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239 lines
7.4 KiB
239 lines
7.4 KiB
"""
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Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
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but are implicitly also testing nsmallest_foo.
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"""
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from string import ascii_lowercase
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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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import pandas._testing as tm
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@pytest.fixture
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def df_duplicates():
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return pd.DataFrame(
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{"a": [1, 2, 3, 4, 4], "b": [1, 1, 1, 1, 1], "c": [0, 1, 2, 5, 4]},
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index=[0, 0, 1, 1, 1],
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)
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@pytest.fixture
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def df_strings():
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return pd.DataFrame(
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{
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"a": np.random.permutation(10),
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"b": list(ascii_lowercase[:10]),
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"c": np.random.permutation(10).astype("float64"),
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}
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)
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@pytest.fixture
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def df_main_dtypes():
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return pd.DataFrame(
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{
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"group": [1, 1, 2],
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"int": [1, 2, 3],
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"float": [4.0, 5.0, 6.0],
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"string": list("abc"),
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"category_string": pd.Series(list("abc")).astype("category"),
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"category_int": [7, 8, 9],
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"datetime": pd.date_range("20130101", periods=3),
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"datetimetz": pd.date_range("20130101", periods=3, tz="US/Eastern"),
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"timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
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},
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columns=[
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"group",
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"int",
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"float",
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"string",
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"category_string",
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"category_int",
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"datetime",
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"datetimetz",
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"timedelta",
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],
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)
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class TestNLargestNSmallest:
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# ----------------------------------------------------------------------
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# Top / bottom
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@pytest.mark.parametrize(
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"order",
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[
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["a"],
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["c"],
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["a", "b"],
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["a", "c"],
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["b", "a"],
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["b", "c"],
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["a", "b", "c"],
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["c", "a", "b"],
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["c", "b", "a"],
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["b", "c", "a"],
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["b", "a", "c"],
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# dups!
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["b", "c", "c"],
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],
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)
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@pytest.mark.parametrize("n", range(1, 11))
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def test_nlargest_n(self, df_strings, nselect_method, n, order):
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# GH#10393
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df = df_strings
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if "b" in order:
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error_msg = (
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f"Column 'b' has dtype object, "
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f"cannot use method '{nselect_method}' with this dtype"
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)
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with pytest.raises(TypeError, match=error_msg):
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getattr(df, nselect_method)(n, order)
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else:
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ascending = nselect_method == "nsmallest"
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result = getattr(df, nselect_method)(n, order)
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expected = df.sort_values(order, ascending=ascending).head(n)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"columns", [["group", "category_string"], ["group", "string"]]
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)
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def test_nlargest_error(self, df_main_dtypes, nselect_method, columns):
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df = df_main_dtypes
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col = columns[1]
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error_msg = (
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f"Column '{col}' has dtype {df[col].dtype}, "
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f"cannot use method '{nselect_method}' with this dtype"
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)
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# escape some characters that may be in the repr
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error_msg = (
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error_msg.replace("(", "\\(")
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.replace(")", "\\)")
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.replace("[", "\\[")
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.replace("]", "\\]")
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)
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with pytest.raises(TypeError, match=error_msg):
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getattr(df, nselect_method)(2, columns)
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def test_nlargest_all_dtypes(self, df_main_dtypes):
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df = df_main_dtypes
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df.nsmallest(2, list(set(df) - {"category_string", "string"}))
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df.nlargest(2, list(set(df) - {"category_string", "string"}))
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def test_nlargest_duplicates_on_starter_columns(self):
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# regression test for GH#22752
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df = pd.DataFrame({"a": [2, 2, 2, 1, 1, 1], "b": [1, 2, 3, 3, 2, 1]})
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result = df.nlargest(4, columns=["a", "b"])
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expected = pd.DataFrame(
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{"a": [2, 2, 2, 1], "b": [3, 2, 1, 3]}, index=[2, 1, 0, 3]
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)
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tm.assert_frame_equal(result, expected)
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result = df.nsmallest(4, columns=["a", "b"])
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expected = pd.DataFrame(
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{"a": [1, 1, 1, 2], "b": [1, 2, 3, 1]}, index=[5, 4, 3, 0]
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)
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tm.assert_frame_equal(result, expected)
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def test_nlargest_n_identical_values(self):
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# GH#15297
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df = pd.DataFrame({"a": [1] * 5, "b": [1, 2, 3, 4, 5]})
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result = df.nlargest(3, "a")
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expected = pd.DataFrame({"a": [1] * 3, "b": [1, 2, 3]}, index=[0, 1, 2])
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tm.assert_frame_equal(result, expected)
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result = df.nsmallest(3, "a")
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expected = pd.DataFrame({"a": [1] * 3, "b": [1, 2, 3]})
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"order",
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[["a", "b", "c"], ["c", "b", "a"], ["a"], ["b"], ["a", "b"], ["c", "b"]],
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)
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@pytest.mark.parametrize("n", range(1, 6))
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def test_nlargest_n_duplicate_index(self, df_duplicates, n, order):
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# GH#13412
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df = df_duplicates
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result = df.nsmallest(n, order)
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expected = df.sort_values(order).head(n)
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tm.assert_frame_equal(result, expected)
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result = df.nlargest(n, order)
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expected = df.sort_values(order, ascending=False).head(n)
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tm.assert_frame_equal(result, expected)
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def test_nlargest_duplicate_keep_all_ties(self):
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# GH#16818
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df = pd.DataFrame(
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{"a": [5, 4, 4, 2, 3, 3, 3, 3], "b": [10, 9, 8, 7, 5, 50, 10, 20]}
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)
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result = df.nlargest(4, "a", keep="all")
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expected = pd.DataFrame(
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{
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"a": {0: 5, 1: 4, 2: 4, 4: 3, 5: 3, 6: 3, 7: 3},
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"b": {0: 10, 1: 9, 2: 8, 4: 5, 5: 50, 6: 10, 7: 20},
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}
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)
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tm.assert_frame_equal(result, expected)
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result = df.nsmallest(2, "a", keep="all")
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expected = pd.DataFrame(
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{
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"a": {3: 2, 4: 3, 5: 3, 6: 3, 7: 3},
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"b": {3: 7, 4: 5, 5: 50, 6: 10, 7: 20},
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}
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)
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tm.assert_frame_equal(result, expected)
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def test_nlargest_multiindex_column_lookup(self):
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# Check whether tuples are correctly treated as multi-level lookups.
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# GH#23033
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df = pd.DataFrame(
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columns=pd.MultiIndex.from_product([["x"], ["a", "b"]]),
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data=[[0.33, 0.13], [0.86, 0.25], [0.25, 0.70], [0.85, 0.91]],
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)
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# nsmallest
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result = df.nsmallest(3, ("x", "a"))
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expected = df.iloc[[2, 0, 3]]
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tm.assert_frame_equal(result, expected)
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# nlargest
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result = df.nlargest(3, ("x", "b"))
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expected = df.iloc[[3, 2, 1]]
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tm.assert_frame_equal(result, expected)
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def test_nlargest_nan(self):
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# GH#43060
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df = pd.DataFrame([np.nan, np.nan, 0, 1, 2, 3])
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result = df.nlargest(5, 0)
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expected = df.sort_values(0, ascending=False).head(5)
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tm.assert_frame_equal(result, expected)
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def test_nsmallest_nan_after_n_element(self):
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# GH#46589
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df = pd.DataFrame(
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{
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"a": [1, 2, 3, 4, 5, None, 7],
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"b": [7, 6, 5, 4, 3, 2, 1],
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"c": [1, 1, 2, 2, 3, 3, 3],
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},
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index=range(7),
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)
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result = df.nsmallest(5, columns=["a", "b"])
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expected = pd.DataFrame(
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{
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"a": [1, 2, 3, 4, 5],
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"b": [7, 6, 5, 4, 3],
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"c": [1, 1, 2, 2, 3],
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},
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index=range(5),
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).astype({"a": "float"})
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tm.assert_frame_equal(result, expected)
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