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Mar 31, 2020
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Json parametrize more2 #33163

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e743888
intframe move
WillAyd Mar 31, 2020
4f75097
tsframe parametrization
WillAyd Mar 31, 2020
123190b
remove mixed_frame
WillAyd Mar 31, 2020
cd9294b
black
WillAyd Mar 31, 2020
a8e0cee
cleanup
WillAyd Mar 31, 2020
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58 changes: 58 additions & 0 deletions pandas/conftest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -526,6 +526,64 @@ def empty_frame():
return DataFrame()


@pytest.fixture
def int_frame():
"""
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does this duplicate?

Fixture for DataFrame of ints with index of unique strings

Columns are ['A', 'B', 'C', 'D']

A B C D
vpBeWjM651 1 0 1 0
5JyxmrP1En -1 0 0 0
qEDaoD49U2 -1 1 0 0
m66TkTfsFe 0 0 0 0
EHPaNzEUFm -1 0 -1 0
fpRJCevQhi 2 0 0 0
OlQvnmfi3Q 0 0 -2 0
... .. .. .. ..
uB1FPlz4uP 0 0 0 1
EcSe6yNzCU 0 0 -1 0
L50VudaiI8 -1 1 -2 0
y3bpw4nwIp 0 -1 0 0
H0RdLLwrCT 1 1 0 0
rY82K0vMwm 0 0 0 0
1OPIUjnkjk 2 0 0 0

[30 rows x 4 columns]
"""
return DataFrame(tm.getSeriesData()).astype("int64")


@pytest.fixture
def datetime_frame():
"""
Fixture for DataFrame of floats with DatetimeIndex

Columns are ['A', 'B', 'C', 'D']

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doesn't this duplicate some existing fixtures?

A B C D
2000-01-03 -1.122153 0.468535 0.122226 1.693711
2000-01-04 0.189378 0.486100 0.007864 -1.216052
2000-01-05 0.041401 -0.835752 -0.035279 -0.414357
2000-01-06 0.430050 0.894352 0.090719 0.036939
2000-01-07 -0.620982 -0.668211 -0.706153 1.466335
2000-01-10 -0.752633 0.328434 -0.815325 0.699674
2000-01-11 -2.236969 0.615737 -0.829076 -1.196106
... ... ... ... ...
2000-02-03 1.642618 -0.579288 0.046005 1.385249
2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
2000-02-07 -2.656149 -0.601387 1.410148 0.444150
2000-02-08 -1.201881 -1.289040 0.772992 -1.445300
2000-02-09 1.377373 0.398619 1.008453 -0.928207
2000-02-10 0.473194 -0.636677 0.984058 0.511519
2000-02-11 -0.965556 0.408313 -1.312844 -0.381948

[30 rows x 4 columns]
"""
return DataFrame(tm.getTimeSeriesData())


@pytest.fixture
def float_frame():
"""
Expand Down
60 changes: 0 additions & 60 deletions pandas/tests/frame/conftest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,66 +79,6 @@ def bool_frame_with_na():
return df


@pytest.fixture
def int_frame():
"""
Fixture for DataFrame of ints with index of unique strings

Columns are ['A', 'B', 'C', 'D']

A B C D
vpBeWjM651 1 0 1 0
5JyxmrP1En -1 0 0 0
qEDaoD49U2 -1 1 0 0
m66TkTfsFe 0 0 0 0
EHPaNzEUFm -1 0 -1 0
fpRJCevQhi 2 0 0 0
OlQvnmfi3Q 0 0 -2 0
... .. .. .. ..
uB1FPlz4uP 0 0 0 1
EcSe6yNzCU 0 0 -1 0
L50VudaiI8 -1 1 -2 0
y3bpw4nwIp 0 -1 0 0
H0RdLLwrCT 1 1 0 0
rY82K0vMwm 0 0 0 0
1OPIUjnkjk 2 0 0 0

[30 rows x 4 columns]
"""
df = DataFrame({k: v.astype(int) for k, v in tm.getSeriesData().items()})
# force these all to int64 to avoid platform testing issues
return DataFrame({c: s for c, s in df.items()}, dtype=np.int64)


@pytest.fixture
def datetime_frame():
"""
Fixture for DataFrame of floats with DatetimeIndex

Columns are ['A', 'B', 'C', 'D']

A B C D
2000-01-03 -1.122153 0.468535 0.122226 1.693711
2000-01-04 0.189378 0.486100 0.007864 -1.216052
2000-01-05 0.041401 -0.835752 -0.035279 -0.414357
2000-01-06 0.430050 0.894352 0.090719 0.036939
2000-01-07 -0.620982 -0.668211 -0.706153 1.466335
2000-01-10 -0.752633 0.328434 -0.815325 0.699674
2000-01-11 -2.236969 0.615737 -0.829076 -1.196106
... ... ... ... ...
2000-02-03 1.642618 -0.579288 0.046005 1.385249
2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
2000-02-07 -2.656149 -0.601387 1.410148 0.444150
2000-02-08 -1.201881 -1.289040 0.772992 -1.445300
2000-02-09 1.377373 0.398619 1.008453 -0.928207
2000-02-10 0.473194 -0.636677 0.984058 0.511519
2000-02-11 -0.965556 0.408313 -1.312844 -0.381948

[30 rows x 4 columns]
"""
return DataFrame(tm.getTimeSeriesData())


@pytest.fixture
def float_string_frame():
"""
Expand Down
53 changes: 20 additions & 33 deletions pandas/tests/io/json/test_pandas.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,20 +16,15 @@
import pandas._testing as tm

_seriesd = tm.getSeriesData()
_tsd = tm.getTimeSeriesData()

_frame = DataFrame(_seriesd)
_intframe = DataFrame({k: v.astype(np.int64) for k, v in _seriesd.items()})

_tsframe = DataFrame(_tsd)
_cat_frame = _frame.copy()
cat = ["bah"] * 5 + ["bar"] * 5 + ["baz"] * 5 + ["foo"] * (len(_cat_frame) - 15)
_cat_frame.index = pd.CategoricalIndex(cat, name="E")
_cat_frame["E"] = list(reversed(cat))
_cat_frame["sort"] = np.arange(len(_cat_frame), dtype="int64")

_mixed_frame = _frame.copy()


def assert_json_roundtrip_equal(result, expected, orient):
if orient == "records" or orient == "values":
Expand All @@ -43,17 +38,10 @@ def assert_json_roundtrip_equal(result, expected, orient):
class TestPandasContainer:
@pytest.fixture(autouse=True)
def setup(self):
self.intframe = _intframe.copy()
self.tsframe = _tsframe.copy()
self.mixed_frame = _mixed_frame.copy()
self.categorical = _cat_frame.copy()

yield

del self.intframe
del self.tsframe
del self.mixed_frame

def test_frame_double_encoded_labels(self, orient):
df = DataFrame(
[["a", "b"], ["c", "d"]],
Expand Down Expand Up @@ -137,12 +125,12 @@ def test_roundtrip_simple(self, orient, convert_axes, numpy, dtype, float_frame)
@pytest.mark.parametrize("dtype", [False, np.int64])
@pytest.mark.parametrize("convert_axes", [True, False])
@pytest.mark.parametrize("numpy", [True, False])
def test_roundtrip_intframe(self, orient, convert_axes, numpy, dtype):
data = self.intframe.to_json(orient=orient)
def test_roundtrip_intframe(self, orient, convert_axes, numpy, dtype, int_frame):
data = int_frame.to_json(orient=orient)
result = pd.read_json(
data, orient=orient, convert_axes=convert_axes, numpy=numpy, dtype=dtype
)
expected = self.intframe.copy()
expected = int_frame
if (
numpy
and (is_platform_32bit() or is_platform_windows())
Expand Down Expand Up @@ -236,13 +224,13 @@ def test_roundtrip_empty(self, orient, convert_axes, numpy, empty_frame):

@pytest.mark.parametrize("convert_axes", [True, False])
@pytest.mark.parametrize("numpy", [True, False])
def test_roundtrip_timestamp(self, orient, convert_axes, numpy):
def test_roundtrip_timestamp(self, orient, convert_axes, numpy, datetime_frame):
# TODO: improve coverage with date_format parameter
data = self.tsframe.to_json(orient=orient)
data = datetime_frame.to_json(orient=orient)
result = pd.read_json(
data, orient=orient, convert_axes=convert_axes, numpy=numpy
)
expected = self.tsframe.copy()
expected = datetime_frame.copy()

if not convert_axes: # one off for ts handling
# DTI gets converted to epoch values
Expand Down Expand Up @@ -730,34 +718,33 @@ def test_reconstruction_index(self):
result = read_json(df.to_json())
tm.assert_frame_equal(result, df)

def test_path(self, float_frame):
def test_path(self, float_frame, int_frame, datetime_frame):
with tm.ensure_clean("test.json") as path:
for df in [
float_frame,
self.intframe,
self.tsframe,
self.mixed_frame,
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I just removed mixed_frame here as this is its only use in the module; doesn't seem important to re-create, and in its current state it was really the same thing as float_frame anyway

int_frame,
datetime_frame,
]:
df.to_json(path)
read_json(path)

def test_axis_dates(self, datetime_series):
def test_axis_dates(self, datetime_series, datetime_frame):

# frame
json = self.tsframe.to_json()
json = datetime_frame.to_json()
result = read_json(json)
tm.assert_frame_equal(result, self.tsframe)
tm.assert_frame_equal(result, datetime_frame)

# series
json = datetime_series.to_json()
result = read_json(json, typ="series")
tm.assert_series_equal(result, datetime_series, check_names=False)
assert result.name is None

def test_convert_dates(self, datetime_series):
def test_convert_dates(self, datetime_series, datetime_frame):

# frame
df = self.tsframe.copy()
df = datetime_frame
df["date"] = Timestamp("20130101")

json = df.to_json()
Expand Down Expand Up @@ -837,8 +824,8 @@ def test_convert_dates_infer(self, infer_word):
("20130101 20:43:42.123456789", "ns"),
],
)
def test_date_format_frame(self, date, date_unit):
df = self.tsframe.copy()
def test_date_format_frame(self, date, date_unit, datetime_frame):
df = datetime_frame

df["date"] = Timestamp(date)
df.iloc[1, df.columns.get_loc("date")] = pd.NaT
Expand All @@ -853,8 +840,8 @@ def test_date_format_frame(self, date, date_unit):
expected["date"] = expected["date"].dt.tz_localize("UTC")
tm.assert_frame_equal(result, expected)

def test_date_format_frame_raises(self):
df = self.tsframe.copy()
def test_date_format_frame_raises(self, datetime_frame):
df = datetime_frame
msg = "Invalid value 'foo' for option 'date_unit'"
with pytest.raises(ValueError, match=msg):
df.to_json(date_format="iso", date_unit="foo")
Expand Down Expand Up @@ -890,8 +877,8 @@ def test_date_format_series_raises(self, datetime_series):
ts.to_json(date_format="iso", date_unit="foo")

@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
def test_date_unit(self, unit):
df = self.tsframe.copy()
def test_date_unit(self, unit, datetime_frame):
df = datetime_frame
df["date"] = Timestamp("20130101 20:43:42")
dl = df.columns.get_loc("date")
df.iloc[1, dl] = Timestamp("19710101 20:43:42")
Expand Down