def test_by_columns():
t = TableSymbol("t", "{name: string, amount: int32, id: int32}")
assert len(by(t["id"], total=t["amount"].sum()).fields) == 2
assert len(by(t["id"], count=t["id"].count()).fields) == 2
print(by(t, count=t.count()).fields)
assert len(by(t, count=t.count()).fields) == 4
def test_by_raises_informative_error_on_old_syntax():
s = symbol('t', 'var * {name: string, amount: int}')
try:
by(s.name, s.amount.sum())
assert False
except ValueError as e:
assert 'please' in str(e).lower()
def test_by_count():
(chunk, chunk_expr), (agg, agg_expr) = \
split(t, by(t.name, total=t.amount.count()))
assert chunk_expr.isidentical(by(chunk.name, total=chunk.amount.count()))
assert agg_expr.isidentical(by(agg.name, total=agg.total.sum()))
def test_by_columns():
t = symbol('t', 'var * {name: string, amount: int32, id: int32}')
assert len(by(t['id'], total=t['amount'].sum()).fields) == 2
assert len(by(t['id'], count=t['id'].count()).fields) == 2
print(by(t, count=t.count()).fields)
assert len(by(t, count=t.count()).fields) == 4
def test_lean_by_with_summary():
assert lean_projection(by(t.x, total=t.y.sum()))._child.isidentical(t[["x", "y"]])
tt = t[["x", "y"]]
result = lean_projection(by(t.x, a=t.y.sum(), b=t.z.sum())[["x", "a"]])
expected = Projection(By(Field(tt, "x"), summary(a=sum(Field(tt, "y")))), ("x", "a"))
assert result.isidentical(expected)
def test_by_columns():
t = TableSymbol('t', '{name: string, amount: int32, id: int32}')
assert len(by(t['id'], t['amount'].sum()).fields) == 2
assert len(by(t['id'], t['id'].count()).fields) == 2
print(by(t, t.count()).fields)
assert len(by(t, t.count()).fields) == 4
def test_count_values():
t = symbol('t', 'var * {name: string, amount: int, city: string}')
assert t.name.count_values(sort=False).isidentical(
by(t.name, count=t.name.count()),
)
assert t.name.count_values(sort=True).isidentical(
by(t.name, count=t.name.count()).sort('count', ascending=False),
)
def test_by():
assert set(compute(by(t.name, sum=t.amount.sum()), c)) == \
set([('Alice', -200), ('Bob', 200),
('Charlie', 400), ('Edith', 200)])
assert set(compute(by(t.name, count=t.amount.count()), c)) == \
set([('Alice', 2), ('Bob', 1),
('Charlie', 1), ('Edith', 1)])
def test_by(bank):
assert set(compute(by(t.name, total=t.amount.sum()), bank)) == \
set([('Alice', 300), ('Bob', 600)])
assert set(compute(by(t.name, min=t.amount.min()), bank)) == \
set([('Alice', 100), ('Bob', 100)])
assert set(compute(by(t.name, max=t.amount.max()), bank)) == \
set([('Alice', 200), ('Bob', 300)])
assert set(compute(by(t.name, count=t.name.count()), bank)) == \
set([('Alice', 2), ('Bob', 3)])
def test_by(bank):
assert set(compute(by(t.name, t.amount.sum()), bank)) == \
set([('Alice', 300), ('Bob', 600)])
assert set(compute(by(t.name, t.amount.min()), bank)) == \
set([('Alice', 100), ('Bob', 100)])
assert set(compute(by(t.name, t.amount.max()), bank)) == \
set([('Alice', 200), ('Bob', 300)])
assert set(compute(by(t.name, t.name.count()), bank)) == \
set([('Alice', 2), ('Bob', 3)])
def test_by_sum():
(chunk, chunk_expr), (agg, agg_expr) = \
split(t, by(t.name, total=t.amount.sum()))
assert chunk.schema == t.schema
assert chunk_expr.isidentical(by(chunk.name, total=chunk.amount.sum()))
assert not isscalar(agg.dshape.measure)
assert agg_expr.isidentical(by(agg.name, total=agg.total.sum()))
def test_by_with_single_field_child():
x = symbol('x', 'var * int')
(chunk, chunk_expr), (agg, agg_expr) = split(x, by(x, total=x.sum()))
assert chunk_expr.isidentical(by(chunk, total=chunk.sum()))
assert (agg_expr.isidentical(
by(agg[agg.fields[0]],
total=agg.total.sum()).relabel({agg.fields[0]: 'x'})))
def test_by_sum():
(chunk, chunk_expr), (agg, agg_expr) = \
split(t, by(t.name, total=t.amount.sum()))
assert chunk.schema == t.schema
assert chunk_expr.isidentical(by(chunk.name, total=chunk.amount.sum()))
assert not isscalar(agg.dshape.measure)
assert agg_expr.isidentical(by(agg.name, total=agg.total.sum()))
def test_by_with_single_field_child():
x = symbol('x', 'var * int')
(chunk, chunk_expr), (agg, agg_expr) = split(x, by(x, total=x.sum()))
assert chunk_expr.isidentical(by(chunk, total=chunk.sum()))
assert (agg_expr.isidentical(by(agg[agg.fields[0]],
total=agg.total.sum())
.relabel({agg.fields[0]: 'x'})))
def test_summary_by():
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum()))
assert str(compute(expr, df)) == \
str(DataFrame([['Alice', 2, 150],
['Bob', 1, 200]], columns=['name', 'count', 'sum']))
expr = by(t.name, summary(count=t.id.count(), sum=(t.amount + 1).sum()))
assert str(compute(expr, df)) == \
str(DataFrame([['Alice', 2, 152],
['Bob', 1, 201]], columns=['name', 'count', 'sum']))
def test_by():
with collection(bank) as coll:
assert set(compute(by(t, t.name, t.amount.sum()), coll)) == \
set([('Alice', 300), ('Bob', 600)])
assert set(compute(by(t, t.name, t.amount.min()), coll)) == \
set([('Alice', 100), ('Bob', 100)])
assert set(compute(by(t, t.name, t.amount.max()), coll)) == \
set([('Alice', 200), ('Bob', 300)])
assert set(compute(by(t, t.name, t.name.count()), coll)) == \
set([('Alice', 2), ('Bob', 3)])
def test_by_mean():
(chunk, chunk_expr), (agg, agg_expr) = \
split(t, by(t.name, avg=t.amount.mean()))
assert chunk_expr.isidentical(by(chunk.name,
avg_total=chunk.amount.sum(),
avg_count=chunk.amount.count()))
assert agg_expr.isidentical(by(agg.name,
avg=(agg.avg_total.sum() / agg.avg_count.sum())))
def test_summary_by():
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum()))
result = compute(expr, df)
expected = DataFrame([['Alice', 2, 150], ['Bob', 1, 200]],
columns=['name', 'count', 'sum'])
expr = by(t.name, summary(count=t.id.count(), sum=(t.amount + 1).sum()))
result = compute(expr, df)
expected = DataFrame([['Alice', 2, 152], ['Bob', 1, 201]],
columns=['name', 'count', 'sum'])
tm.assert_frame_equal(result, expected)
def test_summary_by():
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum()))
result = compute(expr, df)
expected = DataFrame([['Alice', 2, 150],
['Bob', 1, 200]], columns=['name', 'count', 'sum'])
expr = by(t.name, summary(count=t.id.count(), sum=(t.amount + 1).sum()))
result = compute(expr, df)
expected = DataFrame([['Alice', 2, 152],
['Bob', 1, 201]], columns=['name', 'count', 'sum'])
tm.assert_frame_equal(result, expected)
def test_by_mean():
(chunk, chunk_expr), (agg, agg_expr) = \
split(t, by(t.name, avg=t.amount.mean()))
assert chunk_expr.isidentical(
by(chunk.name,
avg_total=chunk.amount.sum(),
avg_count=chunk.amount.count()))
assert agg_expr.isidentical(
by(agg.name, avg=(agg.avg_total.sum() / agg.avg_count.sum())))
def test_lean_by_with_summary():
assert lean_projection(by(t.x, total=t.y.sum()))._child.isidentical(
t[['x', 'y']], )
tt = t[['x', 'y']]
result = lean_projection(by(t.x, a=t.y.sum(), b=t.z.sum())[['x', 'a']])
expected = Projection(
By(Field(tt, 'x'), summary(a=sum(Field(tt, 'y')))),
('x', 'a'),
)
assert result.isidentical(expected)
def test_summary_by():
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum()))
assert set(compute(expr, data)) == set([('Alice', 2, 150),
('Bob', 1, 200)])
expr = by(t.name, summary(count=t.id.count(), sum=(t.amount + 1).sum()))
assert set(compute(expr, data)) == set([('Alice', 2, 152),
('Bob', 1, 201)])
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum() + 1))
assert set(compute(expr, data)) == set([('Alice', 2, 151),
('Bob', 1, 201)])
def test_summary_by():
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum()))
assert set(compute(expr, data)) == set([('Alice', 2, 150),
('Bob', 1, 200)])
expr = by(t.name, summary(count=t.id.count(), sum=(t.amount + 1).sum()))
assert set(compute(expr, data)) == set([('Alice', 2, 152),
('Bob', 1, 201)])
expr = by(t.name, summary(count=t.id.count(), sum=t.amount.sum() + 1))
assert set(compute(expr, data)) == set([('Alice', 2, 151),
('Bob', 1, 201)])
def test_aliased_views_with_two_group_bys():
expr = by(bank.name, total=bank.amount.sum())
expr2 = by(expr.total, count=expr.name.count())
result = compute(expr2, {bank: sql_bank, cities: sql_cities})
assert normalize(str(result)) == normalize("""
SELECT alias.total, count(alias.name) as count
FROM (SELECT bank.name AS name, sum(bank.amount) AS total
FROM bank
GROUP BY bank.name) as alias
GROUP BY alias.total
""")
def test_aliased_views_with_two_group_bys():
expr = by(bank.name, total=bank.amount.sum())
expr2 = by(expr.total, count=expr.name.count())
result = compute(expr2, {bank: sql_bank, cities: sql_cities})
assert normalize(str(result)) == normalize("""
SELECT alias.total, count(alias.name) as count
FROM (SELECT bank.name AS name, sum(bank.amount) AS total
FROM bank
GROUP BY bank.name) as alias
GROUP BY alias.total
""")
def test_aliased_views_more():
metadata = sa.MetaData()
lhs = sa.Table('aaa', metadata,
sa.Column('x', sa.Integer),
sa.Column('y', sa.Integer),
sa.Column('z', sa.Integer))
rhs = sa.Table('bbb', metadata,
sa.Column('w', sa.Integer),
sa.Column('x', sa.Integer),
sa.Column('y', sa.Integer))
L = symbol('L', 'var * {x: int, y: int, z: int}')
R = symbol('R', 'var * {w: int, x: int, y: int}')
expr = join(by(L.x, y_total=L.y.sum()),
R)
result = compute(expr, {L: lhs, R: rhs})
assert normalize(str(result)) == normalize("""
SELECT alias.x, alias.y_total, bbb.w, bbb.y
FROM (SELECT aaa.x as x, sum(aaa.y) as y_total
FROM aaa
GROUP BY aaa.x) AS alias
JOIN bbb ON alias.x = bbb.x """)
expr2 = by(expr.w, count=expr.x.count(), total2=expr.y_total.sum())
result2 = compute(expr2, {L: lhs, R: rhs})
assert (
normalize(str(result2)) == normalize("""
SELECT alias_2.w, count(alias_2.x) as count, sum(alias_2.y_total) as total2
FROM (SELECT alias.x, alias.y_total, bbb.w, bbb.y
FROM (SELECT aaa.x as x, sum(aaa.y) as y_total
FROM aaa
GROUP BY aaa.x) AS alias
JOIN bbb ON alias.x = bbb.x) AS alias_2
GROUP BY alias_2.w""")
or
normalize(str(result2)) == normalize("""
SELECT bbb.w, count(alias.x) as count, sum(alias.y_total) as total2
FROM (SELECT aaa.x as x, sum(aaa.y) as y_total
FROM aaa
GROUP BY aaa.x) as alias
JOIN bbb ON alias.x = bbb.x
GROUP BY bbb.w"""))
def test_aliased_views_with_join():
joined = join(bank, cities)
expr = by(joined.city, total=joined.amount.sum())
expr2 = by(expr.total, count=expr.city.nunique())
result = compute(expr2, {bank: sql_bank, cities: sql_cities})
assert normalize(str(result)) == normalize("""
SELECT alias.total, count(DISTINCT alias.city) AS count
FROM (SELECT cities.city AS city, sum(bank.amount) AS total
FROM bank
JOIN cities ON bank.name = cities.name
GROUP BY cities.city) as alias
GROUP BY alias.total
""")
def test_aliased_views_with_join():
joined = join(bank, cities)
expr = by(joined.city, total=joined.amount.sum())
expr2 = by(expr.total, count=expr.city.nunique())
result = compute(expr2, {bank: sql_bank, cities: sql_cities})
assert normalize(str(result)) == normalize("""
SELECT alias.total, count(DISTINCT alias.city) AS count
FROM (SELECT cities.city AS city, sum(bank.amount) AS total
FROM bank
JOIN cities ON bank.name = cities.name
GROUP BY cities.city) as alias
GROUP BY alias.total
""")
def test_by_multi_column_grouper():
t = TableSymbol('t', '{x: int, y: int, z: int}')
expr = by(t[['x', 'y']], t['z'].count())
data = [(1, 2, 0), (1, 2, 0), (1, 1, 0)]
print(set(compute(expr, data)))
assert set(compute(expr, data)) == set([(1, 2, 2), (1, 1, 1)])
def compute_up(t, rdd, **kwargs):
grouper = optimize(t.grouper, rdd)
apply = optimize(t.apply, rdd)
t = by(grouper, apply)
if ((isinstance(t.apply, Reduction) and type(t.apply) in binops) or
(isinstance(t.apply, Summary)
and builtins.all(type(val) in binops for val in t.apply.values))):
grouper, binop, combiner, initial = reduce_by_funcs(t)
if isscalar(t.grouper.dshape.measure):
keyfunc = lambda x: (x, )
else:
keyfunc = identity
if isscalar(t.apply.dshape.measure):
valfunc = lambda x: (x, )
else:
valfunc = identity
unpack = lambda kv: keyfunc(kv[0]) + valfunc(kv[1])
create = lambda v: binop(initial, v)
return (rdd.keyBy(grouper).combineByKey(create, binop,
combiner).map(unpack))
else:
raise NotImplementedError("By only implemented for common reductions."
"\nGot %s" % type(t.apply))
def test_join_by_arcs():
df_idx = DataFrame([['A', 1],
['B', 2],
['C', 3]],
columns=['name', 'node_id'])
df_arc = DataFrame([[1, 3],
[2, 3],
[3, 1]],
columns=['node_out', 'node_id'])
t_idx = Symbol('t_idx', 'var * {name: string, node_id: int32}')
t_arc = Symbol('t_arc', 'var * {node_out: int32, node_id: int32}')
joined = join(t_arc, t_idx, "node_id")
want = by(joined['name'], joined['node_id'].count())
result = compute(want, {t_arc: df_arc, t_idx:df_idx})
result_pandas = pd.merge(df_arc, df_idx, on='node_id')
expected = result_pandas.groupby('name')['node_id'].count().reset_index()
assert str(result.values) == str(expected.values)
assert list(result.columns) == ['name', 'node_id_count']
def test_by_multi_column_grouper():
t = Symbol('t', 'var * {x: int, y: int, z: int}')
expr = by(t[['x', 'y']], t['z'].count())
data = [(1, 2, 0), (1, 2, 0), (1, 1, 0)]
print(set(compute(expr, data)))
assert set(compute(expr, data)) == set([(1, 2, 2), (1, 1, 1)])
def test_by_two():
result = compute(by(tbig[['name', 'sex']], tbig['amount'].sum()), databig)
expected = [('Alice', 'F', 200), ('Drew', 'F', 100), ('Drew', 'M', 300)]
print(set(result))
assert set(result) == set(expected)
def test_join_by_arcs():
df_idx = DataFrame([['A', 1],
['B', 2],
['C', 3]],
columns=['name', 'node_id'])
df_arc = DataFrame([[1, 3],
[2, 3],
[3, 1]],
columns=['node_out', 'node_id'])
t_idx = TableSymbol('t_idx', '{name: string, node_id: int32}')
t_arc = TableSymbol('t_arc', '{node_out: int32, node_id: int32}')
joined = join(t_arc, t_idx, "node_id")
want = by(joined['name'], joined['node_id'].count())
result = compute(want, {t_arc: df_arc, t_idx:df_idx})
result_pandas = pd.merge(df_arc, df_idx, on='node_id')
expected = result_pandas.groupby('name')['node_id'].count().reset_index()
assert str(result.values) == str(expected.values)
assert list(result.columns) == ['name', 'node_id_count']
def test_by():
t = symbol('t', 'var * {name: string, amount: int32, id: int32}')
r = by(t['name'], total=sum(t['amount']))
print(r.schema)
assert isinstance(r.schema[0], Record)
assert str(r.schema[0]['name']) == 'string'
def test_by():
t = TableSymbol("t", "{name: string, amount: int32, id: int32}")
r = by(t["name"], total=sum(t["amount"]))
print(r.schema)
assert isinstance(r.schema[0], Record)
assert str(r.schema[0]["name"]) == "string"
def test_path_split():
expr = t.amount.sum() + 1
assert path_split(t, expr).isidentical(t.amount.sum())
expr = t.amount.distinct().sort()
assert path_split(t, expr).isidentical(t.amount.distinct())
t2 = transform(t, id=t.id * 2)
expr = by(t2.id, amount=t2.amount.sum()).amount + 1
assert path_split(t, expr).isidentical(by(t2.id, amount=t2.amount.sum()))
expr = count(t.amount.distinct())
assert path_split(t, expr).isidentical(t.amount.distinct())
expr = summary(total=t.amount.sum())
assert path_split(t, expr).isidentical(expr)
def test_by():
t = TableSymbol('t', '{name: string, amount: int32, id: int32}')
r = by(t['name'], sum(t['amount']))
print(r.schema)
assert isinstance(r.schema[0], Record)
assert str(r.schema[0]['name']) == 'string'
def test_join_by_arcs():
df_idx = DataFrame([['A', 1],
['B', 2],
['C', 3]],
columns=['name', 'node_id'])
df_arc = DataFrame([[1, 3],
[2, 3],
[3, 1]],
columns=['node_out', 'node_id'])
t_idx = symbol('t_idx', 'var * {name: string, node_id: int32}')
t_arc = symbol('t_arc', 'var * {node_out: int32, node_id: int32}')
joined = join(t_arc, t_idx, "node_id")
want = by(joined['name'], count=joined['node_id'].count())
result = compute(want, {t_arc: df_arc, t_idx: df_idx})
result_pandas = pd.merge(df_arc, df_idx, on='node_id')
gb = result_pandas.groupby('name')
expected = gb.node_id.count().reset_index().rename(columns={
'node_id': 'count'
})
tm.assert_frame_equal(result, expected)
assert list(result.columns) == ['name', 'count']
def test_path_split():
expr = t.amount.sum() + 1
assert path_split(t, expr).isidentical(t.amount.sum())
expr = t.amount.distinct().sort()
assert path_split(t, expr).isidentical(t.amount.distinct())
t2 = transform(t, id=t.id * 2)
expr = by(t2.id, amount=t2.amount.sum()).amount + 1
assert path_split(t, expr).isidentical(by(t2.id, amount=t2.amount.sum()))
expr = count(t.amount.distinct())
assert path_split(t, expr).isidentical(t.amount.distinct())
expr = summary(total=t.amount.sum())
assert path_split(t, expr).isidentical(expr)
def test_complex_group_by():
expr = by(merge(tbig.amount // 10, tbig.id % 2), count=tbig.name.count())
result = compute(expr, dfbig) # can we do this? yes we can!
expected = dfbig.groupby([dfbig.amount // 10,
dfbig.id % 2])['name'].count().reset_index()
expected = expected.rename(columns={'name': 'count'})
tm.assert_frame_equal(result, expected)
def test_join_by_arcs():
df_idx = DataFrame([['A', 1], ['B', 2], ['C', 3]],
columns=['name', 'node_id'])
df_arc = DataFrame([[1, 3], [2, 3], [3, 1]],
columns=['node_out', 'node_id'])
t_idx = symbol('t_idx', 'var * {name: string, node_id: int32}')
t_arc = symbol('t_arc', 'var * {node_out: int32, node_id: int32}')
joined = join(t_arc, t_idx, "node_id")
want = by(joined['name'], count=joined['node_id'].count())
result = compute(want, {t_arc: df_arc, t_idx: df_idx})
result_pandas = pd.merge(df_arc, df_idx, on='node_id')
gb = result_pandas.groupby('name')
expected = gb.node_id.count().reset_index().rename(
columns={'node_id': 'count'})
tm.assert_frame_equal(result, expected)
assert list(result.columns) == ['name', 'count']
def test_agg_shape_in_tabular_case_with_explicit_chunk():
t = symbol('t', '1000 * {name: string, amount: int, id: int}')
c = symbol('chunk', 100 * t.schema)
expr = by(t.name, total=t.amount.sum())
(chunk, chunk_expr), (agg, agg_expr) = split(t, expr, chunk=c)
assert agg.dshape == dshape('var * {name: string, total: int64}')
def test_complex_group_by():
expr = by(merge(tbig.amount // 10, tbig.id % 2),
count=tbig.name.count())
result = compute(expr, dfbig) # can we do this? yes we can!
expected = dfbig.groupby([dfbig.amount // 10,
dfbig.id % 2])['name'].count().reset_index()
expected = expected.rename(columns={'name': 'count'})
tm.assert_frame_equal(result, expected)
def test_agg_shape_in_tabular_case_with_explicit_chunk():
t = symbol('t', '1000 * {name: string, amount: int, id: int}')
c = symbol('chunk', 100 * t.schema)
expr = by(t.name, total=t.amount.sum())
(chunk, chunk_expr), (agg, agg_expr) = split(t, expr, chunk=c)
assert agg.dshape == dshape('var * {name: string, total: int64}')
def test_by_four():
t = tbig[['sex', 'amount']]
expr = by(t['sex'], max=t['amount'].max())
result = compute(expr, dfbig)
expected = DataFrame([['F', 100], ['M', 200]], columns=['sex', 'max'])
tm.assert_frame_equal(result, expected)
def test_aliased_views_with_computation():
engine = sa.create_engine('sqlite:///:memory:')
df_aaa = DataFrame({
'x': [1, 2, 3, 2, 3],
'y': [2, 1, 2, 3, 1],
'z': [3, 3, 3, 1, 2]
})
df_bbb = DataFrame({
'w': [1, 2, 3, 2, 3],
'x': [2, 1, 2, 3, 1],
'y': [3, 3, 3, 1, 2]
})
df_aaa.to_sql('aaa', engine)
df_bbb.to_sql('bbb', engine)
metadata = sa.MetaData(engine)
metadata.reflect()
sql_aaa = metadata.tables['aaa']
sql_bbb = metadata.tables['bbb']
L = symbol('aaa', discover(df_aaa))
R = symbol('bbb', discover(df_bbb))
expr = join(by(L.x, y_total=L.y.sum()), R)
a = compute(expr, {L: df_aaa, R: df_bbb})
b = compute(expr, {L: sql_aaa, R: sql_bbb})
assert into(set, a) == into(set, b)
expr2 = by(expr.w, count=expr.x.count(), total2=expr.y_total.sum())
a = compute(expr2, {L: df_aaa, R: df_bbb})
b = compute(expr2, {L: sql_aaa, R: sql_bbb})
assert into(set, a) == into(set, b)
expr3 = by(expr.x, count=expr.y_total.count())
a = compute(expr3, {L: df_aaa, R: df_bbb})
b = compute(expr3, {L: sql_aaa, R: sql_bbb})
assert into(set, a) == into(set, b)
expr4 = join(expr2, R)
a = compute(expr4, {L: df_aaa, R: df_bbb})
b = compute(expr4, {L: sql_aaa, R: sql_bbb})
assert into(set, a) == into(set, b)
""" # Takes a while
def test_by():
expr = by(t['name'], total=t['amount'].sum())
result = compute(expr, s)
expected = sa.select([s.c.name,
sa.sql.functions.sum(s.c.amount).label('total')]
).group_by(s.c.name)
assert str(result) == str(expected)
def test_by():
expr = by(t['name'], total=t['amount'].sum())
result = compute(expr, s)
expected = sa.select(
[s.c.name,
sa.sql.functions.sum(s.c.amount).label('total')]).group_by(s.c.name)
assert str(result) == str(expected)
def test_by_four():
t = tbig[['sex', 'amount']]
expr = by(t['sex'], t['amount'].max())
result = compute(expr, dfbig)
expected = DataFrame([['F', 100],
['M', 200]], columns=['sex', 'amount_max'])
assert str(result) == str(expected)
def test_by_on_same_column():
df = pd.DataFrame([[1, 2], [1, 4], [2, 9]], columns=['id', 'value'])
t = symbol('data', 'var * {id: int, value: int}')
gby = by(t['id'], count=t['id'].count())
expected = DataFrame([[1, 2], [2, 1]], columns=['id', 'count'])
result = compute(gby, {t: df})
tm.assert_frame_equal(result, expected)
def test_by_three():
result = compute(
by(tbig[['name', 'sex']], (tbig['id'] + tbig['amount']).sum()),
databig)
expected = [('Alice', 'F', 204), ('Drew', 'F', 104), ('Drew', 'M', 310)]
print(result)
assert set(result) == set(expected)
def test_by_four():
t = tbig[['sex', 'amount']]
expr = by(t['sex'], max=t['amount'].max())
result = compute(expr, dfbig)
expected = DataFrame([['F', 100],
['M', 200]], columns=['sex', 'max'])
tm.assert_frame_equal(result, expected)
def test_by_on_count():
expr = by(t.name, count=t.count())
result = compute(expr, s)
assert normalize(str(result)) == normalize("""
SELECT accounts.name, count(accounts.id) AS count
FROM accounts
GROUP BY accounts.name
""")
def test_by_two():
result = compute(by(tbig[['name', 'sex']], total=sum(tbig['amount'])),
dfbig)
expected = DataFrame(
[['Alice', 'F', 200], ['Drew', 'F', 100], ['Drew', 'M', 300]],
columns=['name', 'sex', 'total'])
tm.assert_frame_equal(result, expected)
def test_summary_by():
expr = by(t.name, summary(a=t.amount.sum(), b=t.id.count()))
result = str(compute(expr, s))
assert 'sum(accounts.amount) as a' in result.lower()
assert 'count(accounts.id) as b' in result.lower()
assert 'group by accounts.name' in result.lower()
def test_by_two():
expr = by(tbig[['name', 'sex']], total=tbig['amount'].sum())
result = compute(expr, sbig)
expected = (sa.select([
sbig.c.name, sbig.c.sex,
sa.sql.functions.sum(sbig.c.amount).label('total')
]).group_by(sbig.c.name, sbig.c.sex))
assert str(result) == str(expected)
def test_reduce_does_not_compose():
expr = by(t.name, counts=t.count()).counts.max()
result = str(compute(expr, s))
expected = """WITH alias AS
(SELECT count(accounts.id) AS counts
FROM accounts GROUP BY accounts.name)
SELECT max(alias.counts) AS counts_max
FROM alias"""
assert normalize(result) == normalize(expected)
def test_by_on_same_column():
df = pd.DataFrame([[1, 2], [1, 4], [2, 9]], columns=['id', 'value'])
t = symbol('data', 'var * {id: int, value: int}')
gby = by(t['id'], count=t['id'].count())
expected = DataFrame([[1, 2], [2, 1]], columns=['id', 'count'])
result = compute(gby, {t: df})
tm.assert_frame_equal(result, expected)
