DatetimeIndex.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 as

MultiIndex.to_hierarchical(n_repeat, n_shuffle=1) [source] Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle. Useful to replicate and rearrange a MultiIndex for combination with another Index with n_repeat items. Parameters: n_repeat : int Number of times to repeat the labels on self n_shuffle : int Controls the reordering of the labels. If the result is going to be an inner level in a MultiIndex, n_shuffle will need to be greater than one. The size o

Series.iloc Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4, 3, 0]. A slice object with ints, e.g. 1:7. A boolean array. A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above) .iloc will raise

Panel.rtruediv(other, axis=0) [source] Floating division of series and other, element-wise (binary operator rtruediv). Equivalent to other / panel. Parameters: other : DataFrame or Panel axis : {items, major_axis, minor_axis} Axis to broadcast over Returns: Panel See also Panel.truediv

CategoricalIndex.groupby(values) [source] Group the index labels by a given array of values. Parameters: values : array Values used to determine the groups. Returns: groups : dict {group name -> group labels}

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

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