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Mar 14, 2020
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CLN: Clean frame/test_constructors.py #32610

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CLN: Clean frame/test_constructors.py
dsaxton Mar 11, 2020
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dsaxton Mar 11, 2020
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106 changes: 52 additions & 54 deletions pandas/tests/frame/test_constructors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,15 +47,15 @@
class TestDataFrameConstructors:
def test_series_with_name_not_matching_column(self):
# GH#9232
x = pd.Series(range(5), name=1)
y = pd.Series(range(5), name=0)
x = Series(range(5), name=1)
y = Series(range(5), name=0)

result = pd.DataFrame(x, columns=[0])
expected = pd.DataFrame([], columns=[0])
result = DataFrame(x, columns=[0])
expected = DataFrame([], columns=[0])
tm.assert_frame_equal(result, expected)

result = pd.DataFrame(y, columns=[1])
expected = pd.DataFrame([], columns=[1])
result = DataFrame(y, columns=[1])
expected = DataFrame([], columns=[1])
tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize(
Expand Down Expand Up @@ -126,7 +126,7 @@ def test_constructor_cast_failure(self):
def test_constructor_dtype_copy(self):
orig_df = DataFrame({"col1": [1.0], "col2": [2.0], "col3": [3.0]})

new_df = pd.DataFrame(orig_df, dtype=float, copy=True)
new_df = DataFrame(orig_df, dtype=float, copy=True)

new_df["col1"] = 200.0
assert orig_df["col1"][0] == 1.0
Expand Down Expand Up @@ -220,10 +220,10 @@ def test_constructor_rec(self, float_frame):
index = float_frame.index

df = DataFrame(rec)
tm.assert_index_equal(df.columns, pd.Index(rec.dtype.names))
tm.assert_index_equal(df.columns, Index(rec.dtype.names))

df2 = DataFrame(rec, index=index)
tm.assert_index_equal(df2.columns, pd.Index(rec.dtype.names))
tm.assert_index_equal(df2.columns, Index(rec.dtype.names))
tm.assert_index_equal(df2.index, index)

rng = np.arange(len(rec))[::-1]
Expand Down Expand Up @@ -298,7 +298,7 @@ def test_constructor_dict(self):

tm.assert_series_equal(frame["col1"], datetime_series.rename("col1"))

exp = pd.Series(
exp = Series(
np.concatenate([[np.nan] * 5, datetime_series_short.values]),
index=datetime_series.index,
name="col2",
Expand All @@ -325,7 +325,7 @@ def test_constructor_dict(self):

# Length-one dict micro-optimization
frame = DataFrame({"A": {"1": 1, "2": 2}})
tm.assert_index_equal(frame.index, pd.Index(["1", "2"]))
tm.assert_index_equal(frame.index, Index(["1", "2"]))

# empty dict plus index
idx = Index([0, 1, 2])
Expand Down Expand Up @@ -418,8 +418,8 @@ def test_constructor_dict_order_insertion(self):

def test_constructor_dict_nan_key_and_columns(self):
# GH 16894
result = pd.DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
expected = pd.DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2])
result = DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
expected = DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2])
tm.assert_frame_equal(result, expected)

def test_constructor_multi_index(self):
Expand All @@ -428,29 +428,29 @@ def test_constructor_multi_index(self):
tuples = [(2, 3), (3, 3), (3, 3)]
mi = MultiIndex.from_tuples(tuples)
df = DataFrame(index=mi, columns=mi)
assert pd.isna(df).values.ravel().all()
assert isna(df).values.ravel().all()

tuples = [(3, 3), (2, 3), (3, 3)]
mi = MultiIndex.from_tuples(tuples)
df = DataFrame(index=mi, columns=mi)
assert pd.isna(df).values.ravel().all()
assert isna(df).values.ravel().all()

def test_constructor_2d_index(self):
# GH 25416
# handling of 2d index in construction
df = pd.DataFrame([[1]], columns=[[1]], index=[1, 2])
expected = pd.DataFrame(
df = DataFrame([[1]], columns=[[1]], index=[1, 2])
expected = DataFrame(
[1, 1],
index=pd.Int64Index([1, 2], dtype="int64"),
columns=pd.MultiIndex(levels=[[1]], codes=[[0]]),
columns=MultiIndex(levels=[[1]], codes=[[0]]),
)
tm.assert_frame_equal(df, expected)

df = pd.DataFrame([[1]], columns=[[1]], index=[[1, 2]])
expected = pd.DataFrame(
df = DataFrame([[1]], columns=[[1]], index=[[1, 2]])
expected = DataFrame(
[1, 1],
index=pd.MultiIndex(levels=[[1, 2]], codes=[[0, 1]]),
columns=pd.MultiIndex(levels=[[1]], codes=[[0]]),
index=MultiIndex(levels=[[1, 2]], codes=[[0, 1]]),
columns=MultiIndex(levels=[[1]], codes=[[0]]),
)
tm.assert_frame_equal(df, expected)

Expand All @@ -471,7 +471,7 @@ def test_constructor_error_msgs(self):
DataFrame(
np.arange(12).reshape((4, 3)),
columns=["foo", "bar", "baz"],
index=pd.date_range("2000-01-01", periods=3),
index=date_range("2000-01-01", periods=3),
)

arr = np.array([[4, 5, 6]])
Expand Down Expand Up @@ -713,14 +713,12 @@ def test_constructor_period(self):
# PeriodIndex
a = pd.PeriodIndex(["2012-01", "NaT", "2012-04"], freq="M")
b = pd.PeriodIndex(["2012-02-01", "2012-03-01", "NaT"], freq="D")
df = pd.DataFrame({"a": a, "b": b})
df = DataFrame({"a": a, "b": b})
assert df["a"].dtype == a.dtype
assert df["b"].dtype == b.dtype

# list of periods
df = pd.DataFrame(
{"a": a.astype(object).tolist(), "b": b.astype(object).tolist()}
)
df = DataFrame({"a": a.astype(object).tolist(), "b": b.astype(object).tolist()})
assert df["a"].dtype == a.dtype
assert df["b"].dtype == b.dtype

Expand Down Expand Up @@ -882,8 +880,8 @@ def test_constructor_maskedarray_nonfloat(self):
def test_constructor_maskedarray_hardened(self):
# Check numpy masked arrays with hard masks -- from GH24574
mat_hard = ma.masked_all((2, 2), dtype=float).harden_mask()
result = pd.DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
expected = pd.DataFrame(
result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
expected = DataFrame(
{"A": [np.nan, np.nan], "B": [np.nan, np.nan]},
columns=["A", "B"],
index=[1, 2],
Expand All @@ -892,8 +890,8 @@ def test_constructor_maskedarray_hardened(self):
tm.assert_frame_equal(result, expected)
# Check case where mask is hard but no data are masked
mat_hard = ma.ones((2, 2), dtype=float).harden_mask()
result = pd.DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
expected = pd.DataFrame(
result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
expected = DataFrame(
{"A": [1.0, 1.0], "B": [1.0, 1.0]},
columns=["A", "B"],
index=[1, 2],
Expand All @@ -907,8 +905,8 @@ def test_constructor_maskedrecarray_dtype(self):
np.ma.zeros(5, dtype=[("date", "<f8"), ("price", "<f8")]), mask=[False] * 5
)
data = data.view(mrecords.mrecarray)
result = pd.DataFrame(data, dtype=int)
expected = pd.DataFrame(np.zeros((5, 2), dtype=int), columns=["date", "price"])
result = DataFrame(data, dtype=int)
expected = DataFrame(np.zeros((5, 2), dtype=int), columns=["date", "price"])
tm.assert_frame_equal(result, expected)

def test_constructor_mrecarray(self):
Expand Down Expand Up @@ -1271,9 +1269,9 @@ def test_constructor_list_of_series(self):
tm.assert_frame_equal(result, expected)

def test_constructor_list_of_series_aligned_index(self):
series = [pd.Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)]
result = pd.DataFrame(series)
expected = pd.DataFrame(
series = [Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)]
result = DataFrame(series)
expected = DataFrame(
{"b": [0, 1, 2], "a": [0, 1, 2], "c": [0, 1, 2]},
columns=["b", "a", "c"],
index=["0", "1", "2"],
Expand Down Expand Up @@ -1500,12 +1498,12 @@ def test_constructor_Series_named_and_columns(self):
s1 = Series(range(5), name=1)

# matching name and column gives standard frame
tm.assert_frame_equal(pd.DataFrame(s0, columns=[0]), s0.to_frame())
tm.assert_frame_equal(pd.DataFrame(s1, columns=[1]), s1.to_frame())
tm.assert_frame_equal(DataFrame(s0, columns=[0]), s0.to_frame())
tm.assert_frame_equal(DataFrame(s1, columns=[1]), s1.to_frame())

# non-matching produces empty frame
assert pd.DataFrame(s0, columns=[1]).empty
assert pd.DataFrame(s1, columns=[0]).empty
assert DataFrame(s0, columns=[1]).empty
assert DataFrame(s1, columns=[0]).empty

def test_constructor_Series_differently_indexed(self):
# name
Expand Down Expand Up @@ -1984,7 +1982,7 @@ def test_from_records_to_records(self):
# TODO(wesm): unused
frame = DataFrame.from_records(arr) # noqa

index = pd.Index(np.arange(len(arr))[::-1])
index = Index(np.arange(len(arr))[::-1])
indexed_frame = DataFrame.from_records(arr, index=index)
tm.assert_index_equal(indexed_frame.index, index)

Expand Down Expand Up @@ -2283,7 +2281,7 @@ def test_from_records_sequencelike(self):
# empty case
result = DataFrame.from_records([], columns=["foo", "bar", "baz"])
assert len(result) == 0
tm.assert_index_equal(result.columns, pd.Index(["foo", "bar", "baz"]))
tm.assert_index_equal(result.columns, Index(["foo", "bar", "baz"]))

result = DataFrame.from_records([])
assert len(result) == 0
Expand Down Expand Up @@ -2442,20 +2440,20 @@ def test_datetime_date_tuple_columns_from_dict(self):
v = date.today()
tup = v, v
result = DataFrame({tup: Series(range(3), index=range(3))}, columns=[tup])
expected = DataFrame([0, 1, 2], columns=pd.Index(pd.Series([tup])))
expected = DataFrame([0, 1, 2], columns=Index(Series([tup])))
tm.assert_frame_equal(result, expected)

def test_construct_with_two_categoricalindex_series(self):
# GH 14600
s1 = pd.Series(
s1 = Series(
[39, 6, 4], index=pd.CategoricalIndex(["female", "male", "unknown"])
)
s2 = pd.Series(
s2 = Series(
[2, 152, 2, 242, 150],
index=pd.CategoricalIndex(["f", "female", "m", "male", "unknown"]),
)
result = pd.DataFrame([s1, s2])
expected = pd.DataFrame(
result = DataFrame([s1, s2])
expected = DataFrame(
np.array(
[[np.nan, 39.0, np.nan, 6.0, 4.0], [2.0, 152.0, 2.0, 242.0, 150.0]]
),
Expand Down Expand Up @@ -2554,19 +2552,19 @@ def test_nested_dict_construction(self):
"Nevada": {2001: 2.4, 2002: 2.9},
"Ohio": {2000: 1.5, 2001: 1.7, 2002: 3.6},
}
result = pd.DataFrame(pop, index=[2001, 2002, 2003], columns=columns)
expected = pd.DataFrame(
result = DataFrame(pop, index=[2001, 2002, 2003], columns=columns)
expected = DataFrame(
[(2.4, 1.7), (2.9, 3.6), (np.nan, np.nan)],
columns=columns,
index=pd.Index([2001, 2002, 2003]),
index=Index([2001, 2002, 2003]),
)
tm.assert_frame_equal(result, expected)

def test_from_tzaware_object_array(self):
# GH#26825 2D object array of tzaware timestamps should not raise
dti = pd.date_range("2016-04-05 04:30", periods=3, tz="UTC")
dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
data = dti._data.astype(object).reshape(1, -1)
df = pd.DataFrame(data)
df = DataFrame(data)
assert df.shape == (1, 3)
assert (df.dtypes == dti.dtype).all()
assert (df == dti).all().all()
Expand Down Expand Up @@ -2605,7 +2603,7 @@ def test_from_tzaware_mixed_object_array(self):
def test_from_2d_ndarray_with_dtype(self):
# GH#12513
array_dim2 = np.arange(10).reshape((5, 2))
df = pd.DataFrame(array_dim2, dtype="datetime64[ns, UTC]")
df = DataFrame(array_dim2, dtype="datetime64[ns, UTC]")

expected = pd.DataFrame(array_dim2).astype("datetime64[ns, UTC]")
expected = DataFrame(array_dim2).astype("datetime64[ns, UTC]")
tm.assert_frame_equal(df, expected)