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DEPR: remove .ix from tests/indexing/multiindex/test_setitem.py #27574

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Jul 25, 2019
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DEPR: remove .ix from tests/indexing/multiindex/test_setitem.py #27574

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268 changes: 118 additions & 150 deletions pandas/tests/indexing/multiindex/test_setitem.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from warnings import catch_warnings, simplefilter

import numpy as np
from numpy.random import randn
import pytest
Expand All @@ -10,133 +8,114 @@
from pandas.util import testing as tm


@pytest.mark.filterwarnings("ignore:\\n.ix:FutureWarning")
class TestMultiIndexSetItem:
def test_setitem_multiindex(self):
with catch_warnings(record=True):

for index_fn in ("ix", "loc"):

def assert_equal(a, b):
assert a == b

def check(target, indexers, value, compare_fn, expected=None):
fn = getattr(target, index_fn)
fn.__setitem__(indexers, value)
result = fn.__getitem__(indexers)
if expected is None:
expected = value
compare_fn(result, expected)

# GH7190
index = MultiIndex.from_product(
[np.arange(0, 100), np.arange(0, 80)], names=["time", "firm"]
)
t, n = 0, 2
df = DataFrame(
np.nan,
columns=["A", "w", "l", "a", "x", "X", "d", "profit"],
index=index,
)
check(
target=df, indexers=((t, n), "X"), value=0, compare_fn=assert_equal
)

df = DataFrame(
-999,
columns=["A", "w", "l", "a", "x", "X", "d", "profit"],
index=index,
)
check(
target=df, indexers=((t, n), "X"), value=1, compare_fn=assert_equal
)

df = DataFrame(
columns=["A", "w", "l", "a", "x", "X", "d", "profit"], index=index
)
check(
target=df, indexers=((t, n), "X"), value=2, compare_fn=assert_equal
)

# gh-7218: assigning with 0-dim arrays
df = DataFrame(
-999,
columns=["A", "w", "l", "a", "x", "X", "d", "profit"],
index=index,
)
check(
target=df,
indexers=((t, n), "X"),
value=np.array(3),
compare_fn=assert_equal,
expected=3,
)

# GH5206
df = DataFrame(
np.arange(25).reshape(5, 5),
columns="A,B,C,D,E".split(","),
dtype=float,
)
df["F"] = 99
row_selection = df["A"] % 2 == 0
col_selection = ["B", "C"]
with catch_warnings(record=True):
df.ix[row_selection, col_selection] = df["F"]
output = DataFrame(99.0, index=[0, 2, 4], columns=["B", "C"])
with catch_warnings(record=True):
tm.assert_frame_equal(df.ix[row_selection, col_selection], output)
check(
target=df,
indexers=(row_selection, col_selection),
value=df["F"],
compare_fn=tm.assert_frame_equal,
expected=output,
)

# GH11372
idx = MultiIndex.from_product(
[["A", "B", "C"], date_range("2015-01-01", "2015-04-01", freq="MS")]
)
cols = MultiIndex.from_product(
[["foo", "bar"], date_range("2016-01-01", "2016-02-01", freq="MS")]
)

df = DataFrame(np.random.random((12, 4)), index=idx, columns=cols)

subidx = MultiIndex.from_tuples(
[("A", Timestamp("2015-01-01")), ("A", Timestamp("2015-02-01"))]
)
subcols = MultiIndex.from_tuples(
[("foo", Timestamp("2016-01-01")), ("foo", Timestamp("2016-02-01"))]
)

vals = DataFrame(
np.random.random((2, 2)), index=subidx, columns=subcols
)
check(
target=df,
indexers=(subidx, subcols),
value=vals,
compare_fn=tm.assert_frame_equal,
)
# set all columns
vals = DataFrame(np.random.random((2, 4)), index=subidx, columns=cols)
check(
target=df,
indexers=(subidx, slice(None, None, None)),
value=vals,
compare_fn=tm.assert_frame_equal,
)
# identity
copy = df.copy()
check(
target=df,
indexers=(df.index, df.columns),
value=df,
compare_fn=tm.assert_frame_equal,
expected=copy,
)
for index_fn in ("loc",):
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Can we just get rid of the loop altogether now?

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yes, there will probably be lots of clean-up opportunities which can be done as a follow-up.

the aim is to just remove for now, deprecations are the prioirty for 1.0 (#27492 (comment)), and keeps the diff easier to review..

here, specificically, more pytest idiom would be nice, so a refactor to just to remove the loop could be just going down another rabbit hole. this pattern of a single loop is not unique to this test.

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@simonjayhawkins this pattern is fine


def assert_equal(a, b):
assert a == b

def check(target, indexers, value, compare_fn, expected=None):
fn = getattr(target, index_fn)
fn.__setitem__(indexers, value)
result = fn.__getitem__(indexers)
if expected is None:
expected = value
compare_fn(result, expected)

# GH7190
index = MultiIndex.from_product(
[np.arange(0, 100), np.arange(0, 80)], names=["time", "firm"]
)
t, n = 0, 2
df = DataFrame(
np.nan,
columns=["A", "w", "l", "a", "x", "X", "d", "profit"],
index=index,
)
check(target=df, indexers=((t, n), "X"), value=0, compare_fn=assert_equal)

df = DataFrame(
-999, columns=["A", "w", "l", "a", "x", "X", "d", "profit"], index=index
)
check(target=df, indexers=((t, n), "X"), value=1, compare_fn=assert_equal)

df = DataFrame(
columns=["A", "w", "l", "a", "x", "X", "d", "profit"], index=index
)
check(target=df, indexers=((t, n), "X"), value=2, compare_fn=assert_equal)

# gh-7218: assigning with 0-dim arrays
df = DataFrame(
-999, columns=["A", "w", "l", "a", "x", "X", "d", "profit"], index=index
)
check(
target=df,
indexers=((t, n), "X"),
value=np.array(3),
compare_fn=assert_equal,
expected=3,
)

# GH5206
df = DataFrame(
np.arange(25).reshape(5, 5), columns="A,B,C,D,E".split(","), dtype=float
)
df["F"] = 99
row_selection = df["A"] % 2 == 0
col_selection = ["B", "C"]
df.loc[row_selection, col_selection] = df["F"]
output = DataFrame(99.0, index=[0, 2, 4], columns=["B", "C"])
tm.assert_frame_equal(df.loc[row_selection, col_selection], output)
check(
target=df,
indexers=(row_selection, col_selection),
value=df["F"],
compare_fn=tm.assert_frame_equal,
expected=output,
)

# GH11372
idx = MultiIndex.from_product(
[["A", "B", "C"], date_range("2015-01-01", "2015-04-01", freq="MS")]
)
cols = MultiIndex.from_product(
[["foo", "bar"], date_range("2016-01-01", "2016-02-01", freq="MS")]
)

df = DataFrame(np.random.random((12, 4)), index=idx, columns=cols)

subidx = MultiIndex.from_tuples(
[("A", Timestamp("2015-01-01")), ("A", Timestamp("2015-02-01"))]
)
subcols = MultiIndex.from_tuples(
[("foo", Timestamp("2016-01-01")), ("foo", Timestamp("2016-02-01"))]
)

vals = DataFrame(np.random.random((2, 2)), index=subidx, columns=subcols)
check(
target=df,
indexers=(subidx, subcols),
value=vals,
compare_fn=tm.assert_frame_equal,
)
# set all columns
vals = DataFrame(np.random.random((2, 4)), index=subidx, columns=cols)
check(
target=df,
indexers=(subidx, slice(None, None, None)),
value=vals,
compare_fn=tm.assert_frame_equal,
)
# identity
copy = df.copy()
check(
target=df,
indexers=(df.index, df.columns),
value=df,
compare_fn=tm.assert_frame_equal,
expected=copy,
)

def test_multiindex_setitem(self):

Expand Down Expand Up @@ -204,9 +183,8 @@ def test_multiindex_assignment(self):
df["d"] = np.nan
arr = np.array([0.0, 1.0])

with catch_warnings(record=True):
df.ix[4, "d"] = arr
tm.assert_series_equal(df.ix[4, "d"], Series(arr, index=[8, 10], name="d"))
df.loc[4, "d"] = arr
tm.assert_series_equal(df.loc[4, "d"], Series(arr, index=[8, 10], name="d"))

# single dtype
df = DataFrame(
Expand All @@ -215,25 +193,21 @@ def test_multiindex_assignment(self):
index=[[4, 4, 8], [8, 10, 12]],
)

with catch_warnings(record=True):
df.ix[4, "c"] = arr
exp = Series(arr, index=[8, 10], name="c", dtype="float64")
tm.assert_series_equal(df.ix[4, "c"], exp)
df.loc[4, "c"] = arr
exp = Series(arr, index=[8, 10], name="c", dtype="float64")
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Would generally prefer long form of expected

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same. since the .ix tests have probably been around for a while, the style used is probably inconsistent with how we would write today.

tm.assert_series_equal(df.loc[4, "c"], exp)

# scalar ok
with catch_warnings(record=True):
df.ix[4, "c"] = 10
exp = Series(10, index=[8, 10], name="c", dtype="float64")
tm.assert_series_equal(df.ix[4, "c"], exp)
df.loc[4, "c"] = 10
exp = Series(10, index=[8, 10], name="c", dtype="float64")
tm.assert_series_equal(df.loc[4, "c"], exp)

# invalid assignments
with pytest.raises(ValueError):
with catch_warnings(record=True):
df.ix[4, "c"] = [0, 1, 2, 3]
df.loc[4, "c"] = [0, 1, 2, 3]

with pytest.raises(ValueError):
with catch_warnings(record=True):
df.ix[4, "c"] = [0]
df.loc[4, "c"] = [0]

# groupby example
NUM_ROWS = 100
Expand Down Expand Up @@ -264,8 +238,7 @@ def f(name, df2):
# but in this case, that's ok
for name, df2 in grp:
new_vals = np.arange(df2.shape[0])
with catch_warnings(record=True):
df.ix[name, "new_col"] = new_vals
df.loc[name, "new_col"] = new_vals

def test_series_setitem(self, multiindex_year_month_day_dataframe_random_data):
ymd = multiindex_year_month_day_dataframe_random_data
Expand Down Expand Up @@ -313,11 +286,6 @@ def test_frame_getitem_setitem_multislice(self):
result = df.loc[:, "value"]
tm.assert_series_equal(df["value"], result)

with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
result = df.ix[:, "value"]
tm.assert_series_equal(df["value"], result)

result = df.loc[df.index[1:3], "value"]
tm.assert_series_equal(df["value"][1:3], result)

Expand Down Expand Up @@ -412,7 +380,7 @@ def test_setitem_change_dtype(self, multiindex_dataframe_random_data):
reindexed = dft.reindex(columns=[("foo", "two")])
tm.assert_series_equal(reindexed["foo", "two"], s > s.median())

def test_set_column_scalar_with_ix(self, multiindex_dataframe_random_data):
def test_set_column_scalar_with_loc(self, multiindex_dataframe_random_data):
frame = multiindex_dataframe_random_data
subset = frame.index[[1, 4, 5]]

Expand Down