def test_construction_with_categorical_index(self):
ci = tm.makeCategoricalIndex(10)
# with Categorical
df = DataFrame({'A': np.random.randn(10),
'B': ci.values})
idf = df.set_index('B')
str(idf)
tm.assert_index_equal(idf.index, ci, check_names=False)
assert idf.index.name == 'B'
# from a CategoricalIndex
df = DataFrame({'A': np.random.randn(10),
'B': ci})
idf = df.set_index('B')
str(idf)
tm.assert_index_equal(idf.index, ci, check_names=False)
assert idf.index.name == 'B'
idf = df.set_index('B').reset_index().set_index('B')
str(idf)
tm.assert_index_equal(idf.index, ci, check_names=False)
assert idf.index.name == 'B'
new_df = idf.reset_index()
new_df.index = df.B
tm.assert_index_equal(new_df.index, ci, check_names=False)
assert idf.index.name == 'B'
def setup_method(self, method):
super(TestIndex, self).setup_method(method)
self.d = {
'string': tm.makeStringIndex(100),
'date': tm.makeDateIndex(100),
'int': tm.makeIntIndex(100),
'rng': tm.makeRangeIndex(100),
'float': tm.makeFloatIndex(100),
'empty': Index([]),
'tuple': Index(zip(['foo', 'bar', 'baz'], [1, 2, 3])),
'period': Index(period_range('2012-1-1', freq='M', periods=3)),
'date2': Index(date_range('2013-01-1', periods=10)),
'bdate': Index(bdate_range('2013-01-02', periods=10)),
'cat': tm.makeCategoricalIndex(100),
'interval': tm.makeIntervalIndex(100),
'timedelta': tm.makeTimedeltaIndex(100, 'H')
}
self.mi = {
'reg': MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'),
('foo', 'two'),
('qux', 'one'), ('qux', 'two')],
names=['first', 'second']),
}
def setup(self):
N = 10**5
ncats = 100
np.random.seed(1234)
self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
self.s_str_cat = self.s_str.astype('category')
self.s_str_cat_ordered = self.s_str.astype('category', ordered=True)
self.s_int = pd.Series(np.random.randint(0, ncats, size=N))
self.s_int_cat = self.s_int.astype('category')
self.s_int_cat_ordered = self.s_int.astype('category', ordered=True)
def setup(self):
N = 10**5
ncats = 100
self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
self.s_str_cat = self.s_str.astype('category')
with warnings.catch_warnings(record=True):
self.s_str_cat_ordered = self.s_str.astype('category',
ordered=True)
self.s_int = pd.Series(np.random.randint(0, ncats, size=N))
self.s_int_cat = self.s_int.astype('category')
with warnings.catch_warnings(record=True):
self.s_int_cat_ordered = self.s_int.astype('category',
ordered=True)
def test_construction_with_categorical_index(self):
ci = tm.makeCategoricalIndex(10)
ci.name = 'B'
# with Categorical
df = DataFrame({'A': np.random.randn(10),
'B': ci.values})
idf = df.set_index('B')
tm.assert_index_equal(idf.index, ci)
# from a CategoricalIndex
df = DataFrame({'A': np.random.randn(10),
'B': ci})
idf = df.set_index('B')
tm.assert_index_equal(idf.index, ci)
# round-trip
idf = idf.reset_index().set_index('B')
tm.assert_index_equal(idf.index, ci)
class TestSeriesMisc(TestData, SharedWithSparse):
series_klass = Series
# SharedWithSparse tests use generic, series_klass-agnostic assertion
_assert_series_equal = staticmethod(tm.assert_series_equal)
def test_tab_completion(self):
# GH 9910
s = Series(list('abcd'))
# Series of str values should have .str but not .dt/.cat in __dir__
assert 'str' in dir(s)
assert 'dt' not in dir(s)
assert 'cat' not in dir(s)
# similarly for .dt
s = Series(date_range('1/1/2015', periods=5))
assert 'dt' in dir(s)
assert 'str' not in dir(s)
assert 'cat' not in dir(s)
# Similarly for .cat, but with the twist that str and dt should be
# there if the categories are of that type first cat and str.
s = Series(list('abbcd'), dtype="category")
assert 'cat' in dir(s)
assert 'str' in dir(s) # as it is a string categorical
assert 'dt' not in dir(s)
# similar to cat and str
s = Series(date_range('1/1/2015', periods=5)).astype("category")
assert 'cat' in dir(s)
assert 'str' not in dir(s)
assert 'dt' in dir(s) # as it is a datetime categorical
def test_tab_completion_with_categorical(self):
# test the tab completion display
ok_for_cat = [
'categories', 'codes', 'ordered', 'set_categories',
'add_categories', 'remove_categories', 'rename_categories',
'reorder_categories', 'remove_unused_categories', 'as_ordered',
'as_unordered'
]
def get_dir(s):
results = [r for r in s.cat.__dir__() if not r.startswith('_')]
return list(sorted(set(results)))
s = Series(list('aabbcde')).astype('category')
results = get_dir(s)
tm.assert_almost_equal(results, list(sorted(set(ok_for_cat))))
@pytest.mark.parametrize("index", [
tm.makeUnicodeIndex(10),
tm.makeStringIndex(10),
tm.makeCategoricalIndex(10),
Index(['foo', 'bar', 'baz'] * 2),
tm.makeDateIndex(10),
tm.makePeriodIndex(10),
tm.makeTimedeltaIndex(10),
tm.makeIntIndex(10),
tm.makeUIntIndex(10),
tm.makeIntIndex(10),
tm.makeFloatIndex(10),
Index([True, False]),
Index(['a{}'.format(i) for i in range(101)]),
pd.MultiIndex.from_tuples(lzip('ABCD', 'EFGH')),
pd.MultiIndex.from_tuples(lzip([0, 1, 2, 3], 'EFGH')),
])
def test_index_tab_completion(self, index):
# dir contains string-like values of the Index.
s = pd.Series(index=index)
dir_s = dir(s)
for i, x in enumerate(s.index.unique(level=0)):
if i < 100:
assert (not isinstance(x, string_types) or not isidentifier(x)
or x in dir_s)
else:
assert x not in dir_s
def test_not_hashable(self):
s_empty = Series()
s = Series([1])
pytest.raises(TypeError, hash, s_empty)
pytest.raises(TypeError, hash, s)
def test_contains(self):
tm.assert_contains_all(self.ts.index, self.ts)
def test_iter(self):
for i, val in enumerate(self.series):
assert val == self.series[i]
for i, val in enumerate(self.ts):
assert val == self.ts[i]
def test_keys(self):
# HACK: By doing this in two stages, we avoid 2to3 wrapping the call
# to .keys() in a list()
getkeys = self.ts.keys
assert getkeys() is self.ts.index
def test_values(self):
tm.assert_almost_equal(self.ts.values, self.ts, check_dtype=False)
def test_iteritems(self):
for idx, val in compat.iteritems(self.series):
assert val == self.series[idx]
for idx, val in compat.iteritems(self.ts):
assert val == self.ts[idx]
# assert is lazy (genrators don't define reverse, lists do)
assert not hasattr(self.series.iteritems(), 'reverse')
def test_items(self):
for idx, val in self.series.items():
assert val == self.series[idx]
for idx, val in self.ts.items():
assert val == self.ts[idx]
# assert is lazy (genrators don't define reverse, lists do)
assert not hasattr(self.series.items(), 'reverse')
def test_raise_on_info(self):
s = Series(np.random.randn(10))
with pytest.raises(AttributeError):
s.info()
def test_copy(self):
for deep in [None, False, True]:
s = Series(np.arange(10), dtype='float64')
# default deep is True
if deep is None:
s2 = s.copy()
else:
s2 = s.copy(deep=deep)
s2[::2] = np.NaN
if deep is None or deep is True:
# Did not modify original Series
assert np.isnan(s2[0])
assert not np.isnan(s[0])
else:
# we DID modify the original Series
assert np.isnan(s2[0])
assert np.isnan(s[0])
# GH 11794
# copy of tz-aware
expected = Series([Timestamp('2012/01/01', tz='UTC')])
expected2 = Series([Timestamp('1999/01/01', tz='UTC')])
for deep in [None, False, True]:
s = Series([Timestamp('2012/01/01', tz='UTC')])
if deep is None:
s2 = s.copy()
else:
s2 = s.copy(deep=deep)
s2[0] = pd.Timestamp('1999/01/01', tz='UTC')
# default deep is True
if deep is None or deep is True:
# Did not modify original Series
assert_series_equal(s2, expected2)
assert_series_equal(s, expected)
else:
# we DID modify the original Series
assert_series_equal(s2, expected2)
assert_series_equal(s, expected2)
def test_axis_alias(self):
s = Series([1, 2, np.nan])
assert_series_equal(s.dropna(axis='rows'), s.dropna(axis='index'))
assert s.dropna().sum('rows') == 3
assert s._get_axis_number('rows') == 0
assert s._get_axis_name('rows') == 'index'
def test_class_axis(self):
# https://github.com/pandas-dev/pandas/issues/18147
# no exception and no empty docstring
assert pydoc.getdoc(Series.index)
def test_numpy_unique(self):
# it works!
np.unique(self.ts)
def test_ndarray_compat(self):
# test numpy compat with Series as sub-class of NDFrame
tsdf = DataFrame(np.random.randn(1000, 3),
columns=['A', 'B', 'C'],
index=date_range('1/1/2000', periods=1000))
def f(x):
return x[x.idxmax()]
result = tsdf.apply(f)
expected = tsdf.max()
tm.assert_series_equal(result, expected)
# .item()
s = Series([1])
result = s.item()
assert result == 1
assert s.item() == s.iloc[0]
# using an ndarray like function
s = Series(np.random.randn(10))
result = Series(np.ones_like(s))
expected = Series(1, index=range(10), dtype='float64')
tm.assert_series_equal(result, expected)
# ravel
s = Series(np.random.randn(10))
tm.assert_almost_equal(s.ravel(order='F'), s.values.ravel(order='F'))
# compress
# GH 6658
s = Series([0, 1., -1], index=list('abc'))
result = np.compress(s > 0, s)
tm.assert_series_equal(result, Series([1.], index=['b']))
result = np.compress(s < -1, s)
# result empty Index(dtype=object) as the same as original
exp = Series([], dtype='float64', index=Index([], dtype='object'))
tm.assert_series_equal(result, exp)
s = Series([0, 1., -1], index=[.1, .2, .3])
result = np.compress(s > 0, s)
tm.assert_series_equal(result, Series([1.], index=[.2]))
result = np.compress(s < -1, s)
# result empty Float64Index as the same as original
exp = Series([], dtype='float64', index=Index([], dtype='float64'))
tm.assert_series_equal(result, exp)
def test_str_attribute(self):
# GH9068
methods = ['strip', 'rstrip', 'lstrip']
s = Series([' jack', 'jill ', ' jesse ', 'frank'])
for method in methods:
expected = Series([getattr(str, method)(x) for x in s.values])
assert_series_equal(getattr(Series.str, method)(s.str), expected)
# str accessor only valid with string values
s = Series(range(5))
with tm.assert_raises_regex(AttributeError, 'only use .str accessor'):
s.str.repeat(2)
def test_empty_method(self):
s_empty = pd.Series()
assert s_empty.empty
for full_series in [pd.Series([1]), pd.Series(index=[1])]:
assert not full_series.empty
def test_tab_complete_warning(self, ip):
# https://github.com/pandas-dev/pandas/issues/16409
pytest.importorskip('IPython', minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; s = pd.Series()"
ip.run_code(code)
with tm.assert_produces_warning(None):
with provisionalcompleter('ignore'):
list(ip.Completer.completions('s.', 1))
def setup_method(self, method):
self.indices = dict(catIndex=tm.makeCategoricalIndex(100))
self.setup_indices()
def setUp(self):
self.indices = dict(catIndex=tm.makeCategoricalIndex(100))
self.setup_indices()
def indices(self, request):
return tm.makeCategoricalIndex(100)
import pandas as pd
from pandas.core.indexes.api import Index, MultiIndex
import pandas.util.testing as tm
@pytest.fixture(params=[tm.makeUnicodeIndex(100),
tm.makeStringIndex(100),
tm.makeDateIndex(100),
tm.makePeriodIndex(100),
tm.makeTimedeltaIndex(100),
tm.makeIntIndex(100),
tm.makeUIntIndex(100),
tm.makeRangeIndex(100),
tm.makeFloatIndex(100),
Index([True, False]),
tm.makeCategoricalIndex(100),
Index([]),
MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
Index([0, 0, 1, 1, 2, 2])],
ids=lambda x: type(x).__name__)
def indices(request):
return request.param
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
# zero-dim integer array behaves like an integer
return request.param
pd.DataFrame({'x': [1, 2, 3]}),
pd.DataFrame({'x': [1., 2., 3.]}),
pd.DataFrame({0: [1, 2, 3]}),
pd.DataFrame({'x': [1., 2., 3.], 'y': [4., 5., 6.]}),
pd.DataFrame({'x': [1., 2., 3.]}, index=pd.Index([4, 5, 6], name='bar')),
pd.Series([1., 2., 3.]),
pd.Series([1., 2., 3.], name='foo'),
pd.Series([1., 2., 3.], name='foo',
index=[4, 5, 6]),
pd.Series([1., 2., 3.], name='foo',
index=pd.Index([4, 5, 6], name='bar')),
pd.DataFrame({'x': ['a', 'b', 'c']}),
pd.DataFrame({'x': [b'a', b'b', b'c']}),
pd.DataFrame({'x': pd.Categorical(['a', 'b', 'a'], ordered=True)}),
pd.DataFrame({'x': pd.Categorical(['a', 'b', 'a'], ordered=False)}),
tm.makeCategoricalIndex(),
tm.makeCustomDataframe(5, 3),
tm.makeDataFrame(),
tm.makeDateIndex(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeObjectSeries(),
tm.makePeriodFrame(),
tm.makeRangeIndex(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeUnicodeIndex(),
]
@pytest.mark.parametrize('df', dfs)
class TestSeriesMisc:
def test_scalarop_preserve_name(self, datetime_series):
result = datetime_series * 2
assert result.name == datetime_series.name
def test_copy_name(self, datetime_series):
result = datetime_series.copy()
assert result.name == datetime_series.name
def test_copy_index_name_checking(self, datetime_series):
# don't want to be able to modify the index stored elsewhere after
# making a copy
datetime_series.index.name = None
assert datetime_series.index.name is None
assert datetime_series is datetime_series
cp = datetime_series.copy()
cp.index.name = "foo"
printing.pprint_thing(datetime_series.index.name)
assert datetime_series.index.name is None
def test_append_preserve_name(self, datetime_series):
result = datetime_series[:5].append(datetime_series[5:])
assert result.name == datetime_series.name
def test_binop_maybe_preserve_name(self, datetime_series):
# names match, preserve
result = datetime_series * datetime_series
assert result.name == datetime_series.name
result = datetime_series.mul(datetime_series)
assert result.name == datetime_series.name
result = datetime_series * datetime_series[:-2]
assert result.name == datetime_series.name
# names don't match, don't preserve
cp = datetime_series.copy()
cp.name = "something else"
result = datetime_series + cp
assert result.name is None
result = datetime_series.add(cp)
assert result.name is None
ops = ["add", "sub", "mul", "div", "truediv", "floordiv", "mod", "pow"]
ops = ops + ["r" + op for op in ops]
for op in ops:
# names match, preserve
s = datetime_series.copy()
result = getattr(s, op)(s)
assert result.name == datetime_series.name
# names don't match, don't preserve
cp = datetime_series.copy()
cp.name = "changed"
result = getattr(s, op)(cp)
assert result.name is None
def test_combine_first_name(self, datetime_series):
result = datetime_series.combine_first(datetime_series[:5])
assert result.name == datetime_series.name
def test_getitem_preserve_name(self, datetime_series):
result = datetime_series[datetime_series > 0]
assert result.name == datetime_series.name
result = datetime_series[[0, 2, 4]]
assert result.name == datetime_series.name
result = datetime_series[5:10]
assert result.name == datetime_series.name
def test_pickle_datetimes(self, datetime_series):
unp_ts = self._pickle_roundtrip(datetime_series)
tm.assert_series_equal(unp_ts, datetime_series)
def test_pickle_strings(self, string_series):
unp_series = self._pickle_roundtrip(string_series)
tm.assert_series_equal(unp_series, string_series)
def _pickle_roundtrip(self, obj):
with tm.ensure_clean() as path:
obj.to_pickle(path)
unpickled = pd.read_pickle(path)
return unpickled
def test_argsort_preserve_name(self, datetime_series):
result = datetime_series.argsort()
assert result.name == datetime_series.name
def test_sort_index_name(self, datetime_series):
result = datetime_series.sort_index(ascending=False)
assert result.name == datetime_series.name
def test_constructor_dict(self):
d = {"a": 0.0, "b": 1.0, "c": 2.0}
result = Series(d)
expected = Series(d, index=sorted(d.keys()))
tm.assert_series_equal(result, expected)
result = Series(d, index=["b", "c", "d", "a"])
expected = Series([1, 2, np.nan, 0], index=["b", "c", "d", "a"])
tm.assert_series_equal(result, expected)
def test_constructor_subclass_dict(self):
data = tm.TestSubDict((x, 10.0 * x) for x in range(10))
series = Series(data)
expected = Series(dict(data.items()))
tm.assert_series_equal(series, expected)
def test_constructor_ordereddict(self):
# GH3283
data = OrderedDict(
("col{i}".format(i=i), np.random.random()) for i in range(12))
series = Series(data)
expected = Series(list(data.values()), list(data.keys()))
tm.assert_series_equal(series, expected)
# Test with subclass
class A(OrderedDict):
pass
series = Series(A(data))
tm.assert_series_equal(series, expected)
def test_constructor_dict_multiindex(self):
d = {("a", "a"): 0.0, ("b", "a"): 1.0, ("b", "c"): 2.0}
_d = sorted(d.items())
result = Series(d)
expected = Series([x[1] for x in _d],
index=pd.MultiIndex.from_tuples([x[0] for x in _d]))
tm.assert_series_equal(result, expected)
d["z"] = 111.0
_d.insert(0, ("z", d["z"]))
result = Series(d)
expected = Series([x[1] for x in _d],
index=pd.Index([x[0] for x in _d],
tupleize_cols=False))
result = result.reindex(index=expected.index)
tm.assert_series_equal(result, expected)
def test_constructor_dict_timedelta_index(self):
# GH #12169 : Resample category data with timedelta index
# construct Series from dict as data and TimedeltaIndex as index
# will result NaN in result Series data
expected = Series(data=["A", "B", "C"],
index=pd.to_timedelta([0, 10, 20], unit="s"))
result = Series(
data={
pd.to_timedelta(0, unit="s"): "A",
pd.to_timedelta(10, unit="s"): "B",
pd.to_timedelta(20, unit="s"): "C",
},
index=pd.to_timedelta([0, 10, 20], unit="s"),
)
tm.assert_series_equal(result, expected)
def test_sparse_accessor_updates_on_inplace(self):
s = pd.Series([1, 1, 2, 3], dtype="Sparse[int]")
s.drop([0, 1], inplace=True)
assert s.sparse.density == 1.0
def test_tab_completion(self):
# GH 9910
s = Series(list("abcd"))
# Series of str values should have .str but not .dt/.cat in __dir__
assert "str" in dir(s)
assert "dt" not in dir(s)
assert "cat" not in dir(s)
# similarly for .dt
s = Series(date_range("1/1/2015", periods=5))
assert "dt" in dir(s)
assert "str" not in dir(s)
assert "cat" not in dir(s)
# Similarly for .cat, but with the twist that str and dt should be
# there if the categories are of that type first cat and str.
s = Series(list("abbcd"), dtype="category")
assert "cat" in dir(s)
assert "str" in dir(s) # as it is a string categorical
assert "dt" not in dir(s)
# similar to cat and str
s = Series(date_range("1/1/2015", periods=5)).astype("category")
assert "cat" in dir(s)
assert "str" not in dir(s)
assert "dt" in dir(s) # as it is a datetime categorical
def test_tab_completion_with_categorical(self):
# test the tab completion display
ok_for_cat = [
"categories",
"codes",
"ordered",
"set_categories",
"add_categories",
"remove_categories",
"rename_categories",
"reorder_categories",
"remove_unused_categories",
"as_ordered",
"as_unordered",
]
def get_dir(s):
results = [r for r in s.cat.__dir__() if not r.startswith("_")]
return sorted(set(results))
s = Series(list("aabbcde")).astype("category")
results = get_dir(s)
tm.assert_almost_equal(results, sorted(set(ok_for_cat)))
@pytest.mark.parametrize(
"index",
[
tm.makeUnicodeIndex(10),
tm.makeStringIndex(10),
tm.makeCategoricalIndex(10),
Index(["foo", "bar", "baz"] * 2),
tm.makeDateIndex(10),
tm.makePeriodIndex(10),
tm.makeTimedeltaIndex(10),
tm.makeIntIndex(10),
tm.makeUIntIndex(10),
tm.makeIntIndex(10),
tm.makeFloatIndex(10),
Index([True, False]),
Index(["a{}".format(i) for i in range(101)]),
pd.MultiIndex.from_tuples(zip("ABCD", "EFGH")),
pd.MultiIndex.from_tuples(zip([0, 1, 2, 3], "EFGH")),
],
)
def test_index_tab_completion(self, index):
# dir contains string-like values of the Index.
s = pd.Series(index=index)
dir_s = dir(s)
for i, x in enumerate(s.index.unique(level=0)):
if i < 100:
assert not isinstance(
x, str) or not x.isidentifier() or x in dir_s
else:
assert x not in dir_s
def test_not_hashable(self):
s_empty = Series()
s = Series([1])
msg = "'Series' objects are mutable, thus they cannot be hashed"
with pytest.raises(TypeError, match=msg):
hash(s_empty)
with pytest.raises(TypeError, match=msg):
hash(s)
def test_contains(self, datetime_series):
tm.assert_contains_all(datetime_series.index, datetime_series)
def test_iter_datetimes(self, datetime_series):
for i, val in enumerate(datetime_series):
assert val == datetime_series[i]
def test_iter_strings(self, string_series):
for i, val in enumerate(string_series):
assert val == string_series[i]
def test_keys(self, datetime_series):
# HACK: By doing this in two stages, we avoid 2to3 wrapping the call
# to .keys() in a list()
getkeys = datetime_series.keys
assert getkeys() is datetime_series.index
def test_values(self, datetime_series):
tm.assert_almost_equal(datetime_series.values,
datetime_series,
check_dtype=False)
def test_iteritems_datetimes(self, datetime_series):
for idx, val in datetime_series.iteritems():
assert val == datetime_series[idx]
def test_iteritems_strings(self, string_series):
for idx, val in string_series.iteritems():
assert val == string_series[idx]
# assert is lazy (genrators don't define reverse, lists do)
assert not hasattr(string_series.iteritems(), "reverse")
def test_items_datetimes(self, datetime_series):
for idx, val in datetime_series.items():
assert val == datetime_series[idx]
def test_items_strings(self, string_series):
for idx, val in string_series.items():
assert val == string_series[idx]
# assert is lazy (genrators don't define reverse, lists do)
assert not hasattr(string_series.items(), "reverse")
def test_raise_on_info(self):
s = Series(np.random.randn(10))
msg = "'Series' object has no attribute 'info'"
with pytest.raises(AttributeError, match=msg):
s.info()
def test_copy(self):
for deep in [None, False, True]:
s = Series(np.arange(10), dtype="float64")
# default deep is True
if deep is None:
s2 = s.copy()
else:
s2 = s.copy(deep=deep)
s2[::2] = np.NaN
if deep is None or deep is True:
# Did not modify original Series
assert np.isnan(s2[0])
assert not np.isnan(s[0])
else:
# we DID modify the original Series
assert np.isnan(s2[0])
assert np.isnan(s[0])
def test_copy_tzaware(self):
# GH#11794
# copy of tz-aware
expected = Series([Timestamp("2012/01/01", tz="UTC")])
expected2 = Series([Timestamp("1999/01/01", tz="UTC")])
for deep in [None, False, True]:
s = Series([Timestamp("2012/01/01", tz="UTC")])
if deep is None:
s2 = s.copy()
else:
s2 = s.copy(deep=deep)
s2[0] = pd.Timestamp("1999/01/01", tz="UTC")
# default deep is True
if deep is None or deep is True:
# Did not modify original Series
tm.assert_series_equal(s2, expected2)
tm.assert_series_equal(s, expected)
else:
# we DID modify the original Series
tm.assert_series_equal(s2, expected2)
tm.assert_series_equal(s, expected2)
def test_axis_alias(self):
s = Series([1, 2, np.nan])
tm.assert_series_equal(s.dropna(axis="rows"), s.dropna(axis="index"))
assert s.dropna().sum("rows") == 3
assert s._get_axis_number("rows") == 0
assert s._get_axis_name("rows") == "index"
def test_class_axis(self):
# https://github.com/pandas-dev/pandas/issues/18147
# no exception and no empty docstring
assert pydoc.getdoc(Series.index)
def test_numpy_unique(self, datetime_series):
# it works!
np.unique(datetime_series)
def test_ndarray_compat(self):
# test numpy compat with Series as sub-class of NDFrame
tsdf = DataFrame(
np.random.randn(1000, 3),
columns=["A", "B", "C"],
index=date_range("1/1/2000", periods=1000),
)
def f(x):
return x[x.idxmax()]
result = tsdf.apply(f)
expected = tsdf.max()
tm.assert_series_equal(result, expected)
# .item()
with tm.assert_produces_warning(FutureWarning):
s = Series([1])
result = s.item()
assert result == 1
assert s.item() == s.iloc[0]
# using an ndarray like function
s = Series(np.random.randn(10))
result = Series(np.ones_like(s))
expected = Series(1, index=range(10), dtype="float64")
tm.assert_series_equal(result, expected)
# ravel
s = Series(np.random.randn(10))
tm.assert_almost_equal(s.ravel(order="F"), s.values.ravel(order="F"))
# compress
# GH 6658
s = Series([0, 1.0, -1], index=list("abc"))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s > 0, s)
tm.assert_series_equal(result, Series([1.0], index=["b"]))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s < -1, s)
# result empty Index(dtype=object) as the same as original
exp = Series([], dtype="float64", index=Index([], dtype="object"))
tm.assert_series_equal(result, exp)
s = Series([0, 1.0, -1], index=[0.1, 0.2, 0.3])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s > 0, s)
tm.assert_series_equal(result, Series([1.0], index=[0.2]))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s < -1, s)
# result empty Float64Index as the same as original
exp = Series([], dtype="float64", index=Index([], dtype="float64"))
tm.assert_series_equal(result, exp)
def test_str_accessor_updates_on_inplace(self):
s = pd.Series(list("abc"))
s.drop([0], inplace=True)
assert len(s.str.lower()) == 2
def test_str_attribute(self):
# GH9068
methods = ["strip", "rstrip", "lstrip"]
s = Series([" jack", "jill ", " jesse ", "frank"])
for method in methods:
expected = Series([getattr(str, method)(x) for x in s.values])
tm.assert_series_equal(
getattr(Series.str, method)(s.str), expected)
# str accessor only valid with string values
s = Series(range(5))
with pytest.raises(AttributeError, match="only use .str accessor"):
s.str.repeat(2)
def test_empty_method(self):
s_empty = pd.Series()
assert s_empty.empty
for full_series in [pd.Series([1]), pd.Series(index=[1])]:
assert not full_series.empty
def test_tab_complete_warning(self, ip):
# https://github.com/pandas-dev/pandas/issues/16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; s = pd.Series()"
ip.run_code(code)
with tm.assert_produces_warning(None):
with provisionalcompleter("ignore"):
list(ip.Completer.completions("s.", 1))
def test_integer_series_size(self):
# GH 25580
s = Series(range(9))
assert s.size == 9
s = Series(range(9), dtype="Int64")
assert s.size == 9
def setup(self):
N = 10**5
self.ci = tm.makeCategoricalIndex(N)
self.c = self.ci.values
self.key = self.ci.categories[0]
def setup(self):
N = 10**5
self.ci = tm.makeCategoricalIndex(N)
self.c = self.ci.values
self.key = self.ci.categories[0]
"x": [1.0, 2.0, 3.0],
"y": [4.0, 5.0, 6.0]
}),
pd.DataFrame({"x": [1.0, 2.0, 3.0]}, index=pd.Index([4, 5, 6],
name="bar")),
pd.Series([1.0, 2.0, 3.0]),
pd.Series([1.0, 2.0, 3.0], name="foo"),
pd.Series([1.0, 2.0, 3.0], name="foo", index=[4, 5, 6]),
pd.Series([1.0, 2.0, 3.0],
name="foo",
index=pd.Index([4, 5, 6], name="bar")),
pd.DataFrame({"x": ["a", "b", "c"]}),
pd.DataFrame({"x": [b"a", b"b", b"c"]}),
pd.DataFrame({"x": pd.Categorical(["a", "b", "a"], ordered=True)}),
pd.DataFrame({"x": pd.Categorical(["a", "b", "a"], ordered=False)}),
tm.makeCategoricalIndex(),
tm.makeCustomDataframe(5, 3),
tm.makeDataFrame(),
tm.makeDateIndex(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeObjectSeries(),
tm.makePeriodFrame(),
tm.makeRangeIndex(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeUnicodeIndex(),
]
@pytest.mark.parametrize("df", dfs)
class TestSeriesMisc(TestData, SharedWithSparse):
series_klass = Series
# SharedWithSparse tests use generic, series_klass-agnostic assertion
_assert_series_equal = staticmethod(tm.assert_series_equal)
def test_tab_completion(self):
# GH 9910
s = Series(list("abcd"))
# Series of str values should have .str but not .dt/.cat in __dir__
assert "str" in dir(s)
assert "dt" not in dir(s)
assert "cat" not in dir(s)
# similarly for .dt
s = Series(date_range("1/1/2015", periods=5))
assert "dt" in dir(s)
assert "str" not in dir(s)
assert "cat" not in dir(s)
# Similarly for .cat, but with the twist that str and dt should be
# there if the categories are of that type first cat and str.
s = Series(list("abbcd"), dtype="category")
assert "cat" in dir(s)
assert "str" in dir(s) # as it is a string categorical
assert "dt" not in dir(s)
# similar to cat and str
s = Series(date_range("1/1/2015", periods=5)).astype("category")
assert "cat" in dir(s)
assert "str" not in dir(s)
assert "dt" in dir(s) # as it is a datetime categorical
def test_tab_completion_with_categorical(self):
# test the tab completion display
ok_for_cat = [
"name",
"index",
"categorical",
"categories",
"codes",
"ordered",
"set_categories",
"add_categories",
"remove_categories",
"rename_categories",
"reorder_categories",
"remove_unused_categories",
"as_ordered",
"as_unordered",
]
def get_dir(s):
results = [r for r in s.cat.__dir__() if not r.startswith("_")]
return list(sorted(set(results)))
s = Series(list("aabbcde")).astype("category")
results = get_dir(s)
tm.assert_almost_equal(results, list(sorted(set(ok_for_cat))))
@pytest.mark.parametrize(
"index",
[
tm.makeUnicodeIndex(10),
tm.makeStringIndex(10),
tm.makeCategoricalIndex(10),
Index(["foo", "bar", "baz"] * 2),
tm.makeDateIndex(10),
tm.makePeriodIndex(10),
tm.makeTimedeltaIndex(10),
tm.makeIntIndex(10),
tm.makeUIntIndex(10),
tm.makeIntIndex(10),
tm.makeFloatIndex(10),
Index([True, False]),
Index(["a{}".format(i) for i in range(101)]),
pd.MultiIndex.from_tuples(zip("ABCD", "EFGH")),
pd.MultiIndex.from_tuples(zip([0, 1, 2, 3], "EFGH")),
],
)
def test_index_tab_completion(self, index):
# dir contains string-like values of the Index.
s = pd.Series(index=index)
dir_s = dir(s)
for i, x in enumerate(s.index.unique(level=0)):
if i < 100:
assert not isinstance(
x, str) or not x.isidentifier() or x in dir_s
else:
assert x not in dir_s
def test_not_hashable(self):
s_empty = Series()
s = Series([1])
msg = "'Series' objects are mutable, thus they cannot be hashed"
with pytest.raises(TypeError, match=msg):
hash(s_empty)
with pytest.raises(TypeError, match=msg):
hash(s)
def test_contains(self):
tm.assert_contains_all(self.ts.index, self.ts)
def test_iter(self):
for i, val in enumerate(self.series):
assert val == self.series[i]
for i, val in enumerate(self.ts):
assert val == self.ts[i]
def test_keys(self):
# HACK: By doing this in two stages, we avoid 2to3 wrapping the call
# to .keys() in a list()
getkeys = self.ts.keys
assert getkeys() is self.ts.index
def test_values(self):
tm.assert_almost_equal(self.ts.values, self.ts, check_dtype=False)
def test_iteritems(self):
for idx, val in self.series.iteritems():
assert val == self.series[idx]
for idx, val in self.ts.iteritems():
assert val == self.ts[idx]
# assert is lazy (genrators don't define reverse, lists do)
assert not hasattr(self.series.iteritems(), "reverse")
def test_items(self):
for idx, val in self.series.items():
assert val == self.series[idx]
for idx, val in self.ts.items():
assert val == self.ts[idx]
# assert is lazy (genrators don't define reverse, lists do)
assert not hasattr(self.series.items(), "reverse")
def test_raise_on_info(self):
s = Series(np.random.randn(10))
msg = "'Series' object has no attribute 'info'"
with pytest.raises(AttributeError, match=msg):
s.info()
def test_copy(self):
for deep in [None, False, True]:
s = Series(np.arange(10), dtype="float64")
# default deep is True
if deep is None:
s2 = s.copy()
else:
s2 = s.copy(deep=deep)
s2[::2] = np.NaN
if deep is None or deep is True:
# Did not modify original Series
assert np.isnan(s2[0])
assert not np.isnan(s[0])
else:
# we DID modify the original Series
assert np.isnan(s2[0])
assert np.isnan(s[0])
def test_copy_tzaware(self):
# GH#11794
# copy of tz-aware
expected = Series([Timestamp("2012/01/01", tz="UTC")])
expected2 = Series([Timestamp("1999/01/01", tz="UTC")])
for deep in [None, False, True]:
s = Series([Timestamp("2012/01/01", tz="UTC")])
if deep is None:
s2 = s.copy()
else:
s2 = s.copy(deep=deep)
s2[0] = pd.Timestamp("1999/01/01", tz="UTC")
# default deep is True
if deep is None or deep is True:
# Did not modify original Series
assert_series_equal(s2, expected2)
assert_series_equal(s, expected)
else:
# we DID modify the original Series
assert_series_equal(s2, expected2)
assert_series_equal(s, expected2)
def test_axis_alias(self):
s = Series([1, 2, np.nan])
assert_series_equal(s.dropna(axis="rows"), s.dropna(axis="index"))
assert s.dropna().sum("rows") == 3
assert s._get_axis_number("rows") == 0
assert s._get_axis_name("rows") == "index"
def test_class_axis(self):
# https://github.com/pandas-dev/pandas/issues/18147
# no exception and no empty docstring
assert pydoc.getdoc(Series.index)
def test_numpy_unique(self):
# it works!
np.unique(self.ts)
def test_ndarray_compat(self):
# test numpy compat with Series as sub-class of NDFrame
tsdf = DataFrame(
np.random.randn(1000, 3),
columns=["A", "B", "C"],
index=date_range("1/1/2000", periods=1000),
)
def f(x):
return x[x.idxmax()]
result = tsdf.apply(f)
expected = tsdf.max()
tm.assert_series_equal(result, expected)
# .item()
with tm.assert_produces_warning(FutureWarning):
s = Series([1])
result = s.item()
assert result == 1
assert s.item() == s.iloc[0]
# using an ndarray like function
s = Series(np.random.randn(10))
result = Series(np.ones_like(s))
expected = Series(1, index=range(10), dtype="float64")
tm.assert_series_equal(result, expected)
# ravel
s = Series(np.random.randn(10))
tm.assert_almost_equal(s.ravel(order="F"), s.values.ravel(order="F"))
# compress
# GH 6658
s = Series([0, 1.0, -1], index=list("abc"))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s > 0, s)
tm.assert_series_equal(result, Series([1.0], index=["b"]))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s < -1, s)
# result empty Index(dtype=object) as the same as original
exp = Series([], dtype="float64", index=Index([], dtype="object"))
tm.assert_series_equal(result, exp)
s = Series([0, 1.0, -1], index=[0.1, 0.2, 0.3])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s > 0, s)
tm.assert_series_equal(result, Series([1.0], index=[0.2]))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.compress(s < -1, s)
# result empty Float64Index as the same as original
exp = Series([], dtype="float64", index=Index([], dtype="float64"))
tm.assert_series_equal(result, exp)
def test_str_accessor_updates_on_inplace(self):
s = pd.Series(list("abc"))
s.drop([0], inplace=True)
assert len(s.str.lower()) == 2
def test_str_attribute(self):
# GH9068
methods = ["strip", "rstrip", "lstrip"]
s = Series([" jack", "jill ", " jesse ", "frank"])
for method in methods:
expected = Series([getattr(str, method)(x) for x in s.values])
assert_series_equal(getattr(Series.str, method)(s.str), expected)
# str accessor only valid with string values
s = Series(range(5))
with pytest.raises(AttributeError, match="only use .str accessor"):
s.str.repeat(2)
def test_empty_method(self):
s_empty = pd.Series()
assert s_empty.empty
for full_series in [pd.Series([1]), pd.Series(index=[1])]:
assert not full_series.empty
def test_tab_complete_warning(self, ip):
# https://github.com/pandas-dev/pandas/issues/16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; s = pd.Series()"
ip.run_code(code)
with tm.assert_produces_warning(None):
with provisionalcompleter("ignore"):
list(ip.Completer.completions("s.", 1))
def test_integer_series_size(self):
# GH 25580
s = Series(range(9))
assert s.size == 9
s = Series(range(9), dtype="Int64")
assert s.size == 9
def test_get_values_deprecation(self):
s = Series(range(9))
with tm.assert_produces_warning(FutureWarning):
res = s.get_values()
tm.assert_numpy_array_equal(res, s.values)
def setup_method(self, method):
self.indices = dict(catIndex=tm.makeCategoricalIndex(100))
self.setup_indices()
def setUp(self):
self.indices = dict(catIndex=tm.makeCategoricalIndex(100))
self.setup_indices()
import pandas.util.testing as tm
from pandas.core.indexes.api import Index, MultiIndex
from pandas.compat import lzip
@pytest.fixture(params=[
tm.makeUnicodeIndex(100),
tm.makeStringIndex(100),
tm.makeDateIndex(100),
tm.makePeriodIndex(100),
tm.makeTimedeltaIndex(100),
tm.makeIntIndex(100),
tm.makeUIntIndex(100),
tm.makeFloatIndex(100),
Index([True, False]),
tm.makeCategoricalIndex(100),
Index([]),
MultiIndex.from_tuples(lzip(['foo', 'bar', 'baz'], [1, 2, 3])),
Index([0, 0, 1, 1, 2, 2])
],
ids=lambda x: type(x).__name__)
def indices(request):
return request.param
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
# zero-dim integer array behaves like an integer
return request.param