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test_analytics.py
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test_analytics.py
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# coding=utf-8
# pylint: disable-msg=E1101,W0612
from itertools import product
from distutils.version import LooseVersion
import nose
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, DataFrame, isnull, notnull, bdate_range,
date_range, _np_version_under1p10)
from pandas.core.index import MultiIndex
from pandas.tseries.index import Timestamp
from pandas.tseries.tdi import Timedelta
import pandas.core.config as cf
import pandas.core.nanops as nanops
from pandas.compat import lrange, range
from pandas import compat
from pandas.util.testing import (assert_series_equal, assert_almost_equal,
assert_frame_equal, assert_index_equal)
import pandas.util.testing as tm
from .common import TestData
class TestSeriesAnalytics(TestData, tm.TestCase):
_multiprocess_can_split_ = True
def test_sum_zero(self):
arr = np.array([])
self.assertEqual(nanops.nansum(arr), 0)
arr = np.empty((10, 0))
self.assertTrue((nanops.nansum(arr, axis=1) == 0).all())
# GH #844
s = Series([], index=[])
self.assertEqual(s.sum(), 0)
df = DataFrame(np.empty((10, 0)))
self.assertTrue((df.sum(1) == 0).all())
def test_nansum_buglet(self):
s = Series([1.0, np.nan], index=[0, 1])
result = np.nansum(s)
assert_almost_equal(result, 1)
def test_overflow(self):
# GH 6915
# overflowing on the smaller int dtypes
for dtype in ['int32', 'int64']:
v = np.arange(5000000, dtype=dtype)
s = Series(v)
# no bottleneck
result = s.sum(skipna=False)
self.assertEqual(int(result), v.sum(dtype='int64'))
result = s.min(skipna=False)
self.assertEqual(int(result), 0)
result = s.max(skipna=False)
self.assertEqual(int(result), v[-1])
# use bottleneck if available
result = s.sum()
self.assertEqual(int(result), v.sum(dtype='int64'))
result = s.min()
self.assertEqual(int(result), 0)
result = s.max()
self.assertEqual(int(result), v[-1])
for dtype in ['float32', 'float64']:
v = np.arange(5000000, dtype=dtype)
s = Series(v)
# no bottleneck
result = s.sum(skipna=False)
self.assertEqual(result, v.sum(dtype=dtype))
result = s.min(skipna=False)
self.assertTrue(np.allclose(float(result), 0.0))
result = s.max(skipna=False)
self.assertTrue(np.allclose(float(result), v[-1]))
# use bottleneck if available
result = s.sum()
self.assertEqual(result, v.sum(dtype=dtype))
result = s.min()
self.assertTrue(np.allclose(float(result), 0.0))
result = s.max()
self.assertTrue(np.allclose(float(result), v[-1]))
def test_sum(self):
self._check_stat_op('sum', np.sum, check_allna=True)
def test_sum_inf(self):
import pandas.core.nanops as nanops
s = Series(np.random.randn(10))
s2 = s.copy()
s[5:8] = np.inf
s2[5:8] = np.nan
self.assertTrue(np.isinf(s.sum()))
arr = np.random.randn(100, 100).astype('f4')
arr[:, 2] = np.inf
with cf.option_context("mode.use_inf_as_null", True):
assert_almost_equal(s.sum(), s2.sum())
res = nanops.nansum(arr, axis=1)
self.assertTrue(np.isinf(res).all())
def test_mean(self):
self._check_stat_op('mean', np.mean)
def test_median(self):
self._check_stat_op('median', np.median)
# test with integers, test failure
int_ts = Series(np.ones(10, dtype=int), index=lrange(10))
self.assertAlmostEqual(np.median(int_ts), int_ts.median())
def test_mode(self):
s = Series([12, 12, 11, 10, 19, 11])
exp = Series([11, 12])
assert_series_equal(s.mode(), exp)
assert_series_equal(
Series([1, 2, 3]).mode(), Series(
[], dtype='int64'))
lst = [5] * 20 + [1] * 10 + [6] * 25
np.random.shuffle(lst)
s = Series(lst)
assert_series_equal(s.mode(), Series([6]))
s = Series([5] * 10)
assert_series_equal(s.mode(), Series([5]))
s = Series(lst)
s[0] = np.nan
assert_series_equal(s.mode(), Series([6.]))
s = Series(list('adfasbasfwewefwefweeeeasdfasnbam'))
assert_series_equal(s.mode(), Series(['e']))
s = Series(['2011-01-03', '2013-01-02', '1900-05-03'], dtype='M8[ns]')
assert_series_equal(s.mode(), Series([], dtype="M8[ns]"))
s = Series(['2011-01-03', '2013-01-02', '1900-05-03', '2011-01-03',
'2013-01-02'], dtype='M8[ns]')
assert_series_equal(s.mode(), Series(['2011-01-03', '2013-01-02'],
dtype='M8[ns]'))
# GH 5986
s = Series(['1 days', '-1 days', '0 days'], dtype='timedelta64[ns]')
assert_series_equal(s.mode(), Series([], dtype='timedelta64[ns]'))
s = Series(['1 day', '1 day', '-1 day', '-1 day 2 min',
'2 min', '2 min'],
dtype='timedelta64[ns]')
assert_series_equal(s.mode(), Series(['2 min', '1 day'],
dtype='timedelta64[ns]'))
def test_prod(self):
self._check_stat_op('prod', np.prod)
def test_min(self):
self._check_stat_op('min', np.min, check_objects=True)
def test_max(self):
self._check_stat_op('max', np.max, check_objects=True)
def test_var_std(self):
alt = lambda x: np.std(x, ddof=1)
self._check_stat_op('std', alt)
alt = lambda x: np.var(x, ddof=1)
self._check_stat_op('var', alt)
result = self.ts.std(ddof=4)
expected = np.std(self.ts.values, ddof=4)
assert_almost_equal(result, expected)
result = self.ts.var(ddof=4)
expected = np.var(self.ts.values, ddof=4)
assert_almost_equal(result, expected)
# 1 - element series with ddof=1
s = self.ts.iloc[[0]]
result = s.var(ddof=1)
self.assertTrue(isnull(result))
result = s.std(ddof=1)
self.assertTrue(isnull(result))
def test_sem(self):
alt = lambda x: np.std(x, ddof=1) / np.sqrt(len(x))
self._check_stat_op('sem', alt)
result = self.ts.sem(ddof=4)
expected = np.std(self.ts.values,
ddof=4) / np.sqrt(len(self.ts.values))
assert_almost_equal(result, expected)
# 1 - element series with ddof=1
s = self.ts.iloc[[0]]
result = s.sem(ddof=1)
self.assertTrue(isnull(result))
def test_skew(self):
tm._skip_if_no_scipy()
from scipy.stats import skew
alt = lambda x: skew(x, bias=False)
self._check_stat_op('skew', alt)
# test corner cases, skew() returns NaN unless there's at least 3
# values
min_N = 3
for i in range(1, min_N + 1):
s = Series(np.ones(i))
df = DataFrame(np.ones((i, i)))
if i < min_N:
self.assertTrue(np.isnan(s.skew()))
self.assertTrue(np.isnan(df.skew()).all())
else:
self.assertEqual(0, s.skew())
self.assertTrue((df.skew() == 0).all())
def test_kurt(self):
tm._skip_if_no_scipy()
from scipy.stats import kurtosis
alt = lambda x: kurtosis(x, bias=False)
self._check_stat_op('kurt', alt)
index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
[0, 1, 0, 1, 0, 1]])
s = Series(np.random.randn(6), index=index)
self.assertAlmostEqual(s.kurt(), s.kurt(level=0)['bar'])
# test corner cases, kurt() returns NaN unless there's at least 4
# values
min_N = 4
for i in range(1, min_N + 1):
s = Series(np.ones(i))
df = DataFrame(np.ones((i, i)))
if i < min_N:
self.assertTrue(np.isnan(s.kurt()))
self.assertTrue(np.isnan(df.kurt()).all())
else:
self.assertEqual(0, s.kurt())
self.assertTrue((df.kurt() == 0).all())
def test_describe(self):
s = Series([0, 1, 2, 3, 4], name='int_data')
result = s.describe()
expected = Series([5, 2, s.std(), 0, 1, 2, 3, 4],
name='int_data',
index=['count', 'mean', 'std', 'min', '25%',
'50%', '75%', 'max'])
self.assert_series_equal(result, expected)
s = Series([True, True, False, False, False], name='bool_data')
result = s.describe()
expected = Series([5, 2, False, 3], name='bool_data',
index=['count', 'unique', 'top', 'freq'])
self.assert_series_equal(result, expected)
s = Series(['a', 'a', 'b', 'c', 'd'], name='str_data')
result = s.describe()
expected = Series([5, 4, 'a', 2], name='str_data',
index=['count', 'unique', 'top', 'freq'])
self.assert_series_equal(result, expected)
def test_argsort(self):
self._check_accum_op('argsort', check_dtype=False)
argsorted = self.ts.argsort()
self.assertTrue(issubclass(argsorted.dtype.type, np.integer))
# GH 2967 (introduced bug in 0.11-dev I think)
s = Series([Timestamp('201301%02d' % (i + 1)) for i in range(5)])
self.assertEqual(s.dtype, 'datetime64[ns]')
shifted = s.shift(-1)
self.assertEqual(shifted.dtype, 'datetime64[ns]')
self.assertTrue(isnull(shifted[4]))
result = s.argsort()
expected = Series(lrange(5), dtype='int64')
assert_series_equal(result, expected)
result = shifted.argsort()
expected = Series(lrange(4) + [-1], dtype='int64')
assert_series_equal(result, expected)
def test_argsort_stable(self):
s = Series(np.random.randint(0, 100, size=10000))
mindexer = s.argsort(kind='mergesort')
qindexer = s.argsort()
mexpected = np.argsort(s.values, kind='mergesort')
qexpected = np.argsort(s.values, kind='quicksort')
self.assert_series_equal(mindexer, Series(mexpected),
check_dtype=False)
self.assert_series_equal(qindexer, Series(qexpected),
check_dtype=False)
self.assertFalse(np.array_equal(qindexer, mindexer))
def test_cumsum(self):
self._check_accum_op('cumsum')
def test_cumprod(self):
self._check_accum_op('cumprod')
def test_cummin(self):
self.assert_numpy_array_equal(self.ts.cummin().values,
np.minimum.accumulate(np.array(self.ts)))
ts = self.ts.copy()
ts[::2] = np.NaN
result = ts.cummin()[1::2]
expected = np.minimum.accumulate(ts.valid())
self.assert_series_equal(result, expected)
def test_cummax(self):
self.assert_numpy_array_equal(self.ts.cummax().values,
np.maximum.accumulate(np.array(self.ts)))
ts = self.ts.copy()
ts[::2] = np.NaN
result = ts.cummax()[1::2]
expected = np.maximum.accumulate(ts.valid())
self.assert_series_equal(result, expected)
def test_cummin_datetime64(self):
s = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT', '2000-1-1',
'NaT', '2000-1-3']))
expected = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT',
'2000-1-1', 'NaT', '2000-1-1']))
result = s.cummin(skipna=True)
self.assert_series_equal(expected, result)
expected = pd.Series(pd.to_datetime(
['NaT', '2000-1-2', '2000-1-2', '2000-1-1', '2000-1-1', '2000-1-1'
]))
result = s.cummin(skipna=False)
self.assert_series_equal(expected, result)
def test_cummax_datetime64(self):
s = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT', '2000-1-1',
'NaT', '2000-1-3']))
expected = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT',
'2000-1-2', 'NaT', '2000-1-3']))
result = s.cummax(skipna=True)
self.assert_series_equal(expected, result)
expected = pd.Series(pd.to_datetime(
['NaT', '2000-1-2', '2000-1-2', '2000-1-2', '2000-1-2', '2000-1-3'
]))
result = s.cummax(skipna=False)
self.assert_series_equal(expected, result)
def test_cummin_timedelta64(self):
s = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'1 min',
'NaT',
'3 min', ]))
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'1 min',
'NaT',
'1 min', ]))
result = s.cummin(skipna=True)
self.assert_series_equal(expected, result)
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'2 min',
'1 min',
'1 min',
'1 min', ]))
result = s.cummin(skipna=False)
self.assert_series_equal(expected, result)
def test_cummax_timedelta64(self):
s = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'1 min',
'NaT',
'3 min', ]))
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'2 min',
'NaT',
'3 min', ]))
result = s.cummax(skipna=True)
self.assert_series_equal(expected, result)
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'2 min',
'2 min',
'2 min',
'3 min', ]))
result = s.cummax(skipna=False)
self.assert_series_equal(expected, result)
def test_npdiff(self):
raise nose.SkipTest("skipping due to Series no longer being an "
"ndarray")
# no longer works as the return type of np.diff is now nd.array
s = Series(np.arange(5))
r = np.diff(s)
assert_series_equal(Series([nan, 0, 0, 0, nan]), r)
def _check_stat_op(self, name, alternate, check_objects=False,
check_allna=False):
import pandas.core.nanops as nanops
def testit():
f = getattr(Series, name)
# add some NaNs
self.series[5:15] = np.NaN
# idxmax, idxmin, min, and max are valid for dates
if name not in ['max', 'min']:
ds = Series(date_range('1/1/2001', periods=10))
self.assertRaises(TypeError, f, ds)
# skipna or no
self.assertTrue(notnull(f(self.series)))
self.assertTrue(isnull(f(self.series, skipna=False)))
# check the result is correct
nona = self.series.dropna()
assert_almost_equal(f(nona), alternate(nona.values))
assert_almost_equal(f(self.series), alternate(nona.values))
allna = self.series * nan
if check_allna:
# xref 9422
# bottleneck >= 1.0 give 0.0 for an allna Series sum
try:
self.assertTrue(nanops._USE_BOTTLENECK)
import bottleneck as bn # noqa
self.assertTrue(bn.__version__ >= LooseVersion('1.0'))
self.assertEqual(f(allna), 0.0)
except:
self.assertTrue(np.isnan(f(allna)))
# dtype=object with None, it works!
s = Series([1, 2, 3, None, 5])
f(s)
# 2888
l = [0]
l.extend(lrange(2 ** 40, 2 ** 40 + 1000))
s = Series(l, dtype='int64')
assert_almost_equal(float(f(s)), float(alternate(s.values)))
# check date range
if check_objects:
s = Series(bdate_range('1/1/2000', periods=10))
res = f(s)
exp = alternate(s)
self.assertEqual(res, exp)
# check on string data
if name not in ['sum', 'min', 'max']:
self.assertRaises(TypeError, f, Series(list('abc')))
# Invalid axis.
self.assertRaises(ValueError, f, self.series, axis=1)
# Unimplemented numeric_only parameter.
if 'numeric_only' in compat.signature(f).args:
self.assertRaisesRegexp(NotImplementedError, name, f,
self.series, numeric_only=True)
testit()
try:
import bottleneck as bn # noqa
nanops._USE_BOTTLENECK = False
testit()
nanops._USE_BOTTLENECK = True
except ImportError:
pass
def _check_accum_op(self, name, check_dtype=True):
func = getattr(np, name)
self.assert_numpy_array_equal(func(self.ts).values,
func(np.array(self.ts)),
check_dtype=check_dtype)
# with missing values
ts = self.ts.copy()
ts[::2] = np.NaN
result = func(ts)[1::2]
expected = func(np.array(ts.valid()))
self.assert_numpy_array_equal(result.values, expected,
check_dtype=False)
def test_compress(self):
cond = [True, False, True, False, False]
s = Series([1, -1, 5, 8, 7],
index=list('abcde'), name='foo')
expected = Series(s.values.compress(cond),
index=list('ac'), name='foo')
tm.assert_series_equal(s.compress(cond), expected)
def test_numpy_compress(self):
cond = [True, False, True, False, False]
s = Series([1, -1, 5, 8, 7],
index=list('abcde'), name='foo')
expected = Series(s.values.compress(cond),
index=list('ac'), name='foo')
tm.assert_series_equal(np.compress(cond, s), expected)
msg = "the 'axis' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.compress,
cond, s, axis=1)
msg = "the 'out' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.compress,
cond, s, out=s)
def test_round(self):
self.ts.index.name = "index_name"
result = self.ts.round(2)
expected = Series(np.round(self.ts.values, 2),
index=self.ts.index, name='ts')
assert_series_equal(result, expected)
self.assertEqual(result.name, self.ts.name)
def test_numpy_round(self):
# See gh-12600
s = Series([1.53, 1.36, 0.06])
out = np.round(s, decimals=0)
expected = Series([2., 1., 0.])
assert_series_equal(out, expected)
msg = "the 'out' parameter is not supported"
with tm.assertRaisesRegexp(ValueError, msg):
np.round(s, decimals=0, out=s)
def test_built_in_round(self):
if not compat.PY3:
raise nose.SkipTest(
'build in round cannot be overriden prior to Python 3')
s = Series([1.123, 2.123, 3.123], index=lrange(3))
result = round(s)
expected_rounded0 = Series([1., 2., 3.], index=lrange(3))
self.assert_series_equal(result, expected_rounded0)
decimals = 2
expected_rounded = Series([1.12, 2.12, 3.12], index=lrange(3))
result = round(s, decimals)
self.assert_series_equal(result, expected_rounded)
def test_prod_numpy16_bug(self):
s = Series([1., 1., 1.], index=lrange(3))
result = s.prod()
self.assertNotIsInstance(result, Series)
def test_all_any(self):
ts = tm.makeTimeSeries()
bool_series = ts > 0
self.assertFalse(bool_series.all())
self.assertTrue(bool_series.any())
# Alternative types, with implicit 'object' dtype.
s = Series(['abc', True])
self.assertEqual('abc', s.any()) # 'abc' || True => 'abc'
def test_all_any_params(self):
# Check skipna, with implicit 'object' dtype.
s1 = Series([np.nan, True])
s2 = Series([np.nan, False])
self.assertTrue(s1.all(skipna=False)) # nan && True => True
self.assertTrue(s1.all(skipna=True))
self.assertTrue(np.isnan(s2.any(skipna=False))) # nan || False => nan
self.assertFalse(s2.any(skipna=True))
# Check level.
s = pd.Series([False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2])
assert_series_equal(s.all(level=0), Series([False, True, False]))
assert_series_equal(s.any(level=0), Series([False, True, True]))
# bool_only is not implemented with level option.
self.assertRaises(NotImplementedError, s.any, bool_only=True, level=0)
self.assertRaises(NotImplementedError, s.all, bool_only=True, level=0)
# bool_only is not implemented alone.
self.assertRaises(NotImplementedError, s.any, bool_only=True)
self.assertRaises(NotImplementedError, s.all, bool_only=True)
def test_modulo(self):
with np.errstate(all='ignore'):
# GH3590, modulo as ints
p = DataFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]})
result = p['first'] % p['second']
expected = Series(p['first'].values % p['second'].values,
dtype='float64')
expected.iloc[0:3] = np.nan
assert_series_equal(result, expected)
result = p['first'] % 0
expected = Series(np.nan, index=p.index, name='first')
assert_series_equal(result, expected)
p = p.astype('float64')
result = p['first'] % p['second']
expected = Series(p['first'].values % p['second'].values)
assert_series_equal(result, expected)
p = p.astype('float64')
result = p['first'] % p['second']
result2 = p['second'] % p['first']
self.assertFalse(np.array_equal(result, result2))
# GH 9144
s = Series([0, 1])
result = s % 0
expected = Series([nan, nan])
assert_series_equal(result, expected)
result = 0 % s
expected = Series([nan, 0.0])
assert_series_equal(result, expected)
def test_ops_consistency_on_empty(self):
# GH 7869
# consistency on empty
# float
result = Series(dtype=float).sum()
self.assertEqual(result, 0)
result = Series(dtype=float).mean()
self.assertTrue(isnull(result))
result = Series(dtype=float).median()
self.assertTrue(isnull(result))
# timedelta64[ns]
result = Series(dtype='m8[ns]').sum()
self.assertEqual(result, Timedelta(0))
result = Series(dtype='m8[ns]').mean()
self.assertTrue(result is pd.NaT)
result = Series(dtype='m8[ns]').median()
self.assertTrue(result is pd.NaT)
def test_corr(self):
tm._skip_if_no_scipy()
import scipy.stats as stats
# full overlap
self.assertAlmostEqual(self.ts.corr(self.ts), 1)
# partial overlap
self.assertAlmostEqual(self.ts[:15].corr(self.ts[5:]), 1)
self.assertTrue(isnull(self.ts[:15].corr(self.ts[5:], min_periods=12)))
ts1 = self.ts[:15].reindex(self.ts.index)
ts2 = self.ts[5:].reindex(self.ts.index)
self.assertTrue(isnull(ts1.corr(ts2, min_periods=12)))
# No overlap
self.assertTrue(np.isnan(self.ts[::2].corr(self.ts[1::2])))
# all NA
cp = self.ts[:10].copy()
cp[:] = np.nan
self.assertTrue(isnull(cp.corr(cp)))
A = tm.makeTimeSeries()
B = tm.makeTimeSeries()
result = A.corr(B)
expected, _ = stats.pearsonr(A, B)
self.assertAlmostEqual(result, expected)
def test_corr_rank(self):
tm._skip_if_no_scipy()
import scipy
import scipy.stats as stats
# kendall and spearman
A = tm.makeTimeSeries()
B = tm.makeTimeSeries()
A[-5:] = A[:5]
result = A.corr(B, method='kendall')
expected = stats.kendalltau(A, B)[0]
self.assertAlmostEqual(result, expected)
result = A.corr(B, method='spearman')
expected = stats.spearmanr(A, B)[0]
self.assertAlmostEqual(result, expected)
# these methods got rewritten in 0.8
if scipy.__version__ < LooseVersion('0.9'):
raise nose.SkipTest("skipping corr rank because of scipy version "
"{0}".format(scipy.__version__))
# results from R
A = Series(
[-0.89926396, 0.94209606, -1.03289164, -0.95445587, 0.76910310, -
0.06430576, -2.09704447, 0.40660407, -0.89926396, 0.94209606])
B = Series(
[-1.01270225, -0.62210117, -1.56895827, 0.59592943, -0.01680292,
1.17258718, -1.06009347, -0.10222060, -0.89076239, 0.89372375])
kexp = 0.4319297
sexp = 0.5853767
self.assertAlmostEqual(A.corr(B, method='kendall'), kexp)
self.assertAlmostEqual(A.corr(B, method='spearman'), sexp)
def test_cov(self):
# full overlap
self.assertAlmostEqual(self.ts.cov(self.ts), self.ts.std() ** 2)
# partial overlap
self.assertAlmostEqual(self.ts[:15].cov(self.ts[5:]),
self.ts[5:15].std() ** 2)
# No overlap
self.assertTrue(np.isnan(self.ts[::2].cov(self.ts[1::2])))
# all NA
cp = self.ts[:10].copy()
cp[:] = np.nan
self.assertTrue(isnull(cp.cov(cp)))
# min_periods
self.assertTrue(isnull(self.ts[:15].cov(self.ts[5:], min_periods=12)))
ts1 = self.ts[:15].reindex(self.ts.index)
ts2 = self.ts[5:].reindex(self.ts.index)
self.assertTrue(isnull(ts1.cov(ts2, min_periods=12)))
def test_count(self):
self.assertEqual(self.ts.count(), len(self.ts))
self.ts[::2] = np.NaN
self.assertEqual(self.ts.count(), np.isfinite(self.ts).sum())
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
left = ts.count(level=1)
right = Series([2, 3, 1], index=[1, 2, nan])
assert_series_equal(left, right)
ts.iloc[[0, 3, 5]] = nan
assert_series_equal(ts.count(level=1), right - 1)
def test_dot(self):
a = Series(np.random.randn(4), index=['p', 'q', 'r', 's'])
b = DataFrame(np.random.randn(3, 4), index=['1', '2', '3'],
columns=['p', 'q', 'r', 's']).T
result = a.dot(b)
expected = Series(np.dot(a.values, b.values), index=['1', '2', '3'])
assert_series_equal(result, expected)
# Check index alignment
b2 = b.reindex(index=reversed(b.index))
result = a.dot(b)
assert_series_equal(result, expected)
# Check ndarray argument
result = a.dot(b.values)
self.assertTrue(np.all(result == expected.values))
assert_almost_equal(a.dot(b['2'].values), expected['2'])
# Check series argument
assert_almost_equal(a.dot(b['1']), expected['1'])
assert_almost_equal(a.dot(b2['1']), expected['1'])
self.assertRaises(Exception, a.dot, a.values[:3])
self.assertRaises(ValueError, a.dot, b.T)
def test_value_counts_nunique(self):
# basics.rst doc example
series = Series(np.random.randn(500))
series[20:500] = np.nan
series[10:20] = 5000
result = series.nunique()
self.assertEqual(result, 11)
def test_unique(self):
# 714 also, dtype=float
s = Series([1.2345] * 100)
s[::2] = np.nan
result = s.unique()
self.assertEqual(len(result), 2)
s = Series([1.2345] * 100, dtype='f4')
s[::2] = np.nan
result = s.unique()
self.assertEqual(len(result), 2)
# NAs in object arrays #714
s = Series(['foo'] * 100, dtype='O')
s[::2] = np.nan
result = s.unique()
self.assertEqual(len(result), 2)
# decision about None
s = Series([1, 2, 3, None, None, None], dtype=object)
result = s.unique()
expected = np.array([1, 2, 3, None], dtype=object)
self.assert_numpy_array_equal(result, expected)
def test_drop_duplicates(self):
# check both int and object
for s in [Series([1, 2, 3, 3]), Series(['1', '2', '3', '3'])]:
expected = Series([False, False, False, True])
assert_series_equal(s.duplicated(), expected)
assert_series_equal(s.drop_duplicates(), s[~expected])
sc = s.copy()
sc.drop_duplicates(inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, False, True, False])
assert_series_equal(s.duplicated(keep='last'), expected)
assert_series_equal(s.drop_duplicates(keep='last'), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep='last', inplace=True)
assert_series_equal(sc, s[~expected])
# deprecate take_last
with tm.assert_produces_warning(FutureWarning):
assert_series_equal(s.duplicated(take_last=True), expected)
with tm.assert_produces_warning(FutureWarning):
assert_series_equal(
s.drop_duplicates(take_last=True), s[~expected])
sc = s.copy()
with tm.assert_produces_warning(FutureWarning):
sc.drop_duplicates(take_last=True, inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, False, True, True])
assert_series_equal(s.duplicated(keep=False), expected)
assert_series_equal(s.drop_duplicates(keep=False), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep=False, inplace=True)
assert_series_equal(sc, s[~expected])
for s in [Series([1, 2, 3, 5, 3, 2, 4]),
Series(['1', '2', '3', '5', '3', '2', '4'])]:
expected = Series([False, False, False, False, True, True, False])
assert_series_equal(s.duplicated(), expected)
assert_series_equal(s.drop_duplicates(), s[~expected])
sc = s.copy()
sc.drop_duplicates(inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, True, True, False, False, False, False])
assert_series_equal(s.duplicated(keep='last'), expected)
assert_series_equal(s.drop_duplicates(keep='last'), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep='last', inplace=True)
assert_series_equal(sc, s[~expected])
# deprecate take_last
with tm.assert_produces_warning(FutureWarning):
assert_series_equal(s.duplicated(take_last=True), expected)
with tm.assert_produces_warning(FutureWarning):
assert_series_equal(
s.drop_duplicates(take_last=True), s[~expected])
sc = s.copy()
with tm.assert_produces_warning(FutureWarning):
sc.drop_duplicates(take_last=True, inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, True, True, False, True, True, False])
assert_series_equal(s.duplicated(keep=False), expected)
assert_series_equal(s.drop_duplicates(keep=False), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep=False, inplace=True)
assert_series_equal(sc, s[~expected])
def test_rank(self):
tm._skip_if_no_scipy()
from scipy.stats import rankdata
self.ts[::2] = np.nan
self.ts[:10][::3] = 4.
ranks = self.ts.rank()
oranks = self.ts.astype('O').rank()
assert_series_equal(ranks, oranks)
mask = np.isnan(self.ts)
filled = self.ts.fillna(np.inf)
# rankdata returns a ndarray
exp = Series(rankdata(filled), index=filled.index, name='ts')
exp[mask] = np.nan
tm.assert_series_equal(ranks, exp)
iseries = Series(np.arange(5).repeat(2))
iranks = iseries.rank()
exp = iseries.astype(float).rank()
assert_series_equal(iranks, exp)
iseries = Series(np.arange(5)) + 1.0
exp = iseries / 5.0
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
iseries = Series(np.repeat(1, 100))
exp = Series(np.repeat(0.505, 100))
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
iseries[1] = np.nan
exp = Series(np.repeat(50.0 / 99.0, 100))
exp[1] = np.nan
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
iseries = Series(np.arange(5)) + 1.0
iseries[4] = np.nan
exp = iseries / 4.0
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
iseries = Series(np.repeat(np.nan, 100))
exp = iseries.copy()
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
iseries = Series(np.arange(5)) + 1
iseries[4] = np.nan
exp = iseries / 4.0
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
rng = date_range('1/1/1990', periods=5)
iseries = Series(np.arange(5), rng) + 1
iseries.ix[4] = np.nan
exp = iseries / 4.0
iranks = iseries.rank(pct=True)
assert_series_equal(iranks, exp)
iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1])
exp = Series([2, 1, 3, 5, 4, 6.0])
iranks = iseries.rank()
assert_series_equal(iranks, exp)
# GH 5968
iseries = Series(['3 day', '1 day 10m', '-2 day', pd.NaT],
dtype='m8[ns]')
exp = Series([3, 2, 1, np.nan])
iranks = iseries.rank()
assert_series_equal(iranks, exp)
values = np.array(
[-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40