针对pulse-transit的工具
This commit is contained in:
827
dist/client/pandas/tests/reshape/test_crosstab.py
vendored
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827
dist/client/pandas/tests/reshape/test_crosstab.py
vendored
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_categorical_dtype
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import pandas as pd
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from pandas import (
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CategoricalIndex,
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DataFrame,
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Index,
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MultiIndex,
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Series,
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crosstab,
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)
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import pandas._testing as tm
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class TestCrosstab:
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def setup_method(self, method):
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df = DataFrame(
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{
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"A": [
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"foo",
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"foo",
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"foo",
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"foo",
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"bar",
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"bar",
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"bar",
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"bar",
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"foo",
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"foo",
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"foo",
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],
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"B": [
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"one",
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"one",
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"one",
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"two",
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"one",
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"one",
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"one",
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"two",
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"two",
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"two",
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"one",
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],
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"C": [
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"dull",
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"dull",
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"shiny",
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"dull",
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"dull",
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"shiny",
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"shiny",
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"dull",
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"shiny",
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"shiny",
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"shiny",
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],
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"D": np.random.randn(11),
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"E": np.random.randn(11),
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"F": np.random.randn(11),
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}
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)
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self.df = pd.concat([df, df], ignore_index=True)
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def test_crosstab_single(self):
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df = self.df
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result = crosstab(df["A"], df["C"])
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expected = df.groupby(["A", "C"]).size().unstack()
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tm.assert_frame_equal(result, expected.fillna(0).astype(np.int64))
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def test_crosstab_multiple(self):
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df = self.df
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result = crosstab(df["A"], [df["B"], df["C"]])
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expected = df.groupby(["A", "B", "C"]).size()
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expected = expected.unstack("B").unstack("C").fillna(0).astype(np.int64)
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tm.assert_frame_equal(result, expected)
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result = crosstab([df["B"], df["C"]], df["A"])
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expected = df.groupby(["B", "C", "A"]).size()
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expected = expected.unstack("A").fillna(0).astype(np.int64)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("box", [np.array, list, tuple])
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def test_crosstab_ndarray(self, box):
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# GH 44076
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a = box(np.random.randint(0, 5, size=100))
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b = box(np.random.randint(0, 3, size=100))
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c = box(np.random.randint(0, 10, size=100))
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df = DataFrame({"a": a, "b": b, "c": c})
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result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"))
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expected = crosstab(df["a"], [df["b"], df["c"]])
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tm.assert_frame_equal(result, expected)
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result = crosstab([b, c], a, colnames=["a"], rownames=("b", "c"))
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expected = crosstab([df["b"], df["c"]], df["a"])
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tm.assert_frame_equal(result, expected)
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# assign arbitrary names
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result = crosstab(a, c)
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expected = crosstab(df["a"], df["c"])
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expected.index.names = ["row_0"]
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expected.columns.names = ["col_0"]
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tm.assert_frame_equal(result, expected)
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def test_crosstab_non_aligned(self):
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# GH 17005
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a = Series([0, 1, 1], index=["a", "b", "c"])
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b = Series([3, 4, 3, 4, 3], index=["a", "b", "c", "d", "f"])
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c = np.array([3, 4, 3])
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expected = DataFrame(
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[[1, 0], [1, 1]],
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index=Index([0, 1], name="row_0"),
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columns=Index([3, 4], name="col_0"),
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)
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result = crosstab(a, b)
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tm.assert_frame_equal(result, expected)
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result = crosstab(a, c)
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tm.assert_frame_equal(result, expected)
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def test_crosstab_margins(self):
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a = np.random.randint(0, 7, size=100)
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b = np.random.randint(0, 3, size=100)
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c = np.random.randint(0, 5, size=100)
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df = DataFrame({"a": a, "b": b, "c": c})
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result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"), margins=True)
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assert result.index.names == ("a",)
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assert result.columns.names == ["b", "c"]
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all_cols = result["All", ""]
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exp_cols = df.groupby(["a"]).size().astype("i8")
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# to keep index.name
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exp_margin = Series([len(df)], index=Index(["All"], name="a"))
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exp_cols = pd.concat([exp_cols, exp_margin])
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exp_cols.name = ("All", "")
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tm.assert_series_equal(all_cols, exp_cols)
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all_rows = result.loc["All"]
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exp_rows = df.groupby(["b", "c"]).size().astype("i8")
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exp_rows = pd.concat([exp_rows, Series([len(df)], index=[("All", "")])])
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exp_rows.name = "All"
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exp_rows = exp_rows.reindex(all_rows.index)
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exp_rows = exp_rows.fillna(0).astype(np.int64)
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tm.assert_series_equal(all_rows, exp_rows)
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def test_crosstab_margins_set_margin_name(self):
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# GH 15972
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a = np.random.randint(0, 7, size=100)
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b = np.random.randint(0, 3, size=100)
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c = np.random.randint(0, 5, size=100)
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df = DataFrame({"a": a, "b": b, "c": c})
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result = crosstab(
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a,
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[b, c],
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rownames=["a"],
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colnames=("b", "c"),
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margins=True,
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margins_name="TOTAL",
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)
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assert result.index.names == ("a",)
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assert result.columns.names == ["b", "c"]
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all_cols = result["TOTAL", ""]
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exp_cols = df.groupby(["a"]).size().astype("i8")
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# to keep index.name
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exp_margin = Series([len(df)], index=Index(["TOTAL"], name="a"))
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exp_cols = pd.concat([exp_cols, exp_margin])
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exp_cols.name = ("TOTAL", "")
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tm.assert_series_equal(all_cols, exp_cols)
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all_rows = result.loc["TOTAL"]
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exp_rows = df.groupby(["b", "c"]).size().astype("i8")
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exp_rows = pd.concat([exp_rows, Series([len(df)], index=[("TOTAL", "")])])
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exp_rows.name = "TOTAL"
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exp_rows = exp_rows.reindex(all_rows.index)
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exp_rows = exp_rows.fillna(0).astype(np.int64)
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tm.assert_series_equal(all_rows, exp_rows)
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msg = "margins_name argument must be a string"
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for margins_name in [666, None, ["a", "b"]]:
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with pytest.raises(ValueError, match=msg):
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crosstab(
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a,
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[b, c],
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rownames=["a"],
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colnames=("b", "c"),
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margins=True,
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margins_name=margins_name,
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)
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def test_crosstab_pass_values(self):
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a = np.random.randint(0, 7, size=100)
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b = np.random.randint(0, 3, size=100)
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c = np.random.randint(0, 5, size=100)
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values = np.random.randn(100)
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table = crosstab(
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[a, b], c, values, aggfunc=np.sum, rownames=["foo", "bar"], colnames=["baz"]
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)
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df = DataFrame({"foo": a, "bar": b, "baz": c, "values": values})
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expected = df.pivot_table(
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"values", index=["foo", "bar"], columns="baz", aggfunc=np.sum
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)
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tm.assert_frame_equal(table, expected)
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def test_crosstab_dropna(self):
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# GH 3820
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a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
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b = np.array(["one", "one", "two", "one", "two", "two", "two"], dtype=object)
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c = np.array(
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["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
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)
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res = crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"], dropna=False)
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m = MultiIndex.from_tuples(
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[("one", "dull"), ("one", "shiny"), ("two", "dull"), ("two", "shiny")],
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names=["b", "c"],
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)
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tm.assert_index_equal(res.columns, m)
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def test_crosstab_no_overlap(self):
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# GS 10291
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s1 = Series([1, 2, 3], index=[1, 2, 3])
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s2 = Series([4, 5, 6], index=[4, 5, 6])
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actual = crosstab(s1, s2)
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expected = DataFrame(
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index=Index([], dtype="int64", name="row_0"),
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columns=Index([], dtype="int64", name="col_0"),
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)
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tm.assert_frame_equal(actual, expected)
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def test_margin_dropna(self):
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# GH 12577
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# pivot_table counts null into margin ('All')
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# when margins=true and dropna=true
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df = DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
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actual = crosstab(df.a, df.b, margins=True, dropna=True)
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expected = DataFrame([[1, 0, 1], [1, 3, 4], [2, 3, 5]])
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expected.index = Index([1.0, 2.0, "All"], name="a")
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expected.columns = Index([3, 4, "All"], name="b")
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tm.assert_frame_equal(actual, expected)
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def test_margin_dropna2(self):
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df = DataFrame(
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{"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
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)
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actual = crosstab(df.a, df.b, margins=True, dropna=True)
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expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
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expected.index = Index([1.0, 2.0, "All"], name="a")
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expected.columns = Index([3.0, 4.0, "All"], name="b")
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tm.assert_frame_equal(actual, expected)
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def test_margin_dropna3(self):
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df = DataFrame(
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{"a": [1, np.nan, np.nan, np.nan, np.nan, 2], "b": [3, 3, 4, 4, 4, 4]}
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)
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actual = crosstab(df.a, df.b, margins=True, dropna=True)
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expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
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expected.index = Index([1.0, 2.0, "All"], name="a")
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expected.columns = Index([3, 4, "All"], name="b")
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tm.assert_frame_equal(actual, expected)
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def test_margin_dropna4(self):
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# GH 12642
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# _add_margins raises KeyError: Level None not found
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# when margins=True and dropna=False
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df = DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
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actual = crosstab(df.a, df.b, margins=True, dropna=False)
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expected = DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]])
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expected.index = Index([1.0, 2.0, "All"], name="a")
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expected.columns = Index([3, 4, "All"], name="b")
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tm.assert_frame_equal(actual, expected)
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def test_margin_dropna5(self):
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df = DataFrame(
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{"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
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)
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actual = crosstab(df.a, df.b, margins=True, dropna=False)
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expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]])
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expected.index = Index([1.0, 2.0, "All"], name="a")
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expected.columns = Index([3.0, 4.0, "All"], name="b")
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tm.assert_frame_equal(actual, expected)
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def test_margin_dropna6(self):
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a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
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b = np.array(["one", "one", "two", "one", "two", np.nan, "two"], dtype=object)
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c = np.array(
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["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
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)
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actual = crosstab(
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a, [b, c], rownames=["a"], colnames=["b", "c"], margins=True, dropna=False
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)
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m = MultiIndex.from_arrays(
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[
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["one", "one", "two", "two", "All"],
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["dull", "shiny", "dull", "shiny", ""],
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],
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names=["b", "c"],
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)
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expected = DataFrame(
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[[1, 0, 1, 0, 2], [2, 0, 1, 1, 5], [3, 0, 2, 1, 7]], columns=m
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)
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expected.index = Index(["bar", "foo", "All"], name="a")
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tm.assert_frame_equal(actual, expected)
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actual = crosstab(
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[a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=False
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)
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m = MultiIndex.from_arrays(
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[["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
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names=["a", "b"],
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)
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expected = DataFrame(
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[[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 2, 7]], index=m
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)
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expected.columns = Index(["dull", "shiny", "All"], name="c")
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tm.assert_frame_equal(actual, expected)
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actual = crosstab(
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[a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=True
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)
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m = MultiIndex.from_arrays(
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[["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
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names=["a", "b"],
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)
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expected = DataFrame(
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[[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 1, 6]], index=m
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)
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expected.columns = Index(["dull", "shiny", "All"], name="c")
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tm.assert_frame_equal(actual, expected)
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def test_crosstab_normalize(self):
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# Issue 12578
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df = DataFrame(
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{"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
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)
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rindex = Index([1, 2], name="a")
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cindex = Index([3, 4], name="b")
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full_normal = DataFrame([[0.2, 0], [0.2, 0.6]], index=rindex, columns=cindex)
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row_normal = DataFrame([[1.0, 0], [0.25, 0.75]], index=rindex, columns=cindex)
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col_normal = DataFrame([[0.5, 0], [0.5, 1.0]], index=rindex, columns=cindex)
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# Check all normalize args
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tm.assert_frame_equal(crosstab(df.a, df.b, normalize="all"), full_normal)
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tm.assert_frame_equal(crosstab(df.a, df.b, normalize=True), full_normal)
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tm.assert_frame_equal(crosstab(df.a, df.b, normalize="index"), row_normal)
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tm.assert_frame_equal(crosstab(df.a, df.b, normalize="columns"), col_normal)
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tm.assert_frame_equal(
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crosstab(df.a, df.b, normalize=1),
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crosstab(df.a, df.b, normalize="columns"),
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)
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tm.assert_frame_equal(
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crosstab(df.a, df.b, normalize=0), crosstab(df.a, df.b, normalize="index")
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)
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row_normal_margins = DataFrame(
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[[1.0, 0], [0.25, 0.75], [0.4, 0.6]],
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index=Index([1, 2, "All"], name="a", dtype="object"),
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columns=Index([3, 4], name="b", dtype="object"),
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)
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col_normal_margins = DataFrame(
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[[0.5, 0, 0.2], [0.5, 1.0, 0.8]],
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index=Index([1, 2], name="a", dtype="object"),
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columns=Index([3, 4, "All"], name="b", dtype="object"),
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)
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all_normal_margins = DataFrame(
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[[0.2, 0, 0.2], [0.2, 0.6, 0.8], [0.4, 0.6, 1]],
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index=Index([1, 2, "All"], name="a", dtype="object"),
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columns=Index([3, 4, "All"], name="b", dtype="object"),
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)
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tm.assert_frame_equal(
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crosstab(df.a, df.b, normalize="index", margins=True), row_normal_margins
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)
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tm.assert_frame_equal(
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crosstab(df.a, df.b, normalize="columns", margins=True), col_normal_margins
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)
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tm.assert_frame_equal(
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crosstab(df.a, df.b, normalize=True, margins=True), all_normal_margins
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)
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def test_crosstab_normalize_arrays(self):
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# GH#12578
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df = DataFrame(
|
||||
{"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
|
||||
)
|
||||
|
||||
# Test arrays
|
||||
crosstab(
|
||||
[np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])], np.array([1, 2, 1, 2])
|
||||
)
|
||||
|
||||
# Test with aggfunc
|
||||
norm_counts = DataFrame(
|
||||
[[0.25, 0, 0.25], [0.25, 0.5, 0.75], [0.5, 0.5, 1]],
|
||||
index=Index([1, 2, "All"], name="a", dtype="object"),
|
||||
columns=Index([3, 4, "All"], name="b"),
|
||||
)
|
||||
test_case = crosstab(
|
||||
df.a, df.b, df.c, aggfunc="count", normalize="all", margins=True
|
||||
)
|
||||
tm.assert_frame_equal(test_case, norm_counts)
|
||||
|
||||
df = DataFrame(
|
||||
{"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [0, 4, np.nan, 3, 3]}
|
||||
)
|
||||
|
||||
norm_sum = DataFrame(
|
||||
[[0, 0, 0.0], [0.4, 0.6, 1], [0.4, 0.6, 1]],
|
||||
index=Index([1, 2, "All"], name="a", dtype="object"),
|
||||
columns=Index([3, 4, "All"], name="b", dtype="object"),
|
||||
)
|
||||
test_case = crosstab(
|
||||
df.a, df.b, df.c, aggfunc=np.sum, normalize="all", margins=True
|
||||
)
|
||||
tm.assert_frame_equal(test_case, norm_sum)
|
||||
|
||||
def test_crosstab_with_empties(self, using_array_manager):
|
||||
# Check handling of empties
|
||||
df = DataFrame(
|
||||
{
|
||||
"a": [1, 2, 2, 2, 2],
|
||||
"b": [3, 3, 4, 4, 4],
|
||||
"c": [np.nan, np.nan, np.nan, np.nan, np.nan],
|
||||
}
|
||||
)
|
||||
|
||||
empty = DataFrame(
|
||||
[[0.0, 0.0], [0.0, 0.0]],
|
||||
index=Index([1, 2], name="a", dtype="int64"),
|
||||
columns=Index([3, 4], name="b"),
|
||||
)
|
||||
|
||||
for i in [True, "index", "columns"]:
|
||||
calculated = crosstab(df.a, df.b, values=df.c, aggfunc="count", normalize=i)
|
||||
tm.assert_frame_equal(empty, calculated)
|
||||
|
||||
nans = DataFrame(
|
||||
[[0.0, np.nan], [0.0, 0.0]],
|
||||
index=Index([1, 2], name="a", dtype="int64"),
|
||||
columns=Index([3, 4], name="b"),
|
||||
)
|
||||
if using_array_manager:
|
||||
# INFO(ArrayManager) column without NaNs can preserve int dtype
|
||||
nans[3] = nans[3].astype("int64")
|
||||
|
||||
calculated = crosstab(df.a, df.b, values=df.c, aggfunc="count", normalize=False)
|
||||
tm.assert_frame_equal(nans, calculated)
|
||||
|
||||
def test_crosstab_errors(self):
|
||||
# Issue 12578
|
||||
|
||||
df = DataFrame(
|
||||
{"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
|
||||
)
|
||||
|
||||
error = "values cannot be used without an aggfunc."
|
||||
with pytest.raises(ValueError, match=error):
|
||||
crosstab(df.a, df.b, values=df.c)
|
||||
|
||||
error = "aggfunc cannot be used without values"
|
||||
with pytest.raises(ValueError, match=error):
|
||||
crosstab(df.a, df.b, aggfunc=np.mean)
|
||||
|
||||
error = "Not a valid normalize argument"
|
||||
with pytest.raises(ValueError, match=error):
|
||||
crosstab(df.a, df.b, normalize="42")
|
||||
|
||||
with pytest.raises(ValueError, match=error):
|
||||
crosstab(df.a, df.b, normalize=42)
|
||||
|
||||
error = "Not a valid margins argument"
|
||||
with pytest.raises(ValueError, match=error):
|
||||
crosstab(df.a, df.b, normalize="all", margins=42)
|
||||
|
||||
def test_crosstab_with_categorial_columns(self):
|
||||
# GH 8860
|
||||
df = DataFrame(
|
||||
{
|
||||
"MAKE": ["Honda", "Acura", "Tesla", "Honda", "Honda", "Acura"],
|
||||
"MODEL": ["Sedan", "Sedan", "Electric", "Pickup", "Sedan", "Sedan"],
|
||||
}
|
||||
)
|
||||
categories = ["Sedan", "Electric", "Pickup"]
|
||||
df["MODEL"] = df["MODEL"].astype("category").cat.set_categories(categories)
|
||||
result = crosstab(df["MAKE"], df["MODEL"])
|
||||
|
||||
expected_index = Index(["Acura", "Honda", "Tesla"], name="MAKE")
|
||||
expected_columns = CategoricalIndex(
|
||||
categories, categories=categories, ordered=False, name="MODEL"
|
||||
)
|
||||
expected_data = [[2, 0, 0], [2, 0, 1], [0, 1, 0]]
|
||||
expected = DataFrame(
|
||||
expected_data, index=expected_index, columns=expected_columns
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_crosstab_with_numpy_size(self):
|
||||
# GH 4003
|
||||
df = DataFrame(
|
||||
{
|
||||
"A": ["one", "one", "two", "three"] * 6,
|
||||
"B": ["A", "B", "C"] * 8,
|
||||
"C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
|
||||
"D": np.random.randn(24),
|
||||
"E": np.random.randn(24),
|
||||
}
|
||||
)
|
||||
result = crosstab(
|
||||
index=[df["A"], df["B"]],
|
||||
columns=[df["C"]],
|
||||
margins=True,
|
||||
aggfunc=np.size,
|
||||
values=df["D"],
|
||||
)
|
||||
expected_index = MultiIndex(
|
||||
levels=[["All", "one", "three", "two"], ["", "A", "B", "C"]],
|
||||
codes=[[1, 1, 1, 2, 2, 2, 3, 3, 3, 0], [1, 2, 3, 1, 2, 3, 1, 2, 3, 0]],
|
||||
names=["A", "B"],
|
||||
)
|
||||
expected_column = Index(["bar", "foo", "All"], dtype="object", name="C")
|
||||
expected_data = np.array(
|
||||
[
|
||||
[2.0, 2.0, 4.0],
|
||||
[2.0, 2.0, 4.0],
|
||||
[2.0, 2.0, 4.0],
|
||||
[2.0, np.nan, 2.0],
|
||||
[np.nan, 2.0, 2.0],
|
||||
[2.0, np.nan, 2.0],
|
||||
[np.nan, 2.0, 2.0],
|
||||
[2.0, np.nan, 2.0],
|
||||
[np.nan, 2.0, 2.0],
|
||||
[12.0, 12.0, 24.0],
|
||||
]
|
||||
)
|
||||
expected = DataFrame(
|
||||
expected_data, index=expected_index, columns=expected_column
|
||||
)
|
||||
# aggfunc is np.size, resulting in integers
|
||||
expected["All"] = expected["All"].astype("int64")
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_crosstab_duplicate_names(self):
|
||||
# GH 13279 / 22529
|
||||
|
||||
s1 = Series(range(3), name="foo")
|
||||
s2_foo = Series(range(1, 4), name="foo")
|
||||
s2_bar = Series(range(1, 4), name="bar")
|
||||
s3 = Series(range(3), name="waldo")
|
||||
|
||||
# check result computed with duplicate labels against
|
||||
# result computed with unique labels, then relabelled
|
||||
mapper = {"bar": "foo"}
|
||||
|
||||
# duplicate row, column labels
|
||||
result = crosstab(s1, s2_foo)
|
||||
expected = crosstab(s1, s2_bar).rename_axis(columns=mapper, axis=1)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# duplicate row, unique column labels
|
||||
result = crosstab([s1, s2_foo], s3)
|
||||
expected = crosstab([s1, s2_bar], s3).rename_axis(index=mapper, axis=0)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# unique row, duplicate column labels
|
||||
result = crosstab(s3, [s1, s2_foo])
|
||||
expected = crosstab(s3, [s1, s2_bar]).rename_axis(columns=mapper, axis=1)
|
||||
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("names", [["a", ("b", "c")], [("a", "b"), "c"]])
|
||||
def test_crosstab_tuple_name(self, names):
|
||||
s1 = Series(range(3), name=names[0])
|
||||
s2 = Series(range(1, 4), name=names[1])
|
||||
|
||||
mi = MultiIndex.from_arrays([range(3), range(1, 4)], names=names)
|
||||
expected = Series(1, index=mi).unstack(1, fill_value=0)
|
||||
|
||||
result = crosstab(s1, s2)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_crosstab_both_tuple_names(self):
|
||||
# GH 18321
|
||||
s1 = Series(range(3), name=("a", "b"))
|
||||
s2 = Series(range(3), name=("c", "d"))
|
||||
|
||||
expected = DataFrame(
|
||||
np.eye(3, dtype="int64"),
|
||||
index=Index(range(3), name=("a", "b")),
|
||||
columns=Index(range(3), name=("c", "d")),
|
||||
)
|
||||
result = crosstab(s1, s2)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_crosstab_unsorted_order(self):
|
||||
df = DataFrame({"b": [3, 1, 2], "a": [5, 4, 6]}, index=["C", "A", "B"])
|
||||
result = crosstab(df.index, [df.b, df.a])
|
||||
e_idx = Index(["A", "B", "C"], name="row_0")
|
||||
e_columns = MultiIndex.from_tuples([(1, 4), (2, 6), (3, 5)], names=["b", "a"])
|
||||
expected = DataFrame(
|
||||
[[1, 0, 0], [0, 1, 0], [0, 0, 1]], index=e_idx, columns=e_columns
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_crosstab_normalize_multiple_columns(self):
|
||||
# GH 15150
|
||||
df = DataFrame(
|
||||
{
|
||||
"A": ["one", "one", "two", "three"] * 6,
|
||||
"B": ["A", "B", "C"] * 8,
|
||||
"C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
|
||||
"D": [0] * 24,
|
||||
"E": [0] * 24,
|
||||
}
|
||||
)
|
||||
result = crosstab(
|
||||
[df.A, df.B],
|
||||
df.C,
|
||||
values=df.D,
|
||||
aggfunc=np.sum,
|
||||
normalize=True,
|
||||
margins=True,
|
||||
)
|
||||
expected = DataFrame(
|
||||
np.array([0] * 29 + [1], dtype=float).reshape(10, 3),
|
||||
columns=Index(["bar", "foo", "All"], dtype="object", name="C"),
|
||||
index=MultiIndex.from_tuples(
|
||||
[
|
||||
("one", "A"),
|
||||
("one", "B"),
|
||||
("one", "C"),
|
||||
("three", "A"),
|
||||
("three", "B"),
|
||||
("three", "C"),
|
||||
("two", "A"),
|
||||
("two", "B"),
|
||||
("two", "C"),
|
||||
("All", ""),
|
||||
],
|
||||
names=["A", "B"],
|
||||
),
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_margin_normalize(self):
|
||||
# GH 27500
|
||||
df = DataFrame(
|
||||
{
|
||||
"A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
|
||||
"B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
|
||||
"C": [
|
||||
"small",
|
||||
"large",
|
||||
"large",
|
||||
"small",
|
||||
"small",
|
||||
"large",
|
||||
"small",
|
||||
"small",
|
||||
"large",
|
||||
],
|
||||
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
|
||||
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
|
||||
}
|
||||
)
|
||||
# normalize on index
|
||||
result = crosstab(
|
||||
[df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=0
|
||||
)
|
||||
expected = DataFrame(
|
||||
[[0.5, 0.5], [0.5, 0.5], [0.666667, 0.333333], [0, 1], [0.444444, 0.555556]]
|
||||
)
|
||||
expected.index = MultiIndex(
|
||||
levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
|
||||
codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
|
||||
names=["A", "B"],
|
||||
)
|
||||
expected.columns = Index(["large", "small"], dtype="object", name="C")
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# normalize on columns
|
||||
result = crosstab(
|
||||
[df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=1
|
||||
)
|
||||
expected = DataFrame(
|
||||
[
|
||||
[0.25, 0.2, 0.222222],
|
||||
[0.25, 0.2, 0.222222],
|
||||
[0.5, 0.2, 0.333333],
|
||||
[0, 0.4, 0.222222],
|
||||
]
|
||||
)
|
||||
expected.columns = Index(
|
||||
["large", "small", "Sub-Total"], dtype="object", name="C"
|
||||
)
|
||||
expected.index = MultiIndex(
|
||||
levels=[["bar", "foo"], ["one", "two"]],
|
||||
codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
|
||||
names=["A", "B"],
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# normalize on both index and column
|
||||
result = crosstab(
|
||||
[df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=True
|
||||
)
|
||||
expected = DataFrame(
|
||||
[
|
||||
[0.111111, 0.111111, 0.222222],
|
||||
[0.111111, 0.111111, 0.222222],
|
||||
[0.222222, 0.111111, 0.333333],
|
||||
[0.000000, 0.222222, 0.222222],
|
||||
[0.444444, 0.555555, 1],
|
||||
]
|
||||
)
|
||||
expected.columns = Index(
|
||||
["large", "small", "Sub-Total"], dtype="object", name="C"
|
||||
)
|
||||
expected.index = MultiIndex(
|
||||
levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
|
||||
codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
|
||||
names=["A", "B"],
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_margin_normalize_multiple_columns(self):
|
||||
# GH 35144
|
||||
# use multiple columns with margins and normalization
|
||||
df = DataFrame(
|
||||
{
|
||||
"A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
|
||||
"B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
|
||||
"C": [
|
||||
"small",
|
||||
"large",
|
||||
"large",
|
||||
"small",
|
||||
"small",
|
||||
"large",
|
||||
"small",
|
||||
"small",
|
||||
"large",
|
||||
],
|
||||
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
|
||||
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
|
||||
}
|
||||
)
|
||||
result = crosstab(
|
||||
index=df.C,
|
||||
columns=[df.A, df.B],
|
||||
margins=True,
|
||||
margins_name="margin",
|
||||
normalize=True,
|
||||
)
|
||||
expected = DataFrame(
|
||||
[
|
||||
[0.111111, 0.111111, 0.222222, 0.000000, 0.444444],
|
||||
[0.111111, 0.111111, 0.111111, 0.222222, 0.555556],
|
||||
[0.222222, 0.222222, 0.333333, 0.222222, 1.0],
|
||||
],
|
||||
index=["large", "small", "margin"],
|
||||
)
|
||||
expected.columns = MultiIndex(
|
||||
levels=[["bar", "foo", "margin"], ["", "one", "two"]],
|
||||
codes=[[0, 0, 1, 1, 2], [1, 2, 1, 2, 0]],
|
||||
names=["A", "B"],
|
||||
)
|
||||
expected.index.name = "C"
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("a_dtype", ["category", "int64"])
|
||||
@pytest.mark.parametrize("b_dtype", ["category", "int64"])
|
||||
def test_categoricals(a_dtype, b_dtype):
|
||||
# https://github.com/pandas-dev/pandas/issues/37465
|
||||
g = np.random.RandomState(25982704)
|
||||
a = Series(g.randint(0, 3, size=100)).astype(a_dtype)
|
||||
b = Series(g.randint(0, 2, size=100)).astype(b_dtype)
|
||||
result = crosstab(a, b, margins=True, dropna=False)
|
||||
columns = Index([0, 1, "All"], dtype="object", name="col_0")
|
||||
index = Index([0, 1, 2, "All"], dtype="object", name="row_0")
|
||||
values = [[18, 16, 34], [18, 16, 34], [16, 16, 32], [52, 48, 100]]
|
||||
expected = DataFrame(values, index, columns)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# Verify when categorical does not have all values present
|
||||
a.loc[a == 1] = 2
|
||||
a_is_cat = is_categorical_dtype(a.dtype)
|
||||
assert not a_is_cat or a.value_counts().loc[1] == 0
|
||||
result = crosstab(a, b, margins=True, dropna=False)
|
||||
values = [[18, 16, 34], [0, 0, 0], [34, 32, 66], [52, 48, 100]]
|
||||
expected = DataFrame(values, index, columns)
|
||||
if not a_is_cat:
|
||||
expected = expected.loc[[0, 2, "All"]]
|
||||
expected["All"] = expected["All"].astype("int64")
|
||||
repr(result)
|
||||
repr(expected)
|
||||
repr(expected.loc[[0, 2, "All"]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
Reference in New Issue
Block a user