Series.asobject return object Series which contains boxed values this is an internal non-public method

Series.argmin(axis=None, skipna=True, *args, **kwargs) [source] Index of first occurrence of minimum of values. Parameters: skipna : boolean, default True Exclude NA/null values Returns: idxmin : Index of minimum of values See also DataFrame.idxmin, numpy.ndarray.argmin Notes This method is the Series version of ndarray.argmin.

Series.argsort(axis=0, kind='quicksort', order=None) [source] Overrides ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values Parameters: axis : int (can only be zero) kind : {?mergesort?, ?quicksort?, ?heapsort?}, default ?quicksort? Choice of sorting algorithm. See np.sort for more information. ?mergesort? is the only stable algorithm order : ignored Returns: argsorted : Series, with -1 indicated where nan value

Series.argmax(axis=None, skipna=True, *args, **kwargs) [source] Index of first occurrence of maximum of values. Parameters: skipna : boolean, default True Exclude NA/null values Returns: idxmax : Index of maximum of values See also DataFrame.idxmax, numpy.ndarray.argmax Notes This method is the Series version of ndarray.argmax.

Series.append(to_append, ignore_index=False, verify_integrity=False) [source] Concatenate two or more Series. Parameters: to_append : Series or list/tuple of Series ignore_index : boolean, default False If True, do not use the index labels. verify_integrity : boolean, default False If True, raise Exception on creating index with duplicates Returns: appended : Series Examples >>> s1 = pd.Series([1, 2, 3]) >>> s2 = pd.Series([4, 5, 6]) >>> s3 = pd.Series([4,

Series.apply(func, convert_dtype=True, args=(), **kwds) [source] Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values Parameters: func : function convert_dtype : boolean, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object args : tuple Positional arguments to pass to function in addition to the value Additional keyword arguments wil

Series.any(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether any element is True over 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 bool_only : boolean, default None Include only boolean columns. If None,

Series.align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None) [source] Align two object on their axes with the specified join method for each axis Index Parameters: other : DataFrame or Series join : {?outer?, ?inner?, ?left?, ?right?}, default ?outer? axis : allowed axis of the other object, default None Align on index (0), columns (1), or both (None) level : int or level name, default None Broadcast acro

Series.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True over 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 bool_only : boolean, default None Include only boolean columns. If Non

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