Python pandas.core.dtypes.generic.ABCIndex() Examples
The following are 20
code examples of pandas.core.dtypes.generic.ABCIndex().
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Example #1
| Source File: datetimelike.py From recruit with Apache License 2.0 | 6 votes |
|
def _join_i8_wrapper(joinf, dtype, with_indexers=True):
"""
Create the join wrapper methods.
"""
from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
@staticmethod
def wrapper(left, right):
if isinstance(left, (np.ndarray, ABCIndex, ABCSeries,
DatetimeLikeArrayMixin)):
left = left.view('i8')
if isinstance(right, (np.ndarray, ABCIndex, ABCSeries,
DatetimeLikeArrayMixin)):
right = right.view('i8')
results = joinf(left, right)
if with_indexers:
join_index, left_indexer, right_indexer = results
join_index = join_index.view(dtype)
return join_index, left_indexer, right_indexer
return results
return wrapper
Example #2
| Source File: common.py From vnpy_crypto with MIT License | 6 votes |
|
def is_bool_indexer(key):
if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
if key.dtype == np.object_:
key = np.asarray(_values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError('cannot index with vector containing '
'NA / NaN values')
return False
return True
elif key.dtype == np.bool_:
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
Example #3
| Source File: common.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def is_bool_indexer(key):
if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
if key.dtype == np.object_:
key = np.asarray(_values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError('cannot index with vector containing '
'NA / NaN values')
return False
return True
elif key.dtype == np.bool_:
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
Example #4
| Source File: datetimelike.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def _join_i8_wrapper(joinf, dtype, with_indexers=True):
"""
Create the join wrapper methods.
"""
from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
@staticmethod
def wrapper(left, right):
if isinstance(left, (np.ndarray, ABCIndex, ABCSeries,
DatetimeLikeArrayMixin)):
left = left.view('i8')
if isinstance(right, (np.ndarray, ABCIndex, ABCSeries,
DatetimeLikeArrayMixin)):
right = right.view('i8')
results = joinf(left, right)
if with_indexers:
join_index, left_indexer, right_indexer = results
join_index = join_index.view(dtype)
return join_index, left_indexer, right_indexer
return results
return wrapper
Example #5
| Source File: common.py From Splunking-Crime with GNU Affero General Public License v3.0 | 6 votes |
|
def is_bool_indexer(key):
if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
if key.dtype == np.object_:
key = np.asarray(_values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError('cannot index with vector containing '
'NA / NaN values')
return False
return True
elif key.dtype == np.bool_:
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
Example #6
| Source File: test_generic.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
Example #7
| Source File: test_base.py From recruit with Apache License 2.0 | 5 votes |
|
def test_generated_op_names(opname, indices):
index = indices
if isinstance(index, ABCIndex) and opname == 'rsub':
# pd.Index.__rsub__ does not exist; though the method does exist
# for subclasses. see GH#19723
return
opname = '__{name}__'.format(name=opname)
method = getattr(index, opname)
assert method.__name__ == opname
Example #8
| Source File: test_base.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_generated_op_names(opname, indices):
index = indices
if isinstance(index, ABCIndex) and opname == 'rsub':
# pd.Index.__rsub__ does not exist; though the method does exist
# for subclasses. see GH#19723
return
opname = '__{name}__'.format(name=opname)
method = getattr(index, opname)
assert method.__name__ == opname
Example #9
| Source File: test_base.py From coffeegrindsize with MIT License | 5 votes |
|
def test_generated_op_names(opname, indices):
index = indices
if isinstance(index, ABCIndex) and opname == 'rsub':
# pd.Index.__rsub__ does not exist; though the method does exist
# for subclasses. see GH#19723
return
opname = '__{name}__'.format(name=opname)
method = getattr(index, opname)
assert method.__name__ == opname
Example #10
| Source File: strings.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _make_accessor(cls, data):
from pandas.core.index import Index
if (isinstance(data, ABCSeries) and
not ((is_categorical_dtype(data.dtype) and
is_object_dtype(data.values.categories)) or
(is_object_dtype(data.dtype)))):
# it's neither a string series not a categorical series with
# strings inside the categories.
# this really should exclude all series with any non-string values
# (instead of test for object dtype), but that isn't practical for
# performance reasons until we have a str dtype (GH 9343)
raise AttributeError("Can only use .str accessor with string "
"values, which use np.object_ dtype in "
"pandas")
elif isinstance(data, Index):
# can't use ABCIndex to exclude non-str
# see scc/inferrence.pyx which can contain string values
allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
if data.inferred_type not in allowed_types:
message = ("Can only use .str accessor with string values "
"(i.e. inferred_type is 'string', 'unicode' or "
"'mixed')")
raise AttributeError(message)
if data.nlevels > 1:
message = ("Can only use .str accessor with Index, not "
"MultiIndex")
raise AttributeError(message)
return cls(data)
Example #11
| Source File: test_generic.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
Example #12
| Source File: strings.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _make_accessor(cls, data):
from pandas.core.index import Index
if (isinstance(data, ABCSeries) and
not ((is_categorical_dtype(data.dtype) and
is_object_dtype(data.values.categories)) or
(is_object_dtype(data.dtype)))):
# it's neither a string series not a categorical series with
# strings inside the categories.
# this really should exclude all series with any non-string values
# (instead of test for object dtype), but that isn't practical for
# performance reasons until we have a str dtype (GH 9343)
raise AttributeError("Can only use .str accessor with string "
"values, which use np.object_ dtype in "
"pandas")
elif isinstance(data, Index):
# can't use ABCIndex to exclude non-str
# see scc/inferrence.pyx which can contain string values
allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
if data.inferred_type not in allowed_types:
message = ("Can only use .str accessor with string values "
"(i.e. inferred_type is 'string', 'unicode' or "
"'mixed')")
raise AttributeError(message)
if data.nlevels > 1:
message = ("Can only use .str accessor with Index, not "
"MultiIndex")
raise AttributeError(message)
return cls(data)
Example #13
| Source File: test_generic.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
simplefilter('ignore', FutureWarning)
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)
assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray)
Example #14
| Source File: test_base.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_generated_op_names(opname, indices):
index = indices
if isinstance(index, ABCIndex) and opname == 'rsub':
# pd.Index.__rsub__ does not exist; though the method does exist
# for subclasses. see GH#19723
return
opname = '__{name}__'.format(name=opname)
method = getattr(index, opname)
assert method.__name__ == opname
Example #15
| Source File: strings.py From vnpy_crypto with MIT License | 5 votes |
|
def _validate(data):
from pandas.core.index import Index
if (isinstance(data, ABCSeries) and
not ((is_categorical_dtype(data.dtype) and
is_object_dtype(data.values.categories)) or
(is_object_dtype(data.dtype)))):
# it's neither a string series not a categorical series with
# strings inside the categories.
# this really should exclude all series with any non-string values
# (instead of test for object dtype), but that isn't practical for
# performance reasons until we have a str dtype (GH 9343)
raise AttributeError("Can only use .str accessor with string "
"values, which use np.object_ dtype in "
"pandas")
elif isinstance(data, Index):
# can't use ABCIndex to exclude non-str
# see src/inference.pyx which can contain string values
allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
if is_categorical_dtype(data.dtype):
inf_type = data.categories.inferred_type
else:
inf_type = data.inferred_type
if inf_type not in allowed_types:
message = ("Can only use .str accessor with string values "
"(i.e. inferred_type is 'string', 'unicode' or "
"'mixed')")
raise AttributeError(message)
if data.nlevels > 1:
message = ("Can only use .str accessor with Index, not "
"MultiIndex")
raise AttributeError(message)
Example #16
| Source File: test_generic.py From vnpy_crypto with MIT License | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
Example #17
| Source File: test_base.py From vnpy_crypto with MIT License | 5 votes |
|
def test_generated_op_names(opname, indices):
index = indices
if isinstance(index, ABCIndex) and opname == 'rsub':
# pd.Index.__rsub__ does not exist; though the method does exist
# for subclasses. see GH#19723
return
opname = '__{name}__'.format(name=opname)
method = getattr(index, opname)
assert method.__name__ == opname
Example #18
| Source File: test_generic.py From recruit with Apache License 2.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
simplefilter('ignore', FutureWarning)
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)
assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray)
Example #19
| Source File: common.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def is_bool_indexer(key):
# type: (Any) -> bool
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values.
"""
na_msg = 'cannot index with vector containing NA / NaN values'
if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
(is_array_like(key) and is_extension_array_dtype(key.dtype))):
if key.dtype == np.object_:
key = np.asarray(values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError(na_msg)
return False
return True
elif is_bool_dtype(key.dtype):
# an ndarray with bool-dtype by definition has no missing values.
# So we only need to check for NAs in ExtensionArrays
if is_extension_array_dtype(key.dtype):
if np.any(key.isna()):
raise ValueError(na_msg)
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
Example #20
| Source File: common.py From recruit with Apache License 2.0 | 4 votes |
|
def is_bool_indexer(key):
# type: (Any) -> bool
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values.
"""
na_msg = 'cannot index with vector containing NA / NaN values'
if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
(is_array_like(key) and is_extension_array_dtype(key.dtype))):
if key.dtype == np.object_:
key = np.asarray(values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError(na_msg)
return False
return True
elif is_bool_dtype(key.dtype):
# an ndarray with bool-dtype by definition has no missing values.
# So we only need to check for NAs in ExtensionArrays
if is_extension_array_dtype(key.dtype):
if np.any(key.isna()):
raise ValueError(na_msg)
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
