Series.argmin(axis=None, skipna=True, *args, **kwargs) [source] Index of first occurrence of minimum of values. Parameters: skipna : boolean, default True Exclude NA/null values Returns: idxmin : Index of minimum of values See also DataFrame.idxmin, numpy.ndarray.argmin Notes This method is the Series version of ndarray.argmin.

MultiIndex.to_datetime(dayfirst=False) [source] DEPRECATED: use pandas.to_datetime() instead. For an Index containing strings or datetime.datetime objects, attempt conversion to DatetimeIndex

MultiIndex.reindex(target, method=None, level=None, limit=None, tolerance=None) [source] Create index with target?s values (move/add/delete values as necessary) Returns: new_index : pd.MultiIndex Resulting index indexer : np.ndarray or None Indices of output values in original index

Panel4D.describe(percentiles=None, include=None, exclude=None) [source] Generate various summary statistics, excluding NaN values. Parameters: percentiles : array-like, optional The percentiles to include in the output. Should all be in the interval [0, 1]. By default percentiles is [.25, .5, .75], returning the 25th, 50th, and 75th percentiles. include, exclude : list-like, ?all?, or None (default) Specify the form of the returned result. Either: None to both (default). The result will

MultiIndex.searchsorted(key, side='left', sorter=None) [source] Find indices where elements should be inserted to maintain order. Find the indices into a sorted IndexOpsMixin self such that, if the corresponding elements in v were inserted before the indices, the order of self would be preserved. Parameters: key : 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

Series.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True over requested axis Parameters: axis : {index (0)} 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 scalar bool_only : boolean, default None Include only boolean columns. If Non

MultiIndex.get_indexer(target, method=None, limit=None, tolerance=None) [source] Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. The mask determines whether labels are found or not in the current index Parameters: target : MultiIndex or Index (of tuples) method : {?pad?, ?ffill?, ?backfill?, ?bfill?} pad / ffill: propagate LAST valid observation forward to next valid bac

DataFrame.to_clipboard(excel=None, sep=None, **kwargs) [source] Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example. Parameters: excel : boolean, defaults to True if True, use the provided separator, writing in a csv format for allowing easy pasting into excel. if False, write a string representation of the object to the clipboard sep : optional, defaults to tab other keywords are passed to to_csv Notes Requirements for your

MultiIndex.get_level_values(level) [source] Return vector of label values for requested level, equal to the length of the index Parameters: level : int or level name Returns: values : ndarray

Series.divide(other, level=None, fill_value=None, axis=0) [source] Floating division of series and other, element-wise (binary operator truediv). 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