c.item(1).pipe(c.ReduceFuncs.Sum(c.this()), label_output="count")
).gen_converter(debug=False)(data) == [1, 2]
def test_iter_method():
assert (c.this.iter(c.this * 3).filter(c.this).as_type(list).execute(
[1, 2, 3, 0, 1],
debug=False,
) == [3, 6, 9, 3])
assert c.group_by(c.item(0)).aggregate(
c([
c.item(0),
c.item(1).pipe(c.ReduceFuncs.Max(c.this)),
]).iter(c.this * 100).as_type(tuple)).execute([(0, 1), (0, 2), (1, 7)],
debug=False) == [
(0, 200),
(100, 700),
]
def test_iter_mut_method():
assert c.iter(c.item(0)).as_type(list).execute([[1], [2]]) == [1, 2]
assert c.iter_mut(c.Mut.custom(c.this.call_method("append", 7))).as_type(
list
).execute([[1], [2]]) == [[1, 7], [2, 7]]
result = (
c.this.iter({"a": c.this})
.iter_mut(
c.Mut.set_item("b", c.item("a") + 1),
c.Mut.set_item("c", c.item("a") + 2),
)
.iter_mut(
c.Mut.set_item("d", c.item("a") + 3),
)
.as_type(list)
.execute([1, 2, 3], debug=False)
)
assert result == [
{"a": 1, "b": 2, "c": 3, "d": 4},
{"a": 2, "b": 3, "c": 4, "d": 5},
{"a": 3, "b": 4, "c": 5, "d": 6},
]
result = (
c.group_by(c.item(0))
.aggregate(
c(
[
{c.item(0): c.item(1).pipe(c.ReduceFuncs.Max(c.this))},
{c.item(1).pipe(c.ReduceFuncs.Max(c.this)): c.item(0)},
]
)
.iter_mut(
c.Mut.set_item(
"x",
c.call_func(sum, c.this.call_method("values"))
+ c.input_arg("base"),
)
)
.as_type(tuple)
)
.execute([(0, 1), (0, 2), (1, 7)], base=100, debug=False)
)
assert result == [
({0: 2, "x": 102}, {2: 0, "x": 100}),
({1: 7, "x": 107}, {7: 1, "x": 101}),
]
def test_zip_in_aggregate():
input_data = [
("kitchen", "size", 10),
("kitchen", "temperature", 40),
("living_room", "size", 12),
("living_room", "color", "white"),
]
converter = (c.group_by(c.item(1)).aggregate({
"prop":
c.item(1),
"values":
c.zip(
room=c.ReduceFuncs.Array(c.item(0)),
value=c.ReduceFuncs.Array(c.item(2)),
).as_type(list),
}).gen_converter())
assert converter(input_data) == [
{
"prop":
"size",
"values": [
{
"room": "kitchen",
"value": 10
},
{
"room": "living_room",
"value": 12
},
],
},
{
"prop": "temperature",
"values": [{
"room": "kitchen",
"value": 40
}]
},
{
"prop": "color",
"values": [{
"room": "living_room",
"value": "white"
}],
},
]
def test_grouping():
data = [
{
"name": "John",
"category": "Games",
"debit": 10,
"balance": 90
},
{
"name": "John",
"category": "Games",
"debit": 200,
"balance": -110
},
{
"name": "John",
"category": "Food",
"debit": 30,
"balance": -140
},
{
"name": "John",
"category": "Games",
"debit": 300,
"balance": 0
},
{
"name": "Nick",
"category": "Food",
"debit": 7,
"balance": 50
},
{
"name": "Nick",
"category": "Games",
"debit": 18,
"balance": 32
},
{
"name": "Bill",
"category": "Games",
"debit": 18,
"balance": 120
},
]
result = (c.group_by(c.item("name")).aggregate((
c.item("name"),
c.item("name").call_method("lower"),
c.call_func(str.lower, c.item("name")),
c.reduce(
lambda a, b: a + b,
c.item("debit"),
initial=c.input_arg("arg1"),
unconditional_init=True,
),
c.reduce(
c.inline_expr("{0} + {1}"),
c.item("debit"),
initial=lambda: 100,
unconditional_init=True,
),
c.reduce(
max,
c.item("debit"),
prepare_first=lambda a: a,
default=c.input_arg("arg1"),
where=c.call_func(lambda x: x < 0, c.item("balance")),
),
c.call_func(
lambda max_debit, n: max_debit * n,
c.reduce(
max,
c.item("debit"),
prepare_first=lambda a: a,
default=0,
where=c.call_func(lambda x: x < 0, c.item("balance")),
),
1000,
),
c.call_func(
lambda max_debit, n: max_debit * n,
c.reduce(
c.ReduceFuncs.Max,
c.item("debit"),
default=1000,
where=c.inline_expr("{0} > {1}").pass_args(
c.item("balance"),
c.input_arg("arg2"),
),
),
-1,
),
c.reduce(c.ReduceFuncs.MaxRow, c.item("debit")).item("balance"),
c.reduce(c.ReduceFuncs.MinRow, c.item("debit")).item("balance"),
)).sort(key=lambda t: t[0].lower(), reverse=True).execute(data,
arg1=100,
arg2=0,
debug=False))
# fmt: off
assert result == [
('Nick', 'nick', 'nick', 125, 125, 100, 0, -18, 32, 50),
('John', 'john', 'john', 640, 640, 200, 200000, -10, 0, 90),
('Bill', 'bill', 'bill', 118, 118, 100, 0, -18, 120, 120),
]
# fmt: on
with pytest.raises(c.ConversionException):
# there's a single group by field, while we use separate items
# of this tuple in aggregate
result = (c.group_by(c.item("name")).aggregate((
c.item("category"),
c.reduce(c.ReduceFuncs.Sum, c.item("debit")),
)).execute(data, debug=False))
aggregation = {
c.call_func(
tuple,
c.ReduceFuncs.Array(c.item("name"), default=None),
):
c.item("category").call_method("lower"),
"count":
c.ReduceFuncs.Count(),
"max":
c.ReduceFuncs.Max(c.item("debit")),
"min":
c.ReduceFuncs.Min(c.item("debit")),
"count_distinct":
c.ReduceFuncs.CountDistinct(c.item("name")),
"array_agg_distinct":
c.ReduceFuncs.ArrayDistinct(c.item("name")),
"dict":
c.ReduceFuncs.Dict(c.item("debit"), c.item("name")),
}
result = (c.group_by(c.item("category")).aggregate(aggregation).execute(
data, debug=False))
result2 = (c.group_by(c.item("category")).aggregate(
c.dict(*aggregation.items())).execute(data, debug=False))
# fmt: off
assert result == result2 == [
{
'array_agg_distinct': ['John', 'Nick', 'Bill'],
'count': 5,
'count_distinct': 3,
'dict': {
10: 'John',
18: 'Bill',
200: 'John',
300: 'John'
},
'max': 300,
'min': 10,
('John', 'John', 'John', 'Nick', 'Bill'): 'games'
}, {
'array_agg_distinct': ['John', 'Nick'],
'count': 2,
'count_distinct': 2,
'dict': {
7: 'Nick',
30: 'John'
},
'max': 30,
'min': 7,
('John', 'Nick'): 'food'
}
]
# fmt: on
result3 = (c.aggregate(c.ReduceFuncs.Sum(c.item("debit"))).pipe(
c.inline_expr("{0} + {1}").pass_args(c.this(),
c.this())).execute(data,
debug=False))
assert result3 == 583 * 2
by = c.item("name"), c.item("category")
result4 = (c.group_by(
*by).aggregate(by + (c.ReduceFuncs.Sum(c.item("debit")), )).execute(
data, debug=False))
# fmt: off
assert result4 == [('John', 'Games', 510), ('John', 'Food', 30),
('Nick', 'Food', 7), ('Nick', 'Games', 18),
('Bill', 'Games', 18)]
# fmt: on
result5 = (c.group_by().aggregate(c.ReduceFuncs.Sum(
c.item("debit"))).execute(data, debug=False))
assert result5 == 583
with pytest.raises(c.ConversionException):
# there's a single group by field, while we use separate items
# of this tuple in aggregate
(c.group_by(by).aggregate(
by + (c.reduce(c.ReduceFuncs.Sum, c.item("debit")), )).execute(
data, debug=False))
def test_blank_aggregate(series):
assert eq(
c.group_by(c.item(0)).aggregate(c.item(0)).execute(series),
list({x[0]: 1
for x in series}),
)
def test_group_by_with_pipes():
# fmt: off
input_data = [
{
"name": "John",
"started_at": date(2020, 1, 1),
"stopped_at": None,
"product": "A"
},
{
"name": "John",
"started_at": date(2020, 1, 1),
"stopped_at": date(2020, 1, 2),
"product": "B"
},
{
"name": "John",
"started_at": date(2020, 1, 1),
"stopped_at": None,
"product": "C"
},
{
"name": "Nick",
"started_at": date(2020, 1, 1),
"stopped_at": None,
"product": "D"
},
{
"name": "Nick",
"started_at": date(2020, 2, 1),
"stopped_at": None,
"product": "D"
},
{
"name": "Nick",
"started_at": date(2020, 2, 1),
"stopped_at": None,
"product": "E"
},
]
# fmt: on
output = (c.group_by(
c.item("name"),
c.item("started_at"),
).aggregate({
"name":
c.item("name"),
"started_at":
c.item("started_at"),
"products":
c.ReduceFuncs.ArrayDistinct(
c.if_(
c.item("stopped_at").is_(None),
c.item("product"),
None,
), ).pipe(c.filter(c.this())).pipe(
c.call_func(sorted, c.this()).pipe(
c(", ").call_method("join", c.this()))).pipe(c.this()),
}).execute(input_data))
# fmt: off
assert output == [{
'name': 'John',
'products': 'A, C',
'started_at': date(2020, 1, 1)
}, {
'name': 'Nick',
'products': 'D',
'started_at': date(2020, 1, 1)
}, {
'name': 'Nick',
'products': 'D, E',
'started_at': date(2020, 2, 1)
}]
# fmt: on
reducer = c.ReduceFuncs.Array(c.this(), default=list)
output = (c.group_by(
c.this()["name"],
c.this()["started_at"],
).aggregate({
"name": c.this()["name"],
"started_at": c.this()["started_at"],
"products": c.this()["product"].pipe(reducer)[:3],
}).execute(input_data))
assert output == [
{
"name": "John",
"products": ["A", "B", "C"],
"started_at": date(2020, 1, 1),
},
{
"name": "Nick",
"products": ["D"],
"started_at": date(2020, 1, 1),
},
{
"name": "Nick",
"products": ["D", "E"],
"started_at": date(2020, 2, 1),
},
]
def test_manually_defined_reducers():
data = [
{
"name": "John",
"category": "Games",
"debit": 10,
"balance": 90
},
{
"name": "John",
"category": "Games",
"debit": 200,
"balance": -110
},
{
"name": "John",
"category": "Food",
"debit": 30,
"balance": -140
},
{
"name": "John",
"category": "Games",
"debit": 300,
"balance": 0
},
{
"name": "Nick",
"category": "Food",
"debit": 7,
"balance": 50
},
{
"name": "Nick",
"category": "Games",
"debit": 18,
"balance": 32
},
{
"name": "Bill",
"category": "Games",
"debit": 18,
"balance": 120
},
]
grouper_base = c.group_by(c.item("name")).aggregate(
c.reduce(
lambda a, b: a + b,
c.item(c.input_arg("group_key")),
initial=int,
default=int,
))
grouper = grouper_base.filter(c.this > 20).gen_converter(
signature="data_, group_key='debit'", debug=False)
assert grouper(data) == [540, 25]
assert list(grouper(data, group_key="balance")) == [82, 120]
grouper = grouper_base.filter(
(c.this > 20),
cast=list).gen_converter(signature="data_, group_key='debit'",
debug=False)
assert grouper(data) == [540, 25]
grouper = grouper_base.filter(
(c.this > 20),
cast=set).gen_converter(signature="data_, group_key='debit'",
debug=False)
assert grouper(data, group_key="balance") == {82, 120}
def test_nested_group_by():
data = [
[0, [1, 2, 3]],
[0, [4, 5, 6]],
[1, [2, 3, 4]],
]
assert c.group_by(c.item(0)).aggregate((
c.item(0),
c.ReduceFuncs.Sum(
c.item(1).pipe(c.aggregate(c.ReduceFuncs.Sum(c.this())))),
)).execute(data, debug=False) == [
(0, 21),
(1, 9),
]
agg_conv = c.aggregate(c.ReduceFuncs.Sum(c.this()))
assert c.group_by(c.item(0)).aggregate((
c.item(0),
c.if_(
c.item(1),
c.item(1),
c.item(1),
).pipe(
c.if_(
c.this(),
c.this(),
c.this(),
).pipe(
c.ReduceFuncs.Sum(
c.if_(
c.this(),
c.this(),
c.this(),
).pipe((agg_conv, agg_conv)).pipe(c.item(1))).pipe(
c.if_(
c.this(),
c.this(),
c.this(),
)), )),
)).execute(data, debug=False) == [
(0, 21),
(1, 9),
]
summer = c.aggregate(c.ReduceFuncs.Sum(c.this()))
merger = c.aggregate({
"value1":
c.ReduceFuncs.First(c.item("value1"), where=c("value1").in_(c.this())),
"value2":
c.ReduceFuncs.First(c.item("value2"),
where=c("value2").in_(c.this())).pipe(
c.if_(c.this(),
c.this().pipe(summer))),
})
converter = (c.group_by(c.item("id_")).aggregate({
"id_":
c.item("id_"),
"data":
c.ReduceFuncs.Array(c.this()).pipe(merger),
}).gen_converter(debug=False))
assert converter([
{
"id_": 1,
"value1": 2
},
{
"id_": 2,
"value1": 3
},
{
"id_": 2,
"value2": [1, 2, 3]
},
]) == [
{
"id_": 1,
"data": {
"value1": 2,
"value2": None
}
},
{
"id_": 2,
"data": {
"value1": 3,
"value2": 6
}
},
]
def g():
yield 1
raise Exception
assert (c.aggregate(c.ReduceFuncs.First(c.this())).execute(
g(), debug=False)) == 1
def test_doc__index_intro():
# ======== #
# GROUP BY #
# ======== #
input_data = [
{
"a": 5,
"b": "foo"
},
{
"a": 10,
"b": "foo"
},
{
"a": 10,
"b": "bar"
},
{
"a": 10,
"b": "bar"
},
{
"a": 20,
"b": "bar"
},
]
conv = (c.group_by(c.item("b")).aggregate({
"b":
c.item("b"),
"a_first":
c.ReduceFuncs.First(c.item("a")),
"a_max":
c.ReduceFuncs.Max(c.item("a")),
}).gen_converter(debug=True))
assert conv(input_data) == [
{
"b": "foo",
"a_first": 5,
"a_max": 10
},
{
"b": "bar",
"a_first": 10,
"a_max": 20
},
]
# ========= #
# AGGREGATE #
# ========= #
conv = c.aggregate({
# list of "a" values where "b" equals to "bar"
"a":
c.ReduceFuncs.Array(c.item("a"), where=c.item("b") == "bar"),
# "b" value of a row where "a" has Max value
"b":
c.ReduceFuncs.MaxRow(c.item("a"), ).item("b", default=None),
}).gen_converter(debug=True)
assert conv(input_data) == {"a": [10, 10, 20], "b": "bar"}
# ==== #
# JOIN #
# ==== #
collection_1 = [
{
"id": 1,
"name": "Nick"
},
{
"id": 2,
"name": "Joash"
},
{
"id": 3,
"name": "Bob"
},
]
collection_2 = [
{
"ID": "3",
"age": 17,
"country": "GB"
},
{
"ID": "2",
"age": 21,
"country": "US"
},
{
"ID": "1",
"age": 18,
"country": "CA"
},
]
input_data = (collection_1, collection_2)
conv = (c.join(
c.item(0),
c.item(1),
c.and_(
c.LEFT.item("id") == c.RIGHT.item("ID").as_type(int),
c.RIGHT.item("age") >= 18,
),
how="left",
).pipe(
c.list_comp({
"id": c.item(0, "id"),
"name": c.item(0, "name"),
"age": c.item(1, "age", default=None),
"country": c.item(1, "country", default=None),
})).gen_converter(debug=True))
assert conv(input_data) == [
{
"id": 1,
"name": "Nick",
"age": 18,
"country": "CA"
},
{
"id": 2,
"name": "Joash",
"age": 21,
"country": "US"
},
{
"id": 3,
"name": "Bob",
"age": None,
"country": None
},
]
def test_grouping():
data = [
{
"name": "John",
"category": "Games",
"debit": 10,
"balance": 90
},
{
"name": "John",
"category": "Games",
"debit": 200,
"balance": -110
},
{
"name": "John",
"category": "Food",
"debit": 30,
"balance": -140
},
{
"name": "John",
"category": "Games",
"debit": 300,
"balance": 0
},
{
"name": "Nick",
"category": "Food",
"debit": 7,
"balance": 50
},
{
"name": "Nick",
"category": "Games",
"debit": 18,
"balance": 32
},
{
"name": "Bill",
"category": "Games",
"debit": 18,
"balance": 120
},
]
result = (c.group_by(c.item("name")).aggregate((
c.item("name"),
c.item("name").call_method("lower"),
c.call_func(str.lower, c.item("name")),
c.reduce(
lambda a, b: a + b,
c.item("debit"),
initial=c.input_arg("arg1"),
),
c.reduce(
c.inline_expr("{0} + {1}"),
c.item("debit"),
initial=lambda: 100,
),
c.reduce(max, c.item("debit"), default=c.input_arg("arg1")).filter(
c.call_func(lambda x: x < 0, c.item("balance"))),
c.call_func(
lambda max_debit, n: max_debit * n,
c.reduce(max, c.item("debit"), default=0).filter(
c.call_func(lambda x: x < 0, c.item("balance"))),
1000,
),
c.call_func(
lambda max_debit, n: max_debit * n,
c.reduce(
c.ReduceFuncs.Max,
c.item("debit"),
default=1000,
).filter(c.inline_expr("{0} > 0").pass_args(c.item("balance"))),
-1,
),
c.reduce(
c.ReduceFuncs.MaxRow,
c.item("debit"),
).item("balance"),
c.reduce(
c.ReduceFuncs.MinRow,
c.item("debit"),
).item("balance"),
)).sort(key=lambda t: t[0].lower(), reverse=True).execute(data,
arg1=100,
debug=False))
# fmt: off
assert result == [
('Nick', 'nick', 'nick', 125, 125, 100, 0, -18, 32, 50),
('John', 'john', 'john', 640, 640, 200, 200000, -10, 0, 90),
('Bill', 'bill', 'bill', 118, 118, 100, 0, -18, 120, 120)
]
# fmt: on
aggregation = {
c.call_func(
tuple,
c.reduce(c.ReduceFuncs.Array, c.item("name"), default=None),
):
c.item("category").call_method("lower"),
"count":
c.reduce(c.ReduceFuncs.Count),
"count_distinct":
c.reduce(c.ReduceFuncs.CountDistinct, c.item("name")),
"array_agg_distinct":
c.reduce(
c.ReduceFuncs.ArrayDistinct,
c.item("name"),
),
"dict":
c.reduce(c.ReduceFuncs.Dict, (c.item("debit"), c.item("name"))),
}
result = (c.group_by(c.item("category")).aggregate(aggregation).execute(
data, debug=False))
result2 = (c.group_by(c.item("category")).aggregate(
c.dict(*aggregation.items())).execute(data, debug=False))
# fmt: off
assert result == result2 == [
{
'array_agg_distinct': ['John', 'Nick', 'Bill'],
'count': 5,
'count_distinct': 3,
'dict': {
10: 'John',
18: 'Bill',
200: 'John',
300: 'John'
},
('John', 'John', 'John', 'Nick', 'Bill'): 'games'
}, {
'array_agg_distinct': ['John', 'Nick'],
'count': 2,
'count_distinct': 2,
'dict': {
7: 'Nick',
30: 'John'
},
('John', 'Nick'): 'food'
}
]
# fmt: on
result3 = (c.aggregate(c.reduce(c.ReduceFuncs.Sum, c.item("debit"))).pipe(
c.inline_expr("{0} + {1}").pass_args(c.this(),
c.this())).execute(data,
debug=False))
assert result3 == 583 * 2
by = c.item("name"), c.item("category")
result4 = (c.group_by(*by).aggregate(by + (
c.reduce(c.ReduceFuncs.Sum, c.item("debit")), )).execute(data,
debug=False))
# fmt: off
assert result4 == [('John', 'Games', 510), ('John', 'Food', 30),
('Nick', 'Food', 7), ('Nick', 'Games', 18),
('Bill', 'Games', 18)]
# fmt: on
result5 = (c.group_by().aggregate(
c.reduce(c.ReduceFuncs.Sum, c.item("debit"))).execute(data,
debug=False))
assert result5 == 583
def test_doc__quickstart_aggregation():
input_data = [
{
"company_name": "Facebrochure",
"company_hq": "CA",
"app_name": "Tardygram",
"date": "2019-01-01",
"country": "US",
"sales": Decimal("45678.98"),
},
{
"company_name": "Facebrochure",
"company_hq": "CA",
"app_name": "Tardygram",
"date": "2019-01-02",
"country": "US",
"sales": Decimal("86869.12"),
},
{
"company_name": "Facebrochure",
"company_hq": "CA",
"app_name": "Tardygram",
"date": "2019-01-03",
"country": "CA",
"sales": Decimal("45000.35"),
},
{
"company_name": "BrainCorp",
"company_hq": "NY",
"app_name": "Learn FT",
"date": "2019-01-01",
"country": "US",
"sales": Decimal("86869.12"),
},
]
# we are going to reuse this reducer
top_sales_day = c.ReduceFuncs.MaxRow(c.item("sales"))
# so the result is going to be a list of dicts
converter = (
c.group_by(c.item("company_name"))
.aggregate(
{
"company_name": c.item("company_name").call_method("upper"),
# this would work as well
# c.item("company_name"): ...,
"none_sensitive_sum": c.ReduceFuncs.SumOrNone(c.item("sales")),
# as you can see, next two reduce objects do the same except taking
# different fields after finding a row with max value.
# but please check the generated code below, you'll see that it is
# calculated just once AND then reused to take necessary fields
"top_sales_app": top_sales_day.item("app_name"),
"top_sales_day": (
top_sales_day.item("date")
.pipe(
datetime.strptime,
"%Y-%m-%d",
)
.call_method("date")
),
"company_hq": c.ReduceFuncs.First(c.item("company_hq")),
"app_name_to_countries": c.ReduceFuncs.DictArrayDistinct(
c.item("app_name"), c.item("country")
),
"app_name_to_sales": c.ReduceFuncs.DictSum(
c.item("app_name"), c.item("sales")
),
}
)
.gen_converter(debug=True)
)
assert converter(input_data) == [
{
"app_name_to_countries": {"Tardygram": ["US", "CA"]},
"app_name_to_sales": {"Tardygram": Decimal("177548.45")},
"company_hq": "CA",
"company_name": "FACEBROCHURE",
"none_sensitive_sum": Decimal("177548.45"),
"top_sales_app": "Tardygram",
"top_sales_day": date(2019, 1, 2),
},
{
"app_name_to_countries": {"Learn FT": ["US"]},
"app_name_to_sales": {"Learn FT": Decimal("86869.12")},
"company_hq": "NY",
"company_name": "BRAINCORP",
"none_sensitive_sum": Decimal("86869.12"),
"top_sales_app": "Learn FT",
"top_sales_day": date(2019, 1, 1),
},
]