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

Series.dt.weekofyear The week ordinal of the year

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

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

Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True) [source] Returns object containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters: normalize : boolean, default False If True then the object returned will contain the relative frequencies of the unique values. sort : boolean, default True Sort by values ascending

Series.add(other, level=None, fill_value=None, axis=0) [source] Addition of series and other, element-wise (binary operator add). Equivalent to series + other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a level, matching

Window.sum(*args, **kwargs) [source] window sum Parameters: how : string, default None (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.window, pandas.DataFrame.window

pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All') [source] Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame Parameters: data : DataFrame values : column to aggregate, optional index : column, Grouper, array, or list of the previous If an array is passed

Index.flags