Series.lt(other, level=None, fill_value=None, axis=0) [source] Less than of series and other, element-wise (binary operator lt). 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, matchin

Index.insert(loc, item) [source] Make new Index inserting new item at location. Follows Python list.append semantics for negative values Parameters: loc : int item : object Returns: new_index : Index

DataFrame.mode(axis=0, numeric_only=False) [source] Gets the mode(s) of each element along the axis selected. Empty if nothing has 2+ occurrences. Adds a row for each mode per label, fills in gaps with nan. Note that there could be multiple values returned for the selected axis (when more than one item share the maximum frequency), which is the reason why a dataframe is returned. If you want to impute missing values with the mode in a dataframe df, you can just do this: df.fillna(df.mode().

StataReader.variable_labels() [source] Returns variable labels as a dict, associating each variable name with corresponding label

Index.slice_locs(start=None, end=None, step=None, kind=None) [source] Compute slice locations for input labels. Parameters: start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the end step : int, defaults None If None, defaults to 1 kind : {?ix?, ?loc?, ?getitem?} or None Returns: start, end : int

Panel4D.select(crit, axis=0) [source] Return data corresponding to axis labels matching criteria Parameters: crit : function To be called on each index (label). Should return True or False axis : int Returns: selection : type of caller

Rolling.var(ddof=1, *args, **kwargs) [source] rolling variance Parameters: ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

Reshaping by pivoting DataFrame objects Data is often stored in CSV files or databases in so-called ?stacked? or ?record? format: In [1]: df Out[1]: date variable value 0 2000-01-03 A 0.469112 1 2000-01-04 A -0.282863 2 2000-01-05 A -1.509059 3 2000-01-03 B -1.135632 4 2000-01-04 B 1.212112 5 2000-01-05 B -0.173215 6 2000-01-03 C 0.119209 7 2000-01-04 C -1.044236 8 2000-01-05 C -0.861849 9 2000-01-03

CategoricalIndex.insert(loc, item) [source] Make new Index inserting new item at location. Follows Python list.append semantics for negative values Parameters: loc : int item : object Returns: new_index : Index Raises: ValueError if the item is not in the categories

Series.str.rsplit(pat=None, n=-1, expand=False) [source] Split each string in the Series/Index by the given delimiter string, starting at the end of the string and working to the front. Equivalent to str.rsplit(). New in version 0.16.2. Parameters: pat : string, default None Separator to split on. If None, splits on whitespace n : int, default -1 (all) None, 0 and -1 will be interpreted as return all splits expand : bool, default False If True, return DataFrame/MultiIndex expanding