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