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DataDict

Warning: The DataDict class is experimental.

Bases: NestedDict, _Arithmetics

A NestedDict that supports arithmetics. Other methods are included that make DataDict similar to DataFrames.

Source code in ndicts\data_dict.py 
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class DataDict(NestedDict, _Arithmetics):
    """A NestedDict that supports arithmetics.
    Other methods are included that make DataDict similar to DataFrames."""

    def _arithmetic_operation(self, other, operation: str, symbol: str):
        """Implements any arithmetic operation, just pass the underlying method as string
        The symbol, passed as a string, will appear in the exception message if any
        The operation is performed only between NestedProperties or with numbers"""
        result = self.copy()
        if isinstance(other, self.__class__):
            for other_key, other_value in other.items():
                if other_key in self:
                    for key, value in self.extract[other_key].items():
                        result[key] = getattr(value, operation).__call__(other_value)
                else:
                    raise TypeError(
                        f"unsupported operand type(s) for {symbol}: incompatible keys"
                    )
            return result

        elif isinstance(other, Number):
            for key, value in self.items():
                result[key] = getattr(value, operation).__call__(other)
            return result

        return TypeError(
            f"unsupported operand type(s) for {symbol}: {type(self)} and {type(other)}"
        )

    def apply(self, func: Callable, inplace: bool = False):
        """Apply func to all values."""
        if inplace:
            for key, leaf in self.items():
                self[key] = func(leaf)
        else:
            new_self = self.copy()
            for key, leaf in new_self.items():
                new_self[key] = func(leaf)
            return new_self

    def reduce(self, func: Callable, *initial: Any):
        """Pass func and initial to functools.reduce and apply it to all values."""
        return reduce(func, self.values(), *initial)

    def total(self):
        """Returns sum of all values."""
        return sum(self.values())

    def mean(self) -> Number:
        """Returns mean of all values."""
        return self.total() / len(self)

    def std(self) -> Number:
        """Returns standard deviation of all values."""
        step = self.reduce(lambda a, b: a + (b - self.mean()) ** 2, 0)
        step /= len(self) - 1
        return step**0.5

apply(func, inplace=False)

Apply func to all values.

Source code in ndicts\data_dict.py 
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def apply(self, func: Callable, inplace: bool = False):
    """Apply func to all values."""
    if inplace:
        for key, leaf in self.items():
            self[key] = func(leaf)
    else:
        new_self = self.copy()
        for key, leaf in new_self.items():
            new_self[key] = func(leaf)
        return new_self

mean()

Returns mean of all values.

Source code in ndicts\data_dict.py 
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def mean(self) -> Number:
    """Returns mean of all values."""
    return self.total() / len(self)

reduce(func, *initial)

Pass func and initial to functools.reduce and apply it to all values.

Source code in ndicts\data_dict.py 
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def reduce(self, func: Callable, *initial: Any):
    """Pass func and initial to functools.reduce and apply it to all values."""
    return reduce(func, self.values(), *initial)

std()

Returns standard deviation of all values.

Source code in ndicts\data_dict.py 
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def std(self) -> Number:
    """Returns standard deviation of all values."""
    step = self.reduce(lambda a, b: a + (b - self.mean()) ** 2, 0)
    step /= len(self) - 1
    return step**0.5

total()

Returns sum of all values.

Source code in ndicts\data_dict.py 
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def total(self):
    """Returns sum of all values."""
    return sum(self.values())