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test_pivot_multilevel.py
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import numpy as np
import pytest
from pandas._libs import lib
import pandas as pd
from pandas import (
Index,
MultiIndex,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"input_index, input_columns, input_values, "
"expected_values, expected_columns, expected_index",
[
(
["lev4"],
"lev3",
"values",
[
[0.0, np.nan],
[np.nan, 1.0],
[2.0, np.nan],
[np.nan, 3.0],
[4.0, np.nan],
[np.nan, 5.0],
[6.0, np.nan],
[np.nan, 7.0],
],
Index([1, 2], name="lev3"),
Index([1, 2, 3, 4, 5, 6, 7, 8], name="lev4"),
),
(
["lev4"],
"lev3",
lib.no_default,
[
[1.0, np.nan, 1.0, np.nan, 0.0, np.nan],
[np.nan, 1.0, np.nan, 1.0, np.nan, 1.0],
[1.0, np.nan, 2.0, np.nan, 2.0, np.nan],
[np.nan, 1.0, np.nan, 2.0, np.nan, 3.0],
[2.0, np.nan, 1.0, np.nan, 4.0, np.nan],
[np.nan, 2.0, np.nan, 1.0, np.nan, 5.0],
[2.0, np.nan, 2.0, np.nan, 6.0, np.nan],
[np.nan, 2.0, np.nan, 2.0, np.nan, 7.0],
],
MultiIndex.from_tuples(
[
("lev1", 1),
("lev1", 2),
("lev2", 1),
("lev2", 2),
("values", 1),
("values", 2),
],
names=[None, "lev3"],
),
Index([1, 2, 3, 4, 5, 6, 7, 8], name="lev4"),
),
(
["lev1", "lev2"],
"lev3",
"values",
[[0, 1], [2, 3], [4, 5], [6, 7]],
Index([1, 2], name="lev3"),
MultiIndex.from_tuples(
[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
),
),
(
["lev1", "lev2"],
"lev3",
lib.no_default,
[[1, 2, 0, 1], [3, 4, 2, 3], [5, 6, 4, 5], [7, 8, 6, 7]],
MultiIndex.from_tuples(
[("lev4", 1), ("lev4", 2), ("values", 1), ("values", 2)],
names=[None, "lev3"],
),
MultiIndex.from_tuples(
[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
),
),
],
)
def test_pivot_list_like_index(
input_index,
input_columns,
input_values,
expected_values,
expected_columns,
expected_index,
):
# GH 21425, test when index is given a list
df = pd.DataFrame(
{
"lev1": [1, 1, 1, 1, 2, 2, 2, 2],
"lev2": [1, 1, 2, 2, 1, 1, 2, 2],
"lev3": [1, 2, 1, 2, 1, 2, 1, 2],
"lev4": [1, 2, 3, 4, 5, 6, 7, 8],
"values": [0, 1, 2, 3, 4, 5, 6, 7],
}
)
result = df.pivot(index=input_index, columns=input_columns, values=input_values)
expected = pd.DataFrame(
expected_values, columns=expected_columns, index=expected_index
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"input_index, input_columns, input_values, "
"expected_values, expected_columns, expected_index",
[
(
"lev4",
["lev3"],
"values",
[
[0.0, np.nan],
[np.nan, 1.0],
[2.0, np.nan],
[np.nan, 3.0],
[4.0, np.nan],
[np.nan, 5.0],
[6.0, np.nan],
[np.nan, 7.0],
],
Index([1, 2], name="lev3"),
Index([1, 2, 3, 4, 5, 6, 7, 8], name="lev4"),
),
(
["lev1", "lev2"],
["lev3"],
"values",
[[0, 1], [2, 3], [4, 5], [6, 7]],
Index([1, 2], name="lev3"),
MultiIndex.from_tuples(
[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
),
),
(
["lev1"],
["lev2", "lev3"],
"values",
[[0, 1, 2, 3], [4, 5, 6, 7]],
MultiIndex.from_tuples(
[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev2", "lev3"]
),
Index([1, 2], name="lev1"),
),
(
["lev1", "lev2"],
["lev3", "lev4"],
"values",
[
[0.0, 1.0, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, 2.0, 3.0, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, 4.0, 5.0, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 6.0, 7.0],
],
MultiIndex.from_tuples(
[(1, 1), (2, 2), (1, 3), (2, 4), (1, 5), (2, 6), (1, 7), (2, 8)],
names=["lev3", "lev4"],
),
MultiIndex.from_tuples(
[(1, 1), (1, 2), (2, 1), (2, 2)], names=["lev1", "lev2"]
),
),
],
)
def test_pivot_list_like_columns(
input_index,
input_columns,
input_values,
expected_values,
expected_columns,
expected_index,
):
# GH 21425, test when columns is given a list
df = pd.DataFrame(
{
"lev1": [1, 1, 1, 1, 2, 2, 2, 2],
"lev2": [1, 1, 2, 2, 1, 1, 2, 2],
"lev3": [1, 2, 1, 2, 1, 2, 1, 2],
"lev4": [1, 2, 3, 4, 5, 6, 7, 8],
"values": [0, 1, 2, 3, 4, 5, 6, 7],
}
)
result = df.pivot(index=input_index, columns=input_columns, values=input_values)
expected = pd.DataFrame(
expected_values, columns=expected_columns, index=expected_index
)
tm.assert_frame_equal(result, expected)
def test_pivot_multiindexed_rows_and_cols():
# GH 36360
df = pd.DataFrame(
data=np.arange(12).reshape(4, 3),
columns=MultiIndex.from_tuples(
[(0, 0), (0, 1), (0, 2)], names=["col_L0", "col_L1"]
),
index=MultiIndex.from_tuples(
[(0, 0, 0), (0, 0, 1), (1, 1, 1), (1, 0, 0)],
names=["idx_L0", "idx_L1", "idx_L2"],
),
)
res = df.pivot_table(
index=["idx_L0"],
columns=["idx_L1"],
values=[(0, 1)],
aggfunc=lambda col: col.values.sum(),
)
expected = pd.DataFrame(
data=[[5, np.nan], [10, 7.0]],
columns=MultiIndex.from_tuples(
[(0, 1, 0), (0, 1, 1)], names=["col_L0", "col_L1", "idx_L1"]
),
index=Index([0, 1], dtype="int64", name="idx_L0"),
)
expected = expected.astype("float64")
tm.assert_frame_equal(res, expected)
def test_pivot_df_multiindex_index_none():
# GH 23955
df = pd.DataFrame(
[
["A", "A1", "label1", 1],
["A", "A2", "label2", 2],
["B", "A1", "label1", 3],
["B", "A2", "label2", 4],
],
columns=["index_1", "index_2", "label", "value"],
)
df = df.set_index(["index_1", "index_2"])
result = df.pivot(columns="label", values="value")
expected = pd.DataFrame(
[[1.0, np.nan], [np.nan, 2.0], [3.0, np.nan], [np.nan, 4.0]],
index=df.index,
columns=Index(["label1", "label2"], name="label"),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"index, columns",
[("index", ["col", "value"]), (["index", "value"], "col")],
ids=["values-and-columns", "values-and-index"],
)
def test_pivot_table_multiindex_values_as_two_params(index, columns, request):
# GH#61292
data = [
["A", 1, 50, -1],
["B", 1, 100, -2],
["A", 2, 100, -2],
["B", 2, 200, -4],
]
df = pd.DataFrame(data=data, columns=["index", "col", "value", "extra"])
result = df.pivot_table(values="value", index=index, columns=columns)
nan = np.nan
if request.node.callspec.id == "values-and-columns":
e_data = [
[50.0, nan, 100.0, nan],
[nan, 100.0, nan, 200.0],
]
e_index = Index(data=["A", "B"], name="index")
e_cols = MultiIndex.from_arrays(
arrays=[[1, 1, 2, 2], [50, 100, 100, 200]], names=["col", "value"]
)
else:
e_data = [
[50.0, nan],
[nan, 100.0],
[100.0, nan],
[nan, 200.0],
]
e_index = MultiIndex.from_arrays(
arrays=[["A", "A", "B", "B"], [50, 100, 100, 200]], names=["index", "value"]
)
e_cols = Index(data=[1, 2], name="col")
expected = pd.DataFrame(data=e_data, index=e_index, columns=e_cols)
tm.assert_frame_equal(result, expected)