EWM.var(bias=False, *args, **kwargs) [source] exponential weighted moving variance Parameters: bias : boolean, default False Use a standard estimation bias correction Returns: same type as input See also pandas.Series.ewm, pandas.DataFrame.ewm

EWM.cov(other=None, pairwise=None, bias=False, **kwargs) [source] exponential weighted 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 inpu

EWM.mean(*args, **kwargs) [source] exponential weighted moving average Returns: same type as input See also pandas.Series.ewm, pandas.DataFrame.ewm

EWM.std(bias=False, *args, **kwargs) [source] exponential weighted moving stddev Parameters: bias : boolean, default False Use a standard estimation bias correction Returns: same type as input See also pandas.Series.ewm, pandas.DataFrame.ewm

EWM.corr(other=None, pairwise=None, **kwargs) [source] exponential weighted 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

Here we discuss a lot of the essential functionality common to the pandas data structures. Here?s how to create some of the objects used in the examples from the previous section: In [1]: index = pd.date_range('1/1/2000', periods=8) In [2]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) In [3]: df = pd.DataFrame(np.random.randn(8, 3), index=index, ...: columns=['A', 'B', 'C']) ...: In [4]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', '

Cython (Writing C extensions for pandas) For many use cases writing pandas in pure python and numpy is sufficient. In some computationally heavy applications however, it can be possible to achieve sizeable speed-ups by offloading work to cython. This tutorial assumes you have refactored as much as possible in python, for example trying to remove for loops and making use of numpy vectorization, it?s always worth optimising in python first. This tutorial walks through a ?typical? process of cyt

class pandas.DatetimeIndex [source] Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information. Parameters: data : array-like (1-dimensional), optional Optional datetime-like data to construct index with copy : bool Make a copy of input ndarray freq : string or pandas offset object, optional One of pandas date offset strings or corresponding objects

DatetimeIndex.year The year of the datetime

DatetimeIndex.where(cond, other=None) [source] New in version 0.19.0. Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. Parameters: cond : boolean same length as self other : scalar, or array-like