Python pandas.core.dtypes.common.is_bool() Examples
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code examples of pandas.core.dtypes.common.is_bool().
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Example #1
| Source File: dtypes.py From recruit with Apache License 2.0 | 6 votes |
|
def validate_ordered(ordered):
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
from pandas.core.dtypes.common import is_bool
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
Example #2
| Source File: dtypes.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def _validate_ordered(ordered):
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
from pandas.core.dtypes.common import is_bool
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
Example #3
| Source File: dtypes.py From vnpy_crypto with MIT License | 6 votes |
|
def validate_ordered(ordered):
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
from pandas.core.dtypes.common import is_bool
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
Example #4
| Source File: dtypes.py From Splunking-Crime with GNU Affero General Public License v3.0 | 6 votes |
|
def _validate_ordered(ordered):
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
from pandas.core.dtypes.common import is_bool
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
Example #5
| Source File: dtypes.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def validate_ordered(ordered):
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
from pandas.core.dtypes.common import is_bool
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
Example #6
| Source File: _validators.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
typ=type(value).__name__))
return value
Example #7
| Source File: test_eval.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_identical(self):
# see gh-10546
x = 1
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1
assert is_scalar(result)
x = 1.5
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1.5
assert is_scalar(result)
x = False
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert not result
assert is_bool(result)
assert is_scalar(result)
x = np.array([1])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1]))
assert result.shape == (1, )
x = np.array([1.5])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1.5]))
assert result.shape == (1, )
x = np.array([False]) # noqa
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([False]))
assert result.shape == (1, )
Example #8
| Source File: function.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #9
| Source File: function.py From coffeegrindsize with MIT License | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #10
| Source File: _validators.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
typ=type(value).__name__))
return value
Example #11
| Source File: _validators.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overriden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
format(fname=fname, arg=key)))
Example #12
| Source File: test_eval.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_identical(self):
# see gh-10546
x = 1
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1
assert is_scalar(result)
x = 1.5
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1.5
assert is_scalar(result)
x = False
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert not result
assert is_bool(result)
assert is_scalar(result)
x = np.array([1])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1]))
assert result.shape == (1, )
x = np.array([1.5])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1.5]))
assert result.shape == (1, )
x = np.array([False]) # noqa
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([False]))
assert result.shape == (1, )
Example #13
| Source File: function.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #14
| Source File: _validators.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
typ=type(value).__name__))
return value
Example #15
| Source File: _validators.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overriden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
format(fname=fname, arg=key)))
Example #16
| Source File: function.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #17
| Source File: function.py From recruit with Apache License 2.0 | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #18
| Source File: _validators.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overridden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except ValueError:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
format(fname=fname, arg=key)))
Example #19
| Source File: test_eval.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_identical(self):
# see gh-10546
x = 1
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1
assert is_scalar(result)
x = 1.5
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1.5
assert is_scalar(result)
x = False
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert not result
assert is_bool(result)
assert is_scalar(result)
x = np.array([1])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1]))
assert result.shape == (1, )
x = np.array([1.5])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1.5]))
assert result.shape == (1, )
x = np.array([False]) # noqa
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([False]))
assert result.shape == (1, )
Example #20
| Source File: function.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #21
| Source File: _validators.py From vnpy_crypto with MIT License | 5 votes |
|
def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
typ=type(value).__name__))
return value
Example #22
| Source File: _validators.py From vnpy_crypto with MIT License | 5 votes |
|
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overridden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
format(fname=fname, arg=key)))
Example #23
| Source File: test_eval.py From vnpy_crypto with MIT License | 5 votes |
|
def test_identical(self):
# see gh-10546
x = 1
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1
assert is_scalar(result)
x = 1.5
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1.5
assert is_scalar(result)
x = False
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert not result
assert is_bool(result)
assert is_scalar(result)
x = np.array([1])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1]))
assert result.shape == (1, )
x = np.array([1.5])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1.5]))
assert result.shape == (1, )
x = np.array([False]) # noqa
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([False]))
assert result.shape == (1, )
Example #24
| Source File: function.py From vnpy_crypto with MIT License | 5 votes |
|
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
Example #25
| Source File: _validators.py From recruit with Apache License 2.0 | 5 votes |
|
def validate_bool_kwarg(value, arg_name):
""" Ensures that argument passed in arg_name is of type bool. """
if not (is_bool(value) or value is None):
raise ValueError('For argument "{arg}" expected type bool, received '
'type {typ}.'.format(arg=arg_name,
typ=type(value).__name__))
return value
Example #26
| Source File: _validators.py From recruit with Apache License 2.0 | 5 votes |
|
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overridden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except ValueError:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
format(fname=fname, arg=key)))
Example #27
| Source File: test_eval.py From recruit with Apache License 2.0 | 5 votes |
|
def test_identical(self):
# see gh-10546
x = 1
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1
assert is_scalar(result)
x = 1.5
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert result == 1.5
assert is_scalar(result)
x = False
result = pd.eval('x', engine=self.engine, parser=self.parser)
assert not result
assert is_bool(result)
assert is_scalar(result)
x = np.array([1])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1]))
assert result.shape == (1, )
x = np.array([1.5])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1.5]))
assert result.shape == (1, )
x = np.array([False]) # noqa
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([False]))
assert result.shape == (1, )
