from copy import deepcopy
from operator import methodcaller
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
MultiIndex,
Series,
date_range,
)
import pandas._testing as tm
class TestDataFrame:
@pytest.mark.parametrize("func", ["_set_axis_name", "rename_axis"])
def test_set_axis_name(self, func):
df = DataFrame([[1, 2], [3, 4]])
result = methodcaller(func, "foo")(df)
assert df.index.name is None
assert result.index.name == "foo"
result = methodcaller(func, "cols", axis=1)(df)
assert df.columns.name is None
assert result.columns.name == "cols"
@pytest.mark.parametrize("func", ["_set_axis_name", "rename_axis"])
def test_set_axis_name_mi(self, func):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list("aBc")]),
columns=MultiIndex.from_tuples([("C", x) for x in list("xyz")]),
)
level_names = ["L1", "L2"]
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
msg_warn = (
"DataFrame.bool is now deprecated and will be removed "
"in future version of pandas"
)
df = DataFrame([[True]])
df1 = DataFrame([[False]])
with tm.assert_produces_warning(FutureWarning, match=msg_warn):
assert df.bool()
with tm.assert_produces_warning(FutureWarning, match=msg_warn):
assert not df1.bool()
df = DataFrame([[False, False]])
msg_err = "The truth value of a DataFrame is ambiguous"
with pytest.raises(ValueError, match=msg_err):
bool(df)
with tm.assert_produces_warning(FutureWarning, match=msg_warn):
with pytest.raises(ValueError, match=msg_err):
df.bool()
def test_metadata_propagation_indiv_groupby(self):
# groupby
df = DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.default_rng(2).standard_normal(8),
"D": np.random.default_rng(2).standard_normal(8),
}
)
result = df.groupby("A").sum()
tm.assert_metadata_equivalent(df, result)
def test_metadata_propagation_indiv_resample(self):
# resample
df = DataFrame(
np.random.default_rng(2).standard_normal((1000, 2)),
index=date_range("20130101", periods=1000, freq="s"),
)
result = df.resample("1min")
tm.assert_metadata_equivalent(df, result)
def test_metadata_propagation_indiv(self, monkeypatch):
# merging with override
# GH 6923
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == "merge":
left, right = other.left, other.right
value = getattr(left, name, "") + "|" + getattr(right, name, "")
object.__setattr__(self, name, value)
elif method == "concat":
value = "+".join(
[getattr(o, name) for o in other.objs if getattr(o, name, None)]
)
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ""))
return self
with monkeypatch.context() as m:
m.setattr(DataFrame, "_metadata", ["filename"])
m.setattr(DataFrame, "__finalize__", finalize)
df1 = DataFrame(
np.random.default_rng(2).integers(0, 4, (3, 2)), columns=["a", "b"]
)
df2 = DataFrame(
np.random.default_rng(2).integers(0, 4, (3, 2)), columns=["c", "d"]
)
DataFrame._metadata = ["filename"]
df1.filename = "fname1.csv"
df2.filename = "fname2.csv"
result = df1.merge(df2, left_on=["a"], right_on=["c"], how="inner")
assert result.filename == "fname1.csv|fname2.csv"
# concat
# GH#6927
df1 = DataFrame(
np.random.default_rng(2).integers(0, 4, (3, 2)), columns=list("ab")
)
df1.filename = "foo"
result = pd.concat([df1, df1])
assert result.filename == "foo+foo"
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({"x": [1, 2, 3]})
df.y = 2
df["y"] = [2, 4, 6]
df.y = 5
assert df.y == 5
tm.assert_series_equal(df["y"], Series([2, 4, 6], name="y"))
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=["A"])
empty_frame_copy = deepcopy(empty_frame)
tm.assert_frame_equal(empty_frame_copy, empty_frame)
# formerly in Generic but only test DataFrame
class TestDataFrame2:
@pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
def test_validate_bool_args(self, value):
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
msg = 'For argument "inplace" expected type bool, received type'
with pytest.raises(ValueError, match=msg):
df.copy().rename_axis(mapper={"a": "x", "b": "y"}, axis=1, inplace=value)
with pytest.raises(ValueError, match=msg):
df.copy().drop("a", axis=1, inplace=value)
with pytest.raises(ValueError, match=msg):
df.copy().fillna(value=0, inplace=value)
with pytest.raises(ValueError, match=msg):
df.copy().replace(to_replace=1, value=7, inplace=value)
with pytest.raises(ValueError, match=msg):
df.copy().interpolate(inplace=value)
with pytest.raises(ValueError, match=msg):
df.copy()._where(cond=df.a > 2, inplace=value)
with pytest.raises(ValueError, match=msg):
df.copy().mask(cond=df.a > 2, inplace=value)
def test_unexpected_keyword(self):
# GH8597
df = DataFrame(
np.random.default_rng(2).standard_normal((5, 2)), columns=["jim", "joe"]
)
ca = pd.Categorical([0, 0, 2, 2, 3, np.nan])
ts = df["joe"].copy()
ts[2] = np.nan
msg = "unexpected keyword"
with pytest.raises(TypeError, match=msg):
df.drop("joe", axis=1, in_place=True)
with pytest.raises(TypeError, match=msg):
df.reindex([1, 0], inplace=True)
with pytest.raises(TypeError, match=msg):
ca.fillna(0, inplace=True)
with pytest.raises(TypeError, match=msg):
ts.fillna(0, in_place=True)