106 lines
3.0 KiB
Python
106 lines
3.0 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from pandas._libs import lib, reduction as libreduction
|
|
|
|
import pandas as pd
|
|
from pandas import Series
|
|
import pandas._testing as tm
|
|
|
|
|
|
def test_series_grouper():
|
|
obj = Series(np.random.randn(10))
|
|
dummy = obj.iloc[:0]
|
|
|
|
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64)
|
|
|
|
grouper = libreduction.SeriesGrouper(obj, np.mean, labels, 2, dummy)
|
|
result, counts = grouper.get_result()
|
|
|
|
expected = np.array([obj[3:6].mean(), obj[6:].mean()])
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
exp_counts = np.array([3, 4], dtype=np.int64)
|
|
tm.assert_almost_equal(counts, exp_counts)
|
|
|
|
|
|
def test_series_grouper_requires_nonempty_raises():
|
|
# GH#29500
|
|
obj = Series(np.random.randn(10))
|
|
dummy = obj.iloc[:0]
|
|
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64)
|
|
|
|
with pytest.raises(ValueError, match="SeriesGrouper requires non-empty `series`"):
|
|
libreduction.SeriesGrouper(dummy, np.mean, labels, 2, dummy)
|
|
|
|
|
|
def test_series_bin_grouper():
|
|
obj = Series(np.random.randn(10))
|
|
dummy = obj[:0]
|
|
|
|
bins = np.array([3, 6])
|
|
|
|
grouper = libreduction.SeriesBinGrouper(obj, np.mean, bins, dummy)
|
|
result, counts = grouper.get_result()
|
|
|
|
expected = np.array([obj[:3].mean(), obj[3:6].mean(), obj[6:].mean()])
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
exp_counts = np.array([3, 3, 4], dtype=np.int64)
|
|
tm.assert_almost_equal(counts, exp_counts)
|
|
|
|
|
|
def assert_block_lengths(x):
|
|
assert len(x) == len(x._mgr.blocks[0].mgr_locs)
|
|
return 0
|
|
|
|
|
|
def cumsum_max(x):
|
|
x.cumsum().max()
|
|
return 0
|
|
|
|
|
|
@pytest.mark.parametrize("func", [cumsum_max, assert_block_lengths])
|
|
def test_mgr_locs_updated(func):
|
|
# https://github.com/pandas-dev/pandas/issues/31802
|
|
# Some operations may require creating new blocks, which requires
|
|
# valid mgr_locs
|
|
df = pd.DataFrame({"A": ["a", "a", "a"], "B": ["a", "b", "b"], "C": [1, 1, 1]})
|
|
result = df.groupby(["A", "B"]).agg(func)
|
|
expected = pd.DataFrame(
|
|
{"C": [0, 0]},
|
|
index=pd.MultiIndex.from_product([["a"], ["a", "b"]], names=["A", "B"]),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"binner,closed,expected",
|
|
[
|
|
(
|
|
np.array([0, 3, 6, 9], dtype=np.int64),
|
|
"left",
|
|
np.array([2, 5, 6], dtype=np.int64),
|
|
),
|
|
(
|
|
np.array([0, 3, 6, 9], dtype=np.int64),
|
|
"right",
|
|
np.array([3, 6, 6], dtype=np.int64),
|
|
),
|
|
(np.array([0, 3, 6], dtype=np.int64), "left", np.array([2, 5], dtype=np.int64)),
|
|
(
|
|
np.array([0, 3, 6], dtype=np.int64),
|
|
"right",
|
|
np.array([3, 6], dtype=np.int64),
|
|
),
|
|
],
|
|
)
|
|
def test_generate_bins(binner, closed, expected):
|
|
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
|
|
result = lib.generate_bins_dt64(values, binner, closed=closed)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
class TestMoments:
|
|
pass
|