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Inzynierka/Lib/site-packages/pandas/tests/frame/test_stack_unstack.py

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2023-06-02 12:51:02 +02:00
from datetime import datetime
from io import StringIO
import itertools
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
Timedelta,
date_range,
)
import pandas._testing as tm
from pandas.core.reshape import reshape as reshape_lib
class TestDataFrameReshape:
def test_stack_unstack(self, float_frame):
df = float_frame.copy()
df[:] = np.arange(np.prod(df.shape)).reshape(df.shape)
stacked = df.stack()
stacked_df = DataFrame({"foo": stacked, "bar": stacked})
unstacked = stacked.unstack()
unstacked_df = stacked_df.unstack()
tm.assert_frame_equal(unstacked, df)
tm.assert_frame_equal(unstacked_df["bar"], df)
unstacked_cols = stacked.unstack(0)
unstacked_cols_df = stacked_df.unstack(0)
tm.assert_frame_equal(unstacked_cols.T, df)
tm.assert_frame_equal(unstacked_cols_df["bar"].T, df)
def test_stack_mixed_level(self):
# GH 18310
levels = [range(3), [3, "a", "b"], [1, 2]]
# flat columns:
df = DataFrame(1, index=levels[0], columns=levels[1])
result = df.stack()
expected = Series(1, index=MultiIndex.from_product(levels[:2]))
tm.assert_series_equal(result, expected)
# MultiIndex columns:
df = DataFrame(1, index=levels[0], columns=MultiIndex.from_product(levels[1:]))
result = df.stack(1)
expected = DataFrame(
1, index=MultiIndex.from_product([levels[0], levels[2]]), columns=levels[1]
)
tm.assert_frame_equal(result, expected)
# as above, but used labels in level are actually of homogeneous type
result = df[["a", "b"]].stack(1)
expected = expected[["a", "b"]]
tm.assert_frame_equal(result, expected)
def test_unstack_not_consolidated(self, using_array_manager):
# Gh#34708
df = DataFrame({"x": [1, 2, np.NaN], "y": [3.0, 4, np.NaN]})
df2 = df[["x"]]
df2["y"] = df["y"]
if not using_array_manager:
assert len(df2._mgr.blocks) == 2
res = df2.unstack()
expected = df.unstack()
tm.assert_series_equal(res, expected)
def test_unstack_fill(self):
# GH #9746: fill_value keyword argument for Series
# and DataFrame unstack
# From a series
data = Series([1, 2, 4, 5], dtype=np.int16)
data.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
result = data.unstack(fill_value=-1)
expected = DataFrame(
{"a": [1, -1, 5], "b": [2, 4, -1]}, index=["x", "y", "z"], dtype=np.int16
)
tm.assert_frame_equal(result, expected)
# From a series with incorrect data type for fill_value
result = data.unstack(fill_value=0.5)
expected = DataFrame(
{"a": [1, 0.5, 5], "b": [2, 4, 0.5]}, index=["x", "y", "z"], dtype=float
)
tm.assert_frame_equal(result, expected)
# GH #13971: fill_value when unstacking multiple levels:
df = DataFrame(
{"x": ["a", "a", "b"], "y": ["j", "k", "j"], "z": [0, 1, 2], "w": [0, 1, 2]}
).set_index(["x", "y", "z"])
unstacked = df.unstack(["x", "y"], fill_value=0)
key = ("w", "b", "j")
expected = unstacked[key]
result = Series([0, 0, 2], index=unstacked.index, name=key)
tm.assert_series_equal(result, expected)
stacked = unstacked.stack(["x", "y"])
stacked.index = stacked.index.reorder_levels(df.index.names)
# Workaround for GH #17886 (unnecessarily casts to float):
stacked = stacked.astype(np.int64)
result = stacked.loc[df.index]
tm.assert_frame_equal(result, df)
# From a series
s = df["w"]
result = s.unstack(["x", "y"], fill_value=0)
expected = unstacked["w"]
tm.assert_frame_equal(result, expected)
def test_unstack_fill_frame(self):
# From a dataframe
rows = [[1, 2], [3, 4], [5, 6], [7, 8]]
df = DataFrame(rows, columns=list("AB"), dtype=np.int32)
df.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
result = df.unstack(fill_value=-1)
rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]]
expected = DataFrame(rows, index=list("xyz"), dtype=np.int32)
expected.columns = MultiIndex.from_tuples(
[("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
)
tm.assert_frame_equal(result, expected)
# From a mixed type dataframe
df["A"] = df["A"].astype(np.int16)
df["B"] = df["B"].astype(np.float64)
result = df.unstack(fill_value=-1)
expected["A"] = expected["A"].astype(np.int16)
expected["B"] = expected["B"].astype(np.float64)
tm.assert_frame_equal(result, expected)
# From a dataframe with incorrect data type for fill_value
result = df.unstack(fill_value=0.5)
rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]]
expected = DataFrame(rows, index=list("xyz"), dtype=float)
expected.columns = MultiIndex.from_tuples(
[("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
)
tm.assert_frame_equal(result, expected)
def test_unstack_fill_frame_datetime(self):
# Test unstacking with date times
dv = date_range("2012-01-01", periods=4).values
data = Series(dv)
data.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
result = data.unstack()
expected = DataFrame(
{"a": [dv[0], pd.NaT, dv[3]], "b": [dv[1], dv[2], pd.NaT]},
index=["x", "y", "z"],
)
tm.assert_frame_equal(result, expected)
result = data.unstack(fill_value=dv[0])
expected = DataFrame(
{"a": [dv[0], dv[0], dv[3]], "b": [dv[1], dv[2], dv[0]]},
index=["x", "y", "z"],
)
tm.assert_frame_equal(result, expected)
def test_unstack_fill_frame_timedelta(self):
# Test unstacking with time deltas
td = [Timedelta(days=i) for i in range(4)]
data = Series(td)
data.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
result = data.unstack()
expected = DataFrame(
{"a": [td[0], pd.NaT, td[3]], "b": [td[1], td[2], pd.NaT]},
index=["x", "y", "z"],
)
tm.assert_frame_equal(result, expected)
result = data.unstack(fill_value=td[1])
expected = DataFrame(
{"a": [td[0], td[1], td[3]], "b": [td[1], td[2], td[1]]},
index=["x", "y", "z"],
)
tm.assert_frame_equal(result, expected)
def test_unstack_fill_frame_period(self):
# Test unstacking with period
periods = [
Period("2012-01"),
Period("2012-02"),
Period("2012-03"),
Period("2012-04"),
]
data = Series(periods)
data.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
result = data.unstack()
expected = DataFrame(
{"a": [periods[0], None, periods[3]], "b": [periods[1], periods[2], None]},
index=["x", "y", "z"],
)
tm.assert_frame_equal(result, expected)
result = data.unstack(fill_value=periods[1])
expected = DataFrame(
{
"a": [periods[0], periods[1], periods[3]],
"b": [periods[1], periods[2], periods[1]],
},
index=["x", "y", "z"],
)
tm.assert_frame_equal(result, expected)
def test_unstack_fill_frame_categorical(self):
# Test unstacking with categorical
data = Series(["a", "b", "c", "a"], dtype="category")
data.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
# By default missing values will be NaN
result = data.unstack()
expected = DataFrame(
{
"a": pd.Categorical(list("axa"), categories=list("abc")),
"b": pd.Categorical(list("bcx"), categories=list("abc")),
},
index=list("xyz"),
)
tm.assert_frame_equal(result, expected)
# Fill with non-category results in a ValueError
msg = r"Cannot setitem on a Categorical with a new category \(d\)"
with pytest.raises(TypeError, match=msg):
data.unstack(fill_value="d")
# Fill with category value replaces missing values as expected
result = data.unstack(fill_value="c")
expected = DataFrame(
{
"a": pd.Categorical(list("aca"), categories=list("abc")),
"b": pd.Categorical(list("bcc"), categories=list("abc")),
},
index=list("xyz"),
)
tm.assert_frame_equal(result, expected)
def test_unstack_tuplename_in_multiindex(self):
# GH 19966
idx = MultiIndex.from_product(
[["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
)
df = DataFrame({"d": [1] * 9, "e": [2] * 9}, index=idx)
result = df.unstack(("A", "a"))
expected = DataFrame(
[[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]],
columns=MultiIndex.from_tuples(
[
("d", "a"),
("d", "b"),
("d", "c"),
("e", "a"),
("e", "b"),
("e", "c"),
],
names=[None, ("A", "a")],
),
index=Index([1, 2, 3], name=("B", "b")),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"unstack_idx, expected_values, expected_index, expected_columns",
[
(
("A", "a"),
[[1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2]],
MultiIndex.from_tuples(
[(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]
),
MultiIndex.from_tuples(
[("d", "a"), ("d", "b"), ("e", "a"), ("e", "b")],
names=[None, ("A", "a")],
),
),
(
(("A", "a"), "B"),
[[1, 1, 1, 1, 2, 2, 2, 2], [1, 1, 1, 1, 2, 2, 2, 2]],
Index([3, 4], name="C"),
MultiIndex.from_tuples(
[
("d", "a", 1),
("d", "a", 2),
("d", "b", 1),
("d", "b", 2),
("e", "a", 1),
("e", "a", 2),
("e", "b", 1),
("e", "b", 2),
],
names=[None, ("A", "a"), "B"],
),
),
],
)
def test_unstack_mixed_type_name_in_multiindex(
self, unstack_idx, expected_values, expected_index, expected_columns
):
# GH 19966
idx = MultiIndex.from_product(
[["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
)
df = DataFrame({"d": [1] * 8, "e": [2] * 8}, index=idx)
result = df.unstack(unstack_idx)
expected = DataFrame(
expected_values, columns=expected_columns, index=expected_index
)
tm.assert_frame_equal(result, expected)
def test_unstack_preserve_dtypes(self):
# Checks fix for #11847
df = DataFrame(
{
"state": ["IL", "MI", "NC"],
"index": ["a", "b", "c"],
"some_categories": Series(["a", "b", "c"]).astype("category"),
"A": np.random.rand(3),
"B": 1,
"C": "foo",
"D": pd.Timestamp("20010102"),
"E": Series([1.0, 50.0, 100.0]).astype("float32"),
"F": Series([3.0, 4.0, 5.0]).astype("float64"),
"G": False,
"H": Series([1, 200, 923442]).astype("int8"),
}
)
def unstack_and_compare(df, column_name):
unstacked1 = df.unstack([column_name])
unstacked2 = df.unstack(column_name)
tm.assert_frame_equal(unstacked1, unstacked2)
df1 = df.set_index(["state", "index"])
unstack_and_compare(df1, "index")
df1 = df.set_index(["state", "some_categories"])
unstack_and_compare(df1, "some_categories")
df1 = df.set_index(["F", "C"])
unstack_and_compare(df1, "F")
df1 = df.set_index(["G", "B", "state"])
unstack_and_compare(df1, "B")
df1 = df.set_index(["E", "A"])
unstack_and_compare(df1, "E")
df1 = df.set_index(["state", "index"])
s = df1["A"]
unstack_and_compare(s, "index")
def test_stack_ints(self):
columns = MultiIndex.from_tuples(list(itertools.product(range(3), repeat=3)))
df = DataFrame(np.random.randn(30, 27), columns=columns)
tm.assert_frame_equal(df.stack(level=[1, 2]), df.stack(level=1).stack(level=1))
tm.assert_frame_equal(
df.stack(level=[-2, -1]), df.stack(level=1).stack(level=1)
)
df_named = df.copy()
return_value = df_named.columns.set_names(range(3), inplace=True)
assert return_value is None
tm.assert_frame_equal(
df_named.stack(level=[1, 2]), df_named.stack(level=1).stack(level=1)
)
def test_stack_mixed_levels(self):
columns = MultiIndex.from_tuples(
[
("A", "cat", "long"),
("B", "cat", "long"),
("A", "dog", "short"),
("B", "dog", "short"),
],
names=["exp", "animal", "hair_length"],
)
df = DataFrame(np.random.randn(4, 4), columns=columns)
animal_hair_stacked = df.stack(level=["animal", "hair_length"])
exp_hair_stacked = df.stack(level=["exp", "hair_length"])
# GH #8584: Need to check that stacking works when a number
# is passed that is both a level name and in the range of
# the level numbers
df2 = df.copy()
df2.columns.names = ["exp", "animal", 1]
tm.assert_frame_equal(
df2.stack(level=["animal", 1]), animal_hair_stacked, check_names=False
)
tm.assert_frame_equal(
df2.stack(level=["exp", 1]), exp_hair_stacked, check_names=False
)
# When mixed types are passed and the ints are not level
# names, raise
msg = (
"level should contain all level names or all level numbers, not "
"a mixture of the two"
)
with pytest.raises(ValueError, match=msg):
df2.stack(level=["animal", 0])
# GH #8584: Having 0 in the level names could raise a
# strange error about lexsort depth
df3 = df.copy()
df3.columns.names = ["exp", "animal", 0]
tm.assert_frame_equal(
df3.stack(level=["animal", 0]), animal_hair_stacked, check_names=False
)
def test_stack_int_level_names(self):
columns = MultiIndex.from_tuples(
[
("A", "cat", "long"),
("B", "cat", "long"),
("A", "dog", "short"),
("B", "dog", "short"),
],
names=["exp", "animal", "hair_length"],
)
df = DataFrame(np.random.randn(4, 4), columns=columns)
exp_animal_stacked = df.stack(level=["exp", "animal"])
animal_hair_stacked = df.stack(level=["animal", "hair_length"])
exp_hair_stacked = df.stack(level=["exp", "hair_length"])
df2 = df.copy()
df2.columns.names = [0, 1, 2]
tm.assert_frame_equal(
df2.stack(level=[1, 2]), animal_hair_stacked, check_names=False
)
tm.assert_frame_equal(
df2.stack(level=[0, 1]), exp_animal_stacked, check_names=False
)
tm.assert_frame_equal(
df2.stack(level=[0, 2]), exp_hair_stacked, check_names=False
)
# Out-of-order int column names
df3 = df.copy()
df3.columns.names = [2, 0, 1]
tm.assert_frame_equal(
df3.stack(level=[0, 1]), animal_hair_stacked, check_names=False
)
tm.assert_frame_equal(
df3.stack(level=[2, 0]), exp_animal_stacked, check_names=False
)
tm.assert_frame_equal(
df3.stack(level=[2, 1]), exp_hair_stacked, check_names=False
)
def test_unstack_bool(self):
df = DataFrame(
[False, False],
index=MultiIndex.from_arrays([["a", "b"], ["c", "l"]]),
columns=["col"],
)
rs = df.unstack()
xp = DataFrame(
np.array([[False, np.nan], [np.nan, False]], dtype=object),
index=["a", "b"],
columns=MultiIndex.from_arrays([["col", "col"], ["c", "l"]]),
)
tm.assert_frame_equal(rs, xp)
def test_unstack_level_binding(self):
# GH9856
mi = MultiIndex(
levels=[["foo", "bar"], ["one", "two"], ["a", "b"]],
codes=[[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 1, 0]],
names=["first", "second", "third"],
)
s = Series(0, index=mi)
result = s.unstack([1, 2]).stack(0)
expected_mi = MultiIndex(
levels=[["foo", "bar"], ["one", "two"]],
codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=["first", "second"],
)
expected = DataFrame(
np.array(
[[np.nan, 0], [0, np.nan], [np.nan, 0], [0, np.nan]], dtype=np.float64
),
index=expected_mi,
columns=Index(["a", "b"], name="third"),
)
tm.assert_frame_equal(result, expected)
def test_unstack_to_series(self, float_frame):
# check reversibility
data = float_frame.unstack()
assert isinstance(data, Series)
undo = data.unstack().T
tm.assert_frame_equal(undo, float_frame)
# check NA handling
data = DataFrame({"x": [1, 2, np.NaN], "y": [3.0, 4, np.NaN]})
data.index = Index(["a", "b", "c"])
result = data.unstack()
midx = MultiIndex(
levels=[["x", "y"], ["a", "b", "c"]],
codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
)
expected = Series([1, 2, np.NaN, 3, 4, np.NaN], index=midx)
tm.assert_series_equal(result, expected)
# check composability of unstack
old_data = data.copy()
for _ in range(4):
data = data.unstack()
tm.assert_frame_equal(old_data, data)
def test_unstack_dtypes(self):
# GH 2929
rows = [[1, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4], [2, 2, 3, 4]]
df = DataFrame(rows, columns=list("ABCD"))
result = df.dtypes
expected = Series([np.dtype("int64")] * 4, index=list("ABCD"))
tm.assert_series_equal(result, expected)
# single dtype
df2 = df.set_index(["A", "B"])
df3 = df2.unstack("B")
result = df3.dtypes
expected = Series(
[np.dtype("int64")] * 4,
index=MultiIndex.from_arrays(
[["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
),
)
tm.assert_series_equal(result, expected)
# mixed
df2 = df.set_index(["A", "B"])
df2["C"] = 3.0
df3 = df2.unstack("B")
result = df3.dtypes
expected = Series(
[np.dtype("float64")] * 2 + [np.dtype("int64")] * 2,
index=MultiIndex.from_arrays(
[["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
),
)
tm.assert_series_equal(result, expected)
df2["D"] = "foo"
df3 = df2.unstack("B")
result = df3.dtypes
expected = Series(
[np.dtype("float64")] * 2 + [np.dtype("object")] * 2,
index=MultiIndex.from_arrays(
[["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
),
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"c, d",
(
(np.zeros(5), np.zeros(5)),
(np.arange(5, dtype="f8"), np.arange(5, 10, dtype="f8")),
),
)
def test_unstack_dtypes_mixed_date(self, c, d):
# GH7405
df = DataFrame(
{
"A": ["a"] * 5,
"C": c,
"D": d,
"B": date_range("2012-01-01", periods=5),
}
)
right = df.iloc[:3].copy(deep=True)
df = df.set_index(["A", "B"])
df["D"] = df["D"].astype("int64")
left = df.iloc[:3].unstack(0)
right = right.set_index(["A", "B"]).unstack(0)
right[("D", "a")] = right[("D", "a")].astype("int64")
assert left.shape == (3, 2)
tm.assert_frame_equal(left, right)
def test_unstack_non_unique_index_names(self):
idx = MultiIndex.from_tuples([("a", "b"), ("c", "d")], names=["c1", "c1"])
df = DataFrame([1, 2], index=idx)
msg = "The name c1 occurs multiple times, use a level number"
with pytest.raises(ValueError, match=msg):
df.unstack("c1")
with pytest.raises(ValueError, match=msg):
df.T.stack("c1")
def test_unstack_unused_levels(self):
# GH 17845: unused codes in index make unstack() cast int to float
idx = MultiIndex.from_product([["a"], ["A", "B", "C", "D"]])[:-1]
df = DataFrame([[1, 0]] * 3, index=idx)
result = df.unstack()
exp_col = MultiIndex.from_product([[0, 1], ["A", "B", "C"]])
expected = DataFrame([[1, 1, 1, 0, 0, 0]], index=["a"], columns=exp_col)
tm.assert_frame_equal(result, expected)
assert (result.columns.levels[1] == idx.levels[1]).all()
# Unused items on both levels
levels = [[0, 1, 7], [0, 1, 2, 3]]
codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
idx = MultiIndex(levels, codes)
block = np.arange(4).reshape(2, 2)
df = DataFrame(np.concatenate([block, block + 4]), index=idx)
result = df.unstack()
expected = DataFrame(
np.concatenate([block * 2, block * 2 + 1], axis=1), columns=idx
)
tm.assert_frame_equal(result, expected)
assert (result.columns.levels[1] == idx.levels[1]).all()
@pytest.mark.parametrize(
"level, idces, col_level, idx_level",
(
(0, [13, 16, 6, 9, 2, 5, 8, 11], [np.nan, "a", 2], [np.nan, 5, 1]),
(1, [8, 11, 1, 4, 12, 15, 13, 16], [np.nan, 5, 1], [np.nan, "a", 2]),
),
)
def test_unstack_unused_levels_mixed_with_nan(
self, level, idces, col_level, idx_level
):
# With mixed dtype and NaN
levels = [["a", 2, "c"], [1, 3, 5, 7]]
codes = [[0, -1, 1, 1], [0, 2, -1, 2]]
idx = MultiIndex(levels, codes)
data = np.arange(8)
df = DataFrame(data.reshape(4, 2), index=idx)
result = df.unstack(level=level)
exp_data = np.zeros(18) * np.nan
exp_data[idces] = data
cols = MultiIndex.from_product([[0, 1], col_level])
expected = DataFrame(exp_data.reshape(3, 6), index=idx_level, columns=cols)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("cols", [["A", "C"], slice(None)])
def test_unstack_unused_level(self, cols):
# GH 18562 : unused codes on the unstacked level
df = DataFrame([[2010, "a", "I"], [2011, "b", "II"]], columns=["A", "B", "C"])
ind = df.set_index(["A", "B", "C"], drop=False)
selection = ind.loc[(slice(None), slice(None), "I"), cols]
result = selection.unstack()
expected = ind.iloc[[0]][cols]
expected.columns = MultiIndex.from_product(
[expected.columns, ["I"]], names=[None, "C"]
)
expected.index = expected.index.droplevel("C")
tm.assert_frame_equal(result, expected)
def test_unstack_long_index(self):
# PH 32624: Error when using a lot of indices to unstack.
# The error occurred only, if a lot of indices are used.
df = DataFrame(
[[1]],
columns=MultiIndex.from_tuples([[0]], names=["c1"]),
index=MultiIndex.from_tuples(
[[0, 0, 1, 0, 0, 0, 1]],
names=["i1", "i2", "i3", "i4", "i5", "i6", "i7"],
),
)
result = df.unstack(["i2", "i3", "i4", "i5", "i6", "i7"])
expected = DataFrame(
[[1]],
columns=MultiIndex.from_tuples(
[[0, 0, 1, 0, 0, 0, 1]],
names=["c1", "i2", "i3", "i4", "i5", "i6", "i7"],
),
index=Index([0], name="i1"),
)
tm.assert_frame_equal(result, expected)
def test_unstack_multi_level_cols(self):
# PH 24729: Unstack a df with multi level columns
df = DataFrame(
[[0.0, 0.0], [0.0, 0.0]],
columns=MultiIndex.from_tuples(
[["B", "C"], ["B", "D"]], names=["c1", "c2"]
),
index=MultiIndex.from_tuples(
[[10, 20, 30], [10, 20, 40]], names=["i1", "i2", "i3"]
),
)
assert df.unstack(["i2", "i1"]).columns.names[-2:] == ["i2", "i1"]
def test_unstack_multi_level_rows_and_cols(self):
# PH 28306: Unstack df with multi level cols and rows
df = DataFrame(
[[1, 2], [3, 4], [-1, -2], [-3, -4]],
columns=MultiIndex.from_tuples([["a", "b", "c"], ["d", "e", "f"]]),
index=MultiIndex.from_tuples(
[
["m1", "P3", 222],
["m1", "A5", 111],
["m2", "P3", 222],
["m2", "A5", 111],
],
names=["i1", "i2", "i3"],
),
)
result = df.unstack(["i3", "i2"])
expected = df.unstack(["i3"]).unstack(["i2"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("idx", [("jim", "joe"), ("joe", "jim")])
@pytest.mark.parametrize("lev", list(range(2)))
def test_unstack_nan_index1(self, idx, lev):
# GH7466
def cast(val):
val_str = "" if val != val else val
return f"{val_str:1}"
df = DataFrame(
{
"jim": ["a", "b", np.nan, "d"],
"joe": ["w", "x", "y", "z"],
"jolie": ["a.w", "b.x", " .y", "d.z"],
}
)
left = df.set_index(["jim", "joe"]).unstack()["jolie"]
right = df.set_index(["joe", "jim"]).unstack()["jolie"].T
tm.assert_frame_equal(left, right)
mi = df.set_index(list(idx))
udf = mi.unstack(level=lev)
assert udf.notna().values.sum() == len(df)
mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
rows, cols = udf["jolie"].notna().values.nonzero()
for i, j in zip(rows, cols):
left = sorted(udf["jolie"].iloc[i, j].split("."))
right = mk_list(udf["jolie"].index[i]) + mk_list(udf["jolie"].columns[j])
right = sorted(map(cast, right))
assert left == right
@pytest.mark.parametrize("idx", itertools.permutations(["1st", "2nd", "3rd"]))
@pytest.mark.parametrize("lev", list(range(3)))
@pytest.mark.parametrize("col", ["4th", "5th"])
def test_unstack_nan_index_repeats(self, idx, lev, col):
def cast(val):
val_str = "" if val != val else val
return f"{val_str:1}"
df = DataFrame(
{
"1st": ["d"] * 3
+ [np.nan] * 5
+ ["a"] * 2
+ ["c"] * 3
+ ["e"] * 2
+ ["b"] * 5,
"2nd": ["y"] * 2
+ ["w"] * 3
+ [np.nan] * 3
+ ["z"] * 4
+ [np.nan] * 3
+ ["x"] * 3
+ [np.nan] * 2,
"3rd": [
67,
39,
53,
72,
57,
80,
31,
18,
11,
30,
59,
50,
62,
59,
76,
52,
14,
53,
60,
51,
],
}
)
df["4th"], df["5th"] = (
df.apply(lambda r: ".".join(map(cast, r)), axis=1),
df.apply(lambda r: ".".join(map(cast, r.iloc[::-1])), axis=1),
)
mi = df.set_index(list(idx))
udf = mi.unstack(level=lev)
assert udf.notna().values.sum() == 2 * len(df)
mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
rows, cols = udf[col].notna().values.nonzero()
for i, j in zip(rows, cols):
left = sorted(udf[col].iloc[i, j].split("."))
right = mk_list(udf[col].index[i]) + mk_list(udf[col].columns[j])
right = sorted(map(cast, right))
assert left == right
def test_unstack_nan_index2(self):
# GH7403
df = DataFrame({"A": list("aaaabbbb"), "B": range(8), "C": range(8)})
# Explicit cast to avoid implicit cast when setting to np.NaN
df = df.astype({"B": "float"})
df.iloc[3, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack(0)
vals = [
[3, 0, 1, 2, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, 4, 5, 6, 7],
]
vals = list(map(list, zip(*vals)))
idx = Index([np.nan, 0, 1, 2, 4, 5, 6, 7], name="B")
cols = MultiIndex(
levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
)
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
# Explicit cast to avoid implicit cast when setting to np.NaN
df = df.astype({"B": "float"})
df.iloc[2, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack(0)
vals = [[2, np.nan], [0, 4], [1, 5], [np.nan, 6], [3, 7]]
cols = MultiIndex(
levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
)
idx = Index([np.nan, 0, 1, 2, 3], name="B")
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
# Explicit cast to avoid implicit cast when setting to np.NaN
df = df.astype({"B": "float"})
df.iloc[3, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack(0)
vals = [[3, np.nan], [0, 4], [1, 5], [2, 6], [np.nan, 7]]
cols = MultiIndex(
levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
)
idx = Index([np.nan, 0, 1, 2, 3], name="B")
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
def test_unstack_nan_index3(self, using_array_manager):
# GH7401
df = DataFrame(
{
"A": list("aaaaabbbbb"),
"B": (date_range("2012-01-01", periods=5).tolist() * 2),
"C": np.arange(10),
}
)
df.iloc[3, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack()
vals = np.array([[3, 0, 1, 2, np.nan, 4], [np.nan, 5, 6, 7, 8, 9]])
idx = Index(["a", "b"], name="A")
cols = MultiIndex(
levels=[["C"], date_range("2012-01-01", periods=5)],
codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
names=[None, "B"],
)
right = DataFrame(vals, columns=cols, index=idx)
if using_array_manager:
# INFO(ArrayManager) with ArrayManager preserve dtype where possible
cols = right.columns[[1, 2, 3, 5]]
right[cols] = right[cols].astype(df["C"].dtype)
tm.assert_frame_equal(left, right)
def test_unstack_nan_index4(self):
# GH4862
vals = [
["Hg", np.nan, np.nan, 680585148],
["U", 0.0, np.nan, 680585148],
["Pb", 7.07e-06, np.nan, 680585148],
["Sn", 2.3614e-05, 0.0133, 680607017],
["Ag", 0.0, 0.0133, 680607017],
["Hg", -0.00015, 0.0133, 680607017],
]
df = DataFrame(
vals,
columns=["agent", "change", "dosage", "s_id"],
index=[17263, 17264, 17265, 17266, 17267, 17268],
)
left = df.copy().set_index(["s_id", "dosage", "agent"]).unstack()
vals = [
[np.nan, np.nan, 7.07e-06, np.nan, 0.0],
[0.0, -0.00015, np.nan, 2.3614e-05, np.nan],
]
idx = MultiIndex(
levels=[[680585148, 680607017], [0.0133]],
codes=[[0, 1], [-1, 0]],
names=["s_id", "dosage"],
)
cols = MultiIndex(
levels=[["change"], ["Ag", "Hg", "Pb", "Sn", "U"]],
codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]],
names=[None, "agent"],
)
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"])
tm.assert_frame_equal(left.unstack(), right)
def test_unstack_nan_index5(self):
# GH9497 - multiple unstack with nulls
df = DataFrame(
{
"1st": [1, 2, 1, 2, 1, 2],
"2nd": date_range("2014-02-01", periods=6, freq="D"),
"jim": 100 + np.arange(6),
"joe": (np.random.randn(6) * 10).round(2),
}
)
df["3rd"] = df["2nd"] - pd.Timestamp("2014-02-02")
df.loc[1, "2nd"] = df.loc[3, "2nd"] = np.nan
df.loc[1, "3rd"] = df.loc[4, "3rd"] = np.nan
left = df.set_index(["1st", "2nd", "3rd"]).unstack(["2nd", "3rd"])
assert left.notna().values.sum() == 2 * len(df)
for col in ["jim", "joe"]:
for _, r in df.iterrows():
key = r["1st"], (col, r["2nd"], r["3rd"])
assert r[col] == left.loc[key]
def test_stack_datetime_column_multiIndex(self):
# GH 8039
t = datetime(2014, 1, 1)
df = DataFrame([1, 2, 3, 4], columns=MultiIndex.from_tuples([(t, "A", "B")]))
result = df.stack()
eidx = MultiIndex.from_product([(0, 1, 2, 3), ("B",)])
ecols = MultiIndex.from_tuples([(t, "A")])
expected = DataFrame([1, 2, 3, 4], index=eidx, columns=ecols)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"multiindex_columns",
[
[0, 1, 2, 3, 4],
[0, 1, 2, 3],
[0, 1, 2, 4],
[0, 1, 2],
[1, 2, 3],
[2, 3, 4],
[0, 1],
[0, 2],
[0, 3],
[0],
[2],
[4],
[4, 3, 2, 1, 0],
[3, 2, 1, 0],
[4, 2, 1, 0],
[2, 1, 0],
[3, 2, 1],
[4, 3, 2],
[1, 0],
[2, 0],
[3, 0],
],
)
@pytest.mark.parametrize("level", (-1, 0, 1, [0, 1], [1, 0]))
def test_stack_partial_multiIndex(self, multiindex_columns, level):
# GH 8844
full_multiindex = MultiIndex.from_tuples(
[("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
names=["Upper", "Lower"],
)
multiindex = full_multiindex[multiindex_columns]
df = DataFrame(
np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
columns=multiindex,
)
result = df.stack(level=level, dropna=False)
if isinstance(level, int):
# Stacking a single level should not make any all-NaN rows,
# so df.stack(level=level, dropna=False) should be the same
# as df.stack(level=level, dropna=True).
expected = df.stack(level=level, dropna=True)
if isinstance(expected, Series):
tm.assert_series_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
df.columns = MultiIndex.from_tuples(
df.columns.to_numpy(), names=df.columns.names
)
expected = df.stack(level=level, dropna=False)
if isinstance(expected, Series):
tm.assert_series_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
def test_stack_full_multiIndex(self):
# GH 8844
full_multiindex = MultiIndex.from_tuples(
[("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
names=["Upper", "Lower"],
)
df = DataFrame(np.arange(6).reshape(2, 3), columns=full_multiindex[[0, 1, 3]])
result = df.stack(dropna=False)
expected = DataFrame(
[[0, 2], [1, np.nan], [3, 5], [4, np.nan]],
index=MultiIndex(
levels=[[0, 1], ["u", "x", "y", "z"]],
codes=[[0, 0, 1, 1], [1, 3, 1, 3]],
names=[None, "Lower"],
),
columns=Index(["B", "C"], name="Upper"),
)
expected["B"] = expected["B"].astype(df.dtypes[0])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("ordered", [False, True])
@pytest.mark.parametrize("labels", [list("yxz"), list("yxy")])
def test_stack_preserve_categorical_dtype(self, ordered, labels):
# GH13854
cidx = pd.CategoricalIndex(labels, categories=list("xyz"), ordered=ordered)
df = DataFrame([[10, 11, 12]], columns=cidx)
result = df.stack()
# `MultiIndex.from_product` preserves categorical dtype -
# it's tested elsewhere.
midx = MultiIndex.from_product([df.index, cidx])
expected = Series([10, 11, 12], index=midx)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("ordered", [False, True])
@pytest.mark.parametrize(
"labels,data",
[
(list("xyz"), [10, 11, 12, 13, 14, 15]),
(list("zyx"), [14, 15, 12, 13, 10, 11]),
],
)
def test_stack_multi_preserve_categorical_dtype(self, ordered, labels, data):
# GH-36991
cidx = pd.CategoricalIndex(labels, categories=sorted(labels), ordered=ordered)
cidx2 = pd.CategoricalIndex(["u", "v"], ordered=ordered)
midx = MultiIndex.from_product([cidx, cidx2])
df = DataFrame([sorted(data)], columns=midx)
result = df.stack([0, 1])
s_cidx = pd.CategoricalIndex(sorted(labels), ordered=ordered)
expected = Series(data, index=MultiIndex.from_product([[0], s_cidx, cidx2]))
tm.assert_series_equal(result, expected)
def test_stack_preserve_categorical_dtype_values(self):
# GH-23077
cat = pd.Categorical(["a", "a", "b", "c"])
df = DataFrame({"A": cat, "B": cat})
result = df.stack()
index = MultiIndex.from_product([[0, 1, 2, 3], ["A", "B"]])
expected = Series(
pd.Categorical(["a", "a", "a", "a", "b", "b", "c", "c"]), index=index
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"index, columns",
[
([0, 0, 1, 1], MultiIndex.from_product([[1, 2], ["a", "b"]])),
([0, 0, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])),
([0, 1, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])),
],
)
def test_stack_multi_columns_non_unique_index(self, index, columns):
# GH-28301
df = DataFrame(index=index, columns=columns).fillna(1)
stacked = df.stack()
new_index = MultiIndex.from_tuples(stacked.index.to_numpy())
expected = DataFrame(
stacked.to_numpy(), index=new_index, columns=stacked.columns
)
tm.assert_frame_equal(stacked, expected)
stacked_codes = np.asarray(stacked.index.codes)
expected_codes = np.asarray(new_index.codes)
tm.assert_numpy_array_equal(stacked_codes, expected_codes)
@pytest.mark.parametrize("level", [0, 1])
def test_unstack_mixed_extension_types(self, level):
index = MultiIndex.from_tuples([("A", 0), ("A", 1), ("B", 1)], names=["a", "b"])
df = DataFrame(
{
"A": pd.array([0, 1, None], dtype="Int64"),
"B": pd.Categorical(["a", "a", "b"]),
},
index=index,
)
result = df.unstack(level=level)
expected = df.astype(object).unstack(level=level)
expected_dtypes = Series(
[df.A.dtype] * 2 + [df.B.dtype] * 2, index=result.columns
)
tm.assert_series_equal(result.dtypes, expected_dtypes)
tm.assert_frame_equal(result.astype(object), expected)
@pytest.mark.parametrize("level", [0, "baz"])
def test_unstack_swaplevel_sortlevel(self, level):
# GH 20994
mi = MultiIndex.from_product([[0], ["d", "c"]], names=["bar", "baz"])
df = DataFrame([[0, 2], [1, 3]], index=mi, columns=["B", "A"])
df.columns.name = "foo"
expected = DataFrame(
[[3, 1, 2, 0]],
columns=MultiIndex.from_tuples(
[("c", "A"), ("c", "B"), ("d", "A"), ("d", "B")], names=["baz", "foo"]
),
)
expected.index.name = "bar"
result = df.unstack().swaplevel(axis=1).sort_index(axis=1, level=level)
tm.assert_frame_equal(result, expected)
def test_unstack_fill_frame_object():
# GH12815 Test unstacking with object.
data = Series(["a", "b", "c", "a"], dtype="object")
data.index = MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
)
# By default missing values will be NaN
result = data.unstack()
expected = DataFrame(
{"a": ["a", np.nan, "a"], "b": ["b", "c", np.nan]}, index=list("xyz")
)
tm.assert_frame_equal(result, expected)
# Fill with any value replaces missing values as expected
result = data.unstack(fill_value="d")
expected = DataFrame(
{"a": ["a", "d", "a"], "b": ["b", "c", "d"]}, index=list("xyz")
)
tm.assert_frame_equal(result, expected)
def test_unstack_timezone_aware_values():
# GH 18338
df = DataFrame(
{
"timestamp": [pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC")],
"a": ["a"],
"b": ["b"],
"c": ["c"],
},
columns=["timestamp", "a", "b", "c"],
)
result = df.set_index(["a", "b"]).unstack()
expected = DataFrame(
[[pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC"), "c"]],
index=Index(["a"], name="a"),
columns=MultiIndex(
levels=[["timestamp", "c"], ["b"]],
codes=[[0, 1], [0, 0]],
names=[None, "b"],
),
)
tm.assert_frame_equal(result, expected)
def test_stack_timezone_aware_values():
# GH 19420
ts = date_range(freq="D", start="20180101", end="20180103", tz="America/New_York")
df = DataFrame({"A": ts}, index=["a", "b", "c"])
result = df.stack()
expected = Series(
ts,
index=MultiIndex(levels=[["a", "b", "c"], ["A"]], codes=[[0, 1, 2], [0, 0, 0]]),
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dropna", [True, False])
def test_stack_empty_frame(dropna):
# GH 36113
levels = [np.array([], dtype=np.int64), np.array([], dtype=np.int64)]
expected = Series(dtype=np.float64, index=MultiIndex(levels=levels, codes=[[], []]))
result = DataFrame(dtype=np.float64).stack(dropna=dropna)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dropna", [True, False])
@pytest.mark.parametrize("fill_value", [None, 0])
def test_stack_unstack_empty_frame(dropna, fill_value):
# GH 36113
result = (
DataFrame(dtype=np.int64).stack(dropna=dropna).unstack(fill_value=fill_value)
)
expected = DataFrame(dtype=np.int64)
tm.assert_frame_equal(result, expected)
def test_unstack_single_index_series():
# GH 36113
msg = r"index must be a MultiIndex to unstack.*"
with pytest.raises(ValueError, match=msg):
Series(dtype=np.int64).unstack()
def test_unstacking_multi_index_df():
# see gh-30740
df = DataFrame(
{
"name": ["Alice", "Bob"],
"score": [9.5, 8],
"employed": [False, True],
"kids": [0, 0],
"gender": ["female", "male"],
}
)
df = df.set_index(["name", "employed", "kids", "gender"])
df = df.unstack(["gender"], fill_value=0)
expected = df.unstack("employed", fill_value=0).unstack("kids", fill_value=0)
result = df.unstack(["employed", "kids"], fill_value=0)
expected = DataFrame(
[[9.5, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 8.0]],
index=Index(["Alice", "Bob"], name="name"),
columns=MultiIndex.from_tuples(
[
("score", "female", False, 0),
("score", "female", True, 0),
("score", "male", False, 0),
("score", "male", True, 0),
],
names=[None, "gender", "employed", "kids"],
),
)
tm.assert_frame_equal(result, expected)
def test_stack_positional_level_duplicate_column_names():
# https://github.com/pandas-dev/pandas/issues/36353
columns = MultiIndex.from_product([("x", "y"), ("y", "z")], names=["a", "a"])
df = DataFrame([[1, 1, 1, 1]], columns=columns)
result = df.stack(0)
new_columns = Index(["y", "z"], name="a")
new_index = MultiIndex.from_tuples([(0, "x"), (0, "y")], names=[None, "a"])
expected = DataFrame([[1, 1], [1, 1]], index=new_index, columns=new_columns)
tm.assert_frame_equal(result, expected)
def test_unstack_non_slice_like_blocks(using_array_manager):
# Case where the mgr_locs of a DataFrame's underlying blocks are not slice-like
mi = MultiIndex.from_product([range(5), ["A", "B", "C"]])
df = DataFrame(
{
0: np.random.randn(15),
1: np.random.randn(15).astype(np.int64),
2: np.random.randn(15),
3: np.random.randn(15),
},
index=mi,
)
if not using_array_manager:
assert any(not x.mgr_locs.is_slice_like for x in df._mgr.blocks)
res = df.unstack()
expected = pd.concat([df[n].unstack() for n in range(4)], keys=range(4), axis=1)
tm.assert_frame_equal(res, expected)
class TestStackUnstackMultiLevel:
def test_unstack(self, multiindex_year_month_day_dataframe_random_data):
# just check that it works for now
ymd = multiindex_year_month_day_dataframe_random_data
unstacked = ymd.unstack()
unstacked.unstack()
# test that ints work
ymd.astype(int).unstack()
# test that int32 work
ymd.astype(np.int32).unstack()
@pytest.mark.parametrize(
"result_rows,result_columns,index_product,expected_row",
[
(
[[1, 1, None, None, 30.0, None], [2, 2, None, None, 30.0, None]],
["ix1", "ix2", "col1", "col2", "col3", "col4"],
2,
[None, None, 30.0, None],
),
(
[[1, 1, None, None, 30.0], [2, 2, None, None, 30.0]],
["ix1", "ix2", "col1", "col2", "col3"],
2,
[None, None, 30.0],
),
(
[[1, 1, None, None, 30.0], [2, None, None, None, 30.0]],
["ix1", "ix2", "col1", "col2", "col3"],
None,
[None, None, 30.0],
),
],
)
def test_unstack_partial(
self, result_rows, result_columns, index_product, expected_row
):
# check for regressions on this issue:
# https://github.com/pandas-dev/pandas/issues/19351
# make sure DataFrame.unstack() works when its run on a subset of the DataFrame
# and the Index levels contain values that are not present in the subset
result = DataFrame(result_rows, columns=result_columns).set_index(
["ix1", "ix2"]
)
result = result.iloc[1:2].unstack("ix2")
expected = DataFrame(
[expected_row],
columns=MultiIndex.from_product(
[result_columns[2:], [index_product]], names=[None, "ix2"]
),
index=Index([2], name="ix1"),
)
tm.assert_frame_equal(result, expected)
def test_unstack_multiple_no_empty_columns(self):
index = MultiIndex.from_tuples(
[(0, "foo", 0), (0, "bar", 0), (1, "baz", 1), (1, "qux", 1)]
)
s = Series(np.random.randn(4), index=index)
unstacked = s.unstack([1, 2])
expected = unstacked.dropna(axis=1, how="all")
tm.assert_frame_equal(unstacked, expected)
def test_stack(self, multiindex_year_month_day_dataframe_random_data):
ymd = multiindex_year_month_day_dataframe_random_data
# regular roundtrip
unstacked = ymd.unstack()
restacked = unstacked.stack()
tm.assert_frame_equal(restacked, ymd)
unlexsorted = ymd.sort_index(level=2)
unstacked = unlexsorted.unstack(2)
restacked = unstacked.stack()
tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
unlexsorted = unlexsorted[::-1]
unstacked = unlexsorted.unstack(1)
restacked = unstacked.stack().swaplevel(1, 2)
tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
unlexsorted = unlexsorted.swaplevel(0, 1)
unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
restacked = unstacked.stack(0).swaplevel(1, 2)
tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
# columns unsorted
unstacked = ymd.unstack()
unstacked = unstacked.sort_index(axis=1, ascending=False)
restacked = unstacked.stack()
tm.assert_frame_equal(restacked, ymd)
# more than 2 levels in the columns
unstacked = ymd.unstack(1).unstack(1)
result = unstacked.stack(1)
expected = ymd.unstack()
tm.assert_frame_equal(result, expected)
result = unstacked.stack(2)
expected = ymd.unstack(1)
tm.assert_frame_equal(result, expected)
result = unstacked.stack(0)
expected = ymd.stack().unstack(1).unstack(1)
tm.assert_frame_equal(result, expected)
# not all levels present in each echelon
unstacked = ymd.unstack(2).loc[:, ::3]
stacked = unstacked.stack().stack()
ymd_stacked = ymd.stack()
tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))
# stack with negative number
result = ymd.unstack(0).stack(-2)
expected = ymd.unstack(0).stack(0)
tm.assert_equal(result, expected)
@pytest.mark.parametrize(
"idx, columns, exp_idx",
[
[
list("abab"),
["1st", "2nd", "3rd"],
MultiIndex(
levels=[["a", "b"], ["1st", "2nd", "3rd"]],
codes=[
np.tile(np.arange(2).repeat(3), 2),
np.tile(np.arange(3), 4),
],
),
],
[
list("abab"),
["1st", "2nd", "1st"],
MultiIndex(
levels=[["a", "b"], ["1st", "2nd"]],
codes=[np.tile(np.arange(2).repeat(3), 2), np.tile([0, 1, 0], 4)],
),
],
[
MultiIndex.from_tuples((("a", 2), ("b", 1), ("a", 1), ("b", 2))),
["1st", "2nd", "1st"],
MultiIndex(
levels=[["a", "b"], [1, 2], ["1st", "2nd"]],
codes=[
np.tile(np.arange(2).repeat(3), 2),
np.repeat([1, 0, 1], [3, 6, 3]),
np.tile([0, 1, 0], 4),
],
),
],
],
)
def test_stack_duplicate_index(self, idx, columns, exp_idx):
# GH10417
df = DataFrame(
np.arange(12).reshape(4, 3),
index=idx,
columns=columns,
)
result = df.stack()
expected = Series(np.arange(12), index=exp_idx)
tm.assert_series_equal(result, expected)
assert result.index.is_unique is False
li, ri = result.index, expected.index
tm.assert_index_equal(li, ri)
def test_unstack_odd_failure(self):
data = """day,time,smoker,sum,len
Fri,Dinner,No,8.25,3.
Fri,Dinner,Yes,27.03,9
Fri,Lunch,No,3.0,1
Fri,Lunch,Yes,13.68,6
Sat,Dinner,No,139.63,45
Sat,Dinner,Yes,120.77,42
Sun,Dinner,No,180.57,57
Sun,Dinner,Yes,66.82,19
Thu,Dinner,No,3.0,1
Thu,Lunch,No,117.32,44
Thu,Lunch,Yes,51.51,17"""
df = pd.read_csv(StringIO(data)).set_index(["day", "time", "smoker"])
# it works, #2100
result = df.unstack(2)
recons = result.stack()
tm.assert_frame_equal(recons, df)
def test_stack_mixed_dtype(self, multiindex_dataframe_random_data):
frame = multiindex_dataframe_random_data
df = frame.T
df["foo", "four"] = "foo"
df = df.sort_index(level=1, axis=1)
stacked = df.stack()
result = df["foo"].stack().sort_index()
tm.assert_series_equal(stacked["foo"], result, check_names=False)
assert result.name is None
assert stacked["bar"].dtype == np.float_
def test_unstack_bug(self):
df = DataFrame(
{
"state": ["naive", "naive", "naive", "active", "active", "active"],
"exp": ["a", "b", "b", "b", "a", "a"],
"barcode": [1, 2, 3, 4, 1, 3],
"v": ["hi", "hi", "bye", "bye", "bye", "peace"],
"extra": np.arange(6.0),
}
)
result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)
unstacked = result.unstack()
restacked = unstacked.stack()
tm.assert_series_equal(restacked, result.reindex(restacked.index).astype(float))
def test_stack_unstack_preserve_names(self, multiindex_dataframe_random_data):
frame = multiindex_dataframe_random_data
unstacked = frame.unstack()
assert unstacked.index.name == "first"
assert unstacked.columns.names == ["exp", "second"]
restacked = unstacked.stack()
assert restacked.index.names == frame.index.names
@pytest.mark.parametrize("method", ["stack", "unstack"])
def test_stack_unstack_wrong_level_name(
self, method, multiindex_dataframe_random_data
):
# GH 18303 - wrong level name should raise
frame = multiindex_dataframe_random_data
# A DataFrame with flat axes:
df = frame.loc["foo"]
with pytest.raises(KeyError, match="does not match index name"):
getattr(df, method)("mistake")
if method == "unstack":
# Same on a Series:
s = df.iloc[:, 0]
with pytest.raises(KeyError, match="does not match index name"):
getattr(s, method)("mistake")
def test_unstack_level_name(self, multiindex_dataframe_random_data):
frame = multiindex_dataframe_random_data
result = frame.unstack("second")
expected = frame.unstack(level=1)
tm.assert_frame_equal(result, expected)
def test_stack_level_name(self, multiindex_dataframe_random_data):
frame = multiindex_dataframe_random_data
unstacked = frame.unstack("second")
result = unstacked.stack("exp")
expected = frame.unstack().stack(0)
tm.assert_frame_equal(result, expected)
result = frame.stack("exp")
expected = frame.stack()
tm.assert_series_equal(result, expected)
def test_stack_unstack_multiple(
self, multiindex_year_month_day_dataframe_random_data
):
ymd = multiindex_year_month_day_dataframe_random_data
unstacked = ymd.unstack(["year", "month"])
expected = ymd.unstack("year").unstack("month")
tm.assert_frame_equal(unstacked, expected)
assert unstacked.columns.names == expected.columns.names
# series
s = ymd["A"]
s_unstacked = s.unstack(["year", "month"])
tm.assert_frame_equal(s_unstacked, expected["A"])
restacked = unstacked.stack(["year", "month"])
restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
restacked = restacked.sort_index(level=0)
tm.assert_frame_equal(restacked, ymd)
assert restacked.index.names == ymd.index.names
# GH #451
unstacked = ymd.unstack([1, 2])
expected = ymd.unstack(1).unstack(1).dropna(axis=1, how="all")
tm.assert_frame_equal(unstacked, expected)
unstacked = ymd.unstack([2, 1])
expected = ymd.unstack(2).unstack(1).dropna(axis=1, how="all")
tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns])
def test_stack_names_and_numbers(
self, multiindex_year_month_day_dataframe_random_data
):
ymd = multiindex_year_month_day_dataframe_random_data
unstacked = ymd.unstack(["year", "month"])
# Can't use mixture of names and numbers to stack
with pytest.raises(ValueError, match="level should contain"):
unstacked.stack([0, "month"])
def test_stack_multiple_out_of_bounds(
self, multiindex_year_month_day_dataframe_random_data
):
# nlevels == 3
ymd = multiindex_year_month_day_dataframe_random_data
unstacked = ymd.unstack(["year", "month"])
with pytest.raises(IndexError, match="Too many levels"):
unstacked.stack([2, 3])
with pytest.raises(IndexError, match="not a valid level number"):
unstacked.stack([-4, -3])
def test_unstack_period_series(self):
# GH4342
idx1 = pd.PeriodIndex(
["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"],
freq="M",
name="period",
)
idx2 = Index(["A", "B"] * 3, name="str")
value = [1, 2, 3, 4, 5, 6]
idx = MultiIndex.from_arrays([idx1, idx2])
s = Series(value, index=idx)
result1 = s.unstack()
result2 = s.unstack(level=1)
result3 = s.unstack(level=0)
e_idx = pd.PeriodIndex(
["2013-01", "2013-02", "2013-03"], freq="M", name="period"
)
expected = DataFrame(
{"A": [1, 3, 5], "B": [2, 4, 6]}, index=e_idx, columns=["A", "B"]
)
expected.columns.name = "str"
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
tm.assert_frame_equal(result3, expected.T)
idx1 = pd.PeriodIndex(
["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"],
freq="M",
name="period1",
)
idx2 = pd.PeriodIndex(
["2013-12", "2013-11", "2013-10", "2013-09", "2013-08", "2013-07"],
freq="M",
name="period2",
)
idx = MultiIndex.from_arrays([idx1, idx2])
s = Series(value, index=idx)
result1 = s.unstack()
result2 = s.unstack(level=1)
result3 = s.unstack(level=0)
e_idx = pd.PeriodIndex(
["2013-01", "2013-02", "2013-03"], freq="M", name="period1"
)
e_cols = pd.PeriodIndex(
["2013-07", "2013-08", "2013-09", "2013-10", "2013-11", "2013-12"],
freq="M",
name="period2",
)
expected = DataFrame(
[
[np.nan, np.nan, np.nan, np.nan, 2, 1],
[np.nan, np.nan, 4, 3, np.nan, np.nan],
[6, 5, np.nan, np.nan, np.nan, np.nan],
],
index=e_idx,
columns=e_cols,
)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
tm.assert_frame_equal(result3, expected.T)
def test_unstack_period_frame(self):
# GH4342
idx1 = pd.PeriodIndex(
["2014-01", "2014-02", "2014-02", "2014-02", "2014-01", "2014-01"],
freq="M",
name="period1",
)
idx2 = pd.PeriodIndex(
["2013-12", "2013-12", "2014-02", "2013-10", "2013-10", "2014-02"],
freq="M",
name="period2",
)
value = {"A": [1, 2, 3, 4, 5, 6], "B": [6, 5, 4, 3, 2, 1]}
idx = MultiIndex.from_arrays([idx1, idx2])
df = DataFrame(value, index=idx)
result1 = df.unstack()
result2 = df.unstack(level=1)
result3 = df.unstack(level=0)
e_1 = pd.PeriodIndex(["2014-01", "2014-02"], freq="M", name="period1")
e_2 = pd.PeriodIndex(
["2013-10", "2013-12", "2014-02", "2013-10", "2013-12", "2014-02"],
freq="M",
name="period2",
)
e_cols = MultiIndex.from_arrays(["A A A B B B".split(), e_2])
expected = DataFrame(
[[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]], index=e_1, columns=e_cols
)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
e_1 = pd.PeriodIndex(
["2014-01", "2014-02", "2014-01", "2014-02"], freq="M", name="period1"
)
e_2 = pd.PeriodIndex(
["2013-10", "2013-12", "2014-02"], freq="M", name="period2"
)
e_cols = MultiIndex.from_arrays(["A A B B".split(), e_1])
expected = DataFrame(
[[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]], index=e_2, columns=e_cols
)
tm.assert_frame_equal(result3, expected)
def test_stack_multiple_bug(self):
# bug when some uniques are not present in the data GH#3170
id_col = ([1] * 3) + ([2] * 3)
name = (["a"] * 3) + (["b"] * 3)
date = pd.to_datetime(["2013-01-03", "2013-01-04", "2013-01-05"] * 2)
var1 = np.random.randint(0, 100, 6)
df = DataFrame({"ID": id_col, "NAME": name, "DATE": date, "VAR1": var1})
multi = df.set_index(["DATE", "ID"])
multi.columns.name = "Params"
unst = multi.unstack("ID")
with pytest.raises(TypeError, match="Could not convert"):
unst.resample("W-THU").mean()
down = unst.resample("W-THU").mean(numeric_only=True)
rs = down.stack("ID")
xp = unst.loc[:, ["VAR1"]].resample("W-THU").mean().stack("ID")
xp.columns.name = "Params"
tm.assert_frame_equal(rs, xp)
def test_stack_dropna(self):
# GH#3997
df = DataFrame({"A": ["a1", "a2"], "B": ["b1", "b2"], "C": [1, 1]})
df = df.set_index(["A", "B"])
stacked = df.unstack().stack(dropna=False)
assert len(stacked) > len(stacked.dropna())
stacked = df.unstack().stack(dropna=True)
tm.assert_frame_equal(stacked, stacked.dropna())
def test_unstack_multiple_hierarchical(self):
df = DataFrame(
index=[
[0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 0, 0, 1, 1],
[0, 1, 0, 1, 0, 1, 0, 1],
],
columns=[[0, 0, 1, 1], [0, 1, 0, 1]],
)
df.index.names = ["a", "b", "c"]
df.columns.names = ["d", "e"]
# it works!
df.unstack(["b", "c"])
def test_unstack_sparse_keyspace(self):
# memory problems with naive impl GH#2278
# Generate Long File & Test Pivot
NUM_ROWS = 1000
df = DataFrame(
{
"A": np.random.randint(100, size=NUM_ROWS),
"B": np.random.randint(300, size=NUM_ROWS),
"C": np.random.randint(-7, 7, size=NUM_ROWS),
"D": np.random.randint(-19, 19, size=NUM_ROWS),
"E": np.random.randint(3000, size=NUM_ROWS),
"F": np.random.randn(NUM_ROWS),
}
)
idf = df.set_index(["A", "B", "C", "D", "E"])
# it works! is sufficient
idf.unstack("E")
def test_unstack_unobserved_keys(self):
# related to GH#2278 refactoring
levels = [[0, 1], [0, 1, 2, 3]]
codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
index = MultiIndex(levels, codes)
df = DataFrame(np.random.randn(4, 2), index=index)
result = df.unstack()
assert len(result.columns) == 4
recons = result.stack()
tm.assert_frame_equal(recons, df)
@pytest.mark.slow
def test_unstack_number_of_levels_larger_than_int32(self, monkeypatch):
# GH#20601
# GH 26314: Change ValueError to PerformanceWarning
class MockUnstacker(reshape_lib._Unstacker):
def __init__(self, *args, **kwargs) -> None:
# __init__ will raise the warning
super().__init__(*args, **kwargs)
raise Exception("Don't compute final result.")
with monkeypatch.context() as m:
m.setattr(reshape_lib, "_Unstacker", MockUnstacker)
df = DataFrame(
np.random.randn(2**16, 2),
index=[np.arange(2**16), np.arange(2**16)],
)
msg = "The following operation may generate"
with tm.assert_produces_warning(PerformanceWarning, match=msg):
with pytest.raises(Exception, match="Don't compute final result."):
df.unstack()
@pytest.mark.parametrize(
"levels",
itertools.chain.from_iterable(
itertools.product(itertools.permutations([0, 1, 2], width), repeat=2)
for width in [2, 3]
),
)
@pytest.mark.parametrize("stack_lev", range(2))
def test_stack_order_with_unsorted_levels(self, levels, stack_lev):
# GH#16323
# deep check for 1-row case
columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
df = DataFrame(columns=columns, data=[range(4)])
df_stacked = df.stack(stack_lev)
assert all(
df.loc[row, col]
== df_stacked.loc[(row, col[stack_lev]), col[1 - stack_lev]]
for row in df.index
for col in df.columns
)
def test_stack_order_with_unsorted_levels_multi_row(self):
# GH#16323
# check multi-row case
mi = MultiIndex(
levels=[["A", "C", "B"], ["B", "A", "C"]],
codes=[np.repeat(range(3), 3), np.tile(range(3), 3)],
)
df = DataFrame(
columns=mi, index=range(5), data=np.arange(5 * len(mi)).reshape(5, -1)
)
assert all(
df.loc[row, col] == df.stack(0).loc[(row, col[0]), col[1]]
for row in df.index
for col in df.columns
)
def test_stack_unstack_unordered_multiindex(self):
# GH# 18265
values = np.arange(5)
data = np.vstack(
[
[f"b{x}" for x in values], # b0, b1, ..
[f"a{x}" for x in values], # a0, a1, ..
]
)
df = DataFrame(data.T, columns=["b", "a"])
df.columns.name = "first"
second_level_dict = {"x": df}
multi_level_df = pd.concat(second_level_dict, axis=1)
multi_level_df.columns.names = ["second", "first"]
df = multi_level_df.reindex(sorted(multi_level_df.columns), axis=1)
result = df.stack(["first", "second"]).unstack(["first", "second"])
expected = DataFrame(
[["a0", "b0"], ["a1", "b1"], ["a2", "b2"], ["a3", "b3"], ["a4", "b4"]],
index=[0, 1, 2, 3, 4],
columns=MultiIndex.from_tuples(
[("a", "x"), ("b", "x")], names=["first", "second"]
),
)
tm.assert_frame_equal(result, expected)
def test_unstack_preserve_types(
self, multiindex_year_month_day_dataframe_random_data
):
# GH#403
ymd = multiindex_year_month_day_dataframe_random_data
ymd["E"] = "foo"
ymd["F"] = 2
unstacked = ymd.unstack("month")
assert unstacked["A", 1].dtype == np.float64
assert unstacked["E", 1].dtype == np.object_
assert unstacked["F", 1].dtype == np.float64
def test_unstack_group_index_overflow(self):
codes = np.tile(np.arange(500), 2)
level = np.arange(500)
index = MultiIndex(
levels=[level] * 8 + [[0, 1]],
codes=[codes] * 8 + [np.arange(2).repeat(500)],
)
s = Series(np.arange(1000), index=index)
result = s.unstack()
assert result.shape == (500, 2)
# test roundtrip
stacked = result.stack()
tm.assert_series_equal(s, stacked.reindex(s.index))
# put it at beginning
index = MultiIndex(
levels=[[0, 1]] + [level] * 8,
codes=[np.arange(2).repeat(500)] + [codes] * 8,
)
s = Series(np.arange(1000), index=index)
result = s.unstack(0)
assert result.shape == (500, 2)
# put it in middle
index = MultiIndex(
levels=[level] * 4 + [[0, 1]] + [level] * 4,
codes=([codes] * 4 + [np.arange(2).repeat(500)] + [codes] * 4),
)
s = Series(np.arange(1000), index=index)
result = s.unstack(4)
assert result.shape == (500, 2)
def test_unstack_with_missing_int_cast_to_float(self, using_array_manager):
# https://github.com/pandas-dev/pandas/issues/37115
df = DataFrame(
{
"a": ["A", "A", "B"],
"b": ["ca", "cb", "cb"],
"v": [10] * 3,
}
).set_index(["a", "b"])
# add another int column to get 2 blocks
df["is_"] = 1
if not using_array_manager:
assert len(df._mgr.blocks) == 2
result = df.unstack("b")
result[("is_", "ca")] = result[("is_", "ca")].fillna(0)
expected = DataFrame(
[[10.0, 10.0, 1.0, 1.0], [np.nan, 10.0, 0.0, 1.0]],
index=Index(["A", "B"], dtype="object", name="a"),
columns=MultiIndex.from_tuples(
[("v", "ca"), ("v", "cb"), ("is_", "ca"), ("is_", "cb")],
names=[None, "b"],
),
)
if using_array_manager:
# INFO(ArrayManager) with ArrayManager preserve dtype where possible
expected[("v", "cb")] = expected[("v", "cb")].astype("int64")
expected[("is_", "cb")] = expected[("is_", "cb")].astype("int64")
tm.assert_frame_equal(result, expected)
def test_unstack_with_level_has_nan(self):
# GH 37510
df1 = DataFrame(
{
"L1": [1, 2, 3, 4],
"L2": [3, 4, 1, 2],
"L3": [1, 1, 1, 1],
"x": [1, 2, 3, 4],
}
)
df1 = df1.set_index(["L1", "L2", "L3"])
new_levels = ["n1", "n2", "n3", None]
df1.index = df1.index.set_levels(levels=new_levels, level="L1")
df1.index = df1.index.set_levels(levels=new_levels, level="L2")
result = df1.unstack("L3")[("x", 1)].sort_index().index
expected = MultiIndex(
levels=[["n1", "n2", "n3", None], ["n1", "n2", "n3", None]],
codes=[[0, 1, 2, 3], [2, 3, 0, 1]],
names=["L1", "L2"],
)
tm.assert_index_equal(result, expected)
def test_stack_nan_in_multiindex_columns(self):
# GH#39481
df = DataFrame(
np.zeros([1, 5]),
columns=MultiIndex.from_tuples(
[
(0, None, None),
(0, 2, 0),
(0, 2, 1),
(0, 3, 0),
(0, 3, 1),
],
),
)
result = df.stack(2)
expected = DataFrame(
[[0.0, np.nan, np.nan], [np.nan, 0.0, 0.0], [np.nan, 0.0, 0.0]],
index=Index([(0, None), (0, 0), (0, 1)]),
columns=Index([(0, None), (0, 2), (0, 3)]),
)
tm.assert_frame_equal(result, expected)
def test_multi_level_stack_categorical(self):
# GH 15239
midx = MultiIndex.from_arrays(
[
["A"] * 2 + ["B"] * 2,
pd.Categorical(list("abab")),
pd.Categorical(list("ccdd")),
]
)
df = DataFrame(np.arange(8).reshape(2, 4), columns=midx)
result = df.stack([1, 2])
expected = DataFrame(
[
[0, np.nan],
[np.nan, 2],
[1, np.nan],
[np.nan, 3],
[4, np.nan],
[np.nan, 6],
[5, np.nan],
[np.nan, 7],
],
columns=["A", "B"],
index=MultiIndex.from_arrays(
[
[0] * 4 + [1] * 4,
pd.Categorical(list("aabbaabb")),
pd.Categorical(list("cdcdcdcd")),
]
),
)
tm.assert_frame_equal(result, expected)
def test_stack_nan_level(self):
# GH 9406
df_nan = DataFrame(
np.arange(4).reshape(2, 2),
columns=MultiIndex.from_tuples(
[("A", np.nan), ("B", "b")], names=["Upper", "Lower"]
),
index=Index([0, 1], name="Num"),
dtype=np.float64,
)
result = df_nan.stack()
expected = DataFrame(
[[0.0, np.nan], [np.nan, 1], [2.0, np.nan], [np.nan, 3.0]],
columns=Index(["A", "B"], name="Upper"),
index=MultiIndex.from_tuples(
[(0, np.nan), (0, "b"), (1, np.nan), (1, "b")], names=["Num", "Lower"]
),
)
tm.assert_frame_equal(result, expected)
def test_unstack_categorical_columns(self):
# GH 14018
idx = MultiIndex.from_product([["A"], [0, 1]])
df = DataFrame({"cat": pd.Categorical(["a", "b"])}, index=idx)
result = df.unstack()
expected = DataFrame(
{
0: pd.Categorical(["a"], categories=["a", "b"]),
1: pd.Categorical(["b"], categories=["a", "b"]),
},
index=["A"],
)
expected.columns = MultiIndex.from_tuples([("cat", 0), ("cat", 1)])
tm.assert_frame_equal(result, expected)
def test_stack_unsorted(self):
# GH 16925
PAE = ["ITA", "FRA"]
VAR = ["A1", "A2"]
TYP = ["CRT", "DBT", "NET"]
MI = MultiIndex.from_product([PAE, VAR, TYP], names=["PAE", "VAR", "TYP"])
V = list(range(len(MI)))
DF = DataFrame(data=V, index=MI, columns=["VALUE"])
DF = DF.unstack(["VAR", "TYP"])
DF.columns = DF.columns.droplevel(0)
DF.loc[:, ("A0", "NET")] = 9999
result = DF.stack(["VAR", "TYP"]).sort_index()
expected = DF.sort_index(axis=1).stack(["VAR", "TYP"]).sort_index()
tm.assert_series_equal(result, expected)
def test_stack_nullable_dtype(self):
# GH#43561
columns = MultiIndex.from_product(
[["54511", "54515"], ["r", "t_mean"]], names=["station", "element"]
)
index = Index([1, 2, 3], name="time")
arr = np.array([[50, 226, 10, 215], [10, 215, 9, 220], [305, 232, 111, 220]])
df = DataFrame(arr, columns=columns, index=index, dtype=pd.Int64Dtype())
result = df.stack("station")
expected = df.astype(np.int64).stack("station").astype(pd.Int64Dtype())
tm.assert_frame_equal(result, expected)
# non-homogeneous case
df[df.columns[0]] = df[df.columns[0]].astype(pd.Float64Dtype())
result = df.stack("station")
# TODO(EA2D): we get object dtype because DataFrame.values can't
# be an EA
expected = df.astype(object).stack("station")
tm.assert_frame_equal(result, expected)
def test_unstack_mixed_level_names(self):
# GH#48763
arrays = [["a", "a"], [1, 2], ["red", "blue"]]
idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))
df = DataFrame({"m": [1, 2]}, index=idx)
result = df.unstack("x")
expected = DataFrame(
[[1], [2]],
columns=MultiIndex.from_tuples([("m", "a")], names=[None, "x"]),
index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
)
tm.assert_frame_equal(result, expected)
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