# -*- coding: utf-8 -*- from __future__ import print_function import re import sys from datetime import datetime, timedelta import pytest import numpy as np import pandas as pd import pandas.compat as compat from pandas.core.dtypes.common import ( is_object_dtype, is_datetimetz, is_datetime64_dtype, needs_i8_conversion) import pandas.util.testing as tm from pandas import (Series, Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta, IntervalIndex, Interval, CategoricalIndex, Timestamp) from pandas.compat import StringIO, PYPY, long from pandas.compat.numpy import np_array_datetime64_compat from pandas.core.accessor import PandasDelegate from pandas.core.base import PandasObject, NoNewAttributesMixin from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin from pandas._libs.tslib import iNaT class CheckStringMixin(object): def test_string_methods_dont_fail(self): repr(self.container) str(self.container) bytes(self.container) if not compat.PY3: unicode(self.container) # noqa def test_tricky_container(self): if not hasattr(self, 'unicode_container'): pytest.skip('Need unicode_container to test with this') repr(self.unicode_container) str(self.unicode_container) bytes(self.unicode_container) if not compat.PY3: unicode(self.unicode_container) # noqa class CheckImmutable(object): mutable_regex = re.compile('does not support mutable operations') def check_mutable_error(self, *args, **kwargs): # Pass whatever function you normally would to assert_raises_regex # (after the Exception kind). tm.assert_raises_regex( TypeError, self.mutable_regex, *args, **kwargs) def test_no_mutable_funcs(self): def setitem(): self.container[0] = 5 self.check_mutable_error(setitem) def setslice(): self.container[1:2] = 3 self.check_mutable_error(setslice) def delitem(): del self.container[0] self.check_mutable_error(delitem) def delslice(): del self.container[0:3] self.check_mutable_error(delslice) mutable_methods = getattr(self, "mutable_methods", []) for meth in mutable_methods: self.check_mutable_error(getattr(self.container, meth)) def test_slicing_maintains_type(self): result = self.container[1:2] expected = self.lst[1:2] self.check_result(result, expected) def check_result(self, result, expected, klass=None): klass = klass or self.klass assert isinstance(result, klass) assert result == expected class TestPandasDelegate(object): class Delegator(object): _properties = ['foo'] _methods = ['bar'] def _set_foo(self, value): self.foo = value def _get_foo(self): return self.foo foo = property(_get_foo, _set_foo, doc="foo property") def bar(self, *args, **kwargs): """ a test bar method """ pass class Delegate(PandasDelegate, PandasObject): def __init__(self, obj): self.obj = obj def setup_method(self, method): pass def test_invalid_delegation(self): # these show that in order for the delegation to work # the _delegate_* methods need to be overridden to not raise # a TypeError self.Delegate._add_delegate_accessors( delegate=self.Delegator, accessors=self.Delegator._properties, typ='property' ) self.Delegate._add_delegate_accessors( delegate=self.Delegator, accessors=self.Delegator._methods, typ='method' ) delegate = self.Delegate(self.Delegator()) def f(): delegate.foo pytest.raises(TypeError, f) def f(): delegate.foo = 5 pytest.raises(TypeError, f) def f(): delegate.foo() pytest.raises(TypeError, f) @pytest.mark.skipif(PYPY, reason="not relevant for PyPy") def test_memory_usage(self): # Delegate does not implement memory_usage. # Check that we fall back to in-built `__sizeof__` # GH 12924 delegate = self.Delegate(self.Delegator()) sys.getsizeof(delegate) class Ops(object): def _allow_na_ops(self, obj): """Whether to skip test cases including NaN""" if (isinstance(obj, Index) and (obj.is_boolean() or not obj._can_hold_na)): # don't test boolean / int64 index return False return True def setup_method(self, method): self.bool_index = tm.makeBoolIndex(10, name='a') self.int_index = tm.makeIntIndex(10, name='a') self.float_index = tm.makeFloatIndex(10, name='a') self.dt_index = tm.makeDateIndex(10, name='a') self.dt_tz_index = tm.makeDateIndex(10, name='a').tz_localize( tz='US/Eastern') self.period_index = tm.makePeriodIndex(10, name='a') self.string_index = tm.makeStringIndex(10, name='a') self.unicode_index = tm.makeUnicodeIndex(10, name='a') arr = np.random.randn(10) self.int_series = Series(arr, index=self.int_index, name='a') self.float_series = Series(arr, index=self.float_index, name='a') self.dt_series = Series(arr, index=self.dt_index, name='a') self.dt_tz_series = self.dt_tz_index.to_series(keep_tz=True) self.period_series = Series(arr, index=self.period_index, name='a') self.string_series = Series(arr, index=self.string_index, name='a') types = ['bool', 'int', 'float', 'dt', 'dt_tz', 'period', 'string', 'unicode'] fmts = ["{0}_{1}".format(t, f) for t in types for f in ['index', 'series']] self.objs = [getattr(self, f) for f in fmts if getattr(self, f, None) is not None] def check_ops_properties(self, props, filter=None, ignore_failures=False): for op in props: for o in self.is_valid_objs: # if a filter, skip if it doesn't match if filter is not None: filt = o.index if isinstance(o, Series) else o if not filter(filt): continue try: if isinstance(o, Series): expected = Series( getattr(o.index, op), index=o.index, name='a') else: expected = getattr(o, op) except (AttributeError): if ignore_failures: continue result = getattr(o, op) # these couuld be series, arrays or scalars if isinstance(result, Series) and isinstance(expected, Series): tm.assert_series_equal(result, expected) elif isinstance(result, Index) and isinstance(expected, Index): tm.assert_index_equal(result, expected) elif isinstance(result, np.ndarray) and isinstance(expected, np.ndarray): tm.assert_numpy_array_equal(result, expected) else: assert result == expected # freq raises AttributeError on an Int64Index because its not # defined we mostly care about Series here anyhow if not ignore_failures: for o in self.not_valid_objs: # an object that is datetimelike will raise a TypeError, # otherwise an AttributeError if issubclass(type(o), DatetimeIndexOpsMixin): pytest.raises(TypeError, lambda: getattr(o, op)) else: pytest.raises(AttributeError, lambda: getattr(o, op)) def test_binary_ops_docs(self): from pandas import DataFrame, Panel op_map = {'add': '+', 'sub': '-', 'mul': '*', 'mod': '%', 'pow': '**', 'truediv': '/', 'floordiv': '//'} for op_name in ['add', 'sub', 'mul', 'mod', 'pow', 'truediv', 'floordiv']: for klass in [Series, DataFrame, Panel]: operand1 = klass.__name__.lower() operand2 = 'other' op = op_map[op_name] expected_str = ' '.join([operand1, op, operand2]) assert expected_str in getattr(klass, op_name).__doc__ # reverse version of the binary ops expected_str = ' '.join([operand2, op, operand1]) assert expected_str in getattr(klass, 'r' + op_name).__doc__ class TestIndexOps(Ops): def setup_method(self, method): super(TestIndexOps, self).setup_method(method) self.is_valid_objs = self.objs self.not_valid_objs = [] def test_none_comparison(self): # bug brought up by #1079 # changed from TypeError in 0.17.0 for o in self.is_valid_objs: if isinstance(o, Series): o[0] = np.nan # noinspection PyComparisonWithNone result = o == None # noqa assert not result.iat[0] assert not result.iat[1] # noinspection PyComparisonWithNone result = o != None # noqa assert result.iat[0] assert result.iat[1] result = None == o # noqa assert not result.iat[0] assert not result.iat[1] # this fails for numpy < 1.9 # and oddly for *some* platforms # result = None != o # noqa # assert result.iat[0] # assert result.iat[1] if (is_datetime64_dtype(o) or is_datetimetz(o)): # Following DatetimeIndex (and Timestamp) convention, # inequality comparisons with Series[datetime64] raise with pytest.raises(TypeError): None > o with pytest.raises(TypeError): o > None else: result = None > o assert not result.iat[0] assert not result.iat[1] result = o < None assert not result.iat[0] assert not result.iat[1] def test_ndarray_compat_properties(self): for o in self.objs: # Check that we work. for p in ['shape', 'dtype', 'T', 'nbytes']: assert getattr(o, p, None) is not None # deprecated properties for p in ['flags', 'strides', 'itemsize']: with tm.assert_produces_warning(FutureWarning): assert getattr(o, p, None) is not None with tm.assert_produces_warning(FutureWarning): assert hasattr(o, 'base') # If we have a datetime-like dtype then needs a view to work # but the user is responsible for that try: with tm.assert_produces_warning(FutureWarning): assert o.data is not None except ValueError: pass with pytest.raises(ValueError): o.item() # len > 1 assert o.ndim == 1 assert o.size == len(o) assert Index([1]).item() == 1 assert Series([1]).item() == 1 def test_ops(self): for op in ['max', 'min']: for o in self.objs: result = getattr(o, op)() if not isinstance(o, PeriodIndex): expected = getattr(o.values, op)() else: expected = pd.Period( ordinal=getattr(o._ndarray_values, op)(), freq=o.freq) try: assert result == expected except TypeError: # comparing tz-aware series with np.array results in # TypeError expected = expected.astype('M8[ns]').astype('int64') assert result.value == expected def test_nanops(self): # GH 7261 for op in ['max', 'min']: for klass in [Index, Series]: obj = klass([np.nan, 2.0]) assert getattr(obj, op)() == 2.0 obj = klass([np.nan]) assert pd.isna(getattr(obj, op)()) obj = klass([]) assert pd.isna(getattr(obj, op)()) obj = klass([pd.NaT, datetime(2011, 11, 1)]) # check DatetimeIndex monotonic path assert getattr(obj, op)() == datetime(2011, 11, 1) obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT]) # check DatetimeIndex non-monotonic path assert getattr(obj, op)(), datetime(2011, 11, 1) # argmin/max obj = Index(np.arange(5, dtype='int64')) assert obj.argmin() == 0 assert obj.argmax() == 4 obj = Index([np.nan, 1, np.nan, 2]) assert obj.argmin() == 1 assert obj.argmax() == 3 obj = Index([np.nan]) assert obj.argmin() == -1 assert obj.argmax() == -1 obj = Index([pd.NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), pd.NaT]) assert obj.argmin() == 1 assert obj.argmax() == 2 obj = Index([pd.NaT]) assert obj.argmin() == -1 assert obj.argmax() == -1 def test_value_counts_unique_nunique(self): for orig in self.objs: o = orig.copy() klass = type(o) values = o._values if isinstance(values, Index): # reset name not to affect latter process values.name = None # create repeated values, 'n'th element is repeated by n+1 times # skip boolean, because it only has 2 values at most if isinstance(o, Index) and o.is_boolean(): continue elif isinstance(o, Index): expected_index = Index(o[::-1]) expected_index.name = None o = o.repeat(range(1, len(o) + 1)) o.name = 'a' else: expected_index = Index(values[::-1]) idx = o.index.repeat(range(1, len(o) + 1)) rep = np.repeat(values, range(1, len(o) + 1)) o = klass(rep, index=idx, name='a') # check values has the same dtype as the original assert o.dtype == orig.dtype expected_s = Series(range(10, 0, -1), index=expected_index, dtype='int64', name='a') result = o.value_counts() tm.assert_series_equal(result, expected_s) assert result.index.name is None assert result.name == 'a' result = o.unique() if isinstance(o, Index): assert isinstance(result, o.__class__) tm.assert_index_equal(result, orig) elif is_datetimetz(o): # datetimetz Series returns array of Timestamp assert result[0] == orig[0] for r in result: assert isinstance(r, Timestamp) tm.assert_numpy_array_equal(result, orig._values.astype(object).values) else: tm.assert_numpy_array_equal(result, orig.values) assert o.nunique() == len(np.unique(o.values)) def test_value_counts_unique_nunique_null(self): for null_obj in [np.nan, None]: for orig in self.objs: o = orig.copy() klass = type(o) values = o._ndarray_values if not self._allow_na_ops(o): continue # special assign to the numpy array if is_datetimetz(o): if isinstance(o, DatetimeIndex): v = o.asi8 v[0:2] = iNaT values = o._shallow_copy(v) else: o = o.copy() o[0:2] = iNaT values = o._values elif needs_i8_conversion(o): values[0:2] = iNaT values = o._shallow_copy(values) else: values[0:2] = null_obj # check values has the same dtype as the original assert values.dtype == o.dtype # create repeated values, 'n'th element is repeated by n+1 # times if isinstance(o, (DatetimeIndex, PeriodIndex)): expected_index = o.copy() expected_index.name = None # attach name to klass o = klass(values.repeat(range(1, len(o) + 1))) o.name = 'a' else: if is_datetimetz(o): expected_index = orig._values._shallow_copy(values) else: expected_index = Index(values) expected_index.name = None o = o.repeat(range(1, len(o) + 1)) o.name = 'a' # check values has the same dtype as the original assert o.dtype == orig.dtype # check values correctly have NaN nanloc = np.zeros(len(o), dtype=np.bool) nanloc[:3] = True if isinstance(o, Index): tm.assert_numpy_array_equal(pd.isna(o), nanloc) else: exp = Series(nanloc, o.index, name='a') tm.assert_series_equal(pd.isna(o), exp) expected_s_na = Series(list(range(10, 2, -1)) + [3], index=expected_index[9:0:-1], dtype='int64', name='a') expected_s = Series(list(range(10, 2, -1)), index=expected_index[9:1:-1], dtype='int64', name='a') result_s_na = o.value_counts(dropna=False) tm.assert_series_equal(result_s_na, expected_s_na) assert result_s_na.index.name is None assert result_s_na.name == 'a' result_s = o.value_counts() tm.assert_series_equal(o.value_counts(), expected_s) assert result_s.index.name is None assert result_s.name == 'a' result = o.unique() if isinstance(o, Index): tm.assert_index_equal(result, Index(values[1:], name='a')) elif is_datetimetz(o): # unable to compare NaT / nan vals = values[2:].astype(object).values tm.assert_numpy_array_equal(result[1:], vals) assert result[0] is pd.NaT else: tm.assert_numpy_array_equal(result[1:], values[2:]) assert pd.isna(result[0]) assert result.dtype == orig.dtype assert o.nunique() == 8 assert o.nunique(dropna=False) == 9 def test_value_counts_inferred(self): klasses = [Index, Series] for klass in klasses: s_values = ['a', 'b', 'b', 'b', 'b', 'c', 'd', 'd', 'a', 'a'] s = klass(s_values) expected = Series([4, 3, 2, 1], index=['b', 'a', 'd', 'c']) tm.assert_series_equal(s.value_counts(), expected) if isinstance(s, Index): exp = Index(np.unique(np.array(s_values, dtype=np.object_))) tm.assert_index_equal(s.unique(), exp) else: exp = np.unique(np.array(s_values, dtype=np.object_)) tm.assert_numpy_array_equal(s.unique(), exp) assert s.nunique() == 4 # don't sort, have to sort after the fact as not sorting is # platform-dep hist = s.value_counts(sort=False).sort_values() expected = Series([3, 1, 4, 2], index=list('acbd')).sort_values() tm.assert_series_equal(hist, expected) # sort ascending hist = s.value_counts(ascending=True) expected = Series([1, 2, 3, 4], index=list('cdab')) tm.assert_series_equal(hist, expected) # relative histogram. hist = s.value_counts(normalize=True) expected = Series([.4, .3, .2, .1], index=['b', 'a', 'd', 'c']) tm.assert_series_equal(hist, expected) def test_value_counts_bins(self): klasses = [Index, Series] for klass in klasses: s_values = ['a', 'b', 'b', 'b', 'b', 'c', 'd', 'd', 'a', 'a'] s = klass(s_values) # bins pytest.raises(TypeError, lambda bins: s.value_counts(bins=bins), 1) s1 = Series([1, 1, 2, 3]) res1 = s1.value_counts(bins=1) exp1 = Series({Interval(0.997, 3.0): 4}) tm.assert_series_equal(res1, exp1) res1n = s1.value_counts(bins=1, normalize=True) exp1n = Series({Interval(0.997, 3.0): 1.0}) tm.assert_series_equal(res1n, exp1n) if isinstance(s1, Index): tm.assert_index_equal(s1.unique(), Index([1, 2, 3])) else: exp = np.array([1, 2, 3], dtype=np.int64) tm.assert_numpy_array_equal(s1.unique(), exp) assert s1.nunique() == 3 # these return the same res4 = s1.value_counts(bins=4, dropna=True) intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0]) exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 3, 1, 2])) tm.assert_series_equal(res4, exp4) res4 = s1.value_counts(bins=4, dropna=False) intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0]) exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 3, 1, 2])) tm.assert_series_equal(res4, exp4) res4n = s1.value_counts(bins=4, normalize=True) exp4n = Series([0.5, 0.25, 0.25, 0], index=intervals.take([0, 3, 1, 2])) tm.assert_series_equal(res4n, exp4n) # handle NA's properly s_values = ['a', 'b', 'b', 'b', np.nan, np.nan, 'd', 'd', 'a', 'a', 'b'] s = klass(s_values) expected = Series([4, 3, 2], index=['b', 'a', 'd']) tm.assert_series_equal(s.value_counts(), expected) if isinstance(s, Index): exp = Index(['a', 'b', np.nan, 'd']) tm.assert_index_equal(s.unique(), exp) else: exp = np.array(['a', 'b', np.nan, 'd'], dtype=object) tm.assert_numpy_array_equal(s.unique(), exp) assert s.nunique() == 3 s = klass({}) expected = Series([], dtype=np.int64) tm.assert_series_equal(s.value_counts(), expected, check_index_type=False) # returned dtype differs depending on original if isinstance(s, Index): tm.assert_index_equal(s.unique(), Index([]), exact=False) else: tm.assert_numpy_array_equal(s.unique(), np.array([]), check_dtype=False) assert s.nunique() == 0 @pytest.mark.parametrize('klass', [Index, Series]) def test_value_counts_datetime64(self, klass): # GH 3002, datetime64[ns] # don't test names though txt = "\n".join(['xxyyzz20100101PIE', 'xxyyzz20100101GUM', 'xxyyzz20100101EGG', 'xxyyww20090101EGG', 'foofoo20080909PIE', 'foofoo20080909GUM']) f = StringIO(txt) df = pd.read_fwf(f, widths=[6, 8, 3], names=["person_id", "dt", "food"], parse_dates=["dt"]) s = klass(df['dt'].copy()) s.name = None idx = pd.to_datetime(['2010-01-01 00:00:00Z', '2008-09-09 00:00:00Z', '2009-01-01 00:00:00Z']) expected_s = Series([3, 2, 1], index=idx) tm.assert_series_equal(s.value_counts(), expected_s) expected = np_array_datetime64_compat(['2010-01-01 00:00:00Z', '2009-01-01 00:00:00Z', '2008-09-09 00:00:00Z'], dtype='datetime64[ns]') if isinstance(s, Index): tm.assert_index_equal(s.unique(), DatetimeIndex(expected)) else: tm.assert_numpy_array_equal(s.unique(), expected) assert s.nunique() == 3 # with NaT s = df['dt'].copy() s = klass([v for v in s.values] + [pd.NaT]) result = s.value_counts() assert result.index.dtype == 'datetime64[ns]' tm.assert_series_equal(result, expected_s) result = s.value_counts(dropna=False) expected_s[pd.NaT] = 1 tm.assert_series_equal(result, expected_s) unique = s.unique() assert unique.dtype == 'datetime64[ns]' # numpy_array_equal cannot compare pd.NaT if isinstance(s, Index): exp_idx = DatetimeIndex(expected.tolist() + [pd.NaT]) tm.assert_index_equal(unique, exp_idx) else: tm.assert_numpy_array_equal(unique[:3], expected) assert pd.isna(unique[3]) assert s.nunique() == 3 assert s.nunique(dropna=False) == 4 # timedelta64[ns] td = df.dt - df.dt + timedelta(1) td = klass(td, name='dt') result = td.value_counts() expected_s = Series([6], index=[Timedelta('1day')], name='dt') tm.assert_series_equal(result, expected_s) expected = TimedeltaIndex(['1 days'], name='dt') if isinstance(td, Index): tm.assert_index_equal(td.unique(), expected) else: tm.assert_numpy_array_equal(td.unique(), expected.values) td2 = timedelta(1) + (df.dt - df.dt) td2 = klass(td2, name='dt') result2 = td2.value_counts() tm.assert_series_equal(result2, expected_s) def test_factorize(self): for orig in self.objs: o = orig.copy() if isinstance(o, Index) and o.is_boolean(): exp_arr = np.array([0, 1] + [0] * 8, dtype=np.intp) exp_uniques = o exp_uniques = Index([False, True]) else: exp_arr = np.array(range(len(o)), dtype=np.intp) exp_uniques = o labels, uniques = o.factorize() tm.assert_numpy_array_equal(labels, exp_arr) if isinstance(o, Series): tm.assert_index_equal(uniques, Index(orig), check_names=False) else: # factorize explicitly resets name tm.assert_index_equal(uniques, exp_uniques, check_names=False) def test_factorize_repeated(self): for orig in self.objs: o = orig.copy() # don't test boolean if isinstance(o, Index) and o.is_boolean(): continue # sort by value, and create duplicates if isinstance(o, Series): o = o.sort_values() n = o.iloc[5:].append(o) else: indexer = o.argsort() o = o.take(indexer) n = o[5:].append(o) exp_arr = np.array([5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.intp) labels, uniques = n.factorize(sort=True) tm.assert_numpy_array_equal(labels, exp_arr) if isinstance(o, Series): tm.assert_index_equal(uniques, Index(orig).sort_values(), check_names=False) else: tm.assert_index_equal(uniques, o, check_names=False) exp_arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4], np.intp) labels, uniques = n.factorize(sort=False) tm.assert_numpy_array_equal(labels, exp_arr) if isinstance(o, Series): expected = Index(o.iloc[5:10].append(o.iloc[:5])) tm.assert_index_equal(uniques, expected, check_names=False) else: expected = o[5:10].append(o[:5]) tm.assert_index_equal(uniques, expected, check_names=False) def test_duplicated_drop_duplicates_index(self): # GH 4060 for original in self.objs: if isinstance(original, Index): # special case if original.is_boolean(): result = original.drop_duplicates() expected = Index([False, True], name='a') tm.assert_index_equal(result, expected) continue # original doesn't have duplicates expected = np.array([False] * len(original), dtype=bool) duplicated = original.duplicated() tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool result = original.drop_duplicates() tm.assert_index_equal(result, original) assert result is not original # has_duplicates assert not original.has_duplicates # create repeated values, 3rd and 5th values are duplicated idx = original[list(range(len(original))) + [5, 3]] expected = np.array([False] * len(original) + [True, True], dtype=bool) duplicated = idx.duplicated() tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool tm.assert_index_equal(idx.drop_duplicates(), original) base = [False] * len(idx) base[3] = True base[5] = True expected = np.array(base) duplicated = idx.duplicated(keep='last') tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool result = idx.drop_duplicates(keep='last') tm.assert_index_equal(result, idx[~expected]) base = [False] * len(original) + [True, True] base[3] = True base[5] = True expected = np.array(base) duplicated = idx.duplicated(keep=False) tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool result = idx.drop_duplicates(keep=False) tm.assert_index_equal(result, idx[~expected]) with tm.assert_raises_regex( TypeError, r"drop_duplicates\(\) got an unexpected " "keyword argument"): idx.drop_duplicates(inplace=True) else: expected = Series([False] * len(original), index=original.index, name='a') tm.assert_series_equal(original.duplicated(), expected) result = original.drop_duplicates() tm.assert_series_equal(result, original) assert result is not original idx = original.index[list(range(len(original))) + [5, 3]] values = original._values[list(range(len(original))) + [5, 3]] s = Series(values, index=idx, name='a') expected = Series([False] * len(original) + [True, True], index=idx, name='a') tm.assert_series_equal(s.duplicated(), expected) tm.assert_series_equal(s.drop_duplicates(), original) base = [False] * len(idx) base[3] = True base[5] = True expected = Series(base, index=idx, name='a') tm.assert_series_equal(s.duplicated(keep='last'), expected) tm.assert_series_equal(s.drop_duplicates(keep='last'), s[~np.array(base)]) base = [False] * len(original) + [True, True] base[3] = True base[5] = True expected = Series(base, index=idx, name='a') tm.assert_series_equal(s.duplicated(keep=False), expected) tm.assert_series_equal(s.drop_duplicates(keep=False), s[~np.array(base)]) s.drop_duplicates(inplace=True) tm.assert_series_equal(s, original) def test_drop_duplicates_series_vs_dataframe(self): # GH 14192 df = pd.DataFrame({'a': [1, 1, 1, 'one', 'one'], 'b': [2, 2, np.nan, np.nan, np.nan], 'c': [3, 3, np.nan, np.nan, 'three'], 'd': [1, 2, 3, 4, 4], 'e': [datetime(2015, 1, 1), datetime(2015, 1, 1), datetime(2015, 2, 1), pd.NaT, pd.NaT] }) for column in df.columns: for keep in ['first', 'last', False]: dropped_frame = df[[column]].drop_duplicates(keep=keep) dropped_series = df[column].drop_duplicates(keep=keep) tm.assert_frame_equal(dropped_frame, dropped_series.to_frame()) def test_fillna(self): # # GH 11343 # though Index.fillna and Series.fillna has separate impl, # test here to confirm these works as the same for orig in self.objs: o = orig.copy() values = o.values # values will not be changed result = o.fillna(o.astype(object).values[0]) if isinstance(o, Index): tm.assert_index_equal(o, result) else: tm.assert_series_equal(o, result) # check shallow_copied assert o is not result for null_obj in [np.nan, None]: for orig in self.objs: o = orig.copy() klass = type(o) if not self._allow_na_ops(o): continue if needs_i8_conversion(o): values = o.astype(object).values fill_value = values[0] values[0:2] = pd.NaT else: values = o.values.copy() fill_value = o.values[0] values[0:2] = null_obj expected = [fill_value] * 2 + list(values[2:]) expected = klass(expected) o = klass(values) # check values has the same dtype as the original assert o.dtype == orig.dtype result = o.fillna(fill_value) if isinstance(o, Index): tm.assert_index_equal(result, expected) else: tm.assert_series_equal(result, expected) # check shallow_copied assert o is not result @pytest.mark.skipif(PYPY, reason="not relevant for PyPy") def test_memory_usage(self): for o in self.objs: res = o.memory_usage() res_deep = o.memory_usage(deep=True) if (is_object_dtype(o) or (isinstance(o, Series) and is_object_dtype(o.index))): # if there are objects, only deep will pick them up assert res_deep > res else: assert res == res_deep if isinstance(o, Series): assert ((o.memory_usage(index=False) + o.index.memory_usage()) == o.memory_usage(index=True)) # sys.getsizeof will call the .memory_usage with # deep=True, and add on some GC overhead diff = res_deep - sys.getsizeof(o) assert abs(diff) < 100 def test_searchsorted(self): # See gh-12238 for o in self.objs: index = np.searchsorted(o, max(o)) assert 0 <= index <= len(o) index = np.searchsorted(o, max(o), sorter=range(len(o))) assert 0 <= index <= len(o) def test_validate_bool_args(self): invalid_values = [1, "True", [1, 2, 3], 5.0] for value in invalid_values: with pytest.raises(ValueError): self.int_series.drop_duplicates(inplace=value) class TestTranspose(Ops): errmsg = "the 'axes' parameter is not supported" def test_transpose(self): for obj in self.objs: if isinstance(obj, Index): tm.assert_index_equal(obj.transpose(), obj) else: tm.assert_series_equal(obj.transpose(), obj) def test_transpose_non_default_axes(self): for obj in self.objs: tm.assert_raises_regex(ValueError, self.errmsg, obj.transpose, 1) tm.assert_raises_regex(ValueError, self.errmsg, obj.transpose, axes=1) def test_numpy_transpose(self): for obj in self.objs: if isinstance(obj, Index): tm.assert_index_equal(np.transpose(obj), obj) else: tm.assert_series_equal(np.transpose(obj), obj) tm.assert_raises_regex(ValueError, self.errmsg, np.transpose, obj, axes=1) class TestNoNewAttributesMixin(object): def test_mixin(self): class T(NoNewAttributesMixin): pass t = T() assert not hasattr(t, "__frozen") t.a = "test" assert t.a == "test" t._freeze() assert "__frozen" in dir(t) assert getattr(t, "__frozen") def f(): t.b = "test" pytest.raises(AttributeError, f) assert not hasattr(t, "b") class TestToIterable(object): # test that we convert an iterable to python types dtypes = [ ('int8', (int, long)), ('int16', (int, long)), ('int32', (int, long)), ('int64', (int, long)), ('uint8', (int, long)), ('uint16', (int, long)), ('uint32', (int, long)), ('uint64', (int, long)), ('float16', float), ('float32', float), ('float64', float), ('datetime64[ns]', Timestamp), ('datetime64[ns, US/Eastern]', Timestamp), ('timedelta64[ns]', Timedelta)] @pytest.mark.parametrize( 'dtype, rdtype', dtypes) @pytest.mark.parametrize( 'method', [ lambda x: x.tolist(), lambda x: list(x), lambda x: list(x.__iter__()), ], ids=['tolist', 'list', 'iter']) @pytest.mark.parametrize('typ', [Series, Index]) def test_iterable(self, typ, method, dtype, rdtype): # gh-10904 # gh-13258 # coerce iteration to underlying python / pandas types s = typ([1], dtype=dtype) result = method(s)[0] assert isinstance(result, rdtype) @pytest.mark.parametrize( 'dtype, rdtype, obj', [ ('object', object, 'a'), ('object', (int, long), 1), ('category', object, 'a'), ('category', (int, long), 1)]) @pytest.mark.parametrize( 'method', [ lambda x: x.tolist(), lambda x: list(x), lambda x: list(x.__iter__()), ], ids=['tolist', 'list', 'iter']) @pytest.mark.parametrize('typ', [Series, Index]) def test_iterable_object_and_category(self, typ, method, dtype, rdtype, obj): # gh-10904 # gh-13258 # coerce iteration to underlying python / pandas types s = typ([obj], dtype=dtype) result = method(s)[0] assert isinstance(result, rdtype) @pytest.mark.parametrize( 'dtype, rdtype', dtypes) def test_iterable_items(self, dtype, rdtype): # gh-13258 # test items / iteritems yields the correct boxed scalars # this only applies to series s = Series([1], dtype=dtype) _, result = list(s.items())[0] assert isinstance(result, rdtype) _, result = list(s.iteritems())[0] assert isinstance(result, rdtype) @pytest.mark.parametrize( 'dtype, rdtype', dtypes + [ ('object', (int, long)), ('category', (int, long))]) @pytest.mark.parametrize('typ', [Series, Index]) def test_iterable_map(self, typ, dtype, rdtype): # gh-13236 # coerce iteration to underlying python / pandas types s = typ([1], dtype=dtype) result = s.map(type)[0] if not isinstance(rdtype, tuple): rdtype = tuple([rdtype]) assert result in rdtype @pytest.mark.parametrize( 'method', [ lambda x: x.tolist(), lambda x: list(x), lambda x: list(x.__iter__()), ], ids=['tolist', 'list', 'iter']) def test_categorial_datetimelike(self, method): i = CategoricalIndex([Timestamp('1999-12-31'), Timestamp('2000-12-31')]) result = method(i)[0] assert isinstance(result, Timestamp) def test_iter_box(self): vals = [Timestamp('2011-01-01'), Timestamp('2011-01-02')] s = Series(vals) assert s.dtype == 'datetime64[ns]' for res, exp in zip(s, vals): assert isinstance(res, Timestamp) assert res.tz is None assert res == exp vals = [Timestamp('2011-01-01', tz='US/Eastern'), Timestamp('2011-01-02', tz='US/Eastern')] s = Series(vals) assert s.dtype == 'datetime64[ns, US/Eastern]' for res, exp in zip(s, vals): assert isinstance(res, Timestamp) assert res.tz == exp.tz assert res == exp # timedelta vals = [Timedelta('1 days'), Timedelta('2 days')] s = Series(vals) assert s.dtype == 'timedelta64[ns]' for res, exp in zip(s, vals): assert isinstance(res, Timedelta) assert res == exp # period (object dtype, not boxed) vals = [pd.Period('2011-01-01', freq='M'), pd.Period('2011-01-02', freq='M')] s = Series(vals) assert s.dtype == 'object' for res, exp in zip(s, vals): assert isinstance(res, pd.Period) assert res.freq == 'M' assert res == exp @pytest.mark.parametrize('array, expected_type, dtype', [ (np.array([0, 1], dtype=np.int64), np.ndarray, 'int64'), (np.array(['a', 'b']), np.ndarray, 'object'), (pd.Categorical(['a', 'b']), pd.Categorical, 'category'), (pd.DatetimeIndex(['2017', '2018']), np.ndarray, 'datetime64[ns]'), (pd.DatetimeIndex(['2017', '2018'], tz="US/Central"), pd.DatetimeIndex, 'datetime64[ns, US/Central]'), (pd.TimedeltaIndex([10**10]), np.ndarray, 'm8[ns]'), (pd.PeriodIndex([2018, 2019], freq='A'), np.ndarray, 'object'), (pd.IntervalIndex.from_breaks([0, 1, 2]), np.ndarray, 'object'), ]) def test_values_consistent(array, expected_type, dtype): l_values = pd.Series(array)._values r_values = pd.Index(array)._values assert type(l_values) is expected_type assert type(l_values) is type(r_values) if isinstance(l_values, np.ndarray): tm.assert_numpy_array_equal(l_values, r_values) elif isinstance(l_values, pd.Index): tm.assert_index_equal(l_values, r_values) elif pd.api.types.is_categorical(l_values): tm.assert_categorical_equal(l_values, r_values) else: raise TypeError("Unexpected type {}".format(type(l_values))) assert l_values.dtype == dtype assert r_values.dtype == dtype @pytest.mark.parametrize('array, expected', [ (np.array([0, 1], dtype=np.int64), np.array([0, 1], dtype=np.int64)), (np.array(['0', '1']), np.array(['0', '1'], dtype=object)), (pd.Categorical(['a', 'a']), np.array([0, 0], dtype='int8')), (pd.DatetimeIndex(['2017-01-01T00:00:00']), np.array(['2017-01-01T00:00:00'], dtype='M8[ns]')), (pd.DatetimeIndex(['2017-01-01T00:00:00'], tz="US/Eastern"), np.array(['2017-01-01T05:00:00'], dtype='M8[ns]')), (pd.TimedeltaIndex([10**10]), np.array([10**10], dtype='m8[ns]')), pytest.param( pd.PeriodIndex(['2017', '2018'], freq='D'), np.array([17167, 17532]), marks=pytest.mark.xfail(reason="PeriodArray Not implemented") ), ]) def test_ndarray_values(array, expected): l_values = pd.Series(array)._ndarray_values r_values = pd.Index(array)._ndarray_values tm.assert_numpy_array_equal(l_values, r_values) tm.assert_numpy_array_equal(l_values, expected)