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255 lines
7.4 KiB
255 lines
7.4 KiB
import numpy as np
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import pytest
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from pandas._libs import lib
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import pandas as pd
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from pandas import (
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Index,
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MultiIndex,
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)
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import pandas._testing as tm
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@pytest.mark.parametrize(
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"input_index, input_columns, input_values, "
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"expected_values, expected_columns, expected_index",
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[
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(
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["lev4"],
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"lev3",
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"values",
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[
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[0.0, np.nan],
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[np.nan, 1.0],
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[2.0, np.nan],
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[np.nan, 3.0],
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[4.0, np.nan],
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[np.nan, 5.0],
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[6.0, np.nan],
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[np.nan, 7.0],
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],
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Index([1, 2], name="lev3"),
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Index([1, 2, 3, 4, 5, 6, 7, 8], name="lev4"),
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),
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(
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["lev4"],
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"lev3",
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lib.no_default,
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[
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[1.0, np.nan, 1.0, np.nan, 0.0, np.nan],
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[np.nan, 1.0, np.nan, 1.0, np.nan, 1.0],
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[1.0, np.nan, 2.0, np.nan, 2.0, np.nan],
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[np.nan, 1.0, np.nan, 2.0, np.nan, 3.0],
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[2.0, np.nan, 1.0, np.nan, 4.0, np.nan],
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[np.nan, 2.0, np.nan, 1.0, np.nan, 5.0],
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[2.0, np.nan, 2.0, np.nan, 6.0, np.nan],
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[np.nan, 2.0, np.nan, 2.0, np.nan, 7.0],
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],
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MultiIndex.from_tuples(
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[
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("lev1", 1),
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("lev1", 2),
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("lev2", 1),
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("lev2", 2),
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("values", 1),
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("values", 2),
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],
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names=[None, "lev3"],
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),
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Index([1, 2, 3, 4, 5, 6, 7, 8], name="lev4"),
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),
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(
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["lev1", "lev2"],
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"lev3",
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"values",
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[[0, 1], [2, 3], [4, 5], [6, 7]],
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Index([1, 2], name="lev3"),
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MultiIndex.from_tuples(
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[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
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),
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),
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(
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["lev1", "lev2"],
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"lev3",
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lib.no_default,
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[[1, 2, 0, 1], [3, 4, 2, 3], [5, 6, 4, 5], [7, 8, 6, 7]],
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MultiIndex.from_tuples(
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[("lev4", 1), ("lev4", 2), ("values", 1), ("values", 2)],
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names=[None, "lev3"],
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),
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MultiIndex.from_tuples(
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[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
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),
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),
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],
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)
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def test_pivot_list_like_index(
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input_index,
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input_columns,
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input_values,
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expected_values,
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expected_columns,
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expected_index,
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):
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# GH 21425, test when index is given a list
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df = pd.DataFrame(
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{
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"lev1": [1, 1, 1, 1, 2, 2, 2, 2],
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"lev2": [1, 1, 2, 2, 1, 1, 2, 2],
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"lev3": [1, 2, 1, 2, 1, 2, 1, 2],
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"lev4": [1, 2, 3, 4, 5, 6, 7, 8],
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"values": [0, 1, 2, 3, 4, 5, 6, 7],
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}
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)
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result = df.pivot(index=input_index, columns=input_columns, values=input_values)
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expected = pd.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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@pytest.mark.parametrize(
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"input_index, input_columns, input_values, "
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"expected_values, expected_columns, expected_index",
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[
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(
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"lev4",
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["lev3"],
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"values",
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[
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[0.0, np.nan],
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[np.nan, 1.0],
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[2.0, np.nan],
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[np.nan, 3.0],
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[4.0, np.nan],
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[np.nan, 5.0],
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[6.0, np.nan],
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[np.nan, 7.0],
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],
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Index([1, 2], name="lev3"),
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Index([1, 2, 3, 4, 5, 6, 7, 8], name="lev4"),
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),
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(
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["lev1", "lev2"],
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["lev3"],
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"values",
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[[0, 1], [2, 3], [4, 5], [6, 7]],
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Index([1, 2], name="lev3"),
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MultiIndex.from_tuples(
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[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
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),
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),
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(
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["lev1"],
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["lev2", "lev3"],
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"values",
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[[0, 1, 2, 3], [4, 5, 6, 7]],
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MultiIndex.from_tuples(
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[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev2", "lev3"]
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),
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Index([1, 2], name="lev1"),
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),
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(
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["lev1", "lev2"],
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["lev3", "lev4"],
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"values",
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[
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[0.0, 1.0, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
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[np.nan, np.nan, 2.0, 3.0, np.nan, np.nan, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, 4.0, 5.0, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 6.0, 7.0],
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],
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MultiIndex.from_tuples(
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[(1, 1), (2, 2), (1, 3), (2, 4), (1, 5), (2, 6), (1, 7), (2, 8)],
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names=["lev3", "lev4"],
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),
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MultiIndex.from_tuples(
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[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
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),
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),
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],
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)
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def test_pivot_list_like_columns(
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input_index,
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input_columns,
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input_values,
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expected_values,
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expected_columns,
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expected_index,
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):
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# GH 21425, test when columns is given a list
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df = pd.DataFrame(
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{
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"lev1": [1, 1, 1, 1, 2, 2, 2, 2],
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"lev2": [1, 1, 2, 2, 1, 1, 2, 2],
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"lev3": [1, 2, 1, 2, 1, 2, 1, 2],
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"lev4": [1, 2, 3, 4, 5, 6, 7, 8],
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"values": [0, 1, 2, 3, 4, 5, 6, 7],
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}
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)
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result = df.pivot(index=input_index, columns=input_columns, values=input_values)
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expected = pd.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_pivot_multiindexed_rows_and_cols(using_array_manager):
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# GH 36360
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df = pd.DataFrame(
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data=np.arange(12).reshape(4, 3),
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columns=MultiIndex.from_tuples(
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[(0, 0), (0, 1), (0, 2)], names=["col_L0", "col_L1"]
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),
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index=MultiIndex.from_tuples(
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[(0, 0, 0), (0, 0, 1), (1, 1, 1), (1, 0, 0)],
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names=["idx_L0", "idx_L1", "idx_L2"],
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),
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)
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res = df.pivot_table(
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index=["idx_L0"],
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columns=["idx_L1"],
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values=[(0, 1)],
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aggfunc=lambda col: col.values.sum(),
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)
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expected = pd.DataFrame(
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data=[[5, np.nan], [10, 7.0]],
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columns=MultiIndex.from_tuples(
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[(0, 1, 0), (0, 1, 1)], names=["col_L0", "col_L1", "idx_L1"]
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),
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index=Index([0, 1], dtype="int64", name="idx_L0"),
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)
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if not using_array_manager:
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# BlockManager does not preserve the dtypes
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expected = expected.astype("float64")
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tm.assert_frame_equal(res, expected)
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def test_pivot_df_multiindex_index_none():
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# GH 23955
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df = pd.DataFrame(
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[
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["A", "A1", "label1", 1],
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["A", "A2", "label2", 2],
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["B", "A1", "label1", 3],
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["B", "A2", "label2", 4],
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],
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columns=["index_1", "index_2", "label", "value"],
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)
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df = df.set_index(["index_1", "index_2"])
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result = df.pivot(columns="label", values="value")
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expected = pd.DataFrame(
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[[1.0, np.nan], [np.nan, 2.0], [3.0, np.nan], [np.nan, 4.0]],
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index=df.index,
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columns=Index(["label1", "label2"], name="label"),
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)
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tm.assert_frame_equal(result, expected)
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