DataFrame.mad(axis=None, skipna=None, level=None) [source] Return the mean absolute deviation 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 float, int, boo

classmethod DataFrame.from_items(items, columns=None, orient='columns') [source] Convert (key, value) pairs to DataFrame. The keys will be the axis index (usually the columns, but depends on the specified orientation). The values should be arrays or Series. Parameters: items : sequence of (key, value) pairs Values should be arrays or Series. columns : sequence of column labels, optional Must be passed if orient=?index?. orient : {?columns?, ?index?}, default ?columns? The ?orientation

Resampler.transform(arg, *args, **kwargs) [source] Call function producing a like-indexed Series on each group and return a Series with the transformed values Parameters: func : function To apply to each group. Should return a Series with the same index Returns: transformed : Series Examples >>> resampled.transform(lambda x: (x - x.mean()) / x.std())

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False) [source] Join columns with other DataFrame either on index or on a key column. Efficiently Join multiple DataFrame objects by index at once by passing a list. Parameters: other : DataFrame, Series with name field set, or list of DataFrame Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joi

DataFrameGroupBy.idxmin(axis=0, skipna=True) Return index of first occurrence of minimum 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 NA Returns: idxmin : Series See also Series.idxmin Notes This method is the DataFrame version of ndarray.argmin.

MultiIndex.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] Examples &g

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

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

Series.idxmin(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.factorize(sort=False, na_sentinel=-1) [source] Encode the object as an enumerated type or categorical variable Parameters: sort : boolean, default False Sort by values na_sentinel: int, default -1 Value to mark ?not found? Returns: labels : the indexer to the original array uniques : the unique Index