Series.rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0) [source] Provides rolling window calculcations. New in version 0.18.0. Parameters: window : int, or offset Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size. If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-per

DataFrame.head(n=5) [source] Returns first n rows

DataFrame.truncate(before=None, after=None, axis=None, copy=True) [source] Truncates a sorted NDFrame before and/or after some particular index value. If the axis contains only datetime values, before/after parameters are converted to datetime values. Parameters: before : date Truncate before index value after : date Truncate after index value axis : the truncation axis, defaults to the stat axis copy : boolean, default is True, return a copy of the truncated section Returns: trunc

Series.combine(other, func, fill_value=nan) [source] Perform elementwise binary operation on two Series using given function with optional fill value when an index is missing from one Series or the other Parameters: other : Series or scalar value func : function fill_value : scalar value Returns: result : Series

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

DataFrame.consolidate(inplace=False) [source] Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray). Mainly an internal API function, but available here to the savvy user Parameters: inplace : boolean, default False If False return new object, otherwise modify existing object Returns: consolidated : type of caller

MultiIndex.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True) [source] Returns object containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters: normalize : boolean, default False If True then the object returned will contain the relative frequencies of the unique values. sort : boolean, default True Sort by values ascen

Panel.product(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the product of the values for the requested axis Parameters: axis : {items (0), major_axis (1), minor_axis (2)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame numeric_only : boolean, default

DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None) [source] Replace values given in ?to_replace? with ?value?. Parameters: to_replace : str, regex, list, dict, Series, numeric, or None str or regex: str: string exactly matching to_replace will be replaced with value regex: regexs matching to_replace will be replaced with value list of str, regex, or numeric: First, if to_replace and value are both lists, they must be the

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