MultiIndex.rename(names, level=None, inplace=False) [source] Set new names on index. Defaults to returning new index. Parameters: names : str or sequence name(s) to set level : int, level name, or sequence of int/level names (default None) If the index is a MultiIndex (hierarchical), level(s) to set (None for all levels). Otherwise level must be None inplace : bool if True, mutates in place Returns: new index (of same type and class...etc) [if inplace, returns None] Examples >&

MultiIndex.searchsorted(key, side='left', sorter=None) [source] Find indices where elements should be inserted to maintain order. Find the indices into a sorted IndexOpsMixin self such that, if the corresponding elements in v were inserted before the indices, the order of self would be preserved. Parameters: key : array_like Values to insert into self. side : {?left?, ?right?}, optional If ?left?, the index of the first suitable location found is given. If ?right?, return the last such

DataFrame.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), columns (1)} 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 Series numeric_only : boolean, default None Include only

Series.ndim return the number of dimensions of the underlying data, by definition 1

GroupBy.sem(ddof=1) [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

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.str.get(i) [source] Extract element from lists, tuples, or strings in each element in the Series/Index. Parameters: i : int Integer index (location) Returns: items : Series/Index of objects

Series.astype(dtype, copy=True, raise_on_error=True, **kwargs) [source] Cast object to input numpy.dtype Return a copy when copy = True (be really careful with this!) Parameters: dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame?s columns to column-specific ty

Series.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 small n

Panel4D.empty True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0. See also pandas.Series.dropna, pandas.DataFrame.dropna Notes If NDFrame contains only NaNs, it is still not considered empty. See the example below. Examples An example of an actual empty DataFrame. Notice the index is empty: >>> df_empty = pd.DataFrame({'A' : []}) >>> df_empty Empty DataFrame Columns: [A] Index: [] >>> df_empty.empty True If we only have NaNs i