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.

Panel.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) [source] Fill NA/NaN values using the specified method Parameters: value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list. method : {?backfil

Panel4D.div(other, axis=0) [source] Floating division of series and other, element-wise (binary operator truediv). Equivalent to panel / other. Parameters: other : Panel or Panel4D axis : {labels, items, major_axis, minor_axis} Axis to broadcast over Returns: Panel4D See also Panel4D.rtruediv

DataFrameGroupBy.describe(percentiles=None, include=None, exclude=None) 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

Expanding.max(*args, **kwargs) [source] expanding maximum Parameters: how : string, default ?max? (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

Rolling.cov(other=None, pairwise=None, ddof=1, **kwargs) [source] rolling sample covariance Parameters: other : Series, DataFrame, or ndarray, optional if not supplied then will default to self and produce pairwise output pairwise : bool, default None If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a Panel in the case of DataFrame inputs. In the ca

Panel4D.clip_upper(threshold, axis=None) [source] Return copy of input with values above 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

GroupBy.sum() [source] Compute sum of group values See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

MultiIndex.truncate(before=None, after=None) [source] Slice index between two labels / tuples, return new MultiIndex Parameters: before : label or tuple, can be partial. Default None None defaults to start after : label or tuple, can be partial. Default None None defaults to end Returns: truncated : MultiIndex

Series.map(arg, na_action=None) [source] Map values of Series using input correspondence (which can be a dict, Series, or function) Parameters: arg : function, dict, or Series na_action : {None, ?ignore?} If ?ignore?, propagate NA values, without passing them to the mapping function Returns: y : Series same index as caller Examples Map inputs to outputs >>> x one 1 two 2 three 3 >>> y 1 foo 2 bar 3 baz >>> x.map(y) one foo two bar three baz U