Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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1072 lines
36 KiB
1072 lines
36 KiB
4 years ago
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, lreshape, melt, wide_to_long
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import pandas._testing as tm
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class TestMelt:
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def setup_method(self, method):
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self.df = tm.makeTimeDataFrame()[:10]
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self.df["id1"] = (self.df["A"] > 0).astype(np.int64)
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self.df["id2"] = (self.df["B"] > 0).astype(np.int64)
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self.var_name = "var"
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self.value_name = "val"
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self.df1 = pd.DataFrame(
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[
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[1.067683, -1.110463, 0.20867],
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[-1.321405, 0.368915, -1.055342],
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[-0.807333, 0.08298, -0.873361],
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]
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)
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self.df1.columns = [list("ABC"), list("abc")]
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self.df1.columns.names = ["CAP", "low"]
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def test_top_level_method(self):
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result = melt(self.df)
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assert result.columns.tolist() == ["variable", "value"]
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def test_method_signatures(self):
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tm.assert_frame_equal(self.df.melt(), melt(self.df))
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tm.assert_frame_equal(
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self.df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"]),
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melt(self.df, id_vars=["id1", "id2"], value_vars=["A", "B"]),
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)
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tm.assert_frame_equal(
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self.df.melt(var_name=self.var_name, value_name=self.value_name),
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melt(self.df, var_name=self.var_name, value_name=self.value_name),
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)
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tm.assert_frame_equal(self.df1.melt(col_level=0), melt(self.df1, col_level=0))
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def test_default_col_names(self):
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result = self.df.melt()
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assert result.columns.tolist() == ["variable", "value"]
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result1 = self.df.melt(id_vars=["id1"])
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assert result1.columns.tolist() == ["id1", "variable", "value"]
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result2 = self.df.melt(id_vars=["id1", "id2"])
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assert result2.columns.tolist() == ["id1", "id2", "variable", "value"]
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def test_value_vars(self):
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result3 = self.df.melt(id_vars=["id1", "id2"], value_vars="A")
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assert len(result3) == 10
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result4 = self.df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"])
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expected4 = DataFrame(
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{
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"id1": self.df["id1"].tolist() * 2,
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"id2": self.df["id2"].tolist() * 2,
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"variable": ["A"] * 10 + ["B"] * 10,
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"value": (self.df["A"].tolist() + self.df["B"].tolist()),
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},
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columns=["id1", "id2", "variable", "value"],
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)
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tm.assert_frame_equal(result4, expected4)
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def test_value_vars_types(self):
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# GH 15348
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expected = DataFrame(
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{
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"id1": self.df["id1"].tolist() * 2,
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"id2": self.df["id2"].tolist() * 2,
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"variable": ["A"] * 10 + ["B"] * 10,
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"value": (self.df["A"].tolist() + self.df["B"].tolist()),
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},
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columns=["id1", "id2", "variable", "value"],
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)
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for type_ in (tuple, list, np.array):
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result = self.df.melt(id_vars=["id1", "id2"], value_vars=type_(("A", "B")))
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tm.assert_frame_equal(result, expected)
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def test_vars_work_with_multiindex(self):
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expected = DataFrame(
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{
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("A", "a"): self.df1[("A", "a")],
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"CAP": ["B"] * len(self.df1),
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"low": ["b"] * len(self.df1),
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"value": self.df1[("B", "b")],
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},
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columns=[("A", "a"), "CAP", "low", "value"],
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)
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result = self.df1.melt(id_vars=[("A", "a")], value_vars=[("B", "b")])
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"id_vars, value_vars, col_level, expected",
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[
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(
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["A"],
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["B"],
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0,
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DataFrame(
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{
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"A": {0: 1.067683, 1: -1.321405, 2: -0.807333},
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"CAP": {0: "B", 1: "B", 2: "B"},
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"value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
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}
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),
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),
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(
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["a"],
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["b"],
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1,
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DataFrame(
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{
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"a": {0: 1.067683, 1: -1.321405, 2: -0.807333},
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"low": {0: "b", 1: "b", 2: "b"},
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"value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
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}
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),
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),
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],
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)
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def test_single_vars_work_with_multiindex(
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self, id_vars, value_vars, col_level, expected
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):
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result = self.df1.melt(id_vars, value_vars, col_level=col_level)
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tm.assert_frame_equal(result, expected)
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def test_tuple_vars_fail_with_multiindex(self):
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# melt should fail with an informative error message if
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# the columns have a MultiIndex and a tuple is passed
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# for id_vars or value_vars.
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tuple_a = ("A", "a")
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list_a = [tuple_a]
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tuple_b = ("B", "b")
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list_b = [tuple_b]
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msg = r"(id|value)_vars must be a list of tuples when columns are a MultiIndex"
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for id_vars, value_vars in (
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(tuple_a, list_b),
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(list_a, tuple_b),
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(tuple_a, tuple_b),
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):
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with pytest.raises(ValueError, match=msg):
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self.df1.melt(id_vars=id_vars, value_vars=value_vars)
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def test_custom_var_name(self):
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result5 = self.df.melt(var_name=self.var_name)
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assert result5.columns.tolist() == ["var", "value"]
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result6 = self.df.melt(id_vars=["id1"], var_name=self.var_name)
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assert result6.columns.tolist() == ["id1", "var", "value"]
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result7 = self.df.melt(id_vars=["id1", "id2"], var_name=self.var_name)
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assert result7.columns.tolist() == ["id1", "id2", "var", "value"]
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result8 = self.df.melt(
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id_vars=["id1", "id2"], value_vars="A", var_name=self.var_name
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)
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assert result8.columns.tolist() == ["id1", "id2", "var", "value"]
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result9 = self.df.melt(
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id_vars=["id1", "id2"], value_vars=["A", "B"], var_name=self.var_name
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)
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expected9 = DataFrame(
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{
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"id1": self.df["id1"].tolist() * 2,
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"id2": self.df["id2"].tolist() * 2,
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self.var_name: ["A"] * 10 + ["B"] * 10,
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"value": (self.df["A"].tolist() + self.df["B"].tolist()),
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},
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columns=["id1", "id2", self.var_name, "value"],
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)
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tm.assert_frame_equal(result9, expected9)
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def test_custom_value_name(self):
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result10 = self.df.melt(value_name=self.value_name)
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assert result10.columns.tolist() == ["variable", "val"]
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result11 = self.df.melt(id_vars=["id1"], value_name=self.value_name)
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assert result11.columns.tolist() == ["id1", "variable", "val"]
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result12 = self.df.melt(id_vars=["id1", "id2"], value_name=self.value_name)
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assert result12.columns.tolist() == ["id1", "id2", "variable", "val"]
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result13 = self.df.melt(
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id_vars=["id1", "id2"], value_vars="A", value_name=self.value_name
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)
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assert result13.columns.tolist() == ["id1", "id2", "variable", "val"]
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result14 = self.df.melt(
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id_vars=["id1", "id2"], value_vars=["A", "B"], value_name=self.value_name
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)
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expected14 = DataFrame(
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{
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"id1": self.df["id1"].tolist() * 2,
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"id2": self.df["id2"].tolist() * 2,
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"variable": ["A"] * 10 + ["B"] * 10,
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self.value_name: (self.df["A"].tolist() + self.df["B"].tolist()),
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},
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columns=["id1", "id2", "variable", self.value_name],
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)
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tm.assert_frame_equal(result14, expected14)
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def test_custom_var_and_value_name(self):
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result15 = self.df.melt(var_name=self.var_name, value_name=self.value_name)
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assert result15.columns.tolist() == ["var", "val"]
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result16 = self.df.melt(
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id_vars=["id1"], var_name=self.var_name, value_name=self.value_name
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)
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assert result16.columns.tolist() == ["id1", "var", "val"]
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result17 = self.df.melt(
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id_vars=["id1", "id2"], var_name=self.var_name, value_name=self.value_name
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)
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assert result17.columns.tolist() == ["id1", "id2", "var", "val"]
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result18 = self.df.melt(
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id_vars=["id1", "id2"],
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value_vars="A",
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var_name=self.var_name,
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value_name=self.value_name,
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)
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assert result18.columns.tolist() == ["id1", "id2", "var", "val"]
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result19 = self.df.melt(
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id_vars=["id1", "id2"],
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value_vars=["A", "B"],
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var_name=self.var_name,
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value_name=self.value_name,
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)
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expected19 = DataFrame(
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{
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"id1": self.df["id1"].tolist() * 2,
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"id2": self.df["id2"].tolist() * 2,
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self.var_name: ["A"] * 10 + ["B"] * 10,
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self.value_name: (self.df["A"].tolist() + self.df["B"].tolist()),
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},
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columns=["id1", "id2", self.var_name, self.value_name],
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)
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tm.assert_frame_equal(result19, expected19)
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df20 = self.df.copy()
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df20.columns.name = "foo"
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result20 = df20.melt()
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assert result20.columns.tolist() == ["foo", "value"]
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def test_col_level(self):
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res1 = self.df1.melt(col_level=0)
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res2 = self.df1.melt(col_level="CAP")
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assert res1.columns.tolist() == ["CAP", "value"]
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assert res2.columns.tolist() == ["CAP", "value"]
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def test_multiindex(self):
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res = self.df1.melt()
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assert res.columns.tolist() == ["CAP", "low", "value"]
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@pytest.mark.parametrize(
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"col",
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[
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pd.Series(pd.date_range("2010", periods=5, tz="US/Pacific")),
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pd.Series(["a", "b", "c", "a", "d"], dtype="category"),
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pd.Series([0, 1, 0, 0, 0]),
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],
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)
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def test_pandas_dtypes(self, col):
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# GH 15785
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df = DataFrame(
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{"klass": range(5), "col": col, "attr1": [1, 0, 0, 0, 0], "attr2": col}
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)
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expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col], ignore_index=True)
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result = melt(
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df, id_vars=["klass", "col"], var_name="attribute", value_name="value"
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)
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expected = DataFrame(
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{
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0: list(range(5)) * 2,
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1: pd.concat([col] * 2, ignore_index=True),
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2: ["attr1"] * 5 + ["attr2"] * 5,
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3: expected_value,
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}
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)
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expected.columns = ["klass", "col", "attribute", "value"]
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tm.assert_frame_equal(result, expected)
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def test_preserve_category(self):
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# GH 15853
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data = DataFrame({"A": [1, 2], "B": pd.Categorical(["X", "Y"])})
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result = pd.melt(data, ["B"], ["A"])
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expected = DataFrame(
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{"B": pd.Categorical(["X", "Y"]), "variable": ["A", "A"], "value": [1, 2]}
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)
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tm.assert_frame_equal(result, expected)
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def test_melt_missing_columns_raises(self):
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# GH-23575
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# This test is to ensure that pandas raises an error if melting is
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# attempted with column names absent from the dataframe
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# Generate data
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df = pd.DataFrame(np.random.randn(5, 4), columns=list("abcd"))
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# Try to melt with missing `value_vars` column name
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msg = "The following '{Var}' are not present in the DataFrame: {Col}"
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with pytest.raises(
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KeyError, match=msg.format(Var="value_vars", Col="\\['C'\\]")
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):
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df.melt(["a", "b"], ["C", "d"])
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# Try to melt with missing `id_vars` column name
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with pytest.raises(KeyError, match=msg.format(Var="id_vars", Col="\\['A'\\]")):
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df.melt(["A", "b"], ["c", "d"])
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# Multiple missing
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with pytest.raises(
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KeyError,
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match=msg.format(Var="id_vars", Col="\\['not_here', 'or_there'\\]"),
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):
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df.melt(["a", "b", "not_here", "or_there"], ["c", "d"])
|
||
|
|
|
||
|
|
# Multiindex melt fails if column is missing from multilevel melt
|
||
|
|
multi = df.copy()
|
||
|
|
multi.columns = [list("ABCD"), list("abcd")]
|
||
|
|
with pytest.raises(KeyError, match=msg.format(Var="id_vars", Col="\\['E'\\]")):
|
||
|
|
multi.melt([("E", "a")], [("B", "b")])
|
||
|
|
# Multiindex fails if column is missing from single level melt
|
||
|
|
with pytest.raises(
|
||
|
|
KeyError, match=msg.format(Var="value_vars", Col="\\['F'\\]")
|
||
|
|
):
|
||
|
|
multi.melt(["A"], ["F"], col_level=0)
|
||
|
|
|
||
|
|
def test_melt_mixed_int_str_id_vars(self):
|
||
|
|
# GH 29718
|
||
|
|
df = DataFrame({0: ["foo"], "a": ["bar"], "b": [1], "d": [2]})
|
||
|
|
result = melt(df, id_vars=[0, "a"], value_vars=["b", "d"])
|
||
|
|
expected = DataFrame(
|
||
|
|
{0: ["foo"] * 2, "a": ["bar"] * 2, "variable": list("bd"), "value": [1, 2]}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_melt_mixed_int_str_value_vars(self):
|
||
|
|
# GH 29718
|
||
|
|
df = DataFrame({0: ["foo"], "a": ["bar"]})
|
||
|
|
result = melt(df, value_vars=[0, "a"])
|
||
|
|
expected = DataFrame({"variable": [0, "a"], "value": ["foo", "bar"]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ignore_index(self):
|
||
|
|
# GH 17440
|
||
|
|
df = DataFrame({"foo": [0], "bar": [1]}, index=["first"])
|
||
|
|
result = melt(df, ignore_index=False)
|
||
|
|
expected = DataFrame(
|
||
|
|
{"variable": ["foo", "bar"], "value": [0, 1]}, index=["first", "first"]
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ignore_multiindex(self):
|
||
|
|
# GH 17440
|
||
|
|
index = pd.MultiIndex.from_tuples(
|
||
|
|
[("first", "second"), ("first", "third")], names=["baz", "foobar"]
|
||
|
|
)
|
||
|
|
df = DataFrame({"foo": [0, 1], "bar": [2, 3]}, index=index)
|
||
|
|
result = melt(df, ignore_index=False)
|
||
|
|
|
||
|
|
expected_index = pd.MultiIndex.from_tuples(
|
||
|
|
[("first", "second"), ("first", "third")] * 2, names=["baz", "foobar"]
|
||
|
|
)
|
||
|
|
expected = DataFrame(
|
||
|
|
{"variable": ["foo"] * 2 + ["bar"] * 2, "value": [0, 1, 2, 3]},
|
||
|
|
index=expected_index,
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ignore_index_name_and_type(self):
|
||
|
|
# GH 17440
|
||
|
|
index = pd.Index(["foo", "bar"], dtype="category", name="baz")
|
||
|
|
df = DataFrame({"x": [0, 1], "y": [2, 3]}, index=index)
|
||
|
|
result = melt(df, ignore_index=False)
|
||
|
|
|
||
|
|
expected_index = pd.Index(["foo", "bar"] * 2, dtype="category", name="baz")
|
||
|
|
expected = DataFrame(
|
||
|
|
{"variable": ["x", "x", "y", "y"], "value": [0, 1, 2, 3]},
|
||
|
|
index=expected_index,
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestLreshape:
|
||
|
|
def test_pairs(self):
|
||
|
|
data = {
|
||
|
|
"birthdt": [
|
||
|
|
"08jan2009",
|
||
|
|
"20dec2008",
|
||
|
|
"30dec2008",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
],
|
||
|
|
"birthwt": [1766, 3301, 1454, 3139, 4133],
|
||
|
|
"id": [101, 102, 103, 104, 105],
|
||
|
|
"sex": ["Male", "Female", "Female", "Female", "Female"],
|
||
|
|
"visitdt1": [
|
||
|
|
"11jan2009",
|
||
|
|
"22dec2008",
|
||
|
|
"04jan2009",
|
||
|
|
"29dec2008",
|
||
|
|
"20jan2009",
|
||
|
|
],
|
||
|
|
"visitdt2": ["21jan2009", np.nan, "22jan2009", "31dec2008", "03feb2009"],
|
||
|
|
"visitdt3": ["05feb2009", np.nan, np.nan, "02jan2009", "15feb2009"],
|
||
|
|
"wt1": [1823, 3338, 1549, 3298, 4306],
|
||
|
|
"wt2": [2011.0, np.nan, 1892.0, 3338.0, 4575.0],
|
||
|
|
"wt3": [2293.0, np.nan, np.nan, 3377.0, 4805.0],
|
||
|
|
}
|
||
|
|
|
||
|
|
df = DataFrame(data)
|
||
|
|
|
||
|
|
spec = {
|
||
|
|
"visitdt": [f"visitdt{i:d}" for i in range(1, 4)],
|
||
|
|
"wt": [f"wt{i:d}" for i in range(1, 4)],
|
||
|
|
}
|
||
|
|
result = lreshape(df, spec)
|
||
|
|
|
||
|
|
exp_data = {
|
||
|
|
"birthdt": [
|
||
|
|
"08jan2009",
|
||
|
|
"20dec2008",
|
||
|
|
"30dec2008",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
"08jan2009",
|
||
|
|
"30dec2008",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
"08jan2009",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
],
|
||
|
|
"birthwt": [
|
||
|
|
1766,
|
||
|
|
3301,
|
||
|
|
1454,
|
||
|
|
3139,
|
||
|
|
4133,
|
||
|
|
1766,
|
||
|
|
1454,
|
||
|
|
3139,
|
||
|
|
4133,
|
||
|
|
1766,
|
||
|
|
3139,
|
||
|
|
4133,
|
||
|
|
],
|
||
|
|
"id": [101, 102, 103, 104, 105, 101, 103, 104, 105, 101, 104, 105],
|
||
|
|
"sex": [
|
||
|
|
"Male",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Male",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Male",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
],
|
||
|
|
"visitdt": [
|
||
|
|
"11jan2009",
|
||
|
|
"22dec2008",
|
||
|
|
"04jan2009",
|
||
|
|
"29dec2008",
|
||
|
|
"20jan2009",
|
||
|
|
"21jan2009",
|
||
|
|
"22jan2009",
|
||
|
|
"31dec2008",
|
||
|
|
"03feb2009",
|
||
|
|
"05feb2009",
|
||
|
|
"02jan2009",
|
||
|
|
"15feb2009",
|
||
|
|
],
|
||
|
|
"wt": [
|
||
|
|
1823.0,
|
||
|
|
3338.0,
|
||
|
|
1549.0,
|
||
|
|
3298.0,
|
||
|
|
4306.0,
|
||
|
|
2011.0,
|
||
|
|
1892.0,
|
||
|
|
3338.0,
|
||
|
|
4575.0,
|
||
|
|
2293.0,
|
||
|
|
3377.0,
|
||
|
|
4805.0,
|
||
|
|
],
|
||
|
|
}
|
||
|
|
exp = DataFrame(exp_data, columns=result.columns)
|
||
|
|
tm.assert_frame_equal(result, exp)
|
||
|
|
|
||
|
|
result = lreshape(df, spec, dropna=False)
|
||
|
|
exp_data = {
|
||
|
|
"birthdt": [
|
||
|
|
"08jan2009",
|
||
|
|
"20dec2008",
|
||
|
|
"30dec2008",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
"08jan2009",
|
||
|
|
"20dec2008",
|
||
|
|
"30dec2008",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
"08jan2009",
|
||
|
|
"20dec2008",
|
||
|
|
"30dec2008",
|
||
|
|
"21dec2008",
|
||
|
|
"11jan2009",
|
||
|
|
],
|
||
|
|
"birthwt": [
|
||
|
|
1766,
|
||
|
|
3301,
|
||
|
|
1454,
|
||
|
|
3139,
|
||
|
|
4133,
|
||
|
|
1766,
|
||
|
|
3301,
|
||
|
|
1454,
|
||
|
|
3139,
|
||
|
|
4133,
|
||
|
|
1766,
|
||
|
|
3301,
|
||
|
|
1454,
|
||
|
|
3139,
|
||
|
|
4133,
|
||
|
|
],
|
||
|
|
"id": [
|
||
|
|
101,
|
||
|
|
102,
|
||
|
|
103,
|
||
|
|
104,
|
||
|
|
105,
|
||
|
|
101,
|
||
|
|
102,
|
||
|
|
103,
|
||
|
|
104,
|
||
|
|
105,
|
||
|
|
101,
|
||
|
|
102,
|
||
|
|
103,
|
||
|
|
104,
|
||
|
|
105,
|
||
|
|
],
|
||
|
|
"sex": [
|
||
|
|
"Male",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Male",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Male",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
"Female",
|
||
|
|
],
|
||
|
|
"visitdt": [
|
||
|
|
"11jan2009",
|
||
|
|
"22dec2008",
|
||
|
|
"04jan2009",
|
||
|
|
"29dec2008",
|
||
|
|
"20jan2009",
|
||
|
|
"21jan2009",
|
||
|
|
np.nan,
|
||
|
|
"22jan2009",
|
||
|
|
"31dec2008",
|
||
|
|
"03feb2009",
|
||
|
|
"05feb2009",
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
"02jan2009",
|
||
|
|
"15feb2009",
|
||
|
|
],
|
||
|
|
"wt": [
|
||
|
|
1823.0,
|
||
|
|
3338.0,
|
||
|
|
1549.0,
|
||
|
|
3298.0,
|
||
|
|
4306.0,
|
||
|
|
2011.0,
|
||
|
|
np.nan,
|
||
|
|
1892.0,
|
||
|
|
3338.0,
|
||
|
|
4575.0,
|
||
|
|
2293.0,
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
3377.0,
|
||
|
|
4805.0,
|
||
|
|
],
|
||
|
|
}
|
||
|
|
exp = DataFrame(exp_data, columns=result.columns)
|
||
|
|
tm.assert_frame_equal(result, exp)
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
result = lreshape(df, spec, dropna=False, label="foo")
|
||
|
|
|
||
|
|
spec = {
|
||
|
|
"visitdt": [f"visitdt{i:d}" for i in range(1, 3)],
|
||
|
|
"wt": [f"wt{i:d}" for i in range(1, 4)],
|
||
|
|
}
|
||
|
|
msg = "All column lists must be same length"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
lreshape(df, spec)
|
||
|
|
|
||
|
|
|
||
|
|
class TestWideToLong:
|
||
|
|
def test_simple(self):
|
||
|
|
np.random.seed(123)
|
||
|
|
x = np.random.randn(3)
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A1970": {0: "a", 1: "b", 2: "c"},
|
||
|
|
"A1980": {0: "d", 1: "e", 2: "f"},
|
||
|
|
"B1970": {0: 2.5, 1: 1.2, 2: 0.7},
|
||
|
|
"B1980": {0: 3.2, 1: 1.3, 2: 0.1},
|
||
|
|
"X": dict(zip(range(3), x)),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
exp_data = {
|
||
|
|
"X": x.tolist() + x.tolist(),
|
||
|
|
"A": ["a", "b", "c", "d", "e", "f"],
|
||
|
|
"B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
|
||
|
|
"year": [1970, 1970, 1970, 1980, 1980, 1980],
|
||
|
|
"id": [0, 1, 2, 0, 1, 2],
|
||
|
|
}
|
||
|
|
expected = DataFrame(exp_data)
|
||
|
|
expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
|
||
|
|
result = wide_to_long(df, ["A", "B"], i="id", j="year")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_stubs(self):
|
||
|
|
# GH9204
|
||
|
|
df = pd.DataFrame([[0, 1, 2, 3, 8], [4, 5, 6, 7, 9]])
|
||
|
|
df.columns = ["id", "inc1", "inc2", "edu1", "edu2"]
|
||
|
|
stubs = ["inc", "edu"]
|
||
|
|
|
||
|
|
# TODO: unused?
|
||
|
|
df_long = pd.wide_to_long(df, stubs, i="id", j="age") # noqa
|
||
|
|
|
||
|
|
assert stubs == ["inc", "edu"]
|
||
|
|
|
||
|
|
def test_separating_character(self):
|
||
|
|
# GH14779
|
||
|
|
np.random.seed(123)
|
||
|
|
x = np.random.randn(3)
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A.1970": {0: "a", 1: "b", 2: "c"},
|
||
|
|
"A.1980": {0: "d", 1: "e", 2: "f"},
|
||
|
|
"B.1970": {0: 2.5, 1: 1.2, 2: 0.7},
|
||
|
|
"B.1980": {0: 3.2, 1: 1.3, 2: 0.1},
|
||
|
|
"X": dict(zip(range(3), x)),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
exp_data = {
|
||
|
|
"X": x.tolist() + x.tolist(),
|
||
|
|
"A": ["a", "b", "c", "d", "e", "f"],
|
||
|
|
"B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
|
||
|
|
"year": [1970, 1970, 1970, 1980, 1980, 1980],
|
||
|
|
"id": [0, 1, 2, 0, 1, 2],
|
||
|
|
}
|
||
|
|
expected = DataFrame(exp_data)
|
||
|
|
expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
|
||
|
|
result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=".")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_escapable_characters(self):
|
||
|
|
np.random.seed(123)
|
||
|
|
x = np.random.randn(3)
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A(quarterly)1970": {0: "a", 1: "b", 2: "c"},
|
||
|
|
"A(quarterly)1980": {0: "d", 1: "e", 2: "f"},
|
||
|
|
"B(quarterly)1970": {0: 2.5, 1: 1.2, 2: 0.7},
|
||
|
|
"B(quarterly)1980": {0: 3.2, 1: 1.3, 2: 0.1},
|
||
|
|
"X": dict(zip(range(3), x)),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
exp_data = {
|
||
|
|
"X": x.tolist() + x.tolist(),
|
||
|
|
"A(quarterly)": ["a", "b", "c", "d", "e", "f"],
|
||
|
|
"B(quarterly)": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
|
||
|
|
"year": [1970, 1970, 1970, 1980, 1980, 1980],
|
||
|
|
"id": [0, 1, 2, 0, 1, 2],
|
||
|
|
}
|
||
|
|
expected = DataFrame(exp_data)
|
||
|
|
expected = expected.set_index(["id", "year"])[
|
||
|
|
["X", "A(quarterly)", "B(quarterly)"]
|
||
|
|
]
|
||
|
|
result = wide_to_long(df, ["A(quarterly)", "B(quarterly)"], i="id", j="year")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_unbalanced(self):
|
||
|
|
# test that we can have a varying amount of time variables
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A2010": [1.0, 2.0],
|
||
|
|
"A2011": [3.0, 4.0],
|
||
|
|
"B2010": [5.0, 6.0],
|
||
|
|
"X": ["X1", "X2"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
exp_data = {
|
||
|
|
"X": ["X1", "X1", "X2", "X2"],
|
||
|
|
"A": [1.0, 3.0, 2.0, 4.0],
|
||
|
|
"B": [5.0, np.nan, 6.0, np.nan],
|
||
|
|
"id": [0, 0, 1, 1],
|
||
|
|
"year": [2010, 2011, 2010, 2011],
|
||
|
|
}
|
||
|
|
expected = pd.DataFrame(exp_data)
|
||
|
|
expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
|
||
|
|
result = wide_to_long(df, ["A", "B"], i="id", j="year")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_character_overlap(self):
|
||
|
|
# Test we handle overlapping characters in both id_vars and value_vars
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A11": ["a11", "a22", "a33"],
|
||
|
|
"A12": ["a21", "a22", "a23"],
|
||
|
|
"B11": ["b11", "b12", "b13"],
|
||
|
|
"B12": ["b21", "b22", "b23"],
|
||
|
|
"BB11": [1, 2, 3],
|
||
|
|
"BB12": [4, 5, 6],
|
||
|
|
"BBBX": [91, 92, 93],
|
||
|
|
"BBBZ": [91, 92, 93],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"BBBX": [91, 92, 93, 91, 92, 93],
|
||
|
|
"BBBZ": [91, 92, 93, 91, 92, 93],
|
||
|
|
"A": ["a11", "a22", "a33", "a21", "a22", "a23"],
|
||
|
|
"B": ["b11", "b12", "b13", "b21", "b22", "b23"],
|
||
|
|
"BB": [1, 2, 3, 4, 5, 6],
|
||
|
|
"id": [0, 1, 2, 0, 1, 2],
|
||
|
|
"year": [11, 11, 11, 12, 12, 12],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = expected.set_index(["id", "year"])[["BBBX", "BBBZ", "A", "B", "BB"]]
|
||
|
|
result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
|
||
|
|
tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
|
||
|
|
|
||
|
|
def test_invalid_separator(self):
|
||
|
|
# if an invalid separator is supplied a empty data frame is returned
|
||
|
|
sep = "nope!"
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A2010": [1.0, 2.0],
|
||
|
|
"A2011": [3.0, 4.0],
|
||
|
|
"B2010": [5.0, 6.0],
|
||
|
|
"X": ["X1", "X2"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
exp_data = {
|
||
|
|
"X": "",
|
||
|
|
"A2010": [],
|
||
|
|
"A2011": [],
|
||
|
|
"B2010": [],
|
||
|
|
"id": [],
|
||
|
|
"year": [],
|
||
|
|
"A": [],
|
||
|
|
"B": [],
|
||
|
|
}
|
||
|
|
expected = pd.DataFrame(exp_data).astype({"year": "int"})
|
||
|
|
expected = expected.set_index(["id", "year"])[
|
||
|
|
["X", "A2010", "A2011", "B2010", "A", "B"]
|
||
|
|
]
|
||
|
|
expected.index.set_levels([0, 1], level=0, inplace=True)
|
||
|
|
result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=sep)
|
||
|
|
tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
|
||
|
|
|
||
|
|
def test_num_string_disambiguation(self):
|
||
|
|
# Test that we can disambiguate number value_vars from
|
||
|
|
# string value_vars
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A11": ["a11", "a22", "a33"],
|
||
|
|
"A12": ["a21", "a22", "a23"],
|
||
|
|
"B11": ["b11", "b12", "b13"],
|
||
|
|
"B12": ["b21", "b22", "b23"],
|
||
|
|
"BB11": [1, 2, 3],
|
||
|
|
"BB12": [4, 5, 6],
|
||
|
|
"Arating": [91, 92, 93],
|
||
|
|
"Arating_old": [91, 92, 93],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"Arating": [91, 92, 93, 91, 92, 93],
|
||
|
|
"Arating_old": [91, 92, 93, 91, 92, 93],
|
||
|
|
"A": ["a11", "a22", "a33", "a21", "a22", "a23"],
|
||
|
|
"B": ["b11", "b12", "b13", "b21", "b22", "b23"],
|
||
|
|
"BB": [1, 2, 3, 4, 5, 6],
|
||
|
|
"id": [0, 1, 2, 0, 1, 2],
|
||
|
|
"year": [11, 11, 11, 12, 12, 12],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = expected.set_index(["id", "year"])[
|
||
|
|
["Arating", "Arating_old", "A", "B", "BB"]
|
||
|
|
]
|
||
|
|
result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
|
||
|
|
tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
|
||
|
|
|
||
|
|
def test_invalid_suffixtype(self):
|
||
|
|
# If all stubs names end with a string, but a numeric suffix is
|
||
|
|
# assumed, an empty data frame is returned
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"Aone": [1.0, 2.0],
|
||
|
|
"Atwo": [3.0, 4.0],
|
||
|
|
"Bone": [5.0, 6.0],
|
||
|
|
"X": ["X1", "X2"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["id"] = df.index
|
||
|
|
exp_data = {
|
||
|
|
"X": "",
|
||
|
|
"Aone": [],
|
||
|
|
"Atwo": [],
|
||
|
|
"Bone": [],
|
||
|
|
"id": [],
|
||
|
|
"year": [],
|
||
|
|
"A": [],
|
||
|
|
"B": [],
|
||
|
|
}
|
||
|
|
expected = pd.DataFrame(exp_data).astype({"year": "int"})
|
||
|
|
|
||
|
|
expected = expected.set_index(["id", "year"])
|
||
|
|
expected.index.set_levels([0, 1], level=0, inplace=True)
|
||
|
|
result = wide_to_long(df, ["A", "B"], i="id", j="year")
|
||
|
|
tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
|
||
|
|
|
||
|
|
def test_multiple_id_columns(self):
|
||
|
|
# Taken from http://www.ats.ucla.edu/stat/stata/modules/reshapel.htm
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
|
||
|
|
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
|
||
|
|
"ht1": [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
|
||
|
|
"ht2": [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"ht": [
|
||
|
|
2.8,
|
||
|
|
3.4,
|
||
|
|
2.9,
|
||
|
|
3.8,
|
||
|
|
2.2,
|
||
|
|
2.9,
|
||
|
|
2.0,
|
||
|
|
3.2,
|
||
|
|
1.8,
|
||
|
|
2.8,
|
||
|
|
1.9,
|
||
|
|
2.4,
|
||
|
|
2.2,
|
||
|
|
3.3,
|
||
|
|
2.3,
|
||
|
|
3.4,
|
||
|
|
2.1,
|
||
|
|
2.9,
|
||
|
|
],
|
||
|
|
"famid": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3],
|
||
|
|
"birth": [1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3],
|
||
|
|
"age": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = expected.set_index(["famid", "birth", "age"])[["ht"]]
|
||
|
|
result = wide_to_long(df, "ht", i=["famid", "birth"], j="age")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_non_unique_idvars(self):
|
||
|
|
# GH16382
|
||
|
|
# Raise an error message if non unique id vars (i) are passed
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"A_A1": [1, 2, 3, 4, 5], "B_B1": [1, 2, 3, 4, 5], "x": [1, 1, 1, 1, 1]}
|
||
|
|
)
|
||
|
|
msg = "the id variables need to uniquely identify each row"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
wide_to_long(df, ["A_A", "B_B"], i="x", j="colname")
|
||
|
|
|
||
|
|
def test_cast_j_int(self):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"actor_1": ["CCH Pounder", "Johnny Depp", "Christoph Waltz"],
|
||
|
|
"actor_2": ["Joel David Moore", "Orlando Bloom", "Rory Kinnear"],
|
||
|
|
"actor_fb_likes_1": [1000.0, 40000.0, 11000.0],
|
||
|
|
"actor_fb_likes_2": [936.0, 5000.0, 393.0],
|
||
|
|
"title": ["Avatar", "Pirates of the Caribbean", "Spectre"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"actor": [
|
||
|
|
"CCH Pounder",
|
||
|
|
"Johnny Depp",
|
||
|
|
"Christoph Waltz",
|
||
|
|
"Joel David Moore",
|
||
|
|
"Orlando Bloom",
|
||
|
|
"Rory Kinnear",
|
||
|
|
],
|
||
|
|
"actor_fb_likes": [1000.0, 40000.0, 11000.0, 936.0, 5000.0, 393.0],
|
||
|
|
"num": [1, 1, 1, 2, 2, 2],
|
||
|
|
"title": [
|
||
|
|
"Avatar",
|
||
|
|
"Pirates of the Caribbean",
|
||
|
|
"Spectre",
|
||
|
|
"Avatar",
|
||
|
|
"Pirates of the Caribbean",
|
||
|
|
"Spectre",
|
||
|
|
],
|
||
|
|
}
|
||
|
|
).set_index(["title", "num"])
|
||
|
|
result = wide_to_long(
|
||
|
|
df, ["actor", "actor_fb_likes"], i="title", j="num", sep="_"
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_identical_stubnames(self):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A2010": [1.0, 2.0],
|
||
|
|
"A2011": [3.0, 4.0],
|
||
|
|
"B2010": [5.0, 6.0],
|
||
|
|
"A": ["X1", "X2"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
msg = "stubname can't be identical to a column name"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
wide_to_long(df, ["A", "B"], i="A", j="colname")
|
||
|
|
|
||
|
|
def test_nonnumeric_suffix(self):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"treatment_placebo": [1.0, 2.0],
|
||
|
|
"treatment_test": [3.0, 4.0],
|
||
|
|
"result_placebo": [5.0, 6.0],
|
||
|
|
"A": ["X1", "X2"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": ["X1", "X1", "X2", "X2"],
|
||
|
|
"colname": ["placebo", "test", "placebo", "test"],
|
||
|
|
"result": [5.0, np.nan, 6.0, np.nan],
|
||
|
|
"treatment": [1.0, 3.0, 2.0, 4.0],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = expected.set_index(["A", "colname"])
|
||
|
|
result = wide_to_long(
|
||
|
|
df, ["result", "treatment"], i="A", j="colname", suffix="[a-z]+", sep="_"
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_mixed_type_suffix(self):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": ["X1", "X2"],
|
||
|
|
"result_1": [0, 9],
|
||
|
|
"result_foo": [5.0, 6.0],
|
||
|
|
"treatment_1": [1.0, 2.0],
|
||
|
|
"treatment_foo": [3.0, 4.0],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": ["X1", "X2", "X1", "X2"],
|
||
|
|
"colname": ["1", "1", "foo", "foo"],
|
||
|
|
"result": [0.0, 9.0, 5.0, 6.0],
|
||
|
|
"treatment": [1.0, 2.0, 3.0, 4.0],
|
||
|
|
}
|
||
|
|
).set_index(["A", "colname"])
|
||
|
|
result = wide_to_long(
|
||
|
|
df, ["result", "treatment"], i="A", j="colname", suffix=".+", sep="_"
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_float_suffix(self):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"treatment_1.1": [1.0, 2.0],
|
||
|
|
"treatment_2.1": [3.0, 4.0],
|
||
|
|
"result_1.2": [5.0, 6.0],
|
||
|
|
"result_1": [0, 9],
|
||
|
|
"A": ["X1", "X2"],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": ["X1", "X1", "X1", "X1", "X2", "X2", "X2", "X2"],
|
||
|
|
"colname": [1, 1.1, 1.2, 2.1, 1, 1.1, 1.2, 2.1],
|
||
|
|
"result": [0.0, np.nan, 5.0, np.nan, 9.0, np.nan, 6.0, np.nan],
|
||
|
|
"treatment": [np.nan, 1.0, np.nan, 3.0, np.nan, 2.0, np.nan, 4.0],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
expected = expected.set_index(["A", "colname"])
|
||
|
|
result = wide_to_long(
|
||
|
|
df, ["result", "treatment"], i="A", j="colname", suffix="[0-9.]+", sep="_"
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_col_substring_of_stubname(self):
|
||
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# GH22468
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# Don't raise ValueError when a column name is a substring
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# of a stubname that's been passed as a string
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wide_data = {
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"node_id": {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
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"A": {0: 0.80, 1: 0.0, 2: 0.25, 3: 1.0, 4: 0.81},
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"PA0": {0: 0.74, 1: 0.56, 2: 0.56, 3: 0.98, 4: 0.6},
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"PA1": {0: 0.77, 1: 0.64, 2: 0.52, 3: 0.98, 4: 0.67},
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"PA3": {0: 0.34, 1: 0.70, 2: 0.52, 3: 0.98, 4: 0.67},
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}
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wide_df = pd.DataFrame.from_dict(wide_data)
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expected = pd.wide_to_long(
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wide_df, stubnames=["PA"], i=["node_id", "A"], j="time"
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)
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result = pd.wide_to_long(wide_df, stubnames="PA", i=["node_id", "A"], j="time")
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tm.assert_frame_equal(result, expected)
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def test_warn_of_column_name_value(self):
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# GH34731
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# raise a warning if the resultant value column name matches
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# a name in the dataframe already (default name is "value")
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df = pd.DataFrame({"col": list("ABC"), "value": range(10, 16, 2)})
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expected = pd.DataFrame(
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[["A", "col", "A"], ["B", "col", "B"], ["C", "col", "C"]],
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columns=["value", "variable", "value"],
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)
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with tm.assert_produces_warning(FutureWarning):
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result = df.melt(id_vars="value")
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tm.assert_frame_equal(result, expected)
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