inference.py

""" 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