How to use the pydash.is_number function in pydash

To help you get started, we’ve selected a few pydash examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

github dgilland / pydash / src / pydash / utilities.py View on Github external
def to_path_tokens(value):
    """Parse `value` into :class:`PathToken` objects."""
    if pyd.is_string(value) and ('.' in value or '[' in value):
        # Since we can't tell whether a bare number is supposed to be dict key
        # or a list index, we support a special syntax where any string-integer
        # surrounded by brackets is treated as a list index and converted to an
        # integer.
        keys = [PathToken(int(key[1:-1]), default_factory=list)
                if RE_PATH_LIST_INDEX.match(key)
                else PathToken(unescape_path_key(key), default_factory=dict)
                for key in filter(None, RE_PATH_KEY_DELIM.split(value))]
    elif pyd.is_string(value) or pyd.is_number(value):
        keys = [PathToken(value, default_factory=dict)]
    elif value is NoValue:
        keys = []
    else:
        keys = value

    return keys
github dgilland / pydash / src / pydash / numerical.py View on Github external
Returns:
        number: Result of calculation.

    Example:

        >>> power(5, 2)
        25
        >>> power(12.5, 3)
        1953.125

    .. versionadded:: 2.1.0

    .. versionchanged:: 4.0.0
        Removed alias ``pow_``.
    """
    if pyd.is_number(x):
        result = pow(x, n)
    elif pyd.is_list(x):
        result = [pow(item, n) for item in x]
    else:
        result = None

    return result
github dgilland / pydash / src / pydash / numerical.py View on Github external
def call_math_operator(value1, value2, op, default):
    """Return the result of the math operation on the given values."""
    if not value1:
        value1 = default

    if not value2:
        value2 = default

    if not pyd.is_number(value1):
        try:
            value1 = float(value1)
        except Exception:
            pass

    if not pyd.is_number(value2):
        try:
            value2 = float(value2)
        except Exception:
            pass

    return op(value1, value2)
github dgilland / pydash / src / pydash / numerical.py View on Github external
def call_math_operator(value1, value2, op, default):
    """Return the result of the math operation on the given values."""
    if not value1:
        value1 = default

    if not value2:
        value2 = default

    if not pyd.is_number(value1):
        try:
            value1 = float(value1)
        except Exception:
            pass

    if not pyd.is_number(value2):
        try:
            value2 = float(value2)
        except Exception:
            pass

    return op(value1, value2)
github dgilland / pydash / src / pydash / functions.py View on Github external
def __init__(self, func, wait, max_wait=False):
        self.func = func
        self.wait = wait
        self.max_wait = max_wait

        self.last_result = None

        # Initialize last_* times to be prior to the wait periods so that func
        # is primed to be executed on first call.
        self.last_call = pyd.now() - self.wait
        self.last_execution = (pyd.now() - max_wait if pyd.is_number(max_wait)
                               else None)
github dgilland / pydash / src / pydash / numerical.py View on Github external
def rounder(func, x, precision):
    precision = pow(10, precision)

    def rounder_func(item):
        return func(item * precision) / precision

    result = None

    if pyd.is_number(x):
        result = rounder_func(x)
    elif pyd.is_iterable(x):
        try:
            result = [rounder_func(item) for item in x]
        except TypeError:
            pass

    return result

pydash

The kitchen sink of Python utility libraries for doing "stuff" in a functional way. Based on the Lo-Dash Javascript library.

MIT
Latest version published 17 days ago

Package Health Score

90 / 100
Full package analysis

Similar packages