TimedeltaIndex.asobject return object Index which contains boxed values this is an internal non-public method

DataFrame.hist(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds) [source] Draw histogram of the DataFrame?s series using matplotlib / pylab. Parameters: data : DataFrame column : string or sequence If passed, will be used to limit data to a subset of columns by : object, optional If passed, then used to form histograms for separate groups grid : boolean, default

Series.dot(other) [source] Matrix multiplication with DataFrame or inner-product with Series objects Parameters: other : Series or DataFrame Returns: dot_product : scalar or Series

Series.dt.floor(*args, **kwargs) [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

Series.nonzero() [source] Return the indices of the elements that are non-zero This method is equivalent to calling numpy.nonzero on the series data. For compatability with NumPy, the return value is the same (a tuple with an array of indices for each dimension), but it will always be a one-item tuple because series only have one dimension. See also numpy.nonzero Examples >>> s = pd.Series([0, 3, 0, 4]) >>> s.nonzero() (array([1, 3]),) >>> s.iloc[s.nonzero()[0]]

CategoricalIndex.astype(dtype, copy=True) [source] Create an Index with values cast to dtypes. The class of a new Index is determined by dtype. When conversion is impossible, a ValueError exception is raised. Parameters: dtype : numpy dtype or pandas type copy : bool, default True By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is retu

Series.between(left, right, inclusive=True) [source] Return boolean Series equivalent to left <= series <= right. NA values will be treated as False Parameters: left : scalar Left boundary right : scalar Right boundary Returns: is_between : Series

TimedeltaIndex.dtype

HDFStore.put(key, value, format=None, append=False, **kwargs) [source] Store object in HDFStore Parameters: key : object value : {Series, DataFrame, Panel} format : ?fixed(f)|table(t)?, default is ?fixed? fixed(f) : Fixed format Fast writing/reading. Not-appendable, nor searchable table(t) : Table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default False This w

Panel.align(other, **kwargs) [source]