1246 lines
46 KiB
Python
1246 lines
46 KiB
Python
from io import StringIO
|
|
import operator
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas.util._test_decorators as td
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Index, MultiIndex, Series, date_range
|
|
import pandas._testing as tm
|
|
from pandas.core.computation.check import NUMEXPR_INSTALLED
|
|
|
|
PARSERS = "python", "pandas"
|
|
ENGINES = "python", pytest.param("numexpr", marks=td.skip_if_no_ne)
|
|
|
|
|
|
@pytest.fixture(params=PARSERS, ids=lambda x: x)
|
|
def parser(request):
|
|
return request.param
|
|
|
|
|
|
@pytest.fixture(params=ENGINES, ids=lambda x: x)
|
|
def engine(request):
|
|
return request.param
|
|
|
|
|
|
def skip_if_no_pandas_parser(parser):
|
|
if parser != "pandas":
|
|
pytest.skip(f"cannot evaluate with parser {repr(parser)}")
|
|
|
|
|
|
class TestCompat:
|
|
def setup_method(self, method):
|
|
self.df = DataFrame({"A": [1, 2, 3]})
|
|
self.expected1 = self.df[self.df.A > 0]
|
|
self.expected2 = self.df.A + 1
|
|
|
|
def test_query_default(self):
|
|
|
|
# GH 12749
|
|
# this should always work, whether NUMEXPR_INSTALLED or not
|
|
df = self.df
|
|
result = df.query("A>0")
|
|
tm.assert_frame_equal(result, self.expected1)
|
|
result = df.eval("A+1")
|
|
tm.assert_series_equal(result, self.expected2, check_names=False)
|
|
|
|
def test_query_None(self):
|
|
|
|
df = self.df
|
|
result = df.query("A>0", engine=None)
|
|
tm.assert_frame_equal(result, self.expected1)
|
|
result = df.eval("A+1", engine=None)
|
|
tm.assert_series_equal(result, self.expected2, check_names=False)
|
|
|
|
def test_query_python(self):
|
|
|
|
df = self.df
|
|
result = df.query("A>0", engine="python")
|
|
tm.assert_frame_equal(result, self.expected1)
|
|
result = df.eval("A+1", engine="python")
|
|
tm.assert_series_equal(result, self.expected2, check_names=False)
|
|
|
|
def test_query_numexpr(self):
|
|
|
|
df = self.df
|
|
if NUMEXPR_INSTALLED:
|
|
result = df.query("A>0", engine="numexpr")
|
|
tm.assert_frame_equal(result, self.expected1)
|
|
result = df.eval("A+1", engine="numexpr")
|
|
tm.assert_series_equal(result, self.expected2, check_names=False)
|
|
else:
|
|
msg = (
|
|
r"'numexpr' is not installed or an unsupported version. "
|
|
r"Cannot use engine='numexpr' for query/eval if 'numexpr' is "
|
|
r"not installed"
|
|
)
|
|
with pytest.raises(ImportError, match=msg):
|
|
df.query("A>0", engine="numexpr")
|
|
with pytest.raises(ImportError, match=msg):
|
|
df.eval("A+1", engine="numexpr")
|
|
|
|
|
|
class TestDataFrameEval:
|
|
|
|
# smaller hits python, larger hits numexpr
|
|
@pytest.mark.parametrize("n", [4, 4000])
|
|
@pytest.mark.parametrize(
|
|
"op_str,op,rop",
|
|
[
|
|
("+", "__add__", "__radd__"),
|
|
("-", "__sub__", "__rsub__"),
|
|
("*", "__mul__", "__rmul__"),
|
|
("/", "__truediv__", "__rtruediv__"),
|
|
],
|
|
)
|
|
def test_ops(self, op_str, op, rop, n):
|
|
|
|
# tst ops and reversed ops in evaluation
|
|
# GH7198
|
|
|
|
df = DataFrame(1, index=range(n), columns=list("abcd"))
|
|
df.iloc[0] = 2
|
|
m = df.mean()
|
|
|
|
base = DataFrame( # noqa
|
|
np.tile(m.values, n).reshape(n, -1), columns=list("abcd")
|
|
)
|
|
|
|
expected = eval(f"base {op_str} df")
|
|
|
|
# ops as strings
|
|
result = eval(f"m {op_str} df")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# these are commutative
|
|
if op in ["+", "*"]:
|
|
result = getattr(df, op)(m)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# these are not
|
|
elif op in ["-", "/"]:
|
|
result = getattr(df, rop)(m)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_sub_numexpr_path(self):
|
|
# GH7192: Note we need a large number of rows to ensure this
|
|
# goes through the numexpr path
|
|
df = DataFrame({"A": np.random.randn(25000)})
|
|
df.iloc[0:5] = np.nan
|
|
expected = 1 - np.isnan(df.iloc[0:25])
|
|
result = (1 - np.isnan(df)).iloc[0:25]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_query_non_str(self):
|
|
# GH 11485
|
|
df = DataFrame({"A": [1, 2, 3], "B": ["a", "b", "b"]})
|
|
|
|
msg = "expr must be a string to be evaluated"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.query(lambda x: x.B == "b")
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.query(111)
|
|
|
|
def test_query_empty_string(self):
|
|
# GH 13139
|
|
df = DataFrame({"A": [1, 2, 3]})
|
|
|
|
msg = "expr cannot be an empty string"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.query("")
|
|
|
|
def test_eval_resolvers_as_list(self):
|
|
# GH 14095
|
|
df = DataFrame(np.random.randn(10, 2), columns=list("ab"))
|
|
dict1 = {"a": 1}
|
|
dict2 = {"b": 2}
|
|
assert df.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
|
|
assert pd.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
|
|
|
|
def test_eval_object_dtype_binop(self):
|
|
# GH#24883
|
|
df = DataFrame({"a1": ["Y", "N"]})
|
|
res = df.eval("c = ((a1 == 'Y') & True)")
|
|
expected = DataFrame({"a1": ["Y", "N"], "c": [True, False]})
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
|
|
class TestDataFrameQueryWithMultiIndex:
|
|
def test_query_with_named_multiindex(self, parser, engine):
|
|
skip_if_no_pandas_parser(parser)
|
|
a = np.random.choice(["red", "green"], size=10)
|
|
b = np.random.choice(["eggs", "ham"], size=10)
|
|
index = MultiIndex.from_arrays([a, b], names=["color", "food"])
|
|
df = DataFrame(np.random.randn(10, 2), index=index)
|
|
ind = Series(
|
|
df.index.get_level_values("color").values, index=index, name="color"
|
|
)
|
|
|
|
# equality
|
|
res1 = df.query('color == "red"', parser=parser, engine=engine)
|
|
res2 = df.query('"red" == color', parser=parser, engine=engine)
|
|
exp = df[ind == "red"]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# inequality
|
|
res1 = df.query('color != "red"', parser=parser, engine=engine)
|
|
res2 = df.query('"red" != color', parser=parser, engine=engine)
|
|
exp = df[ind != "red"]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# list equality (really just set membership)
|
|
res1 = df.query('color == ["red"]', parser=parser, engine=engine)
|
|
res2 = df.query('["red"] == color', parser=parser, engine=engine)
|
|
exp = df[ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
res1 = df.query('color != ["red"]', parser=parser, engine=engine)
|
|
res2 = df.query('["red"] != color', parser=parser, engine=engine)
|
|
exp = df[~ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# in/not in ops
|
|
res1 = df.query('["red"] in color', parser=parser, engine=engine)
|
|
res2 = df.query('"red" in color', parser=parser, engine=engine)
|
|
exp = df[ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
res1 = df.query('["red"] not in color', parser=parser, engine=engine)
|
|
res2 = df.query('"red" not in color', parser=parser, engine=engine)
|
|
exp = df[~ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
def test_query_with_unnamed_multiindex(self, parser, engine):
|
|
skip_if_no_pandas_parser(parser)
|
|
a = np.random.choice(["red", "green"], size=10)
|
|
b = np.random.choice(["eggs", "ham"], size=10)
|
|
index = MultiIndex.from_arrays([a, b])
|
|
df = DataFrame(np.random.randn(10, 2), index=index)
|
|
ind = Series(df.index.get_level_values(0).values, index=index)
|
|
|
|
res1 = df.query('ilevel_0 == "red"', parser=parser, engine=engine)
|
|
res2 = df.query('"red" == ilevel_0', parser=parser, engine=engine)
|
|
exp = df[ind == "red"]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# inequality
|
|
res1 = df.query('ilevel_0 != "red"', parser=parser, engine=engine)
|
|
res2 = df.query('"red" != ilevel_0', parser=parser, engine=engine)
|
|
exp = df[ind != "red"]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# list equality (really just set membership)
|
|
res1 = df.query('ilevel_0 == ["red"]', parser=parser, engine=engine)
|
|
res2 = df.query('["red"] == ilevel_0', parser=parser, engine=engine)
|
|
exp = df[ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
res1 = df.query('ilevel_0 != ["red"]', parser=parser, engine=engine)
|
|
res2 = df.query('["red"] != ilevel_0', parser=parser, engine=engine)
|
|
exp = df[~ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# in/not in ops
|
|
res1 = df.query('["red"] in ilevel_0', parser=parser, engine=engine)
|
|
res2 = df.query('"red" in ilevel_0', parser=parser, engine=engine)
|
|
exp = df[ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
res1 = df.query('["red"] not in ilevel_0', parser=parser, engine=engine)
|
|
res2 = df.query('"red" not in ilevel_0', parser=parser, engine=engine)
|
|
exp = df[~ind.isin(["red"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# ## LEVEL 1
|
|
ind = Series(df.index.get_level_values(1).values, index=index)
|
|
res1 = df.query('ilevel_1 == "eggs"', parser=parser, engine=engine)
|
|
res2 = df.query('"eggs" == ilevel_1', parser=parser, engine=engine)
|
|
exp = df[ind == "eggs"]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# inequality
|
|
res1 = df.query('ilevel_1 != "eggs"', parser=parser, engine=engine)
|
|
res2 = df.query('"eggs" != ilevel_1', parser=parser, engine=engine)
|
|
exp = df[ind != "eggs"]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# list equality (really just set membership)
|
|
res1 = df.query('ilevel_1 == ["eggs"]', parser=parser, engine=engine)
|
|
res2 = df.query('["eggs"] == ilevel_1', parser=parser, engine=engine)
|
|
exp = df[ind.isin(["eggs"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
res1 = df.query('ilevel_1 != ["eggs"]', parser=parser, engine=engine)
|
|
res2 = df.query('["eggs"] != ilevel_1', parser=parser, engine=engine)
|
|
exp = df[~ind.isin(["eggs"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
# in/not in ops
|
|
res1 = df.query('["eggs"] in ilevel_1', parser=parser, engine=engine)
|
|
res2 = df.query('"eggs" in ilevel_1', parser=parser, engine=engine)
|
|
exp = df[ind.isin(["eggs"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
res1 = df.query('["eggs"] not in ilevel_1', parser=parser, engine=engine)
|
|
res2 = df.query('"eggs" not in ilevel_1', parser=parser, engine=engine)
|
|
exp = df[~ind.isin(["eggs"])]
|
|
tm.assert_frame_equal(res1, exp)
|
|
tm.assert_frame_equal(res2, exp)
|
|
|
|
def test_query_with_partially_named_multiindex(self, parser, engine):
|
|
skip_if_no_pandas_parser(parser)
|
|
a = np.random.choice(["red", "green"], size=10)
|
|
b = np.arange(10)
|
|
index = MultiIndex.from_arrays([a, b])
|
|
index.names = [None, "rating"]
|
|
df = DataFrame(np.random.randn(10, 2), index=index)
|
|
res = df.query("rating == 1", parser=parser, engine=engine)
|
|
ind = Series(
|
|
df.index.get_level_values("rating").values, index=index, name="rating"
|
|
)
|
|
exp = df[ind == 1]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.query("rating != 1", parser=parser, engine=engine)
|
|
ind = Series(
|
|
df.index.get_level_values("rating").values, index=index, name="rating"
|
|
)
|
|
exp = df[ind != 1]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.query('ilevel_0 == "red"', parser=parser, engine=engine)
|
|
ind = Series(df.index.get_level_values(0).values, index=index)
|
|
exp = df[ind == "red"]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.query('ilevel_0 != "red"', parser=parser, engine=engine)
|
|
ind = Series(df.index.get_level_values(0).values, index=index)
|
|
exp = df[ind != "red"]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_query_multiindex_get_index_resolvers(self):
|
|
df = tm.makeCustomDataframe(
|
|
10, 3, r_idx_nlevels=2, r_idx_names=["spam", "eggs"]
|
|
)
|
|
resolvers = df._get_index_resolvers()
|
|
|
|
def to_series(mi, level):
|
|
level_values = mi.get_level_values(level)
|
|
s = level_values.to_series()
|
|
s.index = mi
|
|
return s
|
|
|
|
col_series = df.columns.to_series()
|
|
expected = {
|
|
"index": df.index,
|
|
"columns": col_series,
|
|
"spam": to_series(df.index, "spam"),
|
|
"eggs": to_series(df.index, "eggs"),
|
|
"C0": col_series,
|
|
}
|
|
for k, v in resolvers.items():
|
|
if isinstance(v, Index):
|
|
assert v.is_(expected[k])
|
|
elif isinstance(v, Series):
|
|
tm.assert_series_equal(v, expected[k])
|
|
else:
|
|
raise AssertionError("object must be a Series or Index")
|
|
|
|
|
|
@td.skip_if_no_ne
|
|
class TestDataFrameQueryNumExprPandas:
|
|
@classmethod
|
|
def setup_class(cls):
|
|
cls.engine = "numexpr"
|
|
cls.parser = "pandas"
|
|
|
|
@classmethod
|
|
def teardown_class(cls):
|
|
del cls.engine, cls.parser
|
|
|
|
def test_date_query_with_attribute_access(self):
|
|
engine, parser = self.engine, self.parser
|
|
skip_if_no_pandas_parser(parser)
|
|
df = DataFrame(np.random.randn(5, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=5)
|
|
df["dates2"] = date_range("1/1/2013", periods=5)
|
|
df["dates3"] = date_range("1/1/2014", periods=5)
|
|
res = df.query(
|
|
"@df.dates1 < 20130101 < @df.dates3", engine=engine, parser=parser
|
|
)
|
|
expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_query_no_attribute_access(self):
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame(np.random.randn(5, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=5)
|
|
df["dates2"] = date_range("1/1/2013", periods=5)
|
|
df["dates3"] = date_range("1/1/2014", periods=5)
|
|
res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
|
|
expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_query_with_NaT(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates2"] = date_range("1/1/2013", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
df.loc[np.random.rand(n) > 0.5, "dates1"] = pd.NaT
|
|
df.loc[np.random.rand(n) > 0.5, "dates3"] = pd.NaT
|
|
res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
|
|
expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_index_query(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
return_value = df.set_index("dates1", inplace=True, drop=True)
|
|
assert return_value is None
|
|
res = df.query("index < 20130101 < dates3", engine=engine, parser=parser)
|
|
expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_index_query_with_NaT(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
df.iloc[0, 0] = pd.NaT
|
|
return_value = df.set_index("dates1", inplace=True, drop=True)
|
|
assert return_value is None
|
|
res = df.query("index < 20130101 < dates3", engine=engine, parser=parser)
|
|
expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_index_query_with_NaT_duplicates(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
d = {}
|
|
d["dates1"] = date_range("1/1/2012", periods=n)
|
|
d["dates3"] = date_range("1/1/2014", periods=n)
|
|
df = DataFrame(d)
|
|
df.loc[np.random.rand(n) > 0.5, "dates1"] = pd.NaT
|
|
return_value = df.set_index("dates1", inplace=True, drop=True)
|
|
assert return_value is None
|
|
res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
|
|
expec = df[(df.index.to_series() < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_query_with_non_date(self):
|
|
engine, parser = self.engine, self.parser
|
|
|
|
n = 10
|
|
df = DataFrame(
|
|
{"dates": date_range("1/1/2012", periods=n), "nondate": np.arange(n)}
|
|
)
|
|
|
|
result = df.query("dates == nondate", parser=parser, engine=engine)
|
|
assert len(result) == 0
|
|
|
|
result = df.query("dates != nondate", parser=parser, engine=engine)
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
msg = r"Invalid comparison between dtype=datetime64\[ns\] and ndarray"
|
|
for op in ["<", ">", "<=", ">="]:
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.query(f"dates {op} nondate", parser=parser, engine=engine)
|
|
|
|
def test_query_syntax_error(self):
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame({"i": range(10), "+": range(3, 13), "r": range(4, 14)})
|
|
msg = "invalid syntax"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("i - +", engine=engine, parser=parser)
|
|
|
|
def test_query_scope(self):
|
|
from pandas.core.computation.ops import UndefinedVariableError
|
|
|
|
engine, parser = self.engine, self.parser
|
|
skip_if_no_pandas_parser(parser)
|
|
|
|
df = DataFrame(np.random.randn(20, 2), columns=list("ab"))
|
|
|
|
a, b = 1, 2 # noqa
|
|
res = df.query("a > b", engine=engine, parser=parser)
|
|
expected = df[df.a > df.b]
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
res = df.query("@a > b", engine=engine, parser=parser)
|
|
expected = df[a > df.b]
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
# no local variable c
|
|
with pytest.raises(
|
|
UndefinedVariableError, match="local variable 'c' is not defined"
|
|
):
|
|
df.query("@a > b > @c", engine=engine, parser=parser)
|
|
|
|
# no column named 'c'
|
|
with pytest.raises(UndefinedVariableError, match="name 'c' is not defined"):
|
|
df.query("@a > b > c", engine=engine, parser=parser)
|
|
|
|
def test_query_doesnt_pickup_local(self):
|
|
from pandas.core.computation.ops import UndefinedVariableError
|
|
|
|
engine, parser = self.engine, self.parser
|
|
n = m = 10
|
|
df = DataFrame(np.random.randint(m, size=(n, 3)), columns=list("abc"))
|
|
|
|
# we don't pick up the local 'sin'
|
|
with pytest.raises(UndefinedVariableError, match="name 'sin' is not defined"):
|
|
df.query("sin > 5", engine=engine, parser=parser)
|
|
|
|
def test_query_builtin(self):
|
|
from pandas.core.computation.engines import NumExprClobberingError
|
|
|
|
engine, parser = self.engine, self.parser
|
|
|
|
n = m = 10
|
|
df = DataFrame(np.random.randint(m, size=(n, 3)), columns=list("abc"))
|
|
|
|
df.index.name = "sin"
|
|
msg = "Variables in expression.+"
|
|
with pytest.raises(NumExprClobberingError, match=msg):
|
|
df.query("sin > 5", engine=engine, parser=parser)
|
|
|
|
def test_query(self):
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame(np.random.randn(10, 3), columns=["a", "b", "c"])
|
|
|
|
tm.assert_frame_equal(
|
|
df.query("a < b", engine=engine, parser=parser), df[df.a < df.b]
|
|
)
|
|
tm.assert_frame_equal(
|
|
df.query("a + b > b * c", engine=engine, parser=parser),
|
|
df[df.a + df.b > df.b * df.c],
|
|
)
|
|
|
|
def test_query_index_with_name(self):
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame(
|
|
np.random.randint(10, size=(10, 3)),
|
|
index=Index(range(10), name="blob"),
|
|
columns=["a", "b", "c"],
|
|
)
|
|
res = df.query("(blob < 5) & (a < b)", engine=engine, parser=parser)
|
|
expec = df[(df.index < 5) & (df.a < df.b)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
res = df.query("blob < b", engine=engine, parser=parser)
|
|
expec = df[df.index < df.b]
|
|
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_query_index_without_name(self):
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame(
|
|
np.random.randint(10, size=(10, 3)),
|
|
index=range(10),
|
|
columns=["a", "b", "c"],
|
|
)
|
|
|
|
# "index" should refer to the index
|
|
res = df.query("index < b", engine=engine, parser=parser)
|
|
expec = df[df.index < df.b]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
# test against a scalar
|
|
res = df.query("index < 5", engine=engine, parser=parser)
|
|
expec = df[df.index < 5]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_nested_scope(self):
|
|
engine = self.engine
|
|
parser = self.parser
|
|
|
|
skip_if_no_pandas_parser(parser)
|
|
|
|
df = DataFrame(np.random.randn(5, 3))
|
|
df2 = DataFrame(np.random.randn(5, 3))
|
|
expected = df[(df > 0) & (df2 > 0)]
|
|
|
|
result = df.query("(@df > 0) & (@df2 > 0)", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = pd.eval("df[df > 0 and df2 > 0]", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = pd.eval(
|
|
"df[df > 0 and df2 > 0 and df[df > 0] > 0]", engine=engine, parser=parser
|
|
)
|
|
expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = pd.eval("df[(df>0) & (df2>0)]", engine=engine, parser=parser)
|
|
expected = df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_nested_raises_on_local_self_reference(self):
|
|
from pandas.core.computation.ops import UndefinedVariableError
|
|
|
|
df = DataFrame(np.random.randn(5, 3))
|
|
|
|
# can't reference ourself b/c we're a local so @ is necessary
|
|
with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"):
|
|
df.query("df > 0", engine=self.engine, parser=self.parser)
|
|
|
|
def test_local_syntax(self):
|
|
skip_if_no_pandas_parser(self.parser)
|
|
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame(np.random.randn(100, 10), columns=list("abcdefghij"))
|
|
b = 1
|
|
expect = df[df.a < b]
|
|
result = df.query("a < @b", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expect)
|
|
|
|
expect = df[df.a < df.b]
|
|
result = df.query("a < b", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expect)
|
|
|
|
def test_chained_cmp_and_in(self):
|
|
skip_if_no_pandas_parser(self.parser)
|
|
engine, parser = self.engine, self.parser
|
|
cols = list("abc")
|
|
df = DataFrame(np.random.randn(100, len(cols)), columns=cols)
|
|
res = df.query(
|
|
"a < b < c and a not in b not in c", engine=engine, parser=parser
|
|
)
|
|
ind = (df.a < df.b) & (df.b < df.c) & ~df.b.isin(df.a) & ~df.c.isin(df.b)
|
|
expec = df[ind]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_local_variable_with_in(self):
|
|
engine, parser = self.engine, self.parser
|
|
skip_if_no_pandas_parser(parser)
|
|
a = Series(np.random.randint(3, size=15), name="a")
|
|
b = Series(np.random.randint(10, size=15), name="b")
|
|
df = DataFrame({"a": a, "b": b})
|
|
|
|
expected = df.loc[(df.b - 1).isin(a)]
|
|
result = df.query("b - 1 in a", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
b = Series(np.random.randint(10, size=15), name="b")
|
|
expected = df.loc[(b - 1).isin(a)]
|
|
result = df.query("@b - 1 in a", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
def test_at_inside_string(self):
|
|
engine, parser = self.engine, self.parser
|
|
skip_if_no_pandas_parser(parser)
|
|
c = 1 # noqa
|
|
df = DataFrame({"a": ["a", "a", "b", "b", "@c", "@c"]})
|
|
result = df.query('a == "@c"', engine=engine, parser=parser)
|
|
expected = df[df.a == "@c"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_query_undefined_local(self):
|
|
from pandas.core.computation.ops import UndefinedVariableError
|
|
|
|
engine, parser = self.engine, self.parser
|
|
skip_if_no_pandas_parser(parser)
|
|
|
|
df = DataFrame(np.random.rand(10, 2), columns=list("ab"))
|
|
with pytest.raises(
|
|
UndefinedVariableError, match="local variable 'c' is not defined"
|
|
):
|
|
df.query("a == @c", engine=engine, parser=parser)
|
|
|
|
def test_index_resolvers_come_after_columns_with_the_same_name(self):
|
|
n = 1 # noqa
|
|
a = np.r_[20:101:20]
|
|
|
|
df = DataFrame({"index": a, "b": np.random.randn(a.size)})
|
|
df.index.name = "index"
|
|
result = df.query("index > 5", engine=self.engine, parser=self.parser)
|
|
expected = df[df["index"] > 5]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df = DataFrame({"index": a, "b": np.random.randn(a.size)})
|
|
result = df.query("ilevel_0 > 5", engine=self.engine, parser=self.parser)
|
|
expected = df.loc[df.index[df.index > 5]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df = DataFrame({"a": a, "b": np.random.randn(a.size)})
|
|
df.index.name = "a"
|
|
result = df.query("a > 5", engine=self.engine, parser=self.parser)
|
|
expected = df[df.a > 5]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.query("index > 5", engine=self.engine, parser=self.parser)
|
|
expected = df.loc[df.index[df.index > 5]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_inf(self):
|
|
n = 10
|
|
df = DataFrame({"a": np.random.rand(n), "b": np.random.rand(n)})
|
|
df.loc[::2, 0] = np.inf
|
|
d = {"==": operator.eq, "!=": operator.ne}
|
|
for op, f in d.items():
|
|
q = f"a {op} inf"
|
|
expected = df[f(df.a, np.inf)]
|
|
result = df.query(q, engine=self.engine, parser=self.parser)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_check_tz_aware_index_query(self, tz_aware_fixture):
|
|
# https://github.com/pandas-dev/pandas/issues/29463
|
|
tz = tz_aware_fixture
|
|
df_index = pd.date_range(
|
|
start="2019-01-01", freq="1d", periods=10, tz=tz, name="time"
|
|
)
|
|
expected = DataFrame(index=df_index)
|
|
df = DataFrame(index=df_index)
|
|
result = df.query('"2018-01-03 00:00:00+00" < time')
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(df_index)
|
|
result = df.reset_index().query('"2018-01-03 00:00:00+00" < time')
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@td.skip_if_no_ne
|
|
class TestDataFrameQueryNumExprPython(TestDataFrameQueryNumExprPandas):
|
|
@classmethod
|
|
def setup_class(cls):
|
|
super().setup_class()
|
|
cls.engine = "numexpr"
|
|
cls.parser = "python"
|
|
|
|
def test_date_query_no_attribute_access(self):
|
|
engine, parser = self.engine, self.parser
|
|
df = DataFrame(np.random.randn(5, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=5)
|
|
df["dates2"] = date_range("1/1/2013", periods=5)
|
|
df["dates3"] = date_range("1/1/2014", periods=5)
|
|
res = df.query(
|
|
"(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
|
|
)
|
|
expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_query_with_NaT(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates2"] = date_range("1/1/2013", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
df.loc[np.random.rand(n) > 0.5, "dates1"] = pd.NaT
|
|
df.loc[np.random.rand(n) > 0.5, "dates3"] = pd.NaT
|
|
res = df.query(
|
|
"(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
|
|
)
|
|
expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_index_query(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
return_value = df.set_index("dates1", inplace=True, drop=True)
|
|
assert return_value is None
|
|
res = df.query(
|
|
"(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
|
|
)
|
|
expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_index_query_with_NaT(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
df.iloc[0, 0] = pd.NaT
|
|
return_value = df.set_index("dates1", inplace=True, drop=True)
|
|
assert return_value is None
|
|
res = df.query(
|
|
"(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
|
|
)
|
|
expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_date_index_query_with_NaT_duplicates(self):
|
|
engine, parser = self.engine, self.parser
|
|
n = 10
|
|
df = DataFrame(np.random.randn(n, 3))
|
|
df["dates1"] = date_range("1/1/2012", periods=n)
|
|
df["dates3"] = date_range("1/1/2014", periods=n)
|
|
df.loc[np.random.rand(n) > 0.5, "dates1"] = pd.NaT
|
|
return_value = df.set_index("dates1", inplace=True, drop=True)
|
|
assert return_value is None
|
|
msg = r"'BoolOp' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.query("index < 20130101 < dates3", engine=engine, parser=parser)
|
|
|
|
def test_nested_scope(self):
|
|
from pandas.core.computation.ops import UndefinedVariableError
|
|
|
|
engine = self.engine
|
|
parser = self.parser
|
|
# smoke test
|
|
x = 1 # noqa
|
|
result = pd.eval("x + 1", engine=engine, parser=parser)
|
|
assert result == 2
|
|
|
|
df = DataFrame(np.random.randn(5, 3))
|
|
df2 = DataFrame(np.random.randn(5, 3))
|
|
|
|
# don't have the pandas parser
|
|
msg = r"The '@' prefix is only supported by the pandas parser"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser)
|
|
|
|
with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"):
|
|
df.query("(df>0) & (df2>0)", engine=engine, parser=parser)
|
|
|
|
expected = df[(df > 0) & (df2 > 0)]
|
|
result = pd.eval("df[(df > 0) & (df2 > 0)]", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
|
|
result = pd.eval(
|
|
"df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]", engine=engine, parser=parser
|
|
)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
|
|
class TestDataFrameQueryPythonPandas(TestDataFrameQueryNumExprPandas):
|
|
@classmethod
|
|
def setup_class(cls):
|
|
super().setup_class()
|
|
cls.engine = "python"
|
|
cls.parser = "pandas"
|
|
|
|
def test_query_builtin(self):
|
|
engine, parser = self.engine, self.parser
|
|
|
|
n = m = 10
|
|
df = DataFrame(np.random.randint(m, size=(n, 3)), columns=list("abc"))
|
|
|
|
df.index.name = "sin"
|
|
expected = df[df.index > 5]
|
|
result = df.query("sin > 5", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
|
|
class TestDataFrameQueryPythonPython(TestDataFrameQueryNumExprPython):
|
|
@classmethod
|
|
def setup_class(cls):
|
|
super().setup_class()
|
|
cls.engine = cls.parser = "python"
|
|
|
|
def test_query_builtin(self):
|
|
engine, parser = self.engine, self.parser
|
|
|
|
n = m = 10
|
|
df = DataFrame(np.random.randint(m, size=(n, 3)), columns=list("abc"))
|
|
|
|
df.index.name = "sin"
|
|
expected = df[df.index > 5]
|
|
result = df.query("sin > 5", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
|
|
class TestDataFrameQueryStrings:
|
|
def test_str_query_method(self, parser, engine):
|
|
df = DataFrame(np.random.randn(10, 1), columns=["b"])
|
|
df["strings"] = Series(list("aabbccddee"))
|
|
expect = df[df.strings == "a"]
|
|
|
|
if parser != "pandas":
|
|
col = "strings"
|
|
lst = '"a"'
|
|
|
|
lhs = [col] * 2 + [lst] * 2
|
|
rhs = lhs[::-1]
|
|
|
|
eq, ne = "==", "!="
|
|
ops = 2 * ([eq] + [ne])
|
|
msg = r"'(Not)?In' nodes are not implemented"
|
|
|
|
for lhs, op, rhs in zip(lhs, ops, rhs):
|
|
ex = f"{lhs} {op} {rhs}"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.query(
|
|
ex,
|
|
engine=engine,
|
|
parser=parser,
|
|
local_dict={"strings": df.strings},
|
|
)
|
|
else:
|
|
res = df.query('"a" == strings', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
res = df.query('strings == "a"', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
tm.assert_frame_equal(res, df[df.strings.isin(["a"])])
|
|
|
|
expect = df[df.strings != "a"]
|
|
res = df.query('strings != "a"', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
res = df.query('"a" != strings', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
tm.assert_frame_equal(res, df[~df.strings.isin(["a"])])
|
|
|
|
def test_str_list_query_method(self, parser, engine):
|
|
df = DataFrame(np.random.randn(10, 1), columns=["b"])
|
|
df["strings"] = Series(list("aabbccddee"))
|
|
expect = df[df.strings.isin(["a", "b"])]
|
|
|
|
if parser != "pandas":
|
|
col = "strings"
|
|
lst = '["a", "b"]'
|
|
|
|
lhs = [col] * 2 + [lst] * 2
|
|
rhs = lhs[::-1]
|
|
|
|
eq, ne = "==", "!="
|
|
ops = 2 * ([eq] + [ne])
|
|
msg = r"'(Not)?In' nodes are not implemented"
|
|
|
|
for lhs, op, rhs in zip(lhs, ops, rhs):
|
|
ex = f"{lhs} {op} {rhs}"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.query(ex, engine=engine, parser=parser)
|
|
else:
|
|
res = df.query('strings == ["a", "b"]', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
res = df.query('["a", "b"] == strings', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
expect = df[~df.strings.isin(["a", "b"])]
|
|
|
|
res = df.query('strings != ["a", "b"]', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
res = df.query('["a", "b"] != strings', engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_query_with_string_columns(self, parser, engine):
|
|
df = DataFrame(
|
|
{
|
|
"a": list("aaaabbbbcccc"),
|
|
"b": list("aabbccddeeff"),
|
|
"c": np.random.randint(5, size=12),
|
|
"d": np.random.randint(9, size=12),
|
|
}
|
|
)
|
|
if parser == "pandas":
|
|
res = df.query("a in b", parser=parser, engine=engine)
|
|
expec = df[df.a.isin(df.b)]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
res = df.query("a in b and c < d", parser=parser, engine=engine)
|
|
expec = df[df.a.isin(df.b) & (df.c < df.d)]
|
|
tm.assert_frame_equal(res, expec)
|
|
else:
|
|
msg = r"'(Not)?In' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.query("a in b", parser=parser, engine=engine)
|
|
|
|
msg = r"'BoolOp' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.query("a in b and c < d", parser=parser, engine=engine)
|
|
|
|
def test_object_array_eq_ne(self, parser, engine):
|
|
df = DataFrame(
|
|
{
|
|
"a": list("aaaabbbbcccc"),
|
|
"b": list("aabbccddeeff"),
|
|
"c": np.random.randint(5, size=12),
|
|
"d": np.random.randint(9, size=12),
|
|
}
|
|
)
|
|
res = df.query("a == b", parser=parser, engine=engine)
|
|
exp = df[df.a == df.b]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.query("a != b", parser=parser, engine=engine)
|
|
exp = df[df.a != df.b]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_query_with_nested_strings(self, parser, engine):
|
|
skip_if_no_pandas_parser(parser)
|
|
raw = """id event timestamp
|
|
1 "page 1 load" 1/1/2014 0:00:01
|
|
1 "page 1 exit" 1/1/2014 0:00:31
|
|
2 "page 2 load" 1/1/2014 0:01:01
|
|
2 "page 2 exit" 1/1/2014 0:01:31
|
|
3 "page 3 load" 1/1/2014 0:02:01
|
|
3 "page 3 exit" 1/1/2014 0:02:31
|
|
4 "page 1 load" 2/1/2014 1:00:01
|
|
4 "page 1 exit" 2/1/2014 1:00:31
|
|
5 "page 2 load" 2/1/2014 1:01:01
|
|
5 "page 2 exit" 2/1/2014 1:01:31
|
|
6 "page 3 load" 2/1/2014 1:02:01
|
|
6 "page 3 exit" 2/1/2014 1:02:31
|
|
"""
|
|
df = pd.read_csv(
|
|
StringIO(raw), sep=r"\s{2,}", engine="python", parse_dates=["timestamp"]
|
|
)
|
|
expected = df[df.event == '"page 1 load"']
|
|
res = df.query("""'"page 1 load"' in event""", parser=parser, engine=engine)
|
|
tm.assert_frame_equal(expected, res)
|
|
|
|
def test_query_with_nested_special_character(self, parser, engine):
|
|
skip_if_no_pandas_parser(parser)
|
|
df = DataFrame({"a": ["a", "b", "test & test"], "b": [1, 2, 3]})
|
|
res = df.query('a == "test & test"', parser=parser, engine=engine)
|
|
expec = df[df.a == "test & test"]
|
|
tm.assert_frame_equal(res, expec)
|
|
|
|
def test_query_lex_compare_strings(self, parser, engine):
|
|
|
|
a = Series(np.random.choice(list("abcde"), 20))
|
|
b = Series(np.arange(a.size))
|
|
df = DataFrame({"X": a, "Y": b})
|
|
|
|
ops = {"<": operator.lt, ">": operator.gt, "<=": operator.le, ">=": operator.ge}
|
|
|
|
for op, func in ops.items():
|
|
res = df.query(f'X {op} "d"', engine=engine, parser=parser)
|
|
expected = df[func(df.X, "d")]
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
def test_query_single_element_booleans(self, parser, engine):
|
|
columns = "bid", "bidsize", "ask", "asksize"
|
|
data = np.random.randint(2, size=(1, len(columns))).astype(bool)
|
|
df = DataFrame(data, columns=columns)
|
|
res = df.query("bid & ask", engine=engine, parser=parser)
|
|
expected = df[df.bid & df.ask]
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
def test_query_string_scalar_variable(self, parser, engine):
|
|
skip_if_no_pandas_parser(parser)
|
|
df = DataFrame(
|
|
{
|
|
"Symbol": ["BUD US", "BUD US", "IBM US", "IBM US"],
|
|
"Price": [109.70, 109.72, 183.30, 183.35],
|
|
}
|
|
)
|
|
e = df[df.Symbol == "BUD US"]
|
|
symb = "BUD US" # noqa
|
|
r = df.query("Symbol == @symb", parser=parser, engine=engine)
|
|
tm.assert_frame_equal(e, r)
|
|
|
|
|
|
class TestDataFrameEvalWithFrame:
|
|
def setup_method(self, method):
|
|
self.frame = DataFrame(np.random.randn(10, 3), columns=list("abc"))
|
|
|
|
def teardown_method(self, method):
|
|
del self.frame
|
|
|
|
def test_simple_expr(self, parser, engine):
|
|
res = self.frame.eval("a + b", engine=engine, parser=parser)
|
|
expect = self.frame.a + self.frame.b
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_bool_arith_expr(self, parser, engine):
|
|
res = self.frame.eval("a[a < 1] + b", engine=engine, parser=parser)
|
|
expect = self.frame.a[self.frame.a < 1] + self.frame.b
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
@pytest.mark.parametrize("op", ["+", "-", "*", "/"])
|
|
def test_invalid_type_for_operator_raises(self, parser, engine, op):
|
|
df = DataFrame({"a": [1, 2], "b": ["c", "d"]})
|
|
msg = r"unsupported operand type\(s\) for .+: '.+' and '.+'"
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.eval(f"a {op} b", engine=engine, parser=parser)
|
|
|
|
|
|
class TestDataFrameQueryBacktickQuoting:
|
|
@pytest.fixture(scope="class")
|
|
def df(self):
|
|
"""
|
|
Yields a dataframe with strings that may or may not need escaping
|
|
by backticks. The last two columns cannot be escaped by backticks
|
|
and should raise a ValueError.
|
|
"""
|
|
yield DataFrame(
|
|
{
|
|
"A": [1, 2, 3],
|
|
"B B": [3, 2, 1],
|
|
"C C": [4, 5, 6],
|
|
"C C": [7, 4, 3],
|
|
"C_C": [8, 9, 10],
|
|
"D_D D": [11, 1, 101],
|
|
"E.E": [6, 3, 5],
|
|
"F-F": [8, 1, 10],
|
|
"1e1": [2, 4, 8],
|
|
"def": [10, 11, 2],
|
|
"A (x)": [4, 1, 3],
|
|
"B(x)": [1, 1, 5],
|
|
"B (x)": [2, 7, 4],
|
|
" &^ :!€$?(} > <++*'' ": [2, 5, 6],
|
|
"": [10, 11, 1],
|
|
" A": [4, 7, 9],
|
|
" ": [1, 2, 1],
|
|
"it's": [6, 3, 1],
|
|
"that's": [9, 1, 8],
|
|
"☺": [8, 7, 6],
|
|
"foo#bar": [2, 4, 5],
|
|
1: [5, 7, 9],
|
|
}
|
|
)
|
|
|
|
def test_single_backtick_variable_query(self, df):
|
|
res = df.query("1 < `B B`")
|
|
expect = df[1 < df["B B"]]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_two_backtick_variables_query(self, df):
|
|
res = df.query("1 < `B B` and 4 < `C C`")
|
|
expect = df[(1 < df["B B"]) & (4 < df["C C"])]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_single_backtick_variable_expr(self, df):
|
|
res = df.eval("A + `B B`")
|
|
expect = df["A"] + df["B B"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_two_backtick_variables_expr(self, df):
|
|
res = df.eval("`B B` + `C C`")
|
|
expect = df["B B"] + df["C C"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_already_underscore_variable(self, df):
|
|
res = df.eval("`C_C` + A")
|
|
expect = df["C_C"] + df["A"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_same_name_but_underscores(self, df):
|
|
res = df.eval("C_C + `C C`")
|
|
expect = df["C_C"] + df["C C"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_mixed_underscores_and_spaces(self, df):
|
|
res = df.eval("A + `D_D D`")
|
|
expect = df["A"] + df["D_D D"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_backtick_quote_name_with_no_spaces(self, df):
|
|
res = df.eval("A + `C_C`")
|
|
expect = df["A"] + df["C_C"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_special_characters(self, df):
|
|
res = df.eval("`E.E` + `F-F` - A")
|
|
expect = df["E.E"] + df["F-F"] - df["A"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_start_with_digit(self, df):
|
|
res = df.eval("A + `1e1`")
|
|
expect = df["A"] + df["1e1"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_keyword(self, df):
|
|
res = df.eval("A + `def`")
|
|
expect = df["A"] + df["def"]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_unneeded_quoting(self, df):
|
|
res = df.query("`A` > 2")
|
|
expect = df[df["A"] > 2]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_parenthesis(self, df):
|
|
res = df.query("`A (x)` > 2")
|
|
expect = df[df["A (x)"] > 2]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_empty_string(self, df):
|
|
res = df.query("`` > 5")
|
|
expect = df[df[""] > 5]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_multiple_spaces(self, df):
|
|
res = df.query("`C C` > 5")
|
|
expect = df[df["C C"] > 5]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_start_with_spaces(self, df):
|
|
res = df.eval("` A` + ` `")
|
|
expect = df[" A"] + df[" "]
|
|
tm.assert_series_equal(res, expect)
|
|
|
|
def test_lots_of_operators_string(self, df):
|
|
res = df.query("` &^ :!€$?(} > <++*'' ` > 4")
|
|
expect = df[df[" &^ :!€$?(} > <++*'' "] > 4]
|
|
tm.assert_frame_equal(res, expect)
|
|
|
|
def test_missing_attribute(self, df):
|
|
message = "module 'pandas' has no attribute 'thing'"
|
|
with pytest.raises(AttributeError, match=message):
|
|
df.eval("@pd.thing")
|
|
|
|
def test_failing_quote(self, df):
|
|
msg = r"(Could not convert ).*( to a valid Python identifier.)"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("`it's` > `that's`")
|
|
|
|
def test_failing_character_outside_range(self, df):
|
|
msg = r"(Could not convert ).*( to a valid Python identifier.)"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("`☺` > 4")
|
|
|
|
def test_failing_hashtag(self, df):
|
|
msg = "Failed to parse backticks"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("`foo#bar` > 4")
|
|
|
|
def test_call_non_named_expression(self, df):
|
|
"""
|
|
Only attributes and variables ('named functions') can be called.
|
|
.__call__() is not an allowed attribute because that would allow
|
|
calling anything.
|
|
https://github.com/pandas-dev/pandas/pull/32460
|
|
"""
|
|
|
|
def func(*_):
|
|
return 1
|
|
|
|
funcs = [func] # noqa
|
|
|
|
df.eval("@func()")
|
|
|
|
with pytest.raises(TypeError, match="Only named functions are supported"):
|
|
df.eval("@funcs[0]()")
|
|
|
|
with pytest.raises(TypeError, match="Only named functions are supported"):
|
|
df.eval("@funcs[0].__call__()")
|