DatetimeIndex.is_mixed() [source]

Series.keys() [source] Alias for index

DatetimeIndex.format(name=False, formatter=None, **kwargs) [source] Render a string representation of the Index

DataFrame.mask(cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True) [source] Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other. Parameters: cond : boolean NDFrame, array or callable If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn?t check it). New in version

Panel4D.abs() [source] Return an object with absolute value taken?only applicable to objects that are all numeric. Returns: abs: type of caller

Panel4D.filter(*args, **kwargs) [source]

TimedeltaIndex.ceil(freq) [source] ceil the index to the specified freq Parameters: freq : freq string/object Returns: index of same type Raises: ValueError if the freq cannot be converted

Index.strides return the strides of the underlying data

CategoricalIndex.sort(*args, **kwargs) [source]

In this section, we will discuss missing (also referred to as NA) values in pandas. Note The choice of using NaN internally to denote missing data was largely for simplicity and performance reasons. It differs from the MaskedArray approach of, for example, scikits.timeseries. We are hopeful that NumPy will soon be able to provide a native NA type solution (similar to R) performant enough to be used in pandas. See the cookbook for some advanced strategies Missing data basics When / why does