TimedeltaIndex.fillna(value=None, downcast=None) [source] Fill NA/NaN values with the specified value Parameters: value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string ?infer? which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) Returns: filled : %(klass)s

TimedeltaIndex.isin(values) [source] Compute boolean array of whether each index value is found in the passed set of values Parameters: values : set or sequence of values Returns: is_contained : ndarray (boolean dtype)

Panel.iterkv(*args, **kwargs) [source] iteritems alias used to get around 2to3. Deprecated

Rolling.corr(other=None, pairwise=None, **kwargs) [source] rolling sample correlation Parameters: other : Series, DataFrame, or ndarray, optional if not supplied then will default to self and produce pairwise output pairwise : bool, default None If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a Panel in the case of DataFrame inputs. In the case of

Panel.values Numpy representation of NDFrame Notes The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type convention, mixing int64 and uint64 w

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

TimedeltaIndex.name = None

Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. This is encouraging because it means pandas is not only helping users to handle their data tasks but also that it provides a better starting point for developers to build powerful and more focused data tools. The creation of libraries that complement pandas? functionality also allows pandas development to remain focused around it?s original requirements. This is an

Series.align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None) [source] Align two object on their axes with the specified join method for each axis Index Parameters: other : DataFrame or Series join : {?outer?, ?inner?, ?left?, ?right?}, default ?outer? axis : allowed axis of the other object, default None Align on index (0), columns (1), or both (None) level : int or level name, default None Broadcast acro

DatetimeIndex.snap(freq='S') [source] Snap time stamps to nearest occurring frequency