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TST: assert reading of legacy pickles against current data #61792

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42 changes: 32 additions & 10 deletions pandas/tests/io/generate_legacy_storage_files.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,8 @@ def create_pickle_data():
data = {
"A": [0.0, 1.0, 2.0, 3.0, np.nan],
"B": [0, 1, 0, 1, 0],
"C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
# "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
"C": Series(["foo1", "foo2", "foo3", "foo4", "foo5"], dtype=object),
"D": date_range("1/1/2009", periods=5),
"E": [0.0, 1, Timestamp("20100101"), "foo", 2.0],
}
Expand Down Expand Up @@ -180,8 +181,16 @@ def create_pickle_data():
tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=["one", "two"]
),
),
"dup": Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]),
"cat": Series(Categorical(["foo", "bar", "baz"])),
"dup": Series(
np.arange(5).astype(np.float64),
index=Index(["A", "B", "C", "D", "A"], dtype=object),
),
# "cat": Series(Categorical(["foo", "bar", "baz"])),
"cat": Series(
Categorical.from_codes(
[2, 0, 1], categories=Index(["bar", "baz", "foo"], dtype="object")
)
),
"dt": Series(date_range("20130101", periods=5)),
"dt_tz": Series(date_range("20130101", periods=5, tz="US/Eastern")),
"period": Series([Period("2000Q1")] * 5),
Expand Down Expand Up @@ -210,26 +219,36 @@ def create_pickle_data():
"dup": DataFrame(
np.arange(15).reshape(5, 3).astype(np.float64), columns=["A", "B", "A"]
),
"cat_onecol": DataFrame({"A": Categorical(["foo", "bar"])}),
# "cat_onecol": DataFrame({"A": Categorical(["foo", "bar"])}),
"cat_onecol": DataFrame(
{
"A": Categorical.from_codes(
[1, 0], categories=Index(["bar", "foo"], dtype="object")
)
}
),
Comment on lines +222 to +229
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@jorisvandenbossche to get the old behavior here, the code changes are a bit more involved. I've not got round to reviewing all the migration guides/release notes yet. Is this included? if not, should it be?

"cat_and_float": DataFrame(
{
"A": Categorical(["foo", "bar", "baz"]),
# "A": Categorical(["foo", "bar", "baz"]),
"A": Categorical.from_codes(
[2, 0, 1], categories=Index(["bar", "baz", "foo"], dtype="object")
),
"B": np.arange(3).astype(np.int64),
}
),
"mixed_dup": mixed_dup_df,
"dt_mixed_tzs": DataFrame(
{
"A": Timestamp("20130102", tz="US/Eastern"),
"B": Timestamp("20130603", tz="CET"),
"A": Timestamp("20130102", tz="US/Eastern").as_unit("ns"),
"B": Timestamp("20130603", tz="CET").as_unit("ns"),
},
index=range(5),
),
"dt_mixed2_tzs": DataFrame(
{
"A": Timestamp("20130102", tz="US/Eastern"),
"B": Timestamp("20130603", tz="CET"),
"C": Timestamp("20130603", tz="UTC"),
"A": Timestamp("20130102", tz="US/Eastern").as_unit("ns"),
"B": Timestamp("20130603", tz="CET").as_unit("ns"),
"C": Timestamp("20130603", tz="UTC").as_unit("ns"),
},
index=range(5),
),
Expand All @@ -245,6 +264,9 @@ def create_pickle_data():
"normal": Timestamp("2011-01-01"),
"nat": NaT,
"tz": Timestamp("2011-01-01", tz="US/Eastern"),
# kept because those are present in the legacy pickles (<= 1.4)
"freq": Timestamp("2011-01-01"),
"both": Timestamp("2011-01-01", tz="Asia/Tokyo"),
}

off = {
Expand Down
29 changes: 27 additions & 2 deletions pandas/tests/io/test_pickle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@
)
import pandas._testing as tm
from pandas.tests.io.generate_legacy_storage_files import create_pickle_data
from pandas.util.version import Version

import pandas.io.common as icom
from pandas.tseries.offsets import (
Expand All @@ -56,7 +57,7 @@
# ---------------------
def compare_element(result, expected, typ):
if isinstance(expected, Index):
tm.assert_index_equal(expected, result)
tm.assert_index_equal(result, expected)
return

if typ.startswith("sp_"):
Expand All @@ -81,15 +82,39 @@ def test_pickles(datapath):
if not is_platform_little_endian():
pytest.skip("known failure on non-little endian")

current_data = create_pickle_data()

# For loop for compat with --strict-data-files
for legacy_pickle in Path(__file__).parent.glob("data/legacy_pickle/*/*.p*kl*"):
legacy_version = Version(legacy_pickle.parent.name)
legacy_pickle = datapath(legacy_pickle)

data = pd.read_pickle(legacy_pickle)

for typ, dv in data.items():
for dt, result in dv.items():
expected = data[typ][dt]
expected = current_data[typ][dt]

if (
typ == "timestamp"
and dt in ("tz", "both")
and legacy_version < Version("1.3.0")
):
# convert to wall time
# (bug since pandas 2.0 that tz gets dropped for older pickle files)
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is there an issue ref for this

expected = expected.tz_convert(None)

if typ in ("frame", "sp_frame"):
expected.columns = expected.columns.astype("object")

if typ == "frame" and dt == "mi":
expected.index = expected.index.set_levels(
[level.astype("object") for level in expected.index.levels],
)
if typ == "mi":
expected = expected.set_levels(
[level.astype("object") for level in expected.levels],
)

if typ == "series" and dt == "ts":
# GH 7748
Expand Down
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