SeriesGroupBy.unique() Return np.ndarray of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : np.ndarray

SeriesGroupBy.nunique(dropna=True) [source] Returns number of unique elements in the group

SeriesGroupBy.nsmallest(*args, **kwargs) [source] Return the smallest n elements. Parameters: n : int Return this many ascending sorted values keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. take_last : deprecated Returns: bottom_n : Series The n smallest values in the Series, in sorted order See also Series.nlargest Notes Faster than .sort_values().head(n) for small n rela

SeriesGroupBy.nlargest(*args, **kwargs) [source] Return the largest n elements. Parameters: n : int Return this many descending sorted values keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. take_last : deprecated Returns: top_n : Series The n largest values in the Series, in sorted order See also Series.nsmallest Notes Faster than .sort_values(ascending=False).head(n) for s

Series.__iter__() [source] provide iteration over the values of the Series box values if necessary

Series.xs(key, axis=0, level=None, drop_level=True) [source] Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. Defaults to cross-section on the rows (axis=0). Parameters: key : object Some label contained in the index, or partially in a MultiIndex axis : int, default 0 Axis to retrieve cross-section on level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be r

Series.where(cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True) [source] Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. Parameters: cond : boolean NDFrame, array or callable If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn?t check it). New in version 0.1

Series.view(dtype=None) [source]

Series.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return unbiased variance over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument Parameters: axis : {index (0)} 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 scala

Series.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 ascending