475 lines
18 KiB
Python
475 lines
18 KiB
Python
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from collections import defaultdict
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from datetime import datetime
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from itertools import product
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import numpy as np
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import pytest
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from pandas import DataFrame, MultiIndex, Series, array, concat, merge
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import pandas._testing as tm
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from pandas.core.algorithms import safe_sort
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import pandas.core.common as com
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from pandas.core.sorting import (
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decons_group_index,
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get_group_index,
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is_int64_overflow_possible,
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lexsort_indexer,
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nargsort,
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)
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class TestSorting:
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@pytest.mark.slow
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def test_int64_overflow(self):
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B = np.concatenate((np.arange(1000), np.arange(1000), np.arange(500)))
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A = np.arange(2500)
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df = DataFrame(
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{
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"A": A,
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"B": B,
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"C": A,
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"D": B,
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"E": A,
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"F": B,
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"G": A,
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"H": B,
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"values": np.random.randn(2500),
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}
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)
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lg = df.groupby(["A", "B", "C", "D", "E", "F", "G", "H"])
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rg = df.groupby(["H", "G", "F", "E", "D", "C", "B", "A"])
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left = lg.sum()["values"]
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right = rg.sum()["values"]
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exp_index, _ = left.index.sortlevel()
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tm.assert_index_equal(left.index, exp_index)
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exp_index, _ = right.index.sortlevel(0)
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tm.assert_index_equal(right.index, exp_index)
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tups = list(map(tuple, df[["A", "B", "C", "D", "E", "F", "G", "H"]].values))
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tups = com.asarray_tuplesafe(tups)
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expected = df.groupby(tups).sum()["values"]
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for k, v in expected.items():
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assert left[k] == right[k[::-1]]
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assert left[k] == v
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assert len(left) == len(right)
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@pytest.mark.arm_slow
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def test_int64_overflow_moar(self):
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# GH9096
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values = range(55109)
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data = DataFrame.from_dict({"a": values, "b": values, "c": values, "d": values})
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grouped = data.groupby(["a", "b", "c", "d"])
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assert len(grouped) == len(values)
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arr = np.random.randint(-1 << 12, 1 << 12, (1 << 15, 5))
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i = np.random.choice(len(arr), len(arr) * 4)
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arr = np.vstack((arr, arr[i])) # add sume duplicate rows
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i = np.random.permutation(len(arr))
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arr = arr[i] # shuffle rows
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df = DataFrame(arr, columns=list("abcde"))
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df["jim"], df["joe"] = np.random.randn(2, len(df)) * 10
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gr = df.groupby(list("abcde"))
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# verify this is testing what it is supposed to test!
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assert is_int64_overflow_possible(gr.grouper.shape)
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# manually compute groupings
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jim, joe = defaultdict(list), defaultdict(list)
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for key, a, b in zip(map(tuple, arr), df["jim"], df["joe"]):
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jim[key].append(a)
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joe[key].append(b)
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assert len(gr) == len(jim)
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mi = MultiIndex.from_tuples(jim.keys(), names=list("abcde"))
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def aggr(func):
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f = lambda a: np.fromiter(map(func, a), dtype="f8")
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arr = np.vstack((f(jim.values()), f(joe.values()))).T
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res = DataFrame(arr, columns=["jim", "joe"], index=mi)
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return res.sort_index()
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tm.assert_frame_equal(gr.mean(), aggr(np.mean))
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tm.assert_frame_equal(gr.median(), aggr(np.median))
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def test_lexsort_indexer(self):
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keys = [[np.nan] * 5 + list(range(100)) + [np.nan] * 5]
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# orders=True, na_position='last'
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result = lexsort_indexer(keys, orders=True, na_position="last")
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exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110))
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tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp))
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# orders=True, na_position='first'
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result = lexsort_indexer(keys, orders=True, na_position="first")
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exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105))
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tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp))
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# orders=False, na_position='last'
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result = lexsort_indexer(keys, orders=False, na_position="last")
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exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110))
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tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp))
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# orders=False, na_position='first'
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result = lexsort_indexer(keys, orders=False, na_position="first")
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exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1))
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tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp))
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def test_nargsort(self):
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# np.argsort(items) places NaNs last
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items = [np.nan] * 5 + list(range(100)) + [np.nan] * 5
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# np.argsort(items2) may not place NaNs first
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items2 = np.array(items, dtype="O")
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# mergesort is the most difficult to get right because we want it to be
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# stable.
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# According to numpy/core/tests/test_multiarray, """The number of
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# sorted items must be greater than ~50 to check the actual algorithm
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# because quick and merge sort fall over to insertion sort for small
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# arrays."""
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# mergesort, ascending=True, na_position='last'
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result = nargsort(items, kind="mergesort", ascending=True, na_position="last")
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exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=True, na_position='first'
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result = nargsort(items, kind="mergesort", ascending=True, na_position="first")
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exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=False, na_position='last'
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result = nargsort(items, kind="mergesort", ascending=False, na_position="last")
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exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=False, na_position='first'
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result = nargsort(items, kind="mergesort", ascending=False, na_position="first")
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exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=True, na_position='last'
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result = nargsort(items2, kind="mergesort", ascending=True, na_position="last")
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exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=True, na_position='first'
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result = nargsort(items2, kind="mergesort", ascending=True, na_position="first")
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exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=False, na_position='last'
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result = nargsort(items2, kind="mergesort", ascending=False, na_position="last")
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exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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# mergesort, ascending=False, na_position='first'
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result = nargsort(
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items2, kind="mergesort", ascending=False, na_position="first"
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)
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exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1))
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tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
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class TestMerge:
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@pytest.mark.slow
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def test_int64_overflow_issues(self):
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# #2690, combinatorial explosion
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df1 = DataFrame(np.random.randn(1000, 7), columns=list("ABCDEF") + ["G1"])
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df2 = DataFrame(np.random.randn(1000, 7), columns=list("ABCDEF") + ["G2"])
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# it works!
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result = merge(df1, df2, how="outer")
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assert len(result) == 2000
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low, high, n = -1 << 10, 1 << 10, 1 << 20
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left = DataFrame(np.random.randint(low, high, (n, 7)), columns=list("ABCDEFG"))
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left["left"] = left.sum(axis=1)
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# one-2-one match
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i = np.random.permutation(len(left))
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right = left.iloc[i].copy()
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right.columns = right.columns[:-1].tolist() + ["right"]
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right.index = np.arange(len(right))
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right["right"] *= -1
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out = merge(left, right, how="outer")
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assert len(out) == len(left)
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tm.assert_series_equal(out["left"], -out["right"], check_names=False)
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result = out.iloc[:, :-2].sum(axis=1)
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tm.assert_series_equal(out["left"], result, check_names=False)
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assert result.name is None
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out.sort_values(out.columns.tolist(), inplace=True)
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out.index = np.arange(len(out))
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for how in ["left", "right", "outer", "inner"]:
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tm.assert_frame_equal(out, merge(left, right, how=how, sort=True))
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# check that left merge w/ sort=False maintains left frame order
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out = merge(left, right, how="left", sort=False)
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tm.assert_frame_equal(left, out[left.columns.tolist()])
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out = merge(right, left, how="left", sort=False)
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tm.assert_frame_equal(right, out[right.columns.tolist()])
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# one-2-many/none match
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n = 1 << 11
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left = DataFrame(
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np.random.randint(low, high, (n, 7)).astype("int64"),
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columns=list("ABCDEFG"),
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)
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# confirm that this is checking what it is supposed to check
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shape = left.apply(Series.nunique).values
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assert is_int64_overflow_possible(shape)
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# add duplicates to left frame
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left = concat([left, left], ignore_index=True)
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right = DataFrame(
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np.random.randint(low, high, (n // 2, 7)).astype("int64"),
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columns=list("ABCDEFG"),
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)
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# add duplicates & overlap with left to the right frame
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i = np.random.choice(len(left), n)
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right = concat([right, right, left.iloc[i]], ignore_index=True)
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left["left"] = np.random.randn(len(left))
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right["right"] = np.random.randn(len(right))
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# shuffle left & right frames
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i = np.random.permutation(len(left))
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left = left.iloc[i].copy()
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left.index = np.arange(len(left))
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i = np.random.permutation(len(right))
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right = right.iloc[i].copy()
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right.index = np.arange(len(right))
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# manually compute outer merge
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ldict, rdict = defaultdict(list), defaultdict(list)
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for idx, row in left.set_index(list("ABCDEFG")).iterrows():
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ldict[idx].append(row["left"])
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for idx, row in right.set_index(list("ABCDEFG")).iterrows():
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rdict[idx].append(row["right"])
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vals = []
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for k, lval in ldict.items():
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rval = rdict.get(k, [np.nan])
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for lv, rv in product(lval, rval):
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vals.append(
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k
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+ (
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lv,
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rv,
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)
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)
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for k, rval in rdict.items():
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if k not in ldict:
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for rv in rval:
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vals.append(
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k
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+ (
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np.nan,
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rv,
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)
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)
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def align(df):
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df = df.sort_values(df.columns.tolist())
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df.index = np.arange(len(df))
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return df
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def verify_order(df):
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kcols = list("ABCDEFG")
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tm.assert_frame_equal(
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df[kcols].copy(), df[kcols].sort_values(kcols, kind="mergesort")
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)
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out = DataFrame(vals, columns=list("ABCDEFG") + ["left", "right"])
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out = align(out)
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jmask = {
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"left": out["left"].notna(),
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"right": out["right"].notna(),
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"inner": out["left"].notna() & out["right"].notna(),
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"outer": np.ones(len(out), dtype="bool"),
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}
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for how in ["left", "right", "outer", "inner"]:
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mask = jmask[how]
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frame = align(out[mask].copy())
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assert mask.all() ^ mask.any() or how == "outer"
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for sort in [False, True]:
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res = merge(left, right, how=how, sort=sort)
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if sort:
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verify_order(res)
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# as in GH9092 dtypes break with outer/right join
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tm.assert_frame_equal(
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frame, align(res), check_dtype=how not in ("right", "outer")
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)
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def test_decons():
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def testit(codes_list, shape):
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group_index = get_group_index(codes_list, shape, sort=True, xnull=True)
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codes_list2 = decons_group_index(group_index, shape)
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for a, b in zip(codes_list, codes_list2):
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tm.assert_numpy_array_equal(a, b)
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shape = (4, 5, 6)
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codes_list = [
|
||
|
|
np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100).astype(np.int64),
|
||
|
|
np.tile([0, 2, 4, 3, 0, 1, 2, 3], 100).astype(np.int64),
|
||
|
|
np.tile([5, 1, 0, 2, 3, 0, 5, 4], 100).astype(np.int64),
|
||
|
|
]
|
||
|
|
testit(codes_list, shape)
|
||
|
|
|
||
|
|
shape = (10000, 10000)
|
||
|
|
codes_list = [
|
||
|
|
np.tile(np.arange(10000, dtype=np.int64), 5),
|
||
|
|
np.tile(np.arange(10000, dtype=np.int64), 5),
|
||
|
|
]
|
||
|
|
testit(codes_list, shape)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSafeSort:
|
||
|
|
def test_basic_sort(self):
|
||
|
|
values = [3, 1, 2, 0, 4]
|
||
|
|
result = safe_sort(values)
|
||
|
|
expected = np.array([0, 1, 2, 3, 4])
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
values = list("baaacb")
|
||
|
|
result = safe_sort(values)
|
||
|
|
expected = np.array(list("aaabbc"), dtype="object")
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
values = []
|
||
|
|
result = safe_sort(values)
|
||
|
|
expected = np.array([])
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("verify", [True, False])
|
||
|
|
def test_codes(self, verify):
|
||
|
|
values = [3, 1, 2, 0, 4]
|
||
|
|
expected = np.array([0, 1, 2, 3, 4])
|
||
|
|
|
||
|
|
codes = [0, 1, 1, 2, 3, 0, -1, 4]
|
||
|
|
result, result_codes = safe_sort(values, codes, verify=verify)
|
||
|
|
expected_codes = np.array([3, 1, 1, 2, 0, 3, -1, 4], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
tm.assert_numpy_array_equal(result_codes, expected_codes)
|
||
|
|
|
||
|
|
# na_sentinel
|
||
|
|
codes = [0, 1, 1, 2, 3, 0, 99, 4]
|
||
|
|
result, result_codes = safe_sort(values, codes, na_sentinel=99, verify=verify)
|
||
|
|
expected_codes = np.array([3, 1, 1, 2, 0, 3, 99, 4], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
tm.assert_numpy_array_equal(result_codes, expected_codes)
|
||
|
|
|
||
|
|
codes = []
|
||
|
|
result, result_codes = safe_sort(values, codes, verify=verify)
|
||
|
|
expected_codes = np.array([], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
tm.assert_numpy_array_equal(result_codes, expected_codes)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_sentinel", [-1, 99])
|
||
|
|
def test_codes_out_of_bound(self, na_sentinel):
|
||
|
|
values = [3, 1, 2, 0, 4]
|
||
|
|
expected = np.array([0, 1, 2, 3, 4])
|
||
|
|
|
||
|
|
# out of bound indices
|
||
|
|
codes = [0, 101, 102, 2, 3, 0, 99, 4]
|
||
|
|
result, result_codes = safe_sort(values, codes, na_sentinel=na_sentinel)
|
||
|
|
expected_codes = np.array(
|
||
|
|
[3, na_sentinel, na_sentinel, 2, 0, 3, na_sentinel, 4], dtype=np.intp
|
||
|
|
)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
tm.assert_numpy_array_equal(result_codes, expected_codes)
|
||
|
|
|
||
|
|
def test_mixed_integer(self):
|
||
|
|
values = np.array(["b", 1, 0, "a", 0, "b"], dtype=object)
|
||
|
|
result = safe_sort(values)
|
||
|
|
expected = np.array([0, 0, 1, "a", "b", "b"], dtype=object)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
values = np.array(["b", 1, 0, "a"], dtype=object)
|
||
|
|
codes = [0, 1, 2, 3, 0, -1, 1]
|
||
|
|
result, result_codes = safe_sort(values, codes)
|
||
|
|
expected = np.array([0, 1, "a", "b"], dtype=object)
|
||
|
|
expected_codes = np.array([3, 1, 0, 2, 3, -1, 1], dtype=np.intp)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
tm.assert_numpy_array_equal(result_codes, expected_codes)
|
||
|
|
|
||
|
|
def test_mixed_integer_from_list(self):
|
||
|
|
values = ["b", 1, 0, "a", 0, "b"]
|
||
|
|
result = safe_sort(values)
|
||
|
|
expected = np.array([0, 0, 1, "a", "b", "b"], dtype=object)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
def test_unsortable(self):
|
||
|
|
# GH 13714
|
||
|
|
arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
|
||
|
|
msg = (
|
||
|
|
"unorderable types: .* [<>] .*"
|
||
|
|
"|" # the above case happens for numpy < 1.14
|
||
|
|
"'[<>]' not supported between instances of .*"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
safe_sort(arr)
|
||
|
|
|
||
|
|
def test_exceptions(self):
|
||
|
|
with pytest.raises(TypeError, match="Only list-like objects are allowed"):
|
||
|
|
safe_sort(values=1)
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match="Only list-like objects or None"):
|
||
|
|
safe_sort(values=[0, 1, 2], codes=1)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match="values should be unique"):
|
||
|
|
safe_sort(values=[0, 1, 2, 1], codes=[0, 1])
|
||
|
|
|
||
|
|
def test_extension_array(self):
|
||
|
|
# a = array([1, 3, np.nan, 2], dtype='Int64')
|
||
|
|
a = array([1, 3, 2], dtype="Int64")
|
||
|
|
result = safe_sort(a)
|
||
|
|
# expected = array([1, 2, 3, np.nan], dtype='Int64')
|
||
|
|
expected = array([1, 2, 3], dtype="Int64")
|
||
|
|
tm.assert_extension_array_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("verify", [True, False])
|
||
|
|
@pytest.mark.parametrize("na_sentinel", [-1, 99])
|
||
|
|
def test_extension_array_codes(self, verify, na_sentinel):
|
||
|
|
a = array([1, 3, 2], dtype="Int64")
|
||
|
|
result, codes = safe_sort(
|
||
|
|
a, [0, 1, na_sentinel, 2], na_sentinel=na_sentinel, verify=verify
|
||
|
|
)
|
||
|
|
expected_values = array([1, 2, 3], dtype="Int64")
|
||
|
|
expected_codes = np.array([0, 2, na_sentinel, 1], dtype=np.intp)
|
||
|
|
tm.assert_extension_array_equal(result, expected_values)
|
||
|
|
tm.assert_numpy_array_equal(codes, expected_codes)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mixed_str_nan():
|
||
|
|
values = np.array(["b", np.nan, "a", "b"], dtype=object)
|
||
|
|
result = safe_sort(values)
|
||
|
|
expected = np.array([np.nan, "a", "b", "b"], dtype=object)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|