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