Series.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last') [source] Sort by the values along either axis New in version 0.17.0. Parameters: axis : {0, ?index?}, default 0 Axis to direct sorting ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. inplace : bool, default False if True, perform operation in-place kind : {?qu

DataFrame.ffill(axis=None, inplace=False, limit=None, downcast=None) [source] Synonym for NDFrame.fillna(method=?ffill?)

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

Series.str.rindex(sub, start=0, end=None) [source] Return highest indexes in each strings where the substring is fully contained between [start:end]. This is the same as str.rfind except instead of returning -1, it raises a ValueError when the substring is not found. Equivalent to standard str.rindex. Parameters: sub : str Substring being searched start : int Left edge index end : int Right edge index Returns: found : Series/Index of objects See also index Return lowest indexe

Index.append(other) [source] Append a collection of Index options together Parameters: other : Index or list/tuple of indices Returns: appended : Index

Series.plot.box(**kwds) [source] Boxplot New in version 0.17.0. Parameters: **kwds : optional Keyword arguments to pass on to pandas.Series.plot(). Returns: axes : matplotlib.AxesSubplot or np.array of them

DataFrame.nlargest(n, columns, keep='first') [source] Get the rows of a DataFrame sorted by the n largest values of columns. New in version 0.17.0. Parameters: n : int Number of items to retrieve columns : list or str Column name or names to order by keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. Returns: DataFrame Examples >>> df = DataFrame({'a': [1, 10, 8, 11,

DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) [source] Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values Parameters: axis: {0 or ?index?, 1 or ?columns?}, default 0 index to direct ranking method : {?average?, ?min?, ?max?, ?first?, ?dense?} average: average rank of group min: lowest rank in group max: highest rank in group first: ranks assign

classmethod Series.from_csv(path, sep=', ', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False) [source] Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a time Series. This method only differs from pandas.read_csv() in some defaults: index_c

DatetimeIndex.str() [source] Vectorized string functions for Series and Index. NAs stay NA unless handled otherwise by a particular method. Patterned after Python?s string methods, with some inspiration from R?s stringr package. Examples >>> s.str.split('_') >>> s.str.replace('_', '')