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149 lines
4.3 KiB
149 lines
4.3 KiB
import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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MultiIndex,
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Series,
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)
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import pandas._testing as tm
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def test_unstack_preserves_object():
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mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]])
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ser = Series(np.arange(4.0), index=mi, dtype=object)
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res1 = ser.unstack()
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assert (res1.dtypes == object).all()
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res2 = ser.unstack(level=0)
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assert (res2.dtypes == object).all()
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def test_unstack():
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index = MultiIndex(
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levels=[["bar", "foo"], ["one", "three", "two"]],
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codes=[[1, 1, 0, 0], [0, 1, 0, 2]],
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)
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s = Series(np.arange(4.0), index=index)
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unstacked = s.unstack()
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expected = DataFrame(
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[[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]],
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index=["bar", "foo"],
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columns=["one", "three", "two"],
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)
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tm.assert_frame_equal(unstacked, expected)
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unstacked = s.unstack(level=0)
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tm.assert_frame_equal(unstacked, expected.T)
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index = MultiIndex(
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levels=[["bar"], ["one", "two", "three"], [0, 1]],
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codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
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)
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s = Series(np.random.randn(6), index=index)
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exp_index = MultiIndex(
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levels=[["one", "two", "three"], [0, 1]],
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codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
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)
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expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)
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unstacked = s.unstack(0).sort_index()
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tm.assert_frame_equal(unstacked, expected)
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# GH5873
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idx = MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
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ts = Series([1, 2], index=idx)
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left = ts.unstack()
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right = DataFrame(
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[[np.nan, 1], [2, np.nan]], index=[101, 102], columns=[np.nan, 3.5]
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)
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tm.assert_frame_equal(left, right)
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idx = MultiIndex.from_arrays(
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[
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["cat", "cat", "cat", "dog", "dog"],
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["a", "a", "b", "a", "b"],
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[1, 2, 1, 1, np.nan],
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]
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)
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ts = Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
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right = DataFrame(
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[[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]],
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columns=["cat", "dog"],
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)
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tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)]
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right.index = MultiIndex.from_tuples(tpls)
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tm.assert_frame_equal(ts.unstack(level=0), right)
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def test_unstack_tuplename_in_multiindex():
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# GH 19966
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idx = MultiIndex.from_product(
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[["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
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)
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ser = Series(1, index=idx)
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result = ser.unstack(("A", "a"))
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expected = DataFrame(
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[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
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columns=MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]),
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index=pd.Index([1, 2, 3], name=("B", "b")),
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"unstack_idx, expected_values, expected_index, expected_columns",
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[
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(
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("A", "a"),
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[[1, 1], [1, 1], [1, 1], [1, 1]],
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MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]),
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MultiIndex.from_tuples([("a",), ("b",)], names=[("A", "a")]),
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),
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(
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(("A", "a"), "B"),
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[[1, 1, 1, 1], [1, 1, 1, 1]],
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pd.Index([3, 4], name="C"),
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MultiIndex.from_tuples(
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[("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]
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),
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),
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],
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)
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def test_unstack_mixed_type_name_in_multiindex(
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unstack_idx, expected_values, expected_index, expected_columns
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):
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# GH 19966
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idx = MultiIndex.from_product(
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[["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
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)
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ser = Series(1, index=idx)
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result = ser.unstack(unstack_idx)
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expected = DataFrame(
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expected_values, columns=expected_columns, index=expected_index
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)
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tm.assert_frame_equal(result, expected)
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def test_unstack_multi_index_categorical_values():
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mi = tm.makeTimeDataFrame().stack().index.rename(["major", "minor"])
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ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category")
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result = ser.unstack()
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dti = ser.index.levels[0]
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c = pd.Categorical(["foo"] * len(dti))
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expected = DataFrame(
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{"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
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columns=pd.Index(list("ABCD"), name="minor"),
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index=dti.rename("major"),
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)
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tm.assert_frame_equal(result, expected)
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