classmethod Panel.fromDict(data, intersect=False, orient='items', dtype=None) [source] Construct Panel from dict of DataFrame objects Parameters: data : dict {field : DataFrame} intersect : boolean Intersect indexes of input DataFrames orient : {?items?, ?minor?}, default ?items? The ?orientation? of the data. If the keys of the passed dict should be the items of the result panel, pass ?items? (default). Otherwise if the columns of the values of the passed DataFrame objects should be

MultiIndex.get_major_bounds(start=None, end=None, step=None, kind=None) [source] For an ordered MultiIndex, compute the slice locations for input labels. They can be tuples representing partial levels, e.g. for a MultiIndex with 3 levels, you can pass a single value (corresponding to the first level), or a 1-, 2-, or 3-tuple. Parameters: start : label or tuple, default None If None, defaults to the beginning end : label or tuple If None, defaults to the end step : int or None Slice st

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

Series.str.strip(to_strip=None) [source] Strip whitespace (including newlines) from each string in the Series/Index from left and right sides. Equivalent to str.strip(). Returns: stripped : Series/Index of objects

Index.take(indices, axis=0, allow_fill=True, fill_value=None, **kwargs) [source] return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. Parameters: indices : list Indices to be taken axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True fill_value : bool, default None If allow_fill=True and fill_value is not None, indices specified by -1 is regarded as NA. If Index doesn?t hold NA, raise

Panel4D.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True over requested axis Parameters: axis : {labels (0), items (1), major_axis (2), minor_axis (3)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Panel bool_only : boolean, defaul

DataFrame.to_hdf(path_or_buf, key, **kwargs) [source] Write the contained data to an HDF5 file using HDFStore. Parameters: path_or_buf : the path (string) or HDFStore object key : string indentifier for the group in the store mode : optional, {?a?, ?w?, ?r+?}, default ?a? 'w' Write; a new file is created (an existing file with the same name would be deleted). 'a' Append; an existing file is opened for reading and writing, and if the file does not exist it is created. 'r+' It

Series.searchsorted(v, side='left', sorter=None) [source] Find indices where elements should be inserted to maintain order. Find the indices into a sorted Series self such that, if the corresponding elements in v were inserted before the indices, the order of self would be preserved. Parameters: v : array_like Values to insert into self. side : {?left?, ?right?}, optional If ?left?, the index of the first suitable location found is given. If ?right?, return the last such index. If there

DataFrame.rtruediv(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator rtruediv). Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with t

CategoricalIndex.remove_categories(*args, **kwargs) [source] Removes the specified categories. removals must be included in the old categories. Values which were in the removed categories will be set to NaN Parameters: removals : category or list of categories The categories which should be removed. inplace : boolean (default: False) Whether or not to remove the categories inplace or return a copy of this categorical with removed categories. Returns: cat : Categorical with removed ca