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inference.py
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inference.py
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""" basic inference routines """
import collections
import re
import numpy as np
from collections import Iterable
from numbers import Number
from pandas.compat import (PY2, string_types, text_type,
string_and_binary_types, re_type)
from pandas._libs import lib
is_bool = lib.is_bool
is_integer = lib.is_integer
is_float = lib.is_float
is_complex = lib.is_complex
is_scalar = lib.is_scalar
is_decimal = lib.is_decimal
is_interval = lib.is_interval
def is_number(obj):
"""
Check if the object is a number.
Returns True when the object is a number, and False if is not.
Parameters
----------
obj : any type
The object to check if is a number.
Returns
-------
is_number : bool
Whether `obj` is a number or not.
See Also
--------
pandas.api.types.is_integer: checks a subgroup of numbers
Examples
--------
>>> pd.api.types.is_number(1)
True
>>> pd.api.types.is_number(7.15)
True
Booleans are valid because they are int subclass.
>>> pd.api.types.is_number(False)
True
>>> pd.api.types.is_number("foo")
False
>>> pd.api.types.is_number("5")
False
"""
return isinstance(obj, (Number, np.number))
def is_string_like(obj):
"""
Check if the object is a string.
Parameters
----------
obj : The object to check.
Examples
--------
>>> is_string_like("foo")
True
>>> is_string_like(1)
False
Returns
-------
is_str_like : bool
Whether `obj` is a string or not.
"""
return isinstance(obj, (text_type, string_types))
def _iterable_not_string(obj):
"""
Check if the object is an iterable but not a string.
Parameters
----------
obj : The object to check.
Returns
-------
is_iter_not_string : bool
Whether `obj` is a non-string iterable.
Examples
--------
>>> _iterable_not_string([1, 2, 3])
True
>>> _iterable_not_string("foo")
False
>>> _iterable_not_string(1)
False
"""
return (isinstance(obj, collections.Iterable) and
not isinstance(obj, string_types))
def is_iterator(obj):
"""
Check if the object is an iterator.
For example, lists are considered iterators
but not strings or datetime objects.
Parameters
----------
obj : The object to check.
Returns
-------
is_iter : bool
Whether `obj` is an iterator.
Examples
--------
>>> is_iterator([1, 2, 3])
True
>>> is_iterator(datetime(2017, 1, 1))
False
>>> is_iterator("foo")
False
>>> is_iterator(1)
False
"""
if not hasattr(obj, '__iter__'):
return False
if PY2:
return hasattr(obj, 'next')
else:
# Python 3 generators have
# __next__ instead of next
return hasattr(obj, '__next__')
def is_file_like(obj):
"""
Check if the object is a file-like object.
For objects to be considered file-like, they must
be an iterator AND have either a `read` and/or `write`
method as an attribute.
Note: file-like objects must be iterable, but
iterable objects need not be file-like.
.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check.
Returns
-------
is_file_like : bool
Whether `obj` has file-like properties.
Examples
--------
>>> buffer(StringIO("data"))
>>> is_file_like(buffer)
True
>>> is_file_like([1, 2, 3])
False
"""
if not (hasattr(obj, 'read') or hasattr(obj, 'write')):
return False
if not hasattr(obj, "__iter__"):
return False
return True
def is_re(obj):
"""
Check if the object is a regex pattern instance.
Parameters
----------
obj : The object to check.
Returns
-------
is_regex : bool
Whether `obj` is a regex pattern.
Examples
--------
>>> is_re(re.compile(".*"))
True
>>> is_re("foo")
False
"""
return isinstance(obj, re_type)
def is_re_compilable(obj):
"""
Check if the object can be compiled into a regex pattern instance.
Parameters
----------
obj : The object to check.
Returns
-------
is_regex_compilable : bool
Whether `obj` can be compiled as a regex pattern.
Examples
--------
>>> is_re_compilable(".*")
True
>>> is_re_compilable(1)
False
"""
try:
re.compile(obj)
except TypeError:
return False
else:
return True
def is_list_like(obj):
"""
Check if the object is list-like.
Objects that are considered list-like are for example Python
lists, tuples, sets, NumPy arrays, and Pandas Series.
Strings and datetime objects, however, are not considered list-like.
Parameters
----------
obj : The object to check.
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
Examples
--------
>>> is_list_like([1, 2, 3])
True
>>> is_list_like({1, 2, 3})
True
>>> is_list_like(datetime(2017, 1, 1))
False
>>> is_list_like("foo")
False
>>> is_list_like(1)
False
"""
return (isinstance(obj, Iterable) and
not isinstance(obj, string_and_binary_types))
def is_array_like(obj):
"""
Check if the object is array-like.
For an object to be considered array-like, it must be list-like and
have a `dtype` attribute.
Parameters
----------
obj : The object to check.
Returns
-------
is_array_like : bool
Whether `obj` has array-like properties.
Examples
--------
>>> is_array_like(np.array([1, 2, 3]))
True
>>> is_array_like(pd.Series(["a", "b"]))
True
>>> is_array_like(pd.Index(["2016-01-01"]))
True
>>> is_array_like([1, 2, 3])
False
>>> is_array_like(("a", "b"))
False
"""
return is_list_like(obj) and hasattr(obj, "dtype")
def is_nested_list_like(obj):
"""
Check if the object is list-like, and that all of its elements
are also list-like.
.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check.
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
Examples
--------
>>> is_nested_list_like([[1, 2, 3]])
True
>>> is_nested_list_like([{1, 2, 3}, {1, 2, 3}])
True
>>> is_nested_list_like(["foo"])
False
>>> is_nested_list_like([])
False
>>> is_nested_list_like([[1, 2, 3], 1])
False
Notes
-----
This won't reliably detect whether a consumable iterator (e. g.
a generator) is a nested-list-like without consuming the iterator.
To avoid consuming it, we always return False if the outer container
doesn't define `__len__`.
See Also
--------
is_list_like
"""
return (is_list_like(obj) and hasattr(obj, '__len__') and
len(obj) > 0 and all(is_list_like(item) for item in obj))
def is_dict_like(obj):
"""
Check if the object is dict-like.
Parameters
----------
obj : The object to check.
Returns
-------
is_dict_like : bool
Whether `obj` has dict-like properties.
Examples
--------
>>> is_dict_like({1: 2})
True
>>> is_dict_like([1, 2, 3])
False
"""
return hasattr(obj, '__getitem__') and hasattr(obj, 'keys')
def is_named_tuple(obj):
"""
Check if the object is a named tuple.
Parameters
----------
obj : The object to check.
Returns
-------
is_named_tuple : bool
Whether `obj` is a named tuple.
Examples
--------
>>> Point = namedtuple("Point", ["x", "y"])
>>> p = Point(1, 2)
>>>
>>> is_named_tuple(p)
True
>>> is_named_tuple((1, 2))
False
"""
return isinstance(obj, tuple) and hasattr(obj, '_fields')
def is_hashable(obj):
"""Return True if hash(obj) will succeed, False otherwise.
Some types will pass a test against collections.Hashable but fail when they
are actually hashed with hash().
Distinguish between these and other types by trying the call to hash() and
seeing if they raise TypeError.
Examples
--------
>>> a = ([],)
>>> isinstance(a, collections.Hashable)
True
>>> is_hashable(a)
False
"""
# Unfortunately, we can't use isinstance(obj, collections.Hashable), which
# can be faster than calling hash. That is because numpy scalars on Python
# 3 fail this test.
# Reconsider this decision once this numpy bug is fixed:
# https://github.com/numpy/numpy/issues/5562
try:
hash(obj)
except TypeError:
return False
else:
return True
def is_sequence(obj):
"""
Check if the object is a sequence of objects.
String types are not included as sequences here.
Parameters
----------
obj : The object to check.
Returns
-------
is_sequence : bool
Whether `obj` is a sequence of objects.
Examples
--------
>>> l = [1, 2, 3]
>>>
>>> is_sequence(l)
True
>>> is_sequence(iter(l))
False
"""
try:
iter(obj) # Can iterate over it.
len(obj) # Has a length associated with it.
return not isinstance(obj, string_and_binary_types)
except (TypeError, AttributeError):
return False