Series.mul(other, level=None, fill_value=None, axis=0) [source] Multiplication of series and other, element-wise (binary operator mul). 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, mat

Series.mode() [source] Returns the mode(s) of the dataset. Empty if nothing occurs at least 2 times. Always returns Series even if only one value. Parameters: sort : bool, default True If True, will lexicographically sort values, if False skips sorting. Result ordering when sort=False is not defined. Returns: modes : Series (sorted)

Series.mod(other, level=None, fill_value=None, axis=0) [source] Modulo of series and other, element-wise (binary operator mod). 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, matching In

Series.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] This method returns the minimum of the values in the object. If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin. 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

Series.memory_usage(index=True, deep=False) [source] Memory usage of the Series Parameters: index : bool Specifies whether to include memory usage of Series index deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: scalar bytes of memory consumed See also numpy.ndarray.nbytes Notes Memory usage does not include memory consumed by elements that are not components of the array if deep=False

Series.median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the median of the values for the requested axis 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 scalar numeric_only : boolean, default None Include only float, int, bool

Series.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the mean of the values for the requested axis 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 scalar numeric_only : boolean, default None Include only float, int, boolean

Series.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] This method returns the maximum of the values in the object. If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax. 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

Series.mask(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 False 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.map(arg, na_action=None) [source] Map values of Series using input correspondence (which can be a dict, Series, or function) Parameters: arg : function, dict, or Series na_action : {None, ?ignore?} If ?ignore?, propagate NA values, without passing them to the mapping function Returns: y : Series same index as caller Examples Map inputs to outputs >>> x one 1 two 2 three 3 >>> y 1 foo 2 bar 3 baz >>> x.map(y) one foo two bar three baz U