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test_bin_groupby.py
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test_bin_groupby.py
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# -*- coding: utf-8 -*-
from numpy import nan
import numpy as np
from pandas.types.common import _ensure_int64
from pandas import Index, isnull
from pandas.util.testing import assert_almost_equal
import pandas.util.testing as tm
import pandas.lib as lib
import pandas.algos as algos
def test_series_grouper():
from pandas import Series
obj = Series(np.random.randn(10))
dummy = obj[:0]
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64)
grouper = lib.SeriesGrouper(obj, np.mean, labels, 2, dummy)
result, counts = grouper.get_result()
expected = np.array([obj[3:6].mean(), obj[6:].mean()])
assert_almost_equal(result, expected)
exp_counts = np.array([3, 4], dtype=np.int64)
assert_almost_equal(counts, exp_counts)
def test_series_bin_grouper():
from pandas import Series
obj = Series(np.random.randn(10))
dummy = obj[:0]
bins = np.array([3, 6])
grouper = lib.SeriesBinGrouper(obj, np.mean, bins, dummy)
result, counts = grouper.get_result()
expected = np.array([obj[:3].mean(), obj[3:6].mean(), obj[6:].mean()])
assert_almost_equal(result, expected)
exp_counts = np.array([3, 3, 4], dtype=np.int64)
assert_almost_equal(counts, exp_counts)
class TestBinGroupers(tm.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
self.obj = np.random.randn(10, 1)
self.labels = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2], dtype=np.int64)
self.bins = np.array([3, 6], dtype=np.int64)
def test_generate_bins(self):
from pandas.core.groupby import generate_bins_generic
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6, 9], dtype=np.int64)
for func in [lib.generate_bins_dt64, generate_bins_generic]:
bins = func(values, binner, closed='left')
assert ((bins == np.array([2, 5, 6])).all())
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6, 6])).all())
for func in [lib.generate_bins_dt64, generate_bins_generic]:
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6], dtype=np.int64)
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6])).all())
self.assertRaises(ValueError, generate_bins_generic, values, [],
'right')
self.assertRaises(ValueError, generate_bins_generic, values[:0],
binner, 'right')
self.assertRaises(ValueError, generate_bins_generic, values, [4],
'right')
self.assertRaises(ValueError, generate_bins_generic, values, [-3, -1],
'right')
def test_group_ohlc():
def _check(dtype):
obj = np.array(np.random.randn(20), dtype=dtype)
bins = np.array([6, 12, 20])
out = np.zeros((3, 4), dtype)
counts = np.zeros(len(out), dtype=np.int64)
labels = _ensure_int64(np.repeat(np.arange(3),
np.diff(np.r_[0, bins])))
func = getattr(algos, 'group_ohlc_%s' % dtype)
func(out, counts, obj[:, None], labels)
def _ohlc(group):
if isnull(group).all():
return np.repeat(nan, 4)
return [group[0], group.max(), group.min(), group[-1]]
expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]),
_ohlc(obj[12:])])
assert_almost_equal(out, expected)
tm.assert_numpy_array_equal(counts,
np.array([6, 6, 8], dtype=np.int64))
obj[:6] = nan
func(out, counts, obj[:, None], labels)
expected[0] = nan
assert_almost_equal(out, expected)
_check('float32')
_check('float64')
class TestMoments(tm.TestCase):
pass
class TestReducer(tm.TestCase):
def test_int_index(self):
from pandas.core.series import Series
arr = np.random.randn(100, 4)
result = lib.reduce(arr, np.sum, labels=Index(np.arange(4)))
expected = arr.sum(0)
assert_almost_equal(result, expected)
result = lib.reduce(arr, np.sum, axis=1, labels=Index(np.arange(100)))
expected = arr.sum(1)
assert_almost_equal(result, expected)
dummy = Series(0., index=np.arange(100))
result = lib.reduce(arr, np.sum, dummy=dummy,
labels=Index(np.arange(4)))
expected = arr.sum(0)
assert_almost_equal(result, expected)
dummy = Series(0., index=np.arange(4))
result = lib.reduce(arr, np.sum, axis=1, dummy=dummy,
labels=Index(np.arange(100)))
expected = arr.sum(1)
assert_almost_equal(result, expected)
result = lib.reduce(arr, np.sum, axis=1, dummy=dummy,
labels=Index(np.arange(100)))
assert_almost_equal(result, expected)