Rolling.skew(**kwargs) [source] Unbiased rolling skewness Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

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

Rolling.mean(*args, **kwargs) [source] rolling mean Parameters: how : string, default None (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

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

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

Rolling.kurt(**kwargs) [source] Unbiased rolling kurtosis Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

Rolling.count() [source] rolling count of number of non-NaN observations inside provided window. Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

Rolling.corr(other=None, pairwise=None, **kwargs) [source] rolling sample correlation 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 case of

Rolling.apply(func, args=(), kwargs={}) [source] rolling function apply Parameters: func : function Must produce a single value from an ndarray input *args and **kwargs are passed to the function 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