DatetimeIndex.is_normalized = None

DataFrame.clip_lower(threshold, axis=None) [source] Return copy of the input with values below given value(s) truncated. Parameters: threshold : float or array_like axis : int or string axis name, optional Align object with threshold along the given axis. Returns: clipped : same type as input See also clip

MultiIndex.asof_locs(where, mask) [source] where : array of timestamps mask : array of booleans where data is not NA

Panel4D.tz_localize(*args, **kwargs) [source] Localize tz-naive TimeSeries to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to localize level : int, str, default None If axis ia a MultiIndex, localize a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data ambiguous : ?infer?, bool-ndarray, ?NaT?, default ?raise? ?infer? will attempt to infer fall dst-transition hours based on order bool-ndarray

Series.rfloordiv(other, level=None, fill_value=None, axis=0) [source] Integer division of series and other, element-wise (binary operator rfloordiv). Equivalent to other // series, 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 acro

TimedeltaIndex.values return the underlying data as an ndarray

MultiIndex.shift(periods=1, freq=None) [source] Shift Index containing datetime objects by input number of periods and DateOffset Returns: shifted : Index

Series.cat.ordered Gets the ordered attribute

DataFrame.lookup(row_labels, col_labels) [source] Label-based ?fancy indexing? function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. Parameters: row_labels : sequence The row labels to use for lookup col_labels : sequence The column labels to use for lookup Notes Akin to: result = [] for row, col in zip(row_labels, col_labels): result.append(df.get_value(row, col)) Examples values : ndarray

Panel.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True over requested axis Parameters: axis : {items (0), major_axis (1), minor_axis (2)} 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 DataFrame bool_only : boolean, default None In