def setup_method(self, method):
super().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_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']),
}
pd.Series(np.linspace(0,9,5)) # allows to specify the number of values to be created btw boundaries pd.Series(np.random.normal(size=5)) np.random.randint(50,101,10) a = np.array([4] * 16) a[1::] = [42] * 15 a[1:8:2] = 16 import pandas.util.testing as tm tm.N, tm.K = 5,3 tm.makeFloatSeries(), tm.makeBoolIndex(), tm.makeCategoricalIndex() tm.makeCustomIndex(nentries=4,nlevels=2), tm.makeFloatIndex(), tm.makeIntIndex() tm.makeMultiIndex(), tm.makeRangeIndex(), tm.makeIntervalIndex() # All possible combinations (Permutations) from itertools import permutations my_list = [1,2,3] perm = list(permutations(my_list)) #(1, 2, 3) #(1, 3, 2) #(2, 1, 3) #(2, 3, 1) #(3, 1, 2) #(3, 2, 1) # Create dataframes
def test_slice_integer(self):
# same as above, but for Integer based indexes
# these coerce to a like integer
# oob indiciates if we are out of bounds
# of positional indexing
for index, oob in [(tm.makeIntIndex(5), False),
(tm.makeRangeIndex(5), False),
(tm.makeIntIndex(5) + 10, True)]:
# s is an in-range index
s = Series(range(5), index=index)
# getitem
for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]:
for idxr in [lambda x: x.loc, lambda x: x.ix]:
with catch_warnings(record=True):
result = idxr(s)[l]
# these are all label indexing
# except getitem which is positional
# empty
if oob:
indexer = slice(0, 0)
else:
indexer = slice(3, 5)
self.check(result, s, indexer, False)
# positional indexing
def f():
s[l]
pytest.raises(TypeError, f)
# getitem out-of-bounds
for l in [slice(-6, 6), slice(-6.0, 6.0)]:
for idxr in [lambda x: x.loc, lambda x: x.ix]:
with catch_warnings(record=True):
result = idxr(s)[l]
# these are all label indexing
# except getitem which is positional
# empty
if oob:
indexer = slice(0, 0)
else:
indexer = slice(-6, 6)
self.check(result, s, indexer, False)
# positional indexing
def f():
s[slice(-6.0, 6.0)]
pytest.raises(TypeError, f)
# getitem odd floats
for l, res1 in [(slice(2.5, 4), slice(3, 5)),
(slice(2, 3.5), slice(2, 4)),
(slice(2.5, 3.5), slice(3, 4))]:
for idxr in [lambda x: x.loc, lambda x: x.ix]:
with catch_warnings(record=True):
result = idxr(s)[l]
if oob:
res = slice(0, 0)
else:
res = res1
self.check(result, s, res, False)
# positional indexing
def f():
s[l]
pytest.raises(TypeError, f)
# setitem
for l in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]:
for idxr in [lambda x: x.loc, lambda x: x.ix]:
sc = s.copy()
with catch_warnings(record=True):
idxr(sc)[l] = 0
result = idxr(sc)[l].values.ravel()
assert (result == 0).all()
# positional indexing
def f():
s[l] = 0
pytest.raises(TypeError, f)
from pandas.compat import long, lzip
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
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)
def test_dumps_serialize_numpy(df):
header, frames = serialize(df)
if "compression" in header:
frames = decompress(header, frames)
df2 = deserialize(header, frames)
assert_eq(df, df2)
import numpy as np
import pytest
import pandas as pd
from pandas.core.indexes.api import Index, MultiIndex
import pandas.util.testing as tm
indices_dict = {
"unicode": tm.makeUnicodeIndex(100),
"string": tm.makeStringIndex(100),
"datetime": tm.makeDateIndex(100),
"period": tm.makePeriodIndex(100),
"timedelta": tm.makeTimedeltaIndex(100),
"int": tm.makeIntIndex(100),
"uint": tm.makeUIntIndex(100),
"range": tm.makeRangeIndex(100),
"float": tm.makeFloatIndex(100),
"bool": Index([True, False]),
"categorical": tm.makeCategoricalIndex(100),
"interval": tm.makeIntervalIndex(100),
"empty": Index([]),
"tuples": MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])),
"repeats": Index([0, 0, 1, 1, 2, 2]),
}
@pytest.fixture(params=indices_dict.keys())
def indices(request):
# copy to avoid mutation, e.g. setting .name
return indices_dict[request.param].copy()
import numpy as np
import pytest
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(zip(
['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
def test_slice_integer(self):
# same as above, but for Integer based indexes
# these coerce to a like integer
# oob indiciates if we are out of bounds
# of positional indexing
for index, oob in [(tm.makeIntIndex(5), False),
(tm.makeRangeIndex(5), False),
(tm.makeIntIndex(5) + 10, True)]:
# s is an in-range index
s = Series(range(5), index=index)
# getitem
for l in [slice(3.0, 4),
slice(3, 4.0),
slice(3.0, 4.0)]:
for idxr in [lambda x: x.loc,
lambda x: x.ix]:
result = idxr(s)[l]
# these are all label indexing
# except getitem which is positional
# empty
if oob:
indexer = slice(0, 0)
else:
indexer = slice(3, 5)
self.check(result, s, indexer, False)
# positional indexing
def f():
s[l]
self.assertRaises(TypeError, f)
# getitem out-of-bounds
for l in [slice(-6, 6),
slice(-6.0, 6.0)]:
for idxr in [lambda x: x.loc,
lambda x: x.ix]:
result = idxr(s)[l]
# these are all label indexing
# except getitem which is positional
# empty
if oob:
indexer = slice(0, 0)
else:
indexer = slice(-6, 6)
self.check(result, s, indexer, False)
# positional indexing
def f():
s[slice(-6.0, 6.0)]
self.assertRaises(TypeError, f)
# getitem odd floats
for l, res1 in [(slice(2.5, 4), slice(3, 5)),
(slice(2, 3.5), slice(2, 4)),
(slice(2.5, 3.5), slice(3, 4))]:
for idxr in [lambda x: x.loc,
lambda x: x.ix]:
result = idxr(s)[l]
if oob:
res = slice(0, 0)
else:
res = res1
self.check(result, s, res, False)
# positional indexing
def f():
s[l]
self.assertRaises(TypeError, f)
# setitem
for l in [slice(3.0, 4),
slice(3, 4.0),
slice(3.0, 4.0)]:
for idxr in [lambda x: x.loc,
lambda x: x.ix]:
sc = s.copy()
idxr(sc)[l] = 0
result = idxr(sc)[l].values.ravel()
self.assertTrue((result == 0).all())
# positional indexing
def f():
s[l] = 0
self.assertRaises(TypeError, f)
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)
def test_dumps_serialize_numpy(df):
header, frames = serialize(df)
if 'compression' in header:
frames = decompress(header, frames)
df2 = deserialize(header, frames)
assert_eq(df, df2)
