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Jul 13, 2018
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TST: Parameterize more tests #21873

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26 changes: 25 additions & 1 deletion pandas/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,7 +259,10 @@ def string_dtype(request):
return request.param


@pytest.fixture(params=[float, "float32", "float64"])
FLOAT_DTYPES = [float, "float32", "float64"]


@pytest.fixture(params=FLOAT_DTYPES)
def float_dtype(request):
"""
Parameterized fixture for float dtypes.
Expand All @@ -286,6 +289,7 @@ def complex_dtype(request):
UNSIGNED_INT_DTYPES = ["uint8", "uint16", "uint32", "uint64"]
SIGNED_INT_DTYPES = [int, "int8", "int16", "int32", "int64"]
ALL_INT_DTYPES = UNSIGNED_INT_DTYPES + SIGNED_INT_DTYPES
ALL_REAL_DTYPES = FLOAT_DTYPES + ALL_INT_DTYPES


@pytest.fixture(params=SIGNED_INT_DTYPES)
Expand Down Expand Up @@ -334,6 +338,26 @@ def any_int_dtype(request):
return request.param


@pytest.fixture(params=ALL_REAL_DTYPES)
def any_real_dtype(request):
"""
Parameterized fixture for any (purely) real numeric dtypes.

* int8
* uint8
* int16
* uint16
* int32
* uint32
* int64
* uint64
* float32
* float64
"""

return request.param


@pytest.fixture
def mock():
"""
Expand Down
143 changes: 71 additions & 72 deletions pandas/tests/frame/test_dtypes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
import numpy as np
from pandas import (DataFrame, Series, date_range, Timedelta, Timestamp,
Categorical, compat, concat, option_context)
from pandas.compat import u
from pandas.compat import u, PY2
from pandas import _np_version_under1p14

from pandas.core.dtypes.dtypes import DatetimeTZDtype, CategoricalDtype
Expand All @@ -21,6 +21,11 @@
import pandas as pd


@pytest.fixture(params=[str, compat.text_type])
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may consider in the future to move this fixture to conftest (needs a good name though)

def text_dtype(request):
return request.param


class TestDataFrameDataTypes(TestData):

def test_concat_empty_dataframe_dtypes(self):
Expand Down Expand Up @@ -351,27 +356,23 @@ def test_select_dtypes_datetime_with_tz(self):
expected = df3.reindex(columns=[])
assert_frame_equal(result, expected)

def test_select_dtypes_str_raises(self):
df = DataFrame({'a': list('abc'),
'g': list(u('abc')),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.date_range('now', periods=3).values})
string_dtypes = set((str, 'str', np.string_, 'S1',
'unicode', np.unicode_, 'U1'))
try:
string_dtypes.add(unicode)
except NameError:
pass
for dt in string_dtypes:
with tm.assert_raises_regex(TypeError,
'string dtypes are not allowed'):
df.select_dtypes(include=[dt])
with tm.assert_raises_regex(TypeError,
'string dtypes are not allowed'):
df.select_dtypes(exclude=[dt])
@pytest.mark.parametrize(
"dtype", [str, "str", np.string_, "S1",
"unicode", np.unicode_, "U1"] + ([unicode] if PY2 else []))
@pytest.mark.parametrize("arg", ["include", "exclude"])
def test_select_dtypes_str_raises(self, dtype, arg):
df = DataFrame({"a": list("abc"),
"g": list(u("abc")),
"b": list(range(1, 4)),
"c": np.arange(3, 6).astype("u1"),
"d": np.arange(4.0, 7.0, dtype="float64"),
"e": [True, False, True],
"f": pd.date_range("now", periods=3).values})
msg = "string dtypes are not allowed"
kwargs = {arg: [dtype]}

with tm.assert_raises_regex(TypeError, msg):
df.select_dtypes(**kwargs)

def test_select_dtypes_bad_arg_raises(self):
df = DataFrame({'a': list('abc'),
Expand Down Expand Up @@ -502,61 +503,59 @@ def test_astype_with_view(self):
tf = self.frame.astype(np.float64)
casted = tf.astype(np.int64, copy=False) # noqa

def test_astype_cast_nan_inf_int(self):
# GH14265, check nan and inf raise error when converting to int
types = [np.int32, np.int64]
values = [np.nan, np.inf]
msg = 'Cannot convert non-finite values \\(NA or inf\\) to integer'
@pytest.mark.parametrize("dtype", [np.int32, np.int64])
@pytest.mark.parametrize("val", [np.nan, np.inf])
def test_astype_cast_nan_inf_int(self, val, dtype):
# see gh-14265
#
# Check NaN and inf --> raise error when converting to int.
msg = "Cannot convert non-finite values \\(NA or inf\\) to integer"
df = DataFrame([val])

for this_type in types:
for this_val in values:
df = DataFrame([this_val])
with tm.assert_raises_regex(ValueError, msg):
df.astype(this_type)
with tm.assert_raises_regex(ValueError, msg):
df.astype(dtype)

def test_astype_str(self):
# GH9757
a = Series(date_range('2010-01-04', periods=5))
b = Series(date_range('3/6/2012 00:00', periods=5, tz='US/Eastern'))
c = Series([Timedelta(x, unit='d') for x in range(5)])
def test_astype_str(self, text_dtype):
# see gh-9757
a = Series(date_range("2010-01-04", periods=5))
b = Series(date_range("3/6/2012 00:00", periods=5, tz="US/Eastern"))
c = Series([Timedelta(x, unit="d") for x in range(5)])
d = Series(range(5))
e = Series([0.0, 0.2, 0.4, 0.6, 0.8])

df = DataFrame({'a': a, 'b': b, 'c': c, 'd': d, 'e': e})

# datetimelike
# Test str and unicode on python 2.x and just str on python 3.x
for tt in set([str, compat.text_type]):
result = df.astype(tt)

expected = DataFrame({
'a': list(map(tt, map(lambda x: Timestamp(x)._date_repr,
a._values))),
'b': list(map(tt, map(Timestamp, b._values))),
'c': list(map(tt, map(lambda x: Timedelta(x)
._repr_base(format='all'), c._values))),
'd': list(map(tt, d._values)),
'e': list(map(tt, e._values)),
})

assert_frame_equal(result, expected)

# float/nan
# 11302
# consistency in astype(str)
for tt in set([str, compat.text_type]):
result = DataFrame([np.NaN]).astype(tt)
expected = DataFrame(['nan'])
assert_frame_equal(result, expected)

result = DataFrame([1.12345678901234567890]).astype(tt)
if _np_version_under1p14:
# < 1.14 truncates
expected = DataFrame(['1.12345678901'])
else:
# >= 1.14 preserves the full repr
expected = DataFrame(['1.1234567890123457'])
assert_frame_equal(result, expected)
df = DataFrame({"a": a, "b": b, "c": c, "d": d, "e": e})

# Datetime-like
# Test str and unicode on Python 2.x and just str on Python 3.x
result = df.astype(text_dtype)

expected = DataFrame({
"a": list(map(text_dtype,
map(lambda x: Timestamp(x)._date_repr, a._values))),
"b": list(map(text_dtype, map(Timestamp, b._values))),
"c": list(map(text_dtype,
map(lambda x: Timedelta(x)._repr_base(format="all"),
c._values))),
"d": list(map(text_dtype, d._values)),
"e": list(map(text_dtype, e._values)),
})

assert_frame_equal(result, expected)

def test_astype_str_float(self, text_dtype):
# see gh-11302
result = DataFrame([np.NaN]).astype(text_dtype)
expected = DataFrame(["nan"])

assert_frame_equal(result, expected)
result = DataFrame([1.12345678901234567890]).astype(text_dtype)

# < 1.14 truncates
# >= 1.14 preserves the full repr
val = ("1.12345678901" if _np_version_under1p14
else "1.1234567890123457")
expected = DataFrame([val])
assert_frame_equal(result, expected)

@pytest.mark.parametrize("dtype_class", [dict, Series])
def test_astype_dict_like(self, dtype_class):
Expand Down
129 changes: 58 additions & 71 deletions pandas/tests/reshape/merge/test_merge_asof.py
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Original file line number Diff line number Diff line change
Expand Up @@ -892,77 +892,64 @@ def test_on_float(self):

assert_frame_equal(result, expected)

def test_on_specialized_type(self):
# GH13936
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float16, np.float32, np.float64]:
df1 = pd.DataFrame({
'value': [5, 2, 25, 100, 78, 120, 79],
'symbol': list("ABCDEFG")},
columns=['symbol', 'value'])
df1.value = dtype(df1.value)

df2 = pd.DataFrame({
'value': [0, 80, 120, 125],
'result': list('xyzw')},
columns=['value', 'result'])
df2.value = dtype(df2.value)

df1 = df1.sort_values('value').reset_index(drop=True)

if dtype == np.float16:
with pytest.raises(MergeError):
pd.merge_asof(df1, df2, on='value')
continue

result = pd.merge_asof(df1, df2, on='value')

expected = pd.DataFrame(
{'symbol': list("BACEGDF"),
'value': [2, 5, 25, 78, 79, 100, 120],
'result': list('xxxxxyz')
}, columns=['symbol', 'value', 'result'])
expected.value = dtype(expected.value)

assert_frame_equal(result, expected)

def test_on_specialized_type_by_int(self):
# GH13936
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float16, np.float32, np.float64]:
df1 = pd.DataFrame({
'value': [5, 2, 25, 100, 78, 120, 79],
'key': [1, 2, 3, 2, 3, 1, 2],
'symbol': list("ABCDEFG")},
columns=['symbol', 'key', 'value'])
df1.value = dtype(df1.value)

df2 = pd.DataFrame({
'value': [0, 80, 120, 125],
'key': [1, 2, 2, 3],
'result': list('xyzw')},
columns=['value', 'key', 'result'])
df2.value = dtype(df2.value)

df1 = df1.sort_values('value').reset_index(drop=True)

if dtype == np.float16:
with pytest.raises(MergeError):
pd.merge_asof(df1, df2, on='value', by='key')
else:
result = pd.merge_asof(df1, df2, on='value', by='key')

expected = pd.DataFrame({
'symbol': list("BACEGDF"),
'key': [2, 1, 3, 3, 2, 2, 1],
'value': [2, 5, 25, 78, 79, 100, 120],
'result': [np.nan, 'x', np.nan, np.nan, np.nan, 'y', 'x']},
columns=['symbol', 'key', 'value', 'result'])
expected.value = dtype(expected.value)

assert_frame_equal(result, expected)
def test_on_specialized_type(self, any_real_dtype):
# see gh-13936
dtype = np.dtype(any_real_dtype).type

df1 = pd.DataFrame({
"value": [5, 2, 25, 100, 78, 120, 79],
"symbol": list("ABCDEFG")},
columns=["symbol", "value"])
df1.value = dtype(df1.value)

df2 = pd.DataFrame({
"value": [0, 80, 120, 125],
"result": list("xyzw")},
columns=["value", "result"])
df2.value = dtype(df2.value)

df1 = df1.sort_values("value").reset_index(drop=True)
result = pd.merge_asof(df1, df2, on="value")

expected = pd.DataFrame(
{"symbol": list("BACEGDF"),
"value": [2, 5, 25, 78, 79, 100, 120],
"result": list("xxxxxyz")
}, columns=["symbol", "value", "result"])
expected.value = dtype(expected.value)

assert_frame_equal(result, expected)

def test_on_specialized_type_by_int(self, any_real_dtype):
# see gh-13936
dtype = np.dtype(any_real_dtype).type

df1 = pd.DataFrame({
"value": [5, 2, 25, 100, 78, 120, 79],
"key": [1, 2, 3, 2, 3, 1, 2],
"symbol": list("ABCDEFG")},
columns=["symbol", "key", "value"])
df1.value = dtype(df1.value)

df2 = pd.DataFrame({
"value": [0, 80, 120, 125],
"key": [1, 2, 2, 3],
"result": list("xyzw")},
columns=["value", "key", "result"])
df2.value = dtype(df2.value)

df1 = df1.sort_values("value").reset_index(drop=True)
result = pd.merge_asof(df1, df2, on="value", by="key")

expected = pd.DataFrame({
"symbol": list("BACEGDF"),
"key": [2, 1, 3, 3, 2, 2, 1],
"value": [2, 5, 25, 78, 79, 100, 120],
"result": [np.nan, "x", np.nan, np.nan, np.nan, "y", "x"]},
columns=["symbol", "key", "value", "result"])
expected.value = dtype(expected.value)

assert_frame_equal(result, expected)

def test_on_float_by_int(self):
# type specialize both "by" and "on" parameters
Expand Down
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