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Jan 17, 2022
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STYLE: use option_context almost always #45407

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8 changes: 2 additions & 6 deletions pandas/tests/indexes/multi/test_formats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
Index,
MultiIndex,
)
import pandas._testing as tm


def test_format(idx):
Expand All @@ -27,13 +26,10 @@ def test_format_sparse_config(idx):
warn_filters = warnings.filters
warnings.filterwarnings("ignore", category=FutureWarning, module=".*format")
# GH1538
pd.set_option("display.multi_sparse", False)

result = idx.format()
with pd.option_context("display.multi_sparse", False):
result = idx.format()
assert result[1] == "foo two"

tm.reset_display_options()

warnings.filters = warn_filters


Expand Down
19 changes: 10 additions & 9 deletions pandas/tests/indexing/test_chaining_and_caching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,16 +157,17 @@ def test_setitem_chained_setfault(self):
@pytest.mark.arm_slow
def test_detect_chained_assignment(self):

pd.set_option("chained_assignment", "raise")

# work with the chain
expected = DataFrame([[-5, 1], [-6, 3]], columns=list("AB"))
df = DataFrame(np.arange(4).reshape(2, 2), columns=list("AB"), dtype="int64")
assert df._is_copy is None
with option_context("chained_assignment", "raise"):
# work with the chain
expected = DataFrame([[-5, 1], [-6, 3]], columns=list("AB"))
df = DataFrame(
np.arange(4).reshape(2, 2), columns=list("AB"), dtype="int64"
)
assert df._is_copy is None

df["A"][0] = -5
df["A"][1] = -6
tm.assert_frame_equal(df, expected)
df["A"][0] = -5
df["A"][1] = -6
tm.assert_frame_equal(df, expected)

@pytest.mark.arm_slow
def test_detect_chained_assignment_raises(self, using_array_manager):
Expand Down
11 changes: 4 additions & 7 deletions pandas/tests/io/excel/test_writers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,7 @@
DataFrame,
Index,
MultiIndex,
get_option,
set_option,
option_context,
)
import pandas._testing as tm

Expand Down Expand Up @@ -53,10 +52,8 @@ def set_engine(engine, ext):
the test it rolls back said change to the global option.
"""
option_name = f"io.excel.{ext.strip('.')}.writer"
prev_engine = get_option(option_name)
set_option(option_name, engine)
yield
set_option(option_name, prev_engine) # Roll back option change
with option_context(option_name, engine):
yield


@pytest.mark.parametrize(
Expand Down Expand Up @@ -1294,7 +1291,7 @@ def check_called(func):
del called_save[:]
del called_write_cells[:]

with pd.option_context("io.excel.xlsx.writer", "dummy"):
with option_context("io.excel.xlsx.writer", "dummy"):
path = "something.xlsx"
with tm.ensure_clean(path) as filepath:
register_writer(DummyClass)
Expand Down
51 changes: 20 additions & 31 deletions pandas/tests/io/formats/test_format.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1146,20 +1146,18 @@ def test_wide_repr(self):
):
max_cols = get_option("display.max_columns")
df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
set_option("display.expand_frame_repr", False)
rep_str = repr(df)
with option_context("display.expand_frame_repr", False):
rep_str = repr(df)

assert f"10 rows x {max_cols - 1} columns" in rep_str
set_option("display.expand_frame_repr", True)
wide_repr = repr(df)
with option_context("display.expand_frame_repr", True):
wide_repr = repr(df)
assert rep_str != wide_repr

with option_context("display.width", 120):
wider_repr = repr(df)
assert len(wider_repr) < len(wide_repr)

reset_option("display.expand_frame_repr")

def test_wide_repr_wide_columns(self):
with option_context("mode.sim_interactive", True, "display.max_columns", 20):
df = DataFrame(
Expand All @@ -1174,11 +1172,10 @@ def test_wide_repr_named(self):
max_cols = get_option("display.max_columns")
df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
df.index.name = "DataFrame Index"
set_option("display.expand_frame_repr", False)

rep_str = repr(df)
set_option("display.expand_frame_repr", True)
wide_repr = repr(df)
with option_context("display.expand_frame_repr", False):
rep_str = repr(df)
with option_context("display.expand_frame_repr", True):
wide_repr = repr(df)
assert rep_str != wide_repr

with option_context("display.width", 150):
Expand All @@ -1188,18 +1185,16 @@ def test_wide_repr_named(self):
for line in wide_repr.splitlines()[1::13]:
assert "DataFrame Index" in line

reset_option("display.expand_frame_repr")

def test_wide_repr_multiindex(self):
with option_context("mode.sim_interactive", True, "display.max_columns", 20):
midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10)))
max_cols = get_option("display.max_columns")
df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)), index=midx)
df.index.names = ["Level 0", "Level 1"]
set_option("display.expand_frame_repr", False)
rep_str = repr(df)
set_option("display.expand_frame_repr", True)
wide_repr = repr(df)
with option_context("display.expand_frame_repr", False):
rep_str = repr(df)
with option_context("display.expand_frame_repr", True):
wide_repr = repr(df)
assert rep_str != wide_repr

with option_context("display.width", 150):
Expand All @@ -1209,8 +1204,6 @@ def test_wide_repr_multiindex(self):
for line in wide_repr.splitlines()[1::13]:
assert "Level 0 Level 1" in line

reset_option("display.expand_frame_repr")

def test_wide_repr_multiindex_cols(self):
with option_context("mode.sim_interactive", True, "display.max_columns", 20):
max_cols = get_option("display.max_columns")
Expand All @@ -1220,34 +1213,30 @@ def test_wide_repr_multiindex_cols(self):
tm.rands_array(25, (10, max_cols - 1)), index=midx, columns=mcols
)
df.index.names = ["Level 0", "Level 1"]
set_option("display.expand_frame_repr", False)
rep_str = repr(df)
set_option("display.expand_frame_repr", True)
wide_repr = repr(df)
with option_context("display.expand_frame_repr", False):
rep_str = repr(df)
with option_context("display.expand_frame_repr", True):
wide_repr = repr(df)
assert rep_str != wide_repr

with option_context("display.width", 150, "display.max_columns", 20):
wider_repr = repr(df)
assert len(wider_repr) < len(wide_repr)

reset_option("display.expand_frame_repr")

def test_wide_repr_unicode(self):
with option_context("mode.sim_interactive", True, "display.max_columns", 20):
max_cols = 20
df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
set_option("display.expand_frame_repr", False)
rep_str = repr(df)
set_option("display.expand_frame_repr", True)
wide_repr = repr(df)
with option_context("display.expand_frame_repr", False):
rep_str = repr(df)
with option_context("display.expand_frame_repr", True):
wide_repr = repr(df)
assert rep_str != wide_repr

with option_context("display.width", 150):
wider_repr = repr(df)
assert len(wider_repr) < len(wide_repr)

reset_option("display.expand_frame_repr")

def test_wide_repr_wide_long_columns(self):
with option_context("mode.sim_interactive", True):
df = DataFrame({"a": ["a" * 30, "b" * 30], "b": ["c" * 70, "d" * 80]})
Expand Down
107 changes: 54 additions & 53 deletions pandas/tests/io/pytables/test_append.py
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Original file line number Diff line number Diff line change
Expand Up @@ -214,66 +214,66 @@ def test_append_all_nans(setup_path):
tm.assert_frame_equal(store["df2"], df)

# tests the option io.hdf.dropna_table
pd.set_option("io.hdf.dropna_table", False)
_maybe_remove(store, "df3")
store.append("df3", df[:10])
store.append("df3", df[10:])
tm.assert_frame_equal(store["df3"], df)
with pd.option_context("io.hdf.dropna_table", False):
_maybe_remove(store, "df3")
store.append("df3", df[:10])
store.append("df3", df[10:])
tm.assert_frame_equal(store["df3"], df)

pd.set_option("io.hdf.dropna_table", True)
_maybe_remove(store, "df4")
store.append("df4", df[:10])
store.append("df4", df[10:])
tm.assert_frame_equal(store["df4"], df[-4:])
with pd.option_context("io.hdf.dropna_table", True):
_maybe_remove(store, "df4")
store.append("df4", df[:10])
store.append("df4", df[10:])
tm.assert_frame_equal(store["df4"], df[-4:])

# nan some entire rows (string are still written!)
df = DataFrame(
{
"A1": np.random.randn(20),
"A2": np.random.randn(20),
"B": "foo",
"C": "bar",
},
index=np.arange(20),
)
# nan some entire rows (string are still written!)
df = DataFrame(
{
"A1": np.random.randn(20),
"A2": np.random.randn(20),
"B": "foo",
"C": "bar",
},
index=np.arange(20),
)

df.loc[0:15, :] = np.nan
df.loc[0:15, :] = np.nan

_maybe_remove(store, "df")
store.append("df", df[:10], dropna=True)
store.append("df", df[10:], dropna=True)
tm.assert_frame_equal(store["df"], df)
_maybe_remove(store, "df")
store.append("df", df[:10], dropna=True)
store.append("df", df[10:], dropna=True)
tm.assert_frame_equal(store["df"], df)

_maybe_remove(store, "df2")
store.append("df2", df[:10], dropna=False)
store.append("df2", df[10:], dropna=False)
tm.assert_frame_equal(store["df2"], df)
_maybe_remove(store, "df2")
store.append("df2", df[:10], dropna=False)
store.append("df2", df[10:], dropna=False)
tm.assert_frame_equal(store["df2"], df)

# nan some entire rows (but since we have dates they are still
# written!)
df = DataFrame(
{
"A1": np.random.randn(20),
"A2": np.random.randn(20),
"B": "foo",
"C": "bar",
"D": Timestamp("20010101"),
"E": datetime.datetime(2001, 1, 2, 0, 0),
},
index=np.arange(20),
)
# nan some entire rows (but since we have dates they are still
# written!)
df = DataFrame(
{
"A1": np.random.randn(20),
"A2": np.random.randn(20),
"B": "foo",
"C": "bar",
"D": Timestamp("20010101"),
"E": datetime.datetime(2001, 1, 2, 0, 0),
},
index=np.arange(20),
)

df.loc[0:15, :] = np.nan
df.loc[0:15, :] = np.nan

_maybe_remove(store, "df")
store.append("df", df[:10], dropna=True)
store.append("df", df[10:], dropna=True)
tm.assert_frame_equal(store["df"], df)
_maybe_remove(store, "df")
store.append("df", df[:10], dropna=True)
store.append("df", df[10:], dropna=True)
tm.assert_frame_equal(store["df"], df)

_maybe_remove(store, "df2")
store.append("df2", df[:10], dropna=False)
store.append("df2", df[10:], dropna=False)
tm.assert_frame_equal(store["df2"], df)
_maybe_remove(store, "df2")
store.append("df2", df[:10], dropna=False)
store.append("df2", df[10:], dropna=False)
tm.assert_frame_equal(store["df2"], df)


def test_append_frame_column_oriented(setup_path):
Expand Down Expand Up @@ -898,8 +898,9 @@ def test_append_to_multiple_dropna_false(setup_path):
df1.iloc[1, df1.columns.get_indexer(["A", "B"])] = np.nan
df = concat([df1, df2], axis=1)

with ensure_clean_store(setup_path) as store:

with ensure_clean_store(setup_path) as store, pd.option_context(
"io.hdf.dropna_table", True
):
# dropna=False shouldn't synchronize row indexes
store.append_to_multiple(
{"df1a": ["A", "B"], "df2a": None}, df, selector="df1a", dropna=False
Expand Down
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