Python pandas.util.testing.getMixedTypeDict() Examples
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code examples of pandas.util.testing.getMixedTypeDict().
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Example #1
| Source File: test_join.py From recruit with Apache License 2.0 | 6 votes |
|
def setup_method(self, method):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
Example #2
| Source File: test_api.py From recruit with Apache License 2.0 | 6 votes |
|
def test_transpose(self, float_frame):
frame = float_frame
dft = frame.T
for idx, series in compat.iteritems(dft):
for col, value in compat.iteritems(series):
if np.isnan(value):
assert np.isnan(frame[col][idx])
else:
assert value == frame[col][idx]
# mixed type
index, data = tm.getMixedTypeDict()
mixed = self.klass(data, index=index)
mixed_T = mixed.T
for col, s in compat.iteritems(mixed_T):
assert s.dtype == np.object_
Example #3
| Source File: test_api.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_transpose(self):
frame = self.frame
dft = frame.T
for idx, series in compat.iteritems(dft):
for col, value in compat.iteritems(series):
if np.isnan(value):
assert np.isnan(frame[col][idx])
else:
assert value == frame[col][idx]
# mixed type
index, data = tm.getMixedTypeDict()
mixed = self.klass(data, index=index)
mixed_T = mixed.T
for col, s in compat.iteritems(mixed_T):
assert s.dtype == np.object_
Example #4
| Source File: test_join.py From vnpy_crypto with MIT License | 6 votes |
|
def setup_method(self, method):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
Example #5
| Source File: test_join.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def setup_method(self, method):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
Example #6
| Source File: test_api.py From vnpy_crypto with MIT License | 6 votes |
|
def test_transpose(self):
frame = self.frame
dft = frame.T
for idx, series in compat.iteritems(dft):
for col, value in compat.iteritems(series):
if np.isnan(value):
assert np.isnan(frame[col][idx])
else:
assert value == frame[col][idx]
# mixed type
index, data = tm.getMixedTypeDict()
mixed = self.klass(data, index=index)
mixed_T = mixed.T
for col, s in compat.iteritems(mixed_T):
assert s.dtype == np.object_
Example #7
| Source File: test_join.py From coffeegrindsize with MIT License | 6 votes |
|
def setup_method(self, method):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
Example #8
| Source File: test_join.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def setup_method(self, method):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
Example #9
| Source File: test_api.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_transpose(self):
frame = self.frame
dft = frame.T
for idx, series in compat.iteritems(dft):
for col, value in compat.iteritems(series):
if np.isnan(value):
assert np.isnan(frame[col][idx])
else:
assert value == frame[col][idx]
# mixed type
index, data = tm.getMixedTypeDict()
mixed = self.klass(data, index=index)
mixed_T = mixed.T
for col, s in compat.iteritems(mixed_T):
assert s.dtype == np.object_
Example #10
| Source File: test_api.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_transpose(self, float_frame):
frame = float_frame
dft = frame.T
for idx, series in compat.iteritems(dft):
for col, value in compat.iteritems(series):
if np.isnan(value):
assert np.isnan(frame[col][idx])
else:
assert value == frame[col][idx]
# mixed type
index, data = tm.getMixedTypeDict()
mixed = self.klass(data, index=index)
mixed_T = mixed.T
for col, s in compat.iteritems(mixed_T):
assert s.dtype == np.object_
Example #11
| Source File: test_join.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def setup_method(self, method):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
Example #12
| Source File: test_constructors.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_constructor_mixed(self):
index, data = tm.getMixedTypeDict()
# TODO(wesm), incomplete test?
indexed_frame = DataFrame(data, index=index) # noqa
unindexed_frame = DataFrame(data) # noqa
assert self.mixed_frame['foo'].dtype == np.object_
Example #13
| Source File: test_constructors.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_constructor_mixed(self):
index, data = tm.getMixedTypeDict()
# TODO(wesm), incomplete test?
indexed_frame = DataFrame(data, index=index) # noqa
unindexed_frame = DataFrame(data) # noqa
assert self.mixed_frame['foo'].dtype == np.object_
Example #14
| Source File: test_constructors.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_constructor_mixed(self):
index, data = tm.getMixedTypeDict()
# TODO(wesm), incomplete test?
indexed_frame = DataFrame(data, index=index) # noqa
unindexed_frame = DataFrame(data) # noqa
assert self.mixed_frame['foo'].dtype == np.object_
Example #15
| Source File: test_merge.py From Computable with MIT License | 5 votes |
|
def setUp(self):
# aggregate multiple columns
self.df = DataFrame({'key1': get_test_data(),
'key2': get_test_data(),
'data1': np.random.randn(N),
'data2': np.random.randn(N)})
# exclude a couple keys for fun
self.df = self.df[self.df['key2'] > 1]
self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
'key2': get_test_data(ngroups=NGROUPS // 2,
n=N // 5),
'value': np.random.randn(N // 5)})
index, data = tm.getMixedTypeDict()
self.target = DataFrame(data, index=index)
# Join on string value
self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
index=data['C'])
self.left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'],
'v1': np.random.randn(7)})
self.right = DataFrame({'v2': np.random.randn(4)},
index=['d', 'b', 'c', 'a'])
Example #16
| Source File: test_constructors.py From vnpy_crypto with MIT License | 5 votes |
|
def test_constructor_mixed(self):
index, data = tm.getMixedTypeDict()
# TODO(wesm), incomplete test?
indexed_frame = DataFrame(data, index=index) # noqa
unindexed_frame = DataFrame(data) # noqa
assert self.mixed_frame['foo'].dtype == np.object_
Example #17
| Source File: test_constructors.py From recruit with Apache License 2.0 | 5 votes |
|
def test_constructor_mixed(self):
index, data = tm.getMixedTypeDict()
# TODO(wesm), incomplete test?
indexed_frame = DataFrame(data, index=index) # noqa
unindexed_frame = DataFrame(data) # noqa
assert self.mixed_frame['foo'].dtype == np.object_
Example #18
| Source File: test_apply.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def test_map(self):
index, data = tm.getMixedTypeDict()
source = Series(data['B'], index=data['C'])
target = Series(data['C'][:4], index=data['D'][:4])
merged = target.map(source)
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# input could be a dict
merged = target.map(source.to_dict())
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# function
result = self.ts.map(lambda x: x * 2)
tm.assert_series_equal(result, self.ts * 2)
# GH 10324
a = Series([1, 2, 3, 4])
b = Series(["even", "odd", "even", "odd"], dtype="category")
c = Series(["even", "odd", "even", "odd"])
exp = Series(["odd", "even", "odd", np.nan], dtype="category")
tm.assert_series_equal(a.map(b), exp)
exp = Series(["odd", "even", "odd", np.nan])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series([1, 2, 3, 4],
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e']))
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series(['B', 'C', 'D', 'E'], dtype='category',
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e']))
exp = Series(pd.Categorical([np.nan, 'B', 'C', 'D'],
categories=['B', 'C', 'D', 'E']))
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 'B', 'C', 'D'])
tm.assert_series_equal(a.map(c), exp)
Example #19
| Source File: test_apply.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def test_map(self, datetime_series):
index, data = tm.getMixedTypeDict()
source = Series(data['B'], index=data['C'])
target = Series(data['C'][:4], index=data['D'][:4])
merged = target.map(source)
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# input could be a dict
merged = target.map(source.to_dict())
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# function
result = datetime_series.map(lambda x: x * 2)
tm.assert_series_equal(result, datetime_series * 2)
# GH 10324
a = Series([1, 2, 3, 4])
b = Series(["even", "odd", "even", "odd"], dtype="category")
c = Series(["even", "odd", "even", "odd"])
exp = Series(["odd", "even", "odd", np.nan], dtype="category")
tm.assert_series_equal(a.map(b), exp)
exp = Series(["odd", "even", "odd", np.nan])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series([1, 2, 3, 4],
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e']))
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series(['B', 'C', 'D', 'E'], dtype='category',
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e']))
exp = Series(pd.Categorical([np.nan, 'B', 'C', 'D'],
categories=['B', 'C', 'D', 'E']))
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 'B', 'C', 'D'])
tm.assert_series_equal(a.map(c), exp)
Example #20
| Source File: test_apply.py From vnpy_crypto with MIT License | 4 votes |
|
def test_map(self):
index, data = tm.getMixedTypeDict()
source = Series(data['B'], index=data['C'])
target = Series(data['C'][:4], index=data['D'][:4])
merged = target.map(source)
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# input could be a dict
merged = target.map(source.to_dict())
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# function
result = self.ts.map(lambda x: x * 2)
tm.assert_series_equal(result, self.ts * 2)
# GH 10324
a = Series([1, 2, 3, 4])
b = Series(["even", "odd", "even", "odd"], dtype="category")
c = Series(["even", "odd", "even", "odd"])
exp = Series(["odd", "even", "odd", np.nan], dtype="category")
tm.assert_series_equal(a.map(b), exp)
exp = Series(["odd", "even", "odd", np.nan])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series([1, 2, 3, 4],
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e']))
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series(['B', 'C', 'D', 'E'], dtype='category',
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e']))
exp = Series(pd.Categorical([np.nan, 'B', 'C', 'D'],
categories=['B', 'C', 'D', 'E']))
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 'B', 'C', 'D'])
tm.assert_series_equal(a.map(c), exp)
Example #21
| Source File: test_apply.py From twitter-stock-recommendation with MIT License | 4 votes |
|
def test_map(self):
index, data = tm.getMixedTypeDict()
source = Series(data['B'], index=data['C'])
target = Series(data['C'][:4], index=data['D'][:4])
merged = target.map(source)
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# input could be a dict
merged = target.map(source.to_dict())
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# function
result = self.ts.map(lambda x: x * 2)
tm.assert_series_equal(result, self.ts * 2)
# GH 10324
a = Series([1, 2, 3, 4])
b = Series(["even", "odd", "even", "odd"], dtype="category")
c = Series(["even", "odd", "even", "odd"])
exp = Series(["odd", "even", "odd", np.nan], dtype="category")
tm.assert_series_equal(a.map(b), exp)
exp = Series(["odd", "even", "odd", np.nan])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series([1, 2, 3, 4],
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e']))
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series(['B', 'C', 'D', 'E'], dtype='category',
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e']))
exp = Series(pd.Categorical([np.nan, 'B', 'C', 'D'],
categories=['B', 'C', 'D', 'E']))
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 'B', 'C', 'D'])
tm.assert_series_equal(a.map(c), exp)
Example #22
| Source File: test_apply.py From recruit with Apache License 2.0 | 4 votes |
|
def test_map(self, datetime_series):
index, data = tm.getMixedTypeDict()
source = Series(data['B'], index=data['C'])
target = Series(data['C'][:4], index=data['D'][:4])
merged = target.map(source)
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# input could be a dict
merged = target.map(source.to_dict())
for k, v in compat.iteritems(merged):
assert v == source[target[k]]
# function
result = datetime_series.map(lambda x: x * 2)
tm.assert_series_equal(result, datetime_series * 2)
# GH 10324
a = Series([1, 2, 3, 4])
b = Series(["even", "odd", "even", "odd"], dtype="category")
c = Series(["even", "odd", "even", "odd"])
exp = Series(["odd", "even", "odd", np.nan], dtype="category")
tm.assert_series_equal(a.map(b), exp)
exp = Series(["odd", "even", "odd", np.nan])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series([1, 2, 3, 4],
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series([1, 2, 3, 4], index=Index(['b', 'c', 'd', 'e']))
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 1, 2, 3])
tm.assert_series_equal(a.map(c), exp)
a = Series(['a', 'b', 'c', 'd'])
b = Series(['B', 'C', 'D', 'E'], dtype='category',
index=pd.CategoricalIndex(['b', 'c', 'd', 'e']))
c = Series(['B', 'C', 'D', 'E'], index=Index(['b', 'c', 'd', 'e']))
exp = Series(pd.Categorical([np.nan, 'B', 'C', 'D'],
categories=['B', 'C', 'D', 'E']))
tm.assert_series_equal(a.map(b), exp)
exp = Series([np.nan, 'B', 'C', 'D'])
tm.assert_series_equal(a.map(c), exp)
