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import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
Series,
Timestamp,
bdate_range,
date_range,
timedelta_range,
)
import pandas._testing as tm
class TestTranspose:
def test_transpose_td64_intervals(self):
# GH#44917
tdi = timedelta_range("0 Days", "3 Days")
ii = IntervalIndex.from_breaks(tdi)
ii = ii.insert(-1, np.nan)
df = DataFrame(ii)
result = df.T
expected = DataFrame({i: ii[i : i + 1] for i in range(len(ii))})
tm.assert_frame_equal(result, expected)
def test_transpose_empty_preserves_datetimeindex(self):
# GH#41382
dti = DatetimeIndex([], dtype="M8[ns]")
df = DataFrame(index=dti)
expected = DatetimeIndex([], dtype="datetime64[ns]", freq=None)
result1 = df.T.sum().index
result2 = df.sum(axis=1).index
tm.assert_index_equal(result1, expected)
tm.assert_index_equal(result2, expected)
def test_transpose_tzaware_1col_single_tz(self):
# GH#26825
dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
df = DataFrame(dti)
assert (df.dtypes == dti.dtype).all()
res = df.T
assert (res.dtypes == dti.dtype).all()
def test_transpose_tzaware_2col_single_tz(self):
# GH#26825
dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
df3 = DataFrame({"A": dti, "B": dti})
assert (df3.dtypes == dti.dtype).all()
res3 = df3.T
assert (res3.dtypes == dti.dtype).all()
def test_transpose_tzaware_2col_mixed_tz(self):
# GH#26825
dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
dti2 = dti.tz_convert("US/Pacific")
df4 = DataFrame({"A": dti, "B": dti2})
assert (df4.dtypes == [dti.dtype, dti2.dtype]).all()
assert (df4.T.dtypes == object).all()
tm.assert_frame_equal(df4.T.T, df4.astype(object))
@pytest.mark.parametrize("tz", [None, "America/New_York"])
def test_transpose_preserves_dtindex_equality_with_dst(self, tz):
# GH#19970
idx = date_range("20161101", "20161130", freq="4h", tz=tz)
df = DataFrame({"a": range(len(idx)), "b": range(len(idx))}, index=idx)
result = df.T == df.T
expected = DataFrame(True, index=list("ab"), columns=idx)
tm.assert_frame_equal(result, expected)
def test_transpose_object_to_tzaware_mixed_tz(self):
# GH#26825
dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
dti2 = dti.tz_convert("US/Pacific")
# mixed all-tzaware dtypes
df2 = DataFrame([dti, dti2])
assert (df2.dtypes == object).all()
res2 = df2.T
assert (res2.dtypes == object).all()
def test_transpose_uint64(self):
df = DataFrame(
{"A": np.arange(3), "B": [2**63, 2**63 + 5, 2**63 + 10]},
dtype=np.uint64,
)
result = df.T
expected = DataFrame(df.values.T)
expected.index = ["A", "B"]
tm.assert_frame_equal(result, expected)
def test_transpose_float(self, float_frame):
frame = float_frame
dft = frame.T
for idx, series in dft.items():
for col, value in series.items():
if np.isnan(value):
assert np.isnan(frame[col][idx])
else:
assert value == frame[col][idx]
def test_transpose_mixed(self):
# mixed type
mixed = DataFrame(
{
"A": [0.0, 1.0, 2.0, 3.0, 4.0],
"B": [0.0, 1.0, 0.0, 1.0, 0.0],
"C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
"D": bdate_range("1/1/2009", periods=5),
},
index=Index(["a", "b", "c", "d", "e"], dtype=object),
)
mixed_T = mixed.T
for col, s in mixed_T.items():
assert s.dtype == np.object_
@td.skip_array_manager_invalid_test
def test_transpose_get_view(self, float_frame, using_copy_on_write):
dft = float_frame.T
dft.iloc[:, 5:10] = 5
if using_copy_on_write:
assert (float_frame.values[5:10] != 5).all()
else:
assert (float_frame.values[5:10] == 5).all()
@td.skip_array_manager_invalid_test
def test_transpose_get_view_dt64tzget_view(self, using_copy_on_write):
dti = date_range("2016-01-01", periods=6, tz="US/Pacific")
arr = dti._data.reshape(3, 2)
df = DataFrame(arr)
assert df._mgr.nblocks == 1
result = df.T
assert result._mgr.nblocks == 1
rtrip = result._mgr.blocks[0].values
if using_copy_on_write:
assert np.shares_memory(df._mgr.blocks[0].values._ndarray, rtrip._ndarray)
else:
assert np.shares_memory(arr._ndarray, rtrip._ndarray)
def test_transpose_not_inferring_dt(self):
# GH#51546
df = DataFrame(
{
"a": [Timestamp("2019-12-31"), Timestamp("2019-12-31")],
},
dtype=object,
)
result = df.T
expected = DataFrame(
[[Timestamp("2019-12-31"), Timestamp("2019-12-31")]],
columns=[0, 1],
index=["a"],
dtype=object,
)
tm.assert_frame_equal(result, expected)
def test_transpose_not_inferring_dt_mixed_blocks(self):
# GH#51546
df = DataFrame(
{
"a": Series(
[Timestamp("2019-12-31"), Timestamp("2019-12-31")], dtype=object
),
"b": [Timestamp("2019-12-31"), Timestamp("2019-12-31")],
}
)
result = df.T
expected = DataFrame(
[
[Timestamp("2019-12-31"), Timestamp("2019-12-31")],
[Timestamp("2019-12-31"), Timestamp("2019-12-31")],
],
columns=[0, 1],
index=["a", "b"],
dtype=object,
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("dtype1", ["Int64", "Float64"])
@pytest.mark.parametrize("dtype2", ["Int64", "Float64"])
def test_transpose(self, dtype1, dtype2):
# GH#57315 - transpose should have F contiguous blocks
df = DataFrame(
{
"a": pd.array([1, 1, 2], dtype=dtype1),
"b": pd.array([3, 4, 5], dtype=dtype2),
}
)
result = df.T
for blk in result._mgr.blocks:
# When dtypes are unequal, we get NumPy object array
data = blk.values._data if dtype1 == dtype2 else blk.values
assert data.flags["F_CONTIGUOUS"]