2186 lines
75 KiB
Python
2186 lines
75 KiB
Python
from datetime import datetime
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from io import StringIO
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import itertools
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import numpy as np
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import pytest
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from pandas.errors import PerformanceWarning
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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Period,
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Series,
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Timedelta,
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date_range,
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)
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import pandas._testing as tm
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from pandas.core.reshape import reshape as reshape_lib
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class TestDataFrameReshape:
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def test_stack_unstack(self, float_frame, using_array_manager):
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warn = DeprecationWarning if using_array_manager else None
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msg = "will attempt to set the values inplace"
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df = float_frame.copy()
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with tm.assert_produces_warning(warn, match=msg):
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df[:] = np.arange(np.prod(df.shape)).reshape(df.shape)
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stacked = df.stack()
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stacked_df = DataFrame({"foo": stacked, "bar": stacked})
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unstacked = stacked.unstack()
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unstacked_df = stacked_df.unstack()
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tm.assert_frame_equal(unstacked, df)
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tm.assert_frame_equal(unstacked_df["bar"], df)
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unstacked_cols = stacked.unstack(0)
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unstacked_cols_df = stacked_df.unstack(0)
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tm.assert_frame_equal(unstacked_cols.T, df)
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tm.assert_frame_equal(unstacked_cols_df["bar"].T, df)
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def test_stack_mixed_level(self):
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# GH 18310
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levels = [range(3), [3, "a", "b"], [1, 2]]
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# flat columns:
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df = DataFrame(1, index=levels[0], columns=levels[1])
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result = df.stack()
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expected = Series(1, index=MultiIndex.from_product(levels[:2]))
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tm.assert_series_equal(result, expected)
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# MultiIndex columns:
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df = DataFrame(1, index=levels[0], columns=MultiIndex.from_product(levels[1:]))
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result = df.stack(1)
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expected = DataFrame(
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1, index=MultiIndex.from_product([levels[0], levels[2]]), columns=levels[1]
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)
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tm.assert_frame_equal(result, expected)
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# as above, but used labels in level are actually of homogeneous type
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result = df[["a", "b"]].stack(1)
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expected = expected[["a", "b"]]
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tm.assert_frame_equal(result, expected)
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def test_unstack_not_consolidated(self, using_array_manager):
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# Gh#34708
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df = DataFrame({"x": [1, 2, np.NaN], "y": [3.0, 4, np.NaN]})
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df2 = df[["x"]]
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df2["y"] = df["y"]
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if not using_array_manager:
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assert len(df2._mgr.blocks) == 2
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res = df2.unstack()
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expected = df.unstack()
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tm.assert_series_equal(res, expected)
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def test_unstack_fill(self):
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# GH #9746: fill_value keyword argument for Series
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# and DataFrame unstack
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# From a series
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data = Series([1, 2, 4, 5], dtype=np.int16)
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data.index = MultiIndex.from_tuples(
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[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
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)
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result = data.unstack(fill_value=-1)
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expected = DataFrame(
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{"a": [1, -1, 5], "b": [2, 4, -1]}, index=["x", "y", "z"], dtype=np.int16
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)
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tm.assert_frame_equal(result, expected)
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# From a series with incorrect data type for fill_value
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result = data.unstack(fill_value=0.5)
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expected = DataFrame(
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{"a": [1, 0.5, 5], "b": [2, 4, 0.5]}, index=["x", "y", "z"], dtype=float
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)
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tm.assert_frame_equal(result, expected)
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# GH #13971: fill_value when unstacking multiple levels:
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df = DataFrame(
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{"x": ["a", "a", "b"], "y": ["j", "k", "j"], "z": [0, 1, 2], "w": [0, 1, 2]}
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).set_index(["x", "y", "z"])
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unstacked = df.unstack(["x", "y"], fill_value=0)
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key = ("w", "b", "j")
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expected = unstacked[key]
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result = Series([0, 0, 2], index=unstacked.index, name=key)
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tm.assert_series_equal(result, expected)
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stacked = unstacked.stack(["x", "y"])
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stacked.index = stacked.index.reorder_levels(df.index.names)
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# Workaround for GH #17886 (unnecessarily casts to float):
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stacked = stacked.astype(np.int64)
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result = stacked.loc[df.index]
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tm.assert_frame_equal(result, df)
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# From a series
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s = df["w"]
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result = s.unstack(["x", "y"], fill_value=0)
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expected = unstacked["w"]
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tm.assert_frame_equal(result, expected)
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def test_unstack_fill_frame(self):
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# From a dataframe
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rows = [[1, 2], [3, 4], [5, 6], [7, 8]]
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df = DataFrame(rows, columns=list("AB"), dtype=np.int32)
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df.index = MultiIndex.from_tuples(
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[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
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)
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result = df.unstack(fill_value=-1)
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rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]]
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expected = DataFrame(rows, index=list("xyz"), dtype=np.int32)
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expected.columns = MultiIndex.from_tuples(
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[("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
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)
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tm.assert_frame_equal(result, expected)
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# From a mixed type dataframe
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df["A"] = df["A"].astype(np.int16)
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df["B"] = df["B"].astype(np.float64)
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result = df.unstack(fill_value=-1)
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expected["A"] = expected["A"].astype(np.int16)
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expected["B"] = expected["B"].astype(np.float64)
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tm.assert_frame_equal(result, expected)
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# From a dataframe with incorrect data type for fill_value
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result = df.unstack(fill_value=0.5)
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rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]]
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expected = DataFrame(rows, index=list("xyz"), dtype=float)
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expected.columns = MultiIndex.from_tuples(
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[("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
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)
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tm.assert_frame_equal(result, expected)
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def test_unstack_fill_frame_datetime(self):
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# Test unstacking with date times
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dv = date_range("2012-01-01", periods=4).values
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data = Series(dv)
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data.index = MultiIndex.from_tuples(
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[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
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)
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result = data.unstack()
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expected = DataFrame(
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{"a": [dv[0], pd.NaT, dv[3]], "b": [dv[1], dv[2], pd.NaT]},
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index=["x", "y", "z"],
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)
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tm.assert_frame_equal(result, expected)
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result = data.unstack(fill_value=dv[0])
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expected = DataFrame(
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{"a": [dv[0], dv[0], dv[3]], "b": [dv[1], dv[2], dv[0]]},
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index=["x", "y", "z"],
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)
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tm.assert_frame_equal(result, expected)
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def test_unstack_fill_frame_timedelta(self):
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# Test unstacking with time deltas
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td = [Timedelta(days=i) for i in range(4)]
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data = Series(td)
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data.index = MultiIndex.from_tuples(
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[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
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)
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result = data.unstack()
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expected = DataFrame(
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{"a": [td[0], pd.NaT, td[3]], "b": [td[1], td[2], pd.NaT]},
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index=["x", "y", "z"],
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)
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tm.assert_frame_equal(result, expected)
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result = data.unstack(fill_value=td[1])
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expected = DataFrame(
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{"a": [td[0], td[1], td[3]], "b": [td[1], td[2], td[1]]},
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index=["x", "y", "z"],
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)
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tm.assert_frame_equal(result, expected)
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def test_unstack_fill_frame_period(self):
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# Test unstacking with period
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periods = [
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Period("2012-01"),
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Period("2012-02"),
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Period("2012-03"),
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Period("2012-04"),
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]
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data = Series(periods)
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data.index = MultiIndex.from_tuples(
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[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
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)
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result = data.unstack()
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expected = DataFrame(
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{"a": [periods[0], None, periods[3]], "b": [periods[1], periods[2], None]},
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index=["x", "y", "z"],
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)
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tm.assert_frame_equal(result, expected)
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result = data.unstack(fill_value=periods[1])
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expected = DataFrame(
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{
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"a": [periods[0], periods[1], periods[3]],
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"b": [periods[1], periods[2], periods[1]],
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},
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index=["x", "y", "z"],
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)
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tm.assert_frame_equal(result, expected)
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def test_unstack_fill_frame_categorical(self):
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# Test unstacking with categorical
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data = Series(["a", "b", "c", "a"], dtype="category")
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data.index = MultiIndex.from_tuples(
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[("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
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)
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# By default missing values will be NaN
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result = data.unstack()
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expected = DataFrame(
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{
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"a": pd.Categorical(list("axa"), categories=list("abc")),
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"b": pd.Categorical(list("bcx"), categories=list("abc")),
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},
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index=list("xyz"),
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)
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tm.assert_frame_equal(result, expected)
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# Fill with non-category results in a ValueError
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msg = r"Cannot setitem on a Categorical with a new category \(d\)"
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with pytest.raises(TypeError, match=msg):
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data.unstack(fill_value="d")
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# Fill with category value replaces missing values as expected
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result = data.unstack(fill_value="c")
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expected = DataFrame(
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{
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"a": pd.Categorical(list("aca"), categories=list("abc")),
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"b": pd.Categorical(list("bcc"), categories=list("abc")),
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},
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index=list("xyz"),
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)
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tm.assert_frame_equal(result, expected)
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def test_unstack_tuplename_in_multiindex(self):
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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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df = DataFrame({"d": [1] * 9, "e": [2] * 9}, index=idx)
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result = df.unstack(("A", "a"))
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expected = DataFrame(
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[[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]],
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columns=MultiIndex.from_tuples(
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[
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("d", "a"),
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("d", "b"),
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("d", "c"),
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("e", "a"),
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("e", "b"),
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("e", "c"),
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],
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names=[None, ("A", "a")],
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),
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index=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, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2]],
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MultiIndex.from_tuples(
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[(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]
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),
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MultiIndex.from_tuples(
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[("d", "a"), ("d", "b"), ("e", "a"), ("e", "b")],
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names=[None, ("A", "a")],
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),
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),
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(
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(("A", "a"), "B"),
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[[1, 1, 1, 1, 2, 2, 2, 2], [1, 1, 1, 1, 2, 2, 2, 2]],
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Index([3, 4], name="C"),
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MultiIndex.from_tuples(
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[
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("d", "a", 1),
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("d", "a", 2),
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("d", "b", 1),
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("d", "b", 2),
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("e", "a", 1),
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("e", "a", 2),
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("e", "b", 1),
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("e", "b", 2),
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],
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names=[None, ("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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self, 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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df = DataFrame({"d": [1] * 8, "e": [2] * 8}, index=idx)
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result = df.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_preserve_dtypes(self):
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# Checks fix for #11847
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df = DataFrame(
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{
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"state": ["IL", "MI", "NC"],
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"index": ["a", "b", "c"],
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"some_categories": Series(["a", "b", "c"]).astype("category"),
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"A": np.random.rand(3),
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"B": 1,
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"C": "foo",
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"D": pd.Timestamp("20010102"),
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"E": Series([1.0, 50.0, 100.0]).astype("float32"),
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"F": Series([3.0, 4.0, 5.0]).astype("float64"),
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"G": False,
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"H": Series([1, 200, 923442]).astype("int8"),
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}
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)
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def unstack_and_compare(df, column_name):
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unstacked1 = df.unstack([column_name])
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unstacked2 = df.unstack(column_name)
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tm.assert_frame_equal(unstacked1, unstacked2)
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df1 = df.set_index(["state", "index"])
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unstack_and_compare(df1, "index")
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df1 = df.set_index(["state", "some_categories"])
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unstack_and_compare(df1, "some_categories")
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df1 = df.set_index(["F", "C"])
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unstack_and_compare(df1, "F")
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df1 = df.set_index(["G", "B", "state"])
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unstack_and_compare(df1, "B")
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df1 = df.set_index(["E", "A"])
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unstack_and_compare(df1, "E")
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df1 = df.set_index(["state", "index"])
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s = df1["A"]
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unstack_and_compare(s, "index")
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def test_stack_ints(self):
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columns = MultiIndex.from_tuples(list(itertools.product(range(3), repeat=3)))
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df = DataFrame(np.random.randn(30, 27), columns=columns)
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tm.assert_frame_equal(df.stack(level=[1, 2]), df.stack(level=1).stack(level=1))
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tm.assert_frame_equal(
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df.stack(level=[-2, -1]), df.stack(level=1).stack(level=1)
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)
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df_named = df.copy()
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return_value = df_named.columns.set_names(range(3), inplace=True)
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assert return_value is None
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tm.assert_frame_equal(
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df_named.stack(level=[1, 2]), df_named.stack(level=1).stack(level=1)
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)
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def test_stack_mixed_levels(self):
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columns = MultiIndex.from_tuples(
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[
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("A", "cat", "long"),
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("B", "cat", "long"),
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("A", "dog", "short"),
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("B", "dog", "short"),
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],
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names=["exp", "animal", "hair_length"],
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)
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df = DataFrame(np.random.randn(4, 4), columns=columns)
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animal_hair_stacked = df.stack(level=["animal", "hair_length"])
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exp_hair_stacked = df.stack(level=["exp", "hair_length"])
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# GH #8584: Need to check that stacking works when a number
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# is passed that is both a level name and in the range of
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# the level numbers
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df2 = df.copy()
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df2.columns.names = ["exp", "animal", 1]
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tm.assert_frame_equal(
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df2.stack(level=["animal", 1]), animal_hair_stacked, check_names=False
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)
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tm.assert_frame_equal(
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df2.stack(level=["exp", 1]), exp_hair_stacked, check_names=False
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)
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# When mixed types are passed and the ints are not level
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# names, raise
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msg = (
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"level should contain all level names or all level numbers, not "
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"a mixture of the two"
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)
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with pytest.raises(ValueError, match=msg):
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df2.stack(level=["animal", 0])
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# GH #8584: Having 0 in the level names could raise a
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# strange error about lexsort depth
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df3 = df.copy()
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df3.columns.names = ["exp", "animal", 0]
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tm.assert_frame_equal(
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df3.stack(level=["animal", 0]), animal_hair_stacked, check_names=False
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)
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def test_stack_int_level_names(self):
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columns = MultiIndex.from_tuples(
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[
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("A", "cat", "long"),
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("B", "cat", "long"),
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("A", "dog", "short"),
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("B", "dog", "short"),
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],
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names=["exp", "animal", "hair_length"],
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)
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df = DataFrame(np.random.randn(4, 4), columns=columns)
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exp_animal_stacked = df.stack(level=["exp", "animal"])
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animal_hair_stacked = df.stack(level=["animal", "hair_length"])
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exp_hair_stacked = df.stack(level=["exp", "hair_length"])
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|
|
|
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)})
|
|
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)})
|
|
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)})
|
|
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
|
|
expected = Series(index=MultiIndex([[], []], [[], []]), dtype=np.float64)
|
|
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(np.random.randn(15, 4), index=mi)
|
|
df[1] = df[1].astype(np.int64)
|
|
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")
|
|
msg = "The default value of numeric_only"
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
down = unst.resample("W-THU").mean()
|
|
|
|
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)
|