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

DataFrameGroupBy.idxmax(axis=0, skipna=True) Return index of first occurrence of maximum over requested axis. NA/null values are excluded. Parameters: axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? for row-wise, 1 or ?columns? for column-wise skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be first index. Returns: idxmax : Series See also Series.idxmax Notes This method is the DataFrame version of ndarray.argmax.

Series.str.strip(to_strip=None) [source] Strip whitespace (including newlines) from each string in the Series/Index from left and right sides. Equivalent to str.strip(). Returns: stripped : Series/Index of objects

DataFrame.plot.line(x=None, y=None, **kwds) [source] Line plot New in version 0.17.0. Parameters: x, y : label or position, optional Coordinates for each point. **kwds : optional Keyword arguments to pass on to pandas.DataFrame.plot(). Returns: axes : matplotlib.AxesSubplot or np.array of them

Index.drop_duplicates(*args, **kwargs) [source] Return Index with duplicate values removed Parameters: keep : {?first?, ?last?, False}, default ?first? first : Drop duplicates except for the first occurrence. last : Drop duplicates except for the last occurrence. False : Drop all duplicates. take_last : deprecated Returns: deduplicated : Index

Series.gt(other, level=None, fill_value=None, axis=0) [source] Greater than of series and other, element-wise (binary operator gt). 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, matc

Resampler.sem(_method='sem', *args, **kwargs) [source] Compute standard error of the mean of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters: ddof : integer, default 1 degrees of freedom See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DatetimeIndex.union(other) [source] Specialized union for DatetimeIndex objects. If combine overlapping ranges with the same DateOffset, will be much faster than Index.union Parameters: other : DatetimeIndex or array-like Returns: y : Index or DatetimeIndex

Panel4D.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True over requested axis Parameters: axis : {labels (0), items (1), major_axis (2), minor_axis (3)} 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 Panel bool_only : boolean, defaul

TimedeltaIndex.duplicated(*args, **kwargs) [source] Return boolean np.ndarray denoting duplicate values Parameters: keep : {?first?, ?last?, False}, default ?first? first : Mark duplicates as True except for the first occurrence. last : Mark duplicates as True except for the last occurrence. False : Mark all duplicates as True. take_last : deprecated Returns: duplicated : np.ndarray