# coding=utf-8 # pylint: disable-msg=E1101,W0612 from itertools import product from distutils.version import LooseVersion import operator import pytest from numpy import nan import numpy as np import pandas as pd from pandas import (Series, Categorical, DataFrame, isna, notna, bdate_range, date_range, _np_version_under1p10, CategoricalIndex) from pandas.core.index import MultiIndex from pandas.core.indexes.datetimes import Timestamp from pandas.core.indexes.timedeltas import Timedelta import pandas.core.nanops as nanops from pandas.compat import lrange, range, PY35 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 import pandas.util._test_decorators as td from .common import TestData class TestSeriesAnalytics(TestData): @pytest.mark.parametrize("use_bottleneck", [True, False]) @pytest.mark.parametrize("method, unit", [ ("sum", 0.0), ("prod", 1.0) ]) def test_empty(self, method, unit, use_bottleneck): with pd.option_context("use_bottleneck", use_bottleneck): # GH 9422 / 18921 # Entirely empty s = Series([]) # NA by default result = getattr(s, method)() assert result == unit # Explicit result = getattr(s, method)(min_count=0) assert result == unit result = getattr(s, method)(min_count=1) assert isna(result) # Skipna, default result = getattr(s, method)(skipna=True) result == unit # Skipna, explicit result = getattr(s, method)(skipna=True, min_count=0) assert result == unit result = getattr(s, method)(skipna=True, min_count=1) assert isna(result) # All-NA s = Series([np.nan]) # NA by default result = getattr(s, method)() assert result == unit # Explicit result = getattr(s, method)(min_count=0) assert result == unit result = getattr(s, method)(min_count=1) assert isna(result) # Skipna, default result = getattr(s, method)(skipna=True) result == unit # skipna, explicit result = getattr(s, method)(skipna=True, min_count=0) assert result == unit result = getattr(s, method)(skipna=True, min_count=1) assert isna(result) # Mix of valid, empty s = Series([np.nan, 1]) # Default result = getattr(s, method)() assert result == 1.0 # Explicit result = getattr(s, method)(min_count=0) assert result == 1.0 result = getattr(s, method)(min_count=1) assert result == 1.0 # Skipna result = getattr(s, method)(skipna=True) assert result == 1.0 result = getattr(s, method)(skipna=True, min_count=0) assert result == 1.0 result = getattr(s, method)(skipna=True, min_count=1) assert result == 1.0 # GH #844 (changed in 9422) df = DataFrame(np.empty((10, 0))) assert (getattr(df, method)(1) == unit).all() s = pd.Series([1]) result = getattr(s, method)(min_count=2) assert isna(result) s = pd.Series([np.nan]) result = getattr(s, method)(min_count=2) assert isna(result) s = pd.Series([np.nan, 1]) result = getattr(s, method)(min_count=2) assert isna(result) @pytest.mark.parametrize('method, unit', [ ('sum', 0.0), ('prod', 1.0), ]) def test_empty_multi(self, method, unit): s = pd.Series([1, np.nan, np.nan, np.nan], index=pd.MultiIndex.from_product([('a', 'b'), (0, 1)])) # 1 / 0 by default result = getattr(s, method)(level=0) expected = pd.Series([1, unit], index=['a', 'b']) tm.assert_series_equal(result, expected) # min_count=0 result = getattr(s, method)(level=0, min_count=0) expected = pd.Series([1, unit], index=['a', 'b']) tm.assert_series_equal(result, expected) # min_count=1 result = getattr(s, method)(level=0, min_count=1) expected = pd.Series([1, np.nan], index=['a', 'b']) tm.assert_series_equal(result, expected) @pytest.mark.parametrize( "method", ['mean', 'median', 'std', 'var']) def test_ops_consistency_on_empty(self, method): # GH 7869 # consistency on empty # float result = getattr(Series(dtype=float), method)() assert isna(result) # timedelta64[ns] result = getattr(Series(dtype='m8[ns]'), method)() assert result is pd.NaT def test_nansum_buglet(self): s = Series([1.0, np.nan], index=[0, 1]) result = np.nansum(s) assert_almost_equal(result, 1) @pytest.mark.parametrize("use_bottleneck", [True, False]) def test_sum_overflow(self, use_bottleneck): with pd.option_context('use_bottleneck', use_bottleneck): # GH 6915 # overflowing on the smaller int dtypes for dtype in ['int32', 'int64']: v = np.arange(5000000, dtype=dtype) s = Series(v) result = s.sum(skipna=False) assert int(result) == v.sum(dtype='int64') result = s.min(skipna=False) assert int(result) == 0 result = s.max(skipna=False) assert int(result) == v[-1] for dtype in ['float32', 'float64']: v = np.arange(5000000, dtype=dtype) s = Series(v) result = s.sum(skipna=False) assert result == v.sum(dtype=dtype) result = s.min(skipna=False) assert np.allclose(float(result), 0.0) result = s.max(skipna=False) assert np.allclose(float(result), v[-1]) def test_sum(self): self._check_stat_op('sum', np.sum, check_allna=False) def test_sum_inf(self): s = Series(np.random.randn(10)) s2 = s.copy() s[5:8] = np.inf s2[5:8] = np.nan assert np.isinf(s.sum()) arr = np.random.randn(100, 100).astype('f4') arr[:, 2] = np.inf with pd.option_context("mode.use_inf_as_na", True): assert_almost_equal(s.sum(), s2.sum()) res = nanops.nansum(arr, axis=1) assert 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)) tm.assert_almost_equal(np.median(int_ts), int_ts.median()) def test_mode(self): # No mode should be found. exp = Series([], dtype=np.float64) tm.assert_series_equal(Series([]).mode(), exp) exp = Series([1], dtype=np.int64) tm.assert_series_equal(Series([1]).mode(), exp) exp = Series(['a', 'b', 'c'], dtype=np.object) tm.assert_series_equal(Series(['a', 'b', 'c']).mode(), exp) # Test numerical data types. exp_single = [1] data_single = [1] * 5 + [2] * 3 exp_multi = [1, 3] data_multi = [1] * 5 + [2] * 3 + [3] * 5 for dt in np.typecodes['AllInteger'] + np.typecodes['Float']: s = Series(data_single, dtype=dt) exp = Series(exp_single, dtype=dt) tm.assert_series_equal(s.mode(), exp) s = Series(data_multi, dtype=dt) exp = Series(exp_multi, dtype=dt) tm.assert_series_equal(s.mode(), exp) # Test string and object types. exp = ['b'] data = ['a'] * 2 + ['b'] * 3 s = Series(data, dtype='c') exp = Series(exp, dtype='c') tm.assert_series_equal(s.mode(), exp) exp = ['bar'] data = ['foo'] * 2 + ['bar'] * 3 for dt in [str, object]: s = Series(data, dtype=dt) exp = Series(exp, dtype=dt) tm.assert_series_equal(s.mode(), exp) # Test datetime types. exp = Series(['1900-05-03', '2011-01-03', '2013-01-02'], dtype='M8[ns]') s = Series(['2011-01-03', '2013-01-02', '1900-05-03'], dtype='M8[ns]') tm.assert_series_equal(s.mode(), exp) exp = Series(['2011-01-03', '2013-01-02'], dtype='M8[ns]') s = Series(['2011-01-03', '2013-01-02', '1900-05-03', '2011-01-03', '2013-01-02'], dtype='M8[ns]') tm.assert_series_equal(s.mode(), exp) # gh-5986: Test timedelta types. exp = Series(['-1 days', '0 days', '1 days'], dtype='timedelta64[ns]') s = Series(['1 days', '-1 days', '0 days'], dtype='timedelta64[ns]') tm.assert_series_equal(s.mode(), exp) exp = Series(['2 min', '1 day'], dtype='timedelta64[ns]') s = Series(['1 day', '1 day', '-1 day', '-1 day 2 min', '2 min', '2 min'], dtype='timedelta64[ns]') tm.assert_series_equal(s.mode(), exp) # Test mixed dtype. exp = Series(['foo']) s = Series([1, 'foo', 'foo']) tm.assert_series_equal(s.mode(), exp) # Test for uint64 overflow. exp = Series([2**63], dtype=np.uint64) s = Series([1, 2**63, 2**63], dtype=np.uint64) tm.assert_series_equal(s.mode(), exp) exp = Series([1, 2**63], dtype=np.uint64) s = Series([1, 2**63], dtype=np.uint64) tm.assert_series_equal(s.mode(), exp) # Test category dtype. c = Categorical([1, 2]) exp = Categorical([1, 2], categories=[1, 2]) exp = Series(exp, dtype='category') tm.assert_series_equal(Series(c).mode(), exp) c = Categorical([1, 'a', 'a']) exp = Categorical(['a'], categories=[1, 'a']) exp = Series(exp, dtype='category') tm.assert_series_equal(Series(c).mode(), exp) c = Categorical([1, 1, 2, 3, 3]) exp = Categorical([1, 3], categories=[1, 2, 3]) exp = Series(exp, dtype='category') tm.assert_series_equal(Series(c).mode(), exp) 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) assert isna(result) result = s.std(ddof=1) assert isna(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) assert isna(result) @td.skip_if_no_scipy def test_skew(self): 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: assert np.isnan(s.skew()) assert np.isnan(df.skew()).all() else: assert 0 == s.skew() assert (df.skew() == 0).all() @td.skip_if_no_scipy def test_kurt(self): 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) tm.assert_almost_equal(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: assert np.isnan(s.kurt()) assert np.isnan(df.kurt()).all() else: assert 0 == s.kurt() assert (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']) tm.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']) tm.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']) tm.assert_series_equal(result, expected) def test_argsort(self): self._check_accum_op('argsort', check_dtype=False) argsorted = self.ts.argsort() assert 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)]) assert s.dtype == 'datetime64[ns]' shifted = s.shift(-1) assert shifted.dtype == 'datetime64[ns]' assert isna(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') tm.assert_series_equal(mindexer, Series(mexpected), check_dtype=False) tm.assert_series_equal(qindexer, Series(qexpected), check_dtype=False) pytest.raises(AssertionError, tm.assert_numpy_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): tm.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.dropna()) tm.assert_series_equal(result, expected) def test_cummax(self): tm.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.dropna()) tm.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) tm.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) tm.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) tm.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) tm.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) tm.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) tm.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) tm.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) tm.assert_series_equal(expected, result) def test_npdiff(self): pytest.skip("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): with pd.option_context('use_bottleneck', False): 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)) pytest.raises(TypeError, f, ds) # skipna or no assert notna(f(self.series)) assert isna(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: assert 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) assert res == exp # check on string data if name not in ['sum', 'min', 'max']: pytest.raises(TypeError, f, Series(list('abc'))) # Invalid axis. pytest.raises(ValueError, f, self.series, axis=1) # Unimplemented numeric_only parameter. if 'numeric_only' in compat.signature(f).args: tm.assert_raises_regex(NotImplementedError, name, f, self.series, numeric_only=True) def _check_accum_op(self, name, check_dtype=True): func = getattr(np, name) tm.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.dropna())) tm.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.assert_raises_regex(ValueError, msg, np.compress, cond, s, axis=1) msg = "the 'out' parameter is not supported" tm.assert_raises_regex(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) assert 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.assert_raises_regex(ValueError, msg): np.round(s, decimals=0, out=s) def test_built_in_round(self): if not compat.PY3: pytest.skip( 'build in round cannot be overridden 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)) tm.assert_series_equal(result, expected_rounded0) decimals = 2 expected_rounded = Series([1.12, 2.12, 3.12], index=lrange(3)) result = round(s, decimals) tm.assert_series_equal(result, expected_rounded) def test_prod_numpy16_bug(self): s = Series([1., 1., 1.], index=lrange(3)) result = s.prod() assert not isinstance(result, Series) def test_all_any(self): ts = tm.makeTimeSeries() bool_series = ts > 0 assert not bool_series.all() assert bool_series.any() # Alternative types, with implicit 'object' dtype. s = Series(['abc', True]) assert '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]) assert s1.all(skipna=False) # nan && True => True assert s1.all(skipna=True) assert np.isnan(s2.any(skipna=False)) # nan || False => nan assert not 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. pytest.raises(NotImplementedError, s.any, bool_only=True, level=0) pytest.raises(NotImplementedError, s.all, bool_only=True, level=0) # bool_only is not implemented alone. pytest.raises(NotImplementedError, s.any, bool_only=True) pytest.raises(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'] assert not result.equals(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) @td.skip_if_no_scipy def test_corr(self): import scipy.stats as stats # full overlap tm.assert_almost_equal(self.ts.corr(self.ts), 1) # partial overlap tm.assert_almost_equal(self.ts[:15].corr(self.ts[5:]), 1) assert isna(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) assert isna(ts1.corr(ts2, min_periods=12)) # No overlap assert np.isnan(self.ts[::2].corr(self.ts[1::2])) # all NA cp = self.ts[:10].copy() cp[:] = np.nan assert isna(cp.corr(cp)) A = tm.makeTimeSeries() B = tm.makeTimeSeries() result = A.corr(B) expected, _ = stats.pearsonr(A, B) tm.assert_almost_equal(result, expected) @td.skip_if_no_scipy def test_corr_rank(self): 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] tm.assert_almost_equal(result, expected) result = A.corr(B, method='spearman') expected = stats.spearmanr(A, B)[0] tm.assert_almost_equal(result, expected) # these methods got rewritten in 0.8 if LooseVersion(scipy.__version__) < LooseVersion('0.9'): pytest.skip("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 tm.assert_almost_equal(A.corr(B, method='kendall'), kexp) tm.assert_almost_equal(A.corr(B, method='spearman'), sexp) def test_cov(self): # full overlap tm.assert_almost_equal(self.ts.cov(self.ts), self.ts.std() ** 2) # partial overlap tm.assert_almost_equal(self.ts[:15].cov(self.ts[5:]), self.ts[5:15].std() ** 2) # No overlap assert np.isnan(self.ts[::2].cov(self.ts[1::2])) # all NA cp = self.ts[:10].copy() cp[:] = np.nan assert isna(cp.cov(cp)) # min_periods assert isna(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) assert isna(ts1.cov(ts2, min_periods=12)) def test_count(self): assert self.ts.count() == len(self.ts) self.ts[::2] = np.NaN assert 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) assert 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']) pytest.raises(Exception, a.dot, a.values[:3]) pytest.raises(ValueError, a.dot, b.T) @pytest.mark.skipif(not PY35, reason='matmul supported for Python>=3.5') def test_matmul(self): # matmul test is for GH #10259 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 # Series @ DataFrame result = operator.matmul(a, b) expected = Series(np.dot(a.values, b.values), index=['1', '2', '3']) assert_series_equal(result, expected) # DataFrame @ Series result = operator.matmul(b.T, a) expected = Series(np.dot(b.T.values, a.T.values), index=['1', '2', '3']) assert_series_equal(result, expected) # Series @ Series result = operator.matmul(a, a) expected = np.dot(a.values, a.values) assert_almost_equal(result, expected) # np.array @ Series (__rmatmul__) result = operator.matmul(a.values, a) expected = np.dot(a.values, a.values) assert_almost_equal(result, expected) # mixed dtype DataFrame @ Series a['p'] = int(a.p) result = operator.matmul(b.T, a) expected = Series(np.dot(b.T.values, a.T.values), index=['1', '2', '3']) assert_series_equal(result, expected) # different dtypes DataFrame @ Series a = a.astype(int) result = operator.matmul(b.T, a) expected = Series(np.dot(b.T.values, a.T.values), index=['1', '2', '3']) assert_series_equal(result, expected) pytest.raises(Exception, a.dot, a.values[:3]) pytest.raises(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() assert result == 11 # GH 18051 s = pd.Series(pd.Categorical([])) assert s.nunique() == 0 s = pd.Series(pd.Categorical([np.nan])) assert s.nunique() == 0 def test_unique(self): # 714 also, dtype=float s = Series([1.2345] * 100) s[::2] = np.nan result = s.unique() assert len(result) == 2 s = Series([1.2345] * 100, dtype='f4') s[::2] = np.nan result = s.unique() assert len(result) == 2 # NAs in object arrays #714 s = Series(['foo'] * 100, dtype='O') s[::2] = np.nan result = s.unique() assert 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) tm.assert_numpy_array_equal(result, expected) # GH 18051 s = pd.Series(pd.Categorical([])) tm.assert_categorical_equal(s.unique(), pd.Categorical([]), check_dtype=False) s = pd.Series(pd.Categorical([np.nan])) tm.assert_categorical_equal(s.unique(), pd.Categorical([np.nan]), check_dtype=False) @pytest.mark.parametrize( "tc1, tc2", [ ( Series([1, 2, 3, 3], dtype=np.dtype('int_')), Series([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('int_')) ), ( Series([1, 2, 3, 3], dtype=np.dtype('uint')), Series([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('uint')) ), ( Series([1, 2, 3, 3], dtype=np.dtype('float_')), Series([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('float_')) ), ( Series([1, 2, 3, 3], dtype=np.dtype('unicode_')), Series([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('unicode_')) ) ] ) def test_drop_duplicates_non_bool(self, tc1, tc2): # Test case 1 expected = Series([False, False, False, True]) assert_series_equal(tc1.duplicated(), expected) assert_series_equal(tc1.drop_duplicates(), tc1[~expected]) sc = tc1.copy() sc.drop_duplicates(inplace=True) assert_series_equal(sc, tc1[~expected]) expected = Series([False, False, True, False]) assert_series_equal(tc1.duplicated(keep='last'), expected) assert_series_equal(tc1.drop_duplicates(keep='last'), tc1[~expected]) sc = tc1.copy() sc.drop_duplicates(keep='last', inplace=True) assert_series_equal(sc, tc1[~expected]) expected = Series([False, False, True, True]) assert_series_equal(tc1.duplicated(keep=False), expected) assert_series_equal(tc1.drop_duplicates(keep=False), tc1[~expected]) sc = tc1.copy() sc.drop_duplicates(keep=False, inplace=True) assert_series_equal(sc, tc1[~expected]) # Test case 2 expected = Series([False, False, False, False, True, True, False]) assert_series_equal(tc2.duplicated(), expected) assert_series_equal(tc2.drop_duplicates(), tc2[~expected]) sc = tc2.copy() sc.drop_duplicates(inplace=True) assert_series_equal(sc, tc2[~expected]) expected = Series([False, True, True, False, False, False, False]) assert_series_equal(tc2.duplicated(keep='last'), expected) assert_series_equal(tc2.drop_duplicates(keep='last'), tc2[~expected]) sc = tc2.copy() sc.drop_duplicates(keep='last', inplace=True) assert_series_equal(sc, tc2[~expected]) expected = Series([False, True, True, False, True, True, False]) assert_series_equal(tc2.duplicated(keep=False), expected) assert_series_equal(tc2.drop_duplicates(keep=False), tc2[~expected]) sc = tc2.copy() sc.drop_duplicates(keep=False, inplace=True) assert_series_equal(sc, tc2[~expected]) def test_drop_duplicates_bool(self): tc = Series([True, False, True, False]) expected = Series([False, False, True, True]) assert_series_equal(tc.duplicated(), expected) assert_series_equal(tc.drop_duplicates(), tc[~expected]) sc = tc.copy() sc.drop_duplicates(inplace=True) assert_series_equal(sc, tc[~expected]) expected = Series([True, True, False, False]) assert_series_equal(tc.duplicated(keep='last'), expected) assert_series_equal(tc.drop_duplicates(keep='last'), tc[~expected]) sc = tc.copy() sc.drop_duplicates(keep='last', inplace=True) assert_series_equal(sc, tc[~expected]) expected = Series([True, True, True, True]) assert_series_equal(tc.duplicated(keep=False), expected) assert_series_equal(tc.drop_duplicates(keep=False), tc[~expected]) sc = tc.copy() sc.drop_duplicates(keep=False, inplace=True) assert_series_equal(sc, tc[~expected]) def test_clip(self): val = self.ts.median() assert self.ts.clip_lower(val).min() == val assert self.ts.clip_upper(val).max() == val assert self.ts.clip(lower=val).min() == val assert self.ts.clip(upper=val).max() == val result = self.ts.clip(-0.5, 0.5) expected = np.clip(self.ts, -0.5, 0.5) assert_series_equal(result, expected) assert isinstance(expected, Series) def test_clip_types_and_nulls(self): sers = [Series([np.nan, 1.0, 2.0, 3.0]), Series([None, 'a', 'b', 'c']), Series(pd.to_datetime( [np.nan, 1, 2, 3], unit='D'))] for s in sers: thresh = s[2] l = s.clip_lower(thresh) u = s.clip_upper(thresh) assert l[notna(l)].min() == thresh assert u[notna(u)].max() == thresh assert list(isna(s)) == list(isna(l)) assert list(isna(s)) == list(isna(u)) def test_clip_with_na_args(self): """Should process np.nan argument as None """ # GH # 17276 s = Series([1, 2, 3]) assert_series_equal(s.clip(np.nan), Series([1, 2, 3])) assert_series_equal(s.clip(upper=np.nan, lower=np.nan), Series([1, 2, 3])) # GH #19992 assert_series_equal(s.clip(lower=[0, 4, np.nan]), Series([1, 4, np.nan])) assert_series_equal(s.clip(upper=[1, np.nan, 1]), Series([1, np.nan, 1])) def test_clip_against_series(self): # GH #6966 s = Series([1.0, 1.0, 4.0]) threshold = Series([1.0, 2.0, 3.0]) assert_series_equal(s.clip_lower(threshold), Series([1.0, 2.0, 4.0])) assert_series_equal(s.clip_upper(threshold), Series([1.0, 1.0, 3.0])) lower = Series([1.0, 2.0, 3.0]) upper = Series([1.5, 2.5, 3.5]) assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5])) assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5])) @pytest.mark.parametrize("inplace", [True, False]) @pytest.mark.parametrize("upper", [[1, 2, 3], np.asarray([1, 2, 3])]) def test_clip_against_list_like(self, inplace, upper): # GH #15390 original = pd.Series([5, 6, 7]) result = original.clip(upper=upper, inplace=inplace) expected = pd.Series([1, 2, 3]) if inplace: result = original tm.assert_series_equal(result, expected, check_exact=True) def test_clip_with_datetimes(self): # GH 11838 # naive and tz-aware datetimes t = Timestamp('2015-12-01 09:30:30') s = Series([Timestamp('2015-12-01 09:30:00'), Timestamp('2015-12-01 09:31:00')]) result = s.clip(upper=t) expected = Series([Timestamp('2015-12-01 09:30:00'), Timestamp('2015-12-01 09:30:30')]) assert_series_equal(result, expected) t = Timestamp('2015-12-01 09:30:30', tz='US/Eastern') s = Series([Timestamp('2015-12-01 09:30:00', tz='US/Eastern'), Timestamp('2015-12-01 09:31:00', tz='US/Eastern')]) result = s.clip(upper=t) expected = Series([Timestamp('2015-12-01 09:30:00', tz='US/Eastern'), Timestamp('2015-12-01 09:30:30', tz='US/Eastern')]) assert_series_equal(result, expected) def test_cummethods_bool(self): # GH 6270 # looks like a buggy np.maximum.accumulate for numpy 1.6.1, py 3.2 def cummin(x): return np.minimum.accumulate(x) def cummax(x): return np.maximum.accumulate(x) a = pd.Series([False, False, False, True, True, False, False]) b = ~a c = pd.Series([False] * len(b)) d = ~c methods = {'cumsum': np.cumsum, 'cumprod': np.cumprod, 'cummin': cummin, 'cummax': cummax} args = product((a, b, c, d), methods) for s, method in args: expected = Series(methods[method](s.values)) result = getattr(s, method)() assert_series_equal(result, expected) e = pd.Series([False, True, nan, False]) cse = pd.Series([0, 1, nan, 1], dtype=object) cpe = pd.Series([False, 0, nan, 0]) cmin = pd.Series([False, False, nan, False]) cmax = pd.Series([False, True, nan, True]) expecteds = {'cumsum': cse, 'cumprod': cpe, 'cummin': cmin, 'cummax': cmax} for method in methods: res = getattr(e, method)() assert_series_equal(res, expecteds[method]) def test_isin(self): s = Series(['A', 'B', 'C', 'a', 'B', 'B', 'A', 'C']) result = s.isin(['A', 'C']) expected = Series([True, False, True, False, False, False, True, True]) assert_series_equal(result, expected) # GH: 16012 # This specific issue has to have a series over 1e6 in len, but the # comparison array (in_list) must be large enough so that numpy doesn't # do a manual masking trick that will avoid this issue altogether s = Series(list('abcdefghijk' * 10 ** 5)) # If numpy doesn't do the manual comparison/mask, these # unorderable mixed types are what cause the exception in numpy in_list = [-1, 'a', 'b', 'G', 'Y', 'Z', 'E', 'K', 'E', 'S', 'I', 'R', 'R'] * 6 assert s.isin(in_list).sum() == 200000 def test_isin_with_string_scalar(self): # GH4763 s = Series(['A', 'B', 'C', 'a', 'B', 'B', 'A', 'C']) with pytest.raises(TypeError): s.isin('a') with pytest.raises(TypeError): s = Series(['aaa', 'b', 'c']) s.isin('aaa') def test_isin_with_i8(self): # GH 5021 expected = Series([True, True, False, False, False]) expected2 = Series([False, True, False, False, False]) # datetime64[ns] s = Series(date_range('jan-01-2013', 'jan-05-2013')) result = s.isin(s[0:2]) assert_series_equal(result, expected) result = s.isin(s[0:2].values) assert_series_equal(result, expected) # fails on dtype conversion in the first place result = s.isin(s[0:2].values.astype('datetime64[D]')) assert_series_equal(result, expected) result = s.isin([s[1]]) assert_series_equal(result, expected2) result = s.isin([np.datetime64(s[1])]) assert_series_equal(result, expected2) result = s.isin(set(s[0:2])) assert_series_equal(result, expected) # timedelta64[ns] s = Series(pd.to_timedelta(lrange(5), unit='d')) result = s.isin(s[0:2]) assert_series_equal(result, expected) @pytest.mark.parametrize("empty", [[], Series(), np.array([])]) def test_isin_empty(self, empty): # see gh-16991 s = Series(["a", "b"]) expected = Series([False, False]) result = s.isin(empty) tm.assert_series_equal(expected, result) def test_timedelta64_analytics(self): from pandas import date_range # index min/max td = Series(date_range('2012-1-1', periods=3, freq='D')) - \ Timestamp('20120101') result = td.idxmin() assert result == 0 result = td.idxmax() assert result == 2 # GH 2982 # with NaT td[0] = np.nan result = td.idxmin() assert result == 1 result = td.idxmax() assert result == 2 # abs s1 = Series(date_range('20120101', periods=3)) s2 = Series(date_range('20120102', periods=3)) expected = Series(s2 - s1) # this fails as numpy returns timedelta64[us] # result = np.abs(s1-s2) # assert_frame_equal(result,expected) result = (s1 - s2).abs() assert_series_equal(result, expected) # max/min result = td.max() expected = Timedelta('2 days') assert result == expected result = td.min() expected = Timedelta('1 days') assert result == expected def test_idxmin(self): # test idxmin # _check_stat_op approach can not be used here because of isna check. # add some NaNs self.series[5:15] = np.NaN # skipna or no assert self.series[self.series.idxmin()] == self.series.min() assert isna(self.series.idxmin(skipna=False)) # no NaNs nona = self.series.dropna() assert nona[nona.idxmin()] == nona.min() assert (nona.index.values.tolist().index(nona.idxmin()) == nona.values.argmin()) # all NaNs allna = self.series * nan assert isna(allna.idxmin()) # datetime64[ns] from pandas import date_range s = Series(date_range('20130102', periods=6)) result = s.idxmin() assert result == 0 s[0] = np.nan result = s.idxmin() assert result == 1 def test_numpy_argmin_deprecated(self): # See gh-16830 data = np.arange(1, 11) s = Series(data, index=data) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): # The deprecation of Series.argmin also causes a deprecation # warning when calling np.argmin. This behavior is temporary # until the implementation of Series.argmin is corrected. result = np.argmin(s) assert result == 1 with tm.assert_produces_warning(FutureWarning): # argmin is aliased to idxmin result = s.argmin() assert result == 1 if not _np_version_under1p10: with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): msg = "the 'out' parameter is not supported" tm.assert_raises_regex(ValueError, msg, np.argmin, s, out=data) def test_idxmax(self): # test idxmax # _check_stat_op approach can not be used here because of isna check. # add some NaNs self.series[5:15] = np.NaN # skipna or no assert self.series[self.series.idxmax()] == self.series.max() assert isna(self.series.idxmax(skipna=False)) # no NaNs nona = self.series.dropna() assert nona[nona.idxmax()] == nona.max() assert (nona.index.values.tolist().index(nona.idxmax()) == nona.values.argmax()) # all NaNs allna = self.series * nan assert isna(allna.idxmax()) from pandas import date_range s = Series(date_range('20130102', periods=6)) result = s.idxmax() assert result == 5 s[5] = np.nan result = s.idxmax() assert result == 4 # Float64Index # GH 5914 s = pd.Series([1, 2, 3], [1.1, 2.1, 3.1]) result = s.idxmax() assert result == 3.1 result = s.idxmin() assert result == 1.1 s = pd.Series(s.index, s.index) result = s.idxmax() assert result == 3.1 result = s.idxmin() assert result == 1.1 def test_numpy_argmax_deprecated(self): # See gh-16830 data = np.arange(1, 11) s = Series(data, index=data) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): # The deprecation of Series.argmax also causes a deprecation # warning when calling np.argmax. This behavior is temporary # until the implementation of Series.argmax is corrected. result = np.argmax(s) assert result == 10 with tm.assert_produces_warning(FutureWarning): # argmax is aliased to idxmax result = s.argmax() assert result == 10 if not _np_version_under1p10: with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): msg = "the 'out' parameter is not supported" tm.assert_raises_regex(ValueError, msg, np.argmax, s, out=data) def test_ptp(self): N = 1000 arr = np.random.randn(N) ser = Series(arr) assert np.ptp(ser) == np.ptp(arr) # GH11163 s = Series([3, 5, np.nan, -3, 10]) assert s.ptp() == 13 assert pd.isna(s.ptp(skipna=False)) mi = pd.MultiIndex.from_product([['a', 'b'], [1, 2, 3]]) s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi) expected = pd.Series([6, 2], index=['a', 'b'], dtype=np.float64) tm.assert_series_equal(s.ptp(level=0), expected) expected = pd.Series([np.nan, np.nan], index=['a', 'b']) tm.assert_series_equal(s.ptp(level=0, skipna=False), expected) with pytest.raises(ValueError): s.ptp(axis=1) s = pd.Series(['a', 'b', 'c', 'd', 'e']) with pytest.raises(TypeError): s.ptp() with pytest.raises(NotImplementedError): s.ptp(numeric_only=True) def test_empty_timeseries_redections_return_nat(self): # covers #11245 for dtype in ('m8[ns]', 'm8[ns]', 'M8[ns]', 'M8[ns, UTC]'): assert Series([], dtype=dtype).min() is pd.NaT assert Series([], dtype=dtype).max() is pd.NaT def test_unique_data_ownership(self): # it works! #1807 Series(Series(["a", "c", "b"]).unique()).sort_values() def test_repeat(self): s = Series(np.random.randn(3), index=['a', 'b', 'c']) reps = s.repeat(5) exp = Series(s.values.repeat(5), index=s.index.values.repeat(5)) assert_series_equal(reps, exp) with tm.assert_produces_warning(FutureWarning): result = s.repeat(reps=5) assert_series_equal(result, exp) to_rep = [2, 3, 4] reps = s.repeat(to_rep) exp = Series(s.values.repeat(to_rep), index=s.index.values.repeat(to_rep)) assert_series_equal(reps, exp) def test_numpy_repeat(self): s = Series(np.arange(3), name='x') expected = Series(s.values.repeat(2), name='x', index=s.index.values.repeat(2)) assert_series_equal(np.repeat(s, 2), expected) msg = "the 'axis' parameter is not supported" tm.assert_raises_regex(ValueError, msg, np.repeat, s, 2, axis=0) def test_searchsorted(self): s = Series([1, 2, 3]) idx = s.searchsorted(1, side='left') tm.assert_numpy_array_equal(idx, np.array([0], dtype=np.intp)) idx = s.searchsorted(1, side='right') tm.assert_numpy_array_equal(idx, np.array([1], dtype=np.intp)) with tm.assert_produces_warning(FutureWarning): idx = s.searchsorted(v=1, side='left') tm.assert_numpy_array_equal(idx, np.array([0], dtype=np.intp)) def test_searchsorted_numeric_dtypes_scalar(self): s = Series([1, 2, 90, 1000, 3e9]) r = s.searchsorted(30) e = 2 assert r == e r = s.searchsorted([30]) e = np.array([2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_searchsorted_numeric_dtypes_vector(self): s = Series([1, 2, 90, 1000, 3e9]) r = s.searchsorted([91, 2e6]) e = np.array([3, 4], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_search_sorted_datetime64_scalar(self): s = Series(pd.date_range('20120101', periods=10, freq='2D')) v = pd.Timestamp('20120102') r = s.searchsorted(v) e = 1 assert r == e def test_search_sorted_datetime64_list(self): s = Series(pd.date_range('20120101', periods=10, freq='2D')) v = [pd.Timestamp('20120102'), pd.Timestamp('20120104')] r = s.searchsorted(v) e = np.array([1, 2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_searchsorted_sorter(self): # GH8490 s = Series([3, 1, 2]) r = s.searchsorted([0, 3], sorter=np.argsort(s)) e = np.array([0, 2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_is_unique(self): # GH11946 s = Series(np.random.randint(0, 10, size=1000)) assert not s.is_unique s = Series(np.arange(1000)) assert s.is_unique def test_is_unique_class_ne(self, capsys): # GH 20661 class Foo(object): def __init__(self, val): self._value = val def __ne__(self, other): raise Exception("NEQ not supported") li = [Foo(i) for i in range(5)] s = pd.Series(li, index=[i for i in range(5)]) _, err = capsys.readouterr() s.is_unique _, err = capsys.readouterr() assert len(err) == 0 def test_is_monotonic(self): s = Series(np.random.randint(0, 10, size=1000)) assert not s.is_monotonic s = Series(np.arange(1000)) assert s.is_monotonic assert s.is_monotonic_increasing s = Series(np.arange(1000, 0, -1)) assert s.is_monotonic_decreasing s = Series(pd.date_range('20130101', periods=10)) assert s.is_monotonic assert s.is_monotonic_increasing s = Series(list(reversed(s.tolist()))) assert not s.is_monotonic assert s.is_monotonic_decreasing def test_sort_index_level(self): mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC')) s = Series([1, 2], mi) backwards = s.iloc[[1, 0]] res = s.sort_index(level='A') assert_series_equal(backwards, res) res = s.sort_index(level=['A', 'B']) assert_series_equal(backwards, res) res = s.sort_index(level='A', sort_remaining=False) assert_series_equal(s, res) res = s.sort_index(level=['A', 'B'], sort_remaining=False) assert_series_equal(s, res) def test_apply_categorical(self): values = pd.Categorical(list('ABBABCD'), categories=list('DCBA'), ordered=True) s = pd.Series(values, name='XX', index=list('abcdefg')) result = s.apply(lambda x: x.lower()) # should be categorical dtype when the number of categories are # the same values = pd.Categorical(list('abbabcd'), categories=list('dcba'), ordered=True) exp = pd.Series(values, name='XX', index=list('abcdefg')) tm.assert_series_equal(result, exp) tm.assert_categorical_equal(result.values, exp.values) result = s.apply(lambda x: 'A') exp = pd.Series(['A'] * 7, name='XX', index=list('abcdefg')) tm.assert_series_equal(result, exp) assert result.dtype == np.object def test_shift_int(self): ts = self.ts.astype(int) shifted = ts.shift(1) expected = ts.astype(float).shift(1) assert_series_equal(shifted, expected) def test_shift_categorical(self): # GH 9416 s = pd.Series(['a', 'b', 'c', 'd'], dtype='category') assert_series_equal(s.iloc[:-1], s.shift(1).shift(-1).dropna()) sp1 = s.shift(1) assert_index_equal(s.index, sp1.index) assert np.all(sp1.values.codes[:1] == -1) assert np.all(s.values.codes[:-1] == sp1.values.codes[1:]) sn2 = s.shift(-2) assert_index_equal(s.index, sn2.index) assert np.all(sn2.values.codes[-2:] == -1) assert np.all(s.values.codes[2:] == sn2.values.codes[:-2]) assert_index_equal(s.values.categories, sp1.values.categories) assert_index_equal(s.values.categories, sn2.values.categories) def test_unstack(self): from numpy import nan index = MultiIndex(levels=[['bar', 'foo'], ['one', 'three', 'two']], labels=[[1, 1, 0, 0], [0, 1, 0, 2]]) s = Series(np.arange(4.), index=index) unstacked = s.unstack() expected = DataFrame([[2., nan, 3.], [0., 1., nan]], index=['bar', 'foo'], columns=['one', 'three', 'two']) assert_frame_equal(unstacked, expected) unstacked = s.unstack(level=0) assert_frame_equal(unstacked, expected.T) 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) exp_index = MultiIndex(levels=[['one', 'two', 'three'], [0, 1]], labels=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]]) expected = DataFrame({'bar': s.values}, index=exp_index).sort_index(level=0) unstacked = s.unstack(0).sort_index() assert_frame_equal(unstacked, expected) # GH5873 idx = pd.MultiIndex.from_arrays([[101, 102], [3.5, np.nan]]) ts = pd.Series([1, 2], index=idx) left = ts.unstack() right = DataFrame([[nan, 1], [2, nan]], index=[101, 102], columns=[nan, 3.5]) assert_frame_equal(left, right) idx = pd.MultiIndex.from_arrays([['cat', 'cat', 'cat', 'dog', 'dog' ], ['a', 'a', 'b', 'a', 'b'], [1, 2, 1, 1, np.nan]]) ts = pd.Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx) right = DataFrame([[1.0, 1.3], [1.1, nan], [nan, 1.4], [1.2, nan]], columns=['cat', 'dog']) tpls = [('a', 1), ('a', 2), ('b', nan), ('b', 1)] right.index = pd.MultiIndex.from_tuples(tpls) assert_frame_equal(ts.unstack(level=0), right) def test_value_counts_datetime(self): # most dtypes are tested in test_base.py values = [pd.Timestamp('2011-01-01 09:00'), pd.Timestamp('2011-01-01 10:00'), pd.Timestamp('2011-01-01 11:00'), pd.Timestamp('2011-01-01 09:00'), pd.Timestamp('2011-01-01 09:00'), pd.Timestamp('2011-01-01 11:00')] exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00', '2011-01-01 10:00']) exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') s = pd.Series(values, name='xxx') tm.assert_series_equal(s.value_counts(), exp) # check DatetimeIndex outputs the same result idx = pd.DatetimeIndex(values, name='xxx') tm.assert_series_equal(idx.value_counts(), exp) # normalize exp = pd.Series(np.array([3., 2., 1]) / 6., index=exp_idx, name='xxx') tm.assert_series_equal(s.value_counts(normalize=True), exp) tm.assert_series_equal(idx.value_counts(normalize=True), exp) def test_value_counts_datetime_tz(self): values = [pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), pd.Timestamp('2011-01-01 10:00', tz='US/Eastern'), pd.Timestamp('2011-01-01 11:00', tz='US/Eastern'), pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'), pd.Timestamp('2011-01-01 11:00', tz='US/Eastern')] exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00', '2011-01-01 10:00'], tz='US/Eastern') exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') s = pd.Series(values, name='xxx') tm.assert_series_equal(s.value_counts(), exp) idx = pd.DatetimeIndex(values, name='xxx') tm.assert_series_equal(idx.value_counts(), exp) exp = pd.Series(np.array([3., 2., 1]) / 6., index=exp_idx, name='xxx') tm.assert_series_equal(s.value_counts(normalize=True), exp) tm.assert_series_equal(idx.value_counts(normalize=True), exp) def test_value_counts_period(self): values = [pd.Period('2011-01', freq='M'), pd.Period('2011-02', freq='M'), pd.Period('2011-03', freq='M'), pd.Period('2011-01', freq='M'), pd.Period('2011-01', freq='M'), pd.Period('2011-03', freq='M')] exp_idx = pd.PeriodIndex(['2011-01', '2011-03', '2011-02'], freq='M') exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') s = pd.Series(values, name='xxx') tm.assert_series_equal(s.value_counts(), exp) # check DatetimeIndex outputs the same result idx = pd.PeriodIndex(values, name='xxx') tm.assert_series_equal(idx.value_counts(), exp) # normalize exp = pd.Series(np.array([3., 2., 1]) / 6., index=exp_idx, name='xxx') tm.assert_series_equal(s.value_counts(normalize=True), exp) tm.assert_series_equal(idx.value_counts(normalize=True), exp) def test_value_counts_categorical_ordered(self): # most dtypes are tested in test_base.py values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=True) exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], ordered=True) exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') s = pd.Series(values, name='xxx') tm.assert_series_equal(s.value_counts(), exp) # check CategoricalIndex outputs the same result idx = pd.CategoricalIndex(values, name='xxx') tm.assert_series_equal(idx.value_counts(), exp) # normalize exp = pd.Series(np.array([3., 2., 1]) / 6., index=exp_idx, name='xxx') tm.assert_series_equal(s.value_counts(normalize=True), exp) tm.assert_series_equal(idx.value_counts(normalize=True), exp) def test_value_counts_categorical_not_ordered(self): values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=False) exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], ordered=False) exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx') s = pd.Series(values, name='xxx') tm.assert_series_equal(s.value_counts(), exp) # check CategoricalIndex outputs the same result idx = pd.CategoricalIndex(values, name='xxx') tm.assert_series_equal(idx.value_counts(), exp) # normalize exp = pd.Series(np.array([3., 2., 1]) / 6., index=exp_idx, name='xxx') tm.assert_series_equal(s.value_counts(normalize=True), exp) tm.assert_series_equal(idx.value_counts(normalize=True), exp) @pytest.fixture def s_main_dtypes(): df = pd.DataFrame( {'datetime': pd.to_datetime(['2003', '2002', '2001', '2002', '2005']), 'datetimetz': pd.to_datetime( ['2003', '2002', '2001', '2002', '2005']).tz_localize('US/Eastern'), 'timedelta': pd.to_timedelta(['3d', '2d', '1d', '2d', '5d'])}) for dtype in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64', 'uint8', 'uint16', 'uint32', 'uint64']: df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype) return df def assert_check_nselect_boundary(vals, dtype, method): # helper function for 'test_boundary_{dtype}' tests s = Series(vals, dtype=dtype) result = getattr(s, method)(3) expected_idxr = [0, 1, 2] if method == 'nsmallest' else [3, 2, 1] expected = s.loc[expected_idxr] tm.assert_series_equal(result, expected) class TestNLargestNSmallest(object): @pytest.mark.parametrize( "r", [Series([3., 2, 1, 2, '5'], dtype='object'), Series([3., 2, 1, 2, 5], dtype='object'), # not supported on some archs # Series([3., 2, 1, 2, 5], dtype='complex256'), Series([3., 2, 1, 2, 5], dtype='complex128'), Series(list('abcde')), Series(list('abcde'), dtype='category')]) def test_error(self, r): dt = r.dtype msg = ("Cannot use method 'n(larg|small)est' with " "dtype {dt}".format(dt=dt)) args = 2, len(r), 0, -1 methods = r.nlargest, r.nsmallest for method, arg in product(methods, args): with tm.assert_raises_regex(TypeError, msg): method(arg) @pytest.mark.parametrize( "s", [v for k, v in s_main_dtypes().iteritems()]) def test_nsmallest_nlargest(self, s): # float, int, datetime64 (use i8), timedelts64 (same), # object that are numbers, object that are strings assert_series_equal(s.nsmallest(2), s.iloc[[2, 1]]) assert_series_equal(s.nsmallest(2, keep='last'), s.iloc[[2, 3]]) empty = s.iloc[0:0] assert_series_equal(s.nsmallest(0), empty) assert_series_equal(s.nsmallest(-1), empty) assert_series_equal(s.nlargest(0), empty) assert_series_equal(s.nlargest(-1), empty) assert_series_equal(s.nsmallest(len(s)), s.sort_values()) assert_series_equal(s.nsmallest(len(s) + 1), s.sort_values()) assert_series_equal(s.nlargest(len(s)), s.iloc[[4, 0, 1, 3, 2]]) assert_series_equal(s.nlargest(len(s) + 1), s.iloc[[4, 0, 1, 3, 2]]) def test_misc(self): s = Series([3., np.nan, 1, 2, 5]) assert_series_equal(s.nlargest(), s.iloc[[4, 0, 3, 2]]) assert_series_equal(s.nsmallest(), s.iloc[[2, 3, 0, 4]]) msg = 'keep must be either "first", "last"' with tm.assert_raises_regex(ValueError, msg): s.nsmallest(keep='invalid') with tm.assert_raises_regex(ValueError, msg): s.nlargest(keep='invalid') # GH 15297 s = Series([1] * 5, index=[1, 2, 3, 4, 5]) expected_first = Series([1] * 3, index=[1, 2, 3]) expected_last = Series([1] * 3, index=[5, 4, 3]) result = s.nsmallest(3) assert_series_equal(result, expected_first) result = s.nsmallest(3, keep='last') assert_series_equal(result, expected_last) result = s.nlargest(3) assert_series_equal(result, expected_first) result = s.nlargest(3, keep='last') assert_series_equal(result, expected_last) @pytest.mark.parametrize('n', range(1, 5)) def test_n(self, n): # GH 13412 s = Series([1, 4, 3, 2], index=[0, 0, 1, 1]) result = s.nlargest(n) expected = s.sort_values(ascending=False).head(n) assert_series_equal(result, expected) result = s.nsmallest(n) expected = s.sort_values().head(n) assert_series_equal(result, expected) def test_boundary_integer(self, nselect_method, any_int_dtype): # GH 21426 dtype_info = np.iinfo(any_int_dtype) min_val, max_val = dtype_info.min, dtype_info.max vals = [min_val, min_val + 1, max_val - 1, max_val] assert_check_nselect_boundary(vals, any_int_dtype, nselect_method) def test_boundary_float(self, nselect_method, float_dtype): # GH 21426 dtype_info = np.finfo(float_dtype) min_val, max_val = dtype_info.min, dtype_info.max min_2nd, max_2nd = np.nextafter( [min_val, max_val], 0, dtype=float_dtype) vals = [min_val, min_2nd, max_2nd, max_val] assert_check_nselect_boundary(vals, float_dtype, nselect_method) @pytest.mark.parametrize('dtype', ['datetime64[ns]', 'timedelta64[ns]']) def test_boundary_datetimelike(self, nselect_method, dtype): # GH 21426 # use int64 bounds and +1 to min_val since true minimum is NaT # (include min_val/NaT at end to maintain same expected_idxr) dtype_info = np.iinfo('int64') min_val, max_val = dtype_info.min, dtype_info.max vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val] assert_check_nselect_boundary(vals, dtype, nselect_method) class TestCategoricalSeriesAnalytics(object): def test_count(self): s = Series(Categorical([np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True)) result = s.count() assert result == 2 def test_min_max(self): # unordered cats have no min/max cat = Series(Categorical(["a", "b", "c", "d"], ordered=False)) pytest.raises(TypeError, lambda: cat.min()) pytest.raises(TypeError, lambda: cat.max()) cat = Series(Categorical(["a", "b", "c", "d"], ordered=True)) _min = cat.min() _max = cat.max() assert _min == "a" assert _max == "d" cat = Series(Categorical(["a", "b", "c", "d"], categories=[ 'd', 'c', 'b', 'a'], ordered=True)) _min = cat.min() _max = cat.max() assert _min == "d" assert _max == "a" cat = Series(Categorical( [np.nan, "b", "c", np.nan], categories=['d', 'c', 'b', 'a' ], ordered=True)) _min = cat.min() _max = cat.max() assert np.isnan(_min) assert _max == "b" cat = Series(Categorical( [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True)) _min = cat.min() _max = cat.max() assert np.isnan(_min) assert _max == 1 def test_mode(self): s = Series(Categorical([1, 1, 2, 4, 5, 5, 5], categories=[5, 4, 3, 2, 1], ordered=True)) res = s.mode() exp = Series(Categorical([5], categories=[ 5, 4, 3, 2, 1], ordered=True)) tm.assert_series_equal(res, exp) s = Series(Categorical([1, 1, 1, 4, 5, 5, 5], categories=[5, 4, 3, 2, 1], ordered=True)) res = s.mode() exp = Series(Categorical([5, 1], categories=[ 5, 4, 3, 2, 1], ordered=True)) tm.assert_series_equal(res, exp) s = Series(Categorical([1, 2, 3, 4, 5], categories=[5, 4, 3, 2, 1], ordered=True)) res = s.mode() exp = Series(Categorical([5, 4, 3, 2, 1], categories=[5, 4, 3, 2, 1], ordered=True)) tm.assert_series_equal(res, exp) def test_value_counts(self): # GH 12835 cats = Categorical(list('abcccb'), categories=list('cabd')) s = Series(cats, name='xxx') res = s.value_counts(sort=False) exp_index = CategoricalIndex(list('cabd'), categories=cats.categories) exp = Series([3, 1, 2, 0], name='xxx', index=exp_index) tm.assert_series_equal(res, exp) res = s.value_counts(sort=True) exp_index = CategoricalIndex(list('cbad'), categories=cats.categories) exp = Series([3, 2, 1, 0], name='xxx', index=exp_index) tm.assert_series_equal(res, exp) # check object dtype handles the Series.name as the same # (tested in test_base.py) s = Series(["a", "b", "c", "c", "c", "b"], name='xxx') res = s.value_counts() exp = Series([3, 2, 1], name='xxx', index=["c", "b", "a"]) tm.assert_series_equal(res, exp) def test_value_counts_with_nan(self): # see gh-9443 # sanity check s = Series(["a", "b", "a"], dtype="category") exp = Series([2, 1], index=CategoricalIndex(["a", "b"])) res = s.value_counts(dropna=True) tm.assert_series_equal(res, exp) res = s.value_counts(dropna=True) tm.assert_series_equal(res, exp) # same Series via two different constructions --> same behaviour series = [ Series(["a", "b", None, "a", None, None], dtype="category"), Series(Categorical(["a", "b", None, "a", None, None], categories=["a", "b"])) ] for s in series: # None is a NaN value, so we exclude its count here exp = Series([2, 1], index=CategoricalIndex(["a", "b"])) res = s.value_counts(dropna=True) tm.assert_series_equal(res, exp) # we don't exclude the count of None and sort by counts exp = Series([3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"])) res = s.value_counts(dropna=False) tm.assert_series_equal(res, exp) # When we aren't sorting by counts, and np.nan isn't a # category, it should be last. exp = Series([2, 1, 3], index=CategoricalIndex(["a", "b", np.nan])) res = s.value_counts(dropna=False, sort=False) tm.assert_series_equal(res, exp) @pytest.mark.parametrize( "dtype", ["int_", "uint", "float_", "unicode_", "timedelta64[h]", pytest.param("datetime64[D]", marks=pytest.mark.xfail(reason="issue7996"))] ) @pytest.mark.parametrize("is_ordered", [True, False]) def test_drop_duplicates_categorical_non_bool(self, dtype, is_ordered): cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype)) # Test case 1 input1 = np.array([1, 2, 3, 3], dtype=np.dtype(dtype)) tc1 = Series(Categorical(input1, categories=cat_array, ordered=is_ordered)) expected = Series([False, False, False, True]) tm.assert_series_equal(tc1.duplicated(), expected) tm.assert_series_equal(tc1.drop_duplicates(), tc1[~expected]) sc = tc1.copy() sc.drop_duplicates(inplace=True) tm.assert_series_equal(sc, tc1[~expected]) expected = Series([False, False, True, False]) tm.assert_series_equal(tc1.duplicated(keep='last'), expected) tm.assert_series_equal(tc1.drop_duplicates(keep='last'), tc1[~expected]) sc = tc1.copy() sc.drop_duplicates(keep='last', inplace=True) tm.assert_series_equal(sc, tc1[~expected]) expected = Series([False, False, True, True]) tm.assert_series_equal(tc1.duplicated(keep=False), expected) tm.assert_series_equal(tc1.drop_duplicates(keep=False), tc1[~expected]) sc = tc1.copy() sc.drop_duplicates(keep=False, inplace=True) tm.assert_series_equal(sc, tc1[~expected]) # Test case 2 input2 = np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype(dtype)) tc2 = Series(Categorical( input2, categories=cat_array, ordered=is_ordered) ) expected = Series([False, False, False, False, True, True, False]) tm.assert_series_equal(tc2.duplicated(), expected) tm.assert_series_equal(tc2.drop_duplicates(), tc2[~expected]) sc = tc2.copy() sc.drop_duplicates(inplace=True) tm.assert_series_equal(sc, tc2[~expected]) expected = Series([False, True, True, False, False, False, False]) tm.assert_series_equal(tc2.duplicated(keep='last'), expected) tm.assert_series_equal(tc2.drop_duplicates(keep='last'), tc2[~expected]) sc = tc2.copy() sc.drop_duplicates(keep='last', inplace=True) tm.assert_series_equal(sc, tc2[~expected]) expected = Series([False, True, True, False, True, True, False]) tm.assert_series_equal(tc2.duplicated(keep=False), expected) tm.assert_series_equal(tc2.drop_duplicates(keep=False), tc2[~expected]) sc = tc2.copy() sc.drop_duplicates(keep=False, inplace=True) tm.assert_series_equal(sc, tc2[~expected]) @pytest.mark.parametrize("is_ordered", [True, False]) def test_drop_duplicates_categorical_bool(self, is_ordered): tc = Series(Categorical([True, False, True, False], categories=[True, False], ordered=is_ordered)) expected = Series([False, False, True, True]) tm.assert_series_equal(tc.duplicated(), expected) tm.assert_series_equal(tc.drop_duplicates(), tc[~expected]) sc = tc.copy() sc.drop_duplicates(inplace=True) tm.assert_series_equal(sc, tc[~expected]) expected = Series([True, True, False, False]) tm.assert_series_equal(tc.duplicated(keep='last'), expected) tm.assert_series_equal(tc.drop_duplicates(keep='last'), tc[~expected]) sc = tc.copy() sc.drop_duplicates(keep='last', inplace=True) tm.assert_series_equal(sc, tc[~expected]) expected = Series([True, True, True, True]) tm.assert_series_equal(tc.duplicated(keep=False), expected) tm.assert_series_equal(tc.drop_duplicates(keep=False), tc[~expected]) sc = tc.copy() sc.drop_duplicates(keep=False, inplace=True) tm.assert_series_equal(sc, tc[~expected])