def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how="left", sort=sort)
assert len(left) < len(res) + 1
assert not res["4th"].isna().any()
assert not res["5th"].isna().any()
tm.assert_series_equal(res["4th"], -res["5th"], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res["4th"], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(res,
res.sort_values(icols, kind="mergesort"))
out = merge(left,
right.reset_index(),
on=icols,
sort=sort,
how="left")
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = DataFrame(
{
"household_id": [1, 2, 2, 3, 3, 3, 4],
"asset_id": [
"nl0000301109",
"nl0000301109",
"gb00b03mlx29",
"gb00b03mlx29",
"lu0197800237",
"nl0000289965",
np.nan,
],
"share": [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0],
},
columns=["household_id", "asset_id", "share"],
).set_index(["household_id", "asset_id"])
log_return = DataFrame({
"asset_id": [
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"lu0197800237",
"lu0197800237",
],
"t": [233, 234, 235, 180, 181],
"log_return": [
0.09604978,
-0.06524096,
0.03532373,
0.03025441,
0.036997,
],
}).set_index(["asset_id", "t"])
expected = (DataFrame({
"household_id": [2, 2, 2, 3, 3, 3, 3, 3],
"asset_id": [
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"lu0197800237",
"lu0197800237",
],
"t": [233, 234, 235, 233, 234, 235, 180, 181],
"share": [0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
"log_return": [
0.09604978,
-0.06524096,
0.03532373,
0.09604978,
-0.06524096,
0.03532373,
0.03025441,
0.036997,
],
}).set_index(["household_id", "asset_id",
"t"]).reindex(columns=["share", "log_return"]))
# this is the equivalency
result = merge(
household.reset_index(),
log_return.reset_index(),
on=["asset_id"],
how="inner",
).set_index(["household_id", "asset_id", "t"])
tm.assert_frame_equal(result, expected)
expected = (DataFrame({
"household_id": [1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
"asset_id": [
"nl0000301109",
"nl0000301109",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"gb00b03mlx29",
"lu0197800237",
"lu0197800237",
"nl0000289965",
None,
],
"t": [
None,
None,
233,
234,
235,
233,
234,
235,
180,
181,
None,
None,
],
"share": [
1.0,
0.4,
0.6,
0.6,
0.6,
0.15,
0.15,
0.15,
0.6,
0.6,
0.25,
1.0,
],
"log_return": [
None,
None,
0.09604978,
-0.06524096,
0.03532373,
0.09604978,
-0.06524096,
0.03532373,
0.03025441,
0.036997,
None,
None,
],
}).set_index(["household_id", "asset_id",
"t"]).reindex(columns=["share", "log_return"]))
result = merge(
household.reset_index(),
log_return.reset_index(),
on=["asset_id"],
how="outer",
).set_index(["household_id", "asset_id", "t"])
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = DataFrame(
{
"household_id": [1, 2, 3],
"male": [0, 1, 0],
"wealth": [196087.3, 316478.7, 294750],
},
columns=["household_id", "male", "wealth"],
).set_index("household_id")
portfolio = DataFrame(
{
"household_id": [1, 2, 2, 3, 3, 3, 4],
"asset_id": [
"nl0000301109",
"nl0000289783",
"gb00b03mlx29",
"gb00b03mlx29",
"lu0197800237",
"nl0000289965",
np.nan,
],
"name": [
"ABN Amro",
"Robeco",
"Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds",
np.nan,
],
"share": [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0],
},
columns=["household_id", "asset_id", "name", "share"],
).set_index(["household_id", "asset_id"])
result = household.join(portfolio, how="inner")
expected = (DataFrame({
"male": [0, 1, 1, 0, 0, 0],
"wealth": [
196087.3,
316478.7,
316478.7,
294750.0,
294750.0,
294750.0,
],
"name": [
"ABN Amro",
"Robeco",
"Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds",
],
"share": [1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
"household_id": [1, 2, 2, 3, 3, 3],
"asset_id": [
"nl0000301109",
"nl0000289783",
"gb00b03mlx29",
"gb00b03mlx29",
"lu0197800237",
"nl0000289965",
],
}).set_index(["household_id", "asset_id"
]).reindex(columns=["male", "wealth", "name", "share"]))
tm.assert_frame_equal(result, expected)
# equivalency
result = merge(
household.reset_index(),
portfolio.reset_index(),
on=["household_id"],
how="inner",
).set_index(["household_id", "asset_id"])
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how="outer")
expected = concat(
[
expected,
(DataFrame(
{"share": [1.00]},
index=MultiIndex.from_tuples(
[(4, np.nan)], names=["household_id", "asset_id"]),
)),
],
axis=0,
sort=True,
).reindex(columns=expected.columns)
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = "foo"
with pytest.raises(
ValueError,
match="cannot join with no overlapping index names"):
household.join(portfolio, how="inner")
portfolio2 = portfolio.copy()
portfolio2.index.set_names(["household_id", "foo"])
with pytest.raises(ValueError,
match="columns overlap but no suffix specified"):
portfolio2.join(portfolio, how="inner")
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
def f():
household.join(portfolio, how='inner')
pytest.raises(ValueError, f)
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
def f():
portfolio2.join(portfolio, how='inner')
pytest.raises(ValueError, f)
