Python pandas.util.testing.assert_dict_equal() Examples
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code examples of pandas.util.testing.assert_dict_equal().
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Example #1
| Source File: test_categorical.py From vnpy_crypto with MIT License | 6 votes |
|
def test_observed_groups(observed):
# gh-20583
# test that we have the appropriate groups
cat = pd.Categorical(['a', 'c', 'a'], categories=['a', 'b', 'c'])
df = pd.DataFrame({'cat': cat, 'vals': [1, 2, 3]})
g = df.groupby('cat', observed=observed)
result = g.groups
if observed:
expected = {'a': Index([0, 2], dtype='int64'),
'c': Index([1], dtype='int64')}
else:
expected = {'a': Index([0, 2], dtype='int64'),
'b': Index([], dtype='int64'),
'c': Index([1], dtype='int64')}
tm.assert_dict_equal(result, expected)
Example #2
| Source File: test_grouping.py From recruit with Apache License 2.0 | 6 votes |
|
def test_groupby_multiindex_tuple(self):
# GH 17979
df = pd.DataFrame([[1, 2, 3, 4], [3, 4, 5, 6], [1, 4, 2, 3]],
columns=pd.MultiIndex.from_arrays(
[['a', 'b', 'b', 'c'],
[1, 1, 2, 2]]))
expected = df.groupby([('b', 1)]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
df2 = pd.DataFrame(df.values,
columns=pd.MultiIndex.from_arrays(
[['a', 'b', 'b', 'c'],
['d', 'd', 'e', 'e']]))
expected = df2.groupby([('b', 'd')]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
df3 = pd.DataFrame(df.values,
columns=[('a', 'd'), ('b', 'd'), ('b', 'e'), 'c'])
expected = df3.groupby([('b', 'd')]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
Example #3
| Source File: test_grouping.py From recruit with Apache License 2.0 | 6 votes |
|
def test_multiindex_columns_empty_level(self):
lst = [['count', 'values'], ['to filter', '']]
midx = MultiIndex.from_tuples(lst)
df = DataFrame([[long(1), 'A']], columns=midx)
grouped = df.groupby('to filter').groups
assert grouped['A'] == [0]
grouped = df.groupby([('to filter', '')]).groups
assert grouped['A'] == [0]
df = DataFrame([[long(1), 'A'], [long(2), 'B']], columns=midx)
expected = df.groupby('to filter').groups
result = df.groupby([('to filter', '')]).groups
assert result == expected
df = DataFrame([[long(1), 'A'], [long(2), 'A']], columns=midx)
expected = df.groupby('to filter').groups
result = df.groupby([('to filter', '')]).groups
tm.assert_dict_equal(result, expected)
Example #4
| Source File: test_categorical.py From recruit with Apache License 2.0 | 6 votes |
|
def test_observed_groups(observed):
# gh-20583
# test that we have the appropriate groups
cat = pd.Categorical(['a', 'c', 'a'], categories=['a', 'b', 'c'])
df = pd.DataFrame({'cat': cat, 'vals': [1, 2, 3]})
g = df.groupby('cat', observed=observed)
result = g.groups
if observed:
expected = {'a': Index([0, 2], dtype='int64'),
'c': Index([1], dtype='int64')}
else:
expected = {'a': Index([0, 2], dtype='int64'),
'b': Index([], dtype='int64'),
'c': Index([1], dtype='int64')}
tm.assert_dict_equal(result, expected)
Example #5
| Source File: test_grouping.py From recruit with Apache License 2.0 | 6 votes |
|
def test_list_grouper_with_nat(self):
# GH 14715
df = pd.DataFrame({'date': pd.date_range('1/1/2011',
periods=365, freq='D')})
df.iloc[-1] = pd.NaT
grouper = pd.Grouper(key='date', freq='AS')
# Grouper in a list grouping
result = df.groupby([grouper])
expected = {pd.Timestamp('2011-01-01'): pd.Index(list(range(364)))}
tm.assert_dict_equal(result.groups, expected)
# Test case without a list
result = df.groupby(grouper)
expected = {pd.Timestamp('2011-01-01'): 365}
tm.assert_dict_equal(result.groups, expected)
# get_group
# --------------------------------
Example #6
| Source File: test_numeric.py From recruit with Apache License 2.0 | 6 votes |
|
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
Example #7
| Source File: test_numeric.py From vnpy_crypto with MIT License | 6 votes |
|
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
Example #8
| Source File: test_grouping.py From vnpy_crypto with MIT License | 6 votes |
|
def test_multiindex_columns_empty_level(self):
lst = [['count', 'values'], ['to filter', '']]
midx = MultiIndex.from_tuples(lst)
df = DataFrame([[long(1), 'A']], columns=midx)
grouped = df.groupby('to filter').groups
assert grouped['A'] == [0]
grouped = df.groupby([('to filter', '')]).groups
assert grouped['A'] == [0]
df = DataFrame([[long(1), 'A'], [long(2), 'B']], columns=midx)
expected = df.groupby('to filter').groups
result = df.groupby([('to filter', '')]).groups
assert result == expected
df = DataFrame([[long(1), 'A'], [long(2), 'A']], columns=midx)
expected = df.groupby('to filter').groups
result = df.groupby([('to filter', '')]).groups
tm.assert_dict_equal(result, expected)
Example #9
| Source File: test_grouping.py From vnpy_crypto with MIT License | 6 votes |
|
def test_groupby_multiindex_tuple(self):
# GH 17979
df = pd.DataFrame([[1, 2, 3, 4], [3, 4, 5, 6], [1, 4, 2, 3]],
columns=pd.MultiIndex.from_arrays(
[['a', 'b', 'b', 'c'],
[1, 1, 2, 2]]))
expected = df.groupby([('b', 1)]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
df2 = pd.DataFrame(df.values,
columns=pd.MultiIndex.from_arrays(
[['a', 'b', 'b', 'c'],
['d', 'd', 'e', 'e']]))
expected = df2.groupby([('b', 'd')]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
df3 = pd.DataFrame(df.values,
columns=[('a', 'd'), ('b', 'd'), ('b', 'e'), 'c'])
expected = df3.groupby([('b', 'd')]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
Example #10
| Source File: test_numeric.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
Example #11
| Source File: test_categorical.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_observed_groups(observed):
# gh-20583
# test that we have the appropriate groups
cat = pd.Categorical(['a', 'c', 'a'], categories=['a', 'b', 'c'])
df = pd.DataFrame({'cat': cat, 'vals': [1, 2, 3]})
g = df.groupby('cat', observed=observed)
result = g.groups
if observed:
expected = {'a': Index([0, 2], dtype='int64'),
'c': Index([1], dtype='int64')}
else:
expected = {'a': Index([0, 2], dtype='int64'),
'b': Index([], dtype='int64'),
'c': Index([1], dtype='int64')}
tm.assert_dict_equal(result, expected)
Example #12
| Source File: test_grouping.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_multiindex_columns_empty_level(self):
lst = [['count', 'values'], ['to filter', '']]
midx = MultiIndex.from_tuples(lst)
df = DataFrame([[long(1), 'A']], columns=midx)
grouped = df.groupby('to filter').groups
assert grouped['A'] == [0]
grouped = df.groupby([('to filter', '')]).groups
assert grouped['A'] == [0]
df = DataFrame([[long(1), 'A'], [long(2), 'B']], columns=midx)
expected = df.groupby('to filter').groups
result = df.groupby([('to filter', '')]).groups
assert result == expected
df = DataFrame([[long(1), 'A'], [long(2), 'A']], columns=midx)
expected = df.groupby('to filter').groups
result = df.groupby([('to filter', '')]).groups
tm.assert_dict_equal(result, expected)
Example #13
| Source File: test_grouping.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_groupby_multiindex_tuple(self):
# GH 17979
df = pd.DataFrame([[1, 2, 3, 4], [3, 4, 5, 6], [1, 4, 2, 3]],
columns=pd.MultiIndex.from_arrays(
[['a', 'b', 'b', 'c'],
[1, 1, 2, 2]]))
expected = df.groupby([('b', 1)]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
df2 = pd.DataFrame(df.values,
columns=pd.MultiIndex.from_arrays(
[['a', 'b', 'b', 'c'],
['d', 'd', 'e', 'e']]))
expected = df2.groupby([('b', 'd')]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
df3 = pd.DataFrame(df.values,
columns=[('a', 'd'), ('b', 'd'), ('b', 'e'), 'c'])
expected = df3.groupby([('b', 'd')]).groups
result = df.groupby(('b', 1)).groups
tm.assert_dict_equal(expected, result)
Example #14
| Source File: test_grouping.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_list_grouper_with_nat(self):
# GH 14715
df = pd.DataFrame({'date': pd.date_range('1/1/2011',
periods=365, freq='D')})
df.iloc[-1] = pd.NaT
grouper = pd.Grouper(key='date', freq='AS')
# Grouper in a list grouping
result = df.groupby([grouper])
expected = {pd.Timestamp('2011-01-01'): pd.Index(list(range(364)))}
tm.assert_dict_equal(result.groups, expected)
# Test case without a list
result = df.groupby(grouper)
expected = {pd.Timestamp('2011-01-01'): 365}
tm.assert_dict_equal(result.groups, expected)
# get_group
# --------------------------------
Example #15
| Source File: test_numeric.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
Example #16
| Source File: test_numeric.py From coffeegrindsize with MIT License | 6 votes |
|
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
Example #17
| Source File: test_numeric.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
Example #18
| Source File: test_categorical.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_observed_groups(observed):
# gh-20583
# test that we have the appropriate groups
cat = pd.Categorical(['a', 'c', 'a'], categories=['a', 'b', 'c'])
df = pd.DataFrame({'cat': cat, 'vals': [1, 2, 3]})
g = df.groupby('cat', observed=observed)
result = g.groups
if observed:
expected = {'a': Index([0, 2], dtype='int64'),
'c': Index([1], dtype='int64')}
else:
expected = {'a': Index([0, 2], dtype='int64'),
'b': Index([], dtype='int64'),
'c': Index([1], dtype='int64')}
tm.assert_dict_equal(result, expected)
Example #19
| Source File: test_events.py From outrigger with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test__mutually_exclusive_exon(self, splice_graph, exon1_i, exon1_name,
mutually_exclusive_events):
test = splice_graph._mutually_exclusive_exon(exon1_i, exon1_name)
true = mutually_exclusive_events
pdt.assert_dict_equal(test, true)
Example #20
| Source File: test_adjacencies.py From outrigger with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test__junctions_genome_adjacent_to_exon(self, adjacencies, snap25_exon,
adjacent_in_genome):
true = adjacent_in_genome
test = adjacencies._junctions_genome_adjacent_to_exon(snap25_exon)
pdt.assert_dict_equal(test, true)
Example #21
| Source File: test_packers.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_dict_numpy_complex(self):
x = {'foo': np.complex128(1.0 + 1.0j),
'bar': np.complex128(2.0 + 2.0j)}
x_rec = self.encode_decode(x)
tm.assert_dict_equal(x, x_rec)
for key in x:
tm.assert_class_equal(x[key], x_rec[key], obj="numpy complex128")
Example #22
| Source File: test_packers.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_dict_complex(self):
x = {'foo': 1.0 + 1.0j, 'bar': 2.0 + 2.0j}
x_rec = self.encode_decode(x)
tm.assert_dict_equal(x, x_rec)
for key in x:
tm.assert_class_equal(x[key], x_rec[key], obj="complex value")
Example #23
| Source File: test_na_values.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_na_values_dict_aliasing(all_parsers):
parser = all_parsers
na_values = {"a": 2, "b": 1}
na_values_copy = na_values.copy()
names = ["a", "b"]
data = "1,2\n2,1"
expected = DataFrame([[1.0, 2.0], [np.nan, np.nan]], columns=names)
result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
tm.assert_frame_equal(result, expected)
tm.assert_dict_equal(na_values, na_values_copy)
Example #24
| Source File: test_events.py From outrigger with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test__skipped_exon(self, splice_graph, exon1_i, exon1_name,
skipped_exon_events):
test = splice_graph._skipped_exon(exon1_i, exon1_name)
true = skipped_exon_events
pdt.assert_dict_equal(test, true)
Example #25
| Source File: test_bam.py From outrigger with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test__report_read_positions(bamfile):
from outrigger.io.bam import _report_read_positions
bam = pysam.AlignmentFile(bamfile, 'rb')
test = collections.Counter()
for read in bam:
_report_read_positions(read, test)
break
bam.close()
true = {('chr2', 136713559, 136713559, '+'): 1}
pdt.assert_dict_equal(test, true)
Example #26
| Source File: test_multi.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_groupby(self):
groups = self.index.groupby(np.array([1, 1, 1, 2, 2, 2]))
labels = self.index.get_values().tolist()
exp = {1: labels[:3], 2: labels[3:]}
tm.assert_dict_equal(groups, exp)
# GH5620
groups = self.index.groupby(self.index)
exp = dict((key, [key]) for key in self.index)
tm.assert_dict_equal(groups, exp)
Example #27
| Source File: test_base.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_groupby(self):
idx = Index(range(5))
groups = idx.groupby(np.array([1, 1, 2, 2, 2]))
exp = {1: pd.Index([0, 1]), 2: pd.Index([2, 3, 4])}
tm.assert_dict_equal(groups, exp)
Example #28
| Source File: test_eval.py From recruit with Apache License 2.0 | 5 votes |
|
def test_query_inplace(self):
# see gh-11149
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
expected = df.copy()
expected = expected[expected['a'] == 2]
df.query('a == 2', inplace=True)
assert_frame_equal(expected, df)
df = {}
expected = {"a": 3}
self.eval("a = 1 + 2", target=df, inplace=True)
tm.assert_dict_equal(df, expected)
Example #29
| Source File: test_eval.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_query_inplace(self):
# see gh-11149
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
expected = df.copy()
expected = expected[expected['a'] == 2]
df.query('a == 2', inplace=True)
assert_frame_equal(expected, df)
df = {}
expected = {"a": 3}
self.eval("a = 1 + 2", target=df, inplace=True)
tm.assert_dict_equal(df, expected)
Example #30
| Source File: test_events.py From outrigger with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_single_exon_alternative_events(self, splice_graph, exon1_i,
exon1_name,
mutually_exclusive_events,
skipped_exon_events):
test = splice_graph.single_exon_alternative_events(
exon1_i, exon1_name)
true = {'se': skipped_exon_events, 'mxe': mutually_exclusive_events}
pdt.assert_dict_equal(test, true)
