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.

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.pow(other, axis='columns', level=None, fill_value=None) [source] Exponential power of dataframe and other, element-wise (binary operator pow). Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this value

Panel4D.get(key, default=None) [source] Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found. Parameters: key : object Returns: value : type of items contained in object

DataFrame.itertuples(index=True, name='Pandas') [source] Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple. Parameters: index : boolean, default True If True, return the index as the first element of the tuple. name : string, default ?Pandas? The name of the returned namedtuples or None to return regular tuples. See also iterrows Iterate over DataFrame rows as (index, Series) pairs. iteritems Iterate over (column name, Series) pairs. No

DataFrame.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), 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

DataFrame.plot.hexbin(x, y, C=None, reduce_C_function=None, gridsize=None, **kwds) [source] Hexbin plot New in version 0.17.0. Parameters: x, y : label or position, optional Coordinates for each point. C : label or position, optional The value at each (x, y) point. reduce_C_function : callable, optional Function of one argument that reduces all the values in a bin to a single number (e.g. mean, max, sum, std). gridsize : int, optional Number of bins. **kwds : optional Keyword ar

CategoricalIndex.set_names(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] Examp

GroupBy.head(n=5) [source] Returns first n rows of each group. Essentially equivalent to .apply(lambda x: x.head(n)), except ignores as_index flag. See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby Examples >>> df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) >>> df.groupby('A', as_index=False).head(1) A B 0 1 2 2 5 6 >>> df.groupby('A').head(1) A B 0 1 2 2 5 6

Series.to_pickle(path) [source] Pickle (serialize) object to input file path. Parameters: path : string File path