DataFrame.add(other, axis='columns', level=None, fill_value=None) [source] Addition of dataframe and other, element-wise (binary operator add). 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. If both

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.ne(other, level=None, fill_value=None, axis=0) [source] Not equal to of series and other, element-wise (binary operator ne). 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, matchi

DataFrame.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs) [source] Percent change over given number of periods. Parameters: periods : int, default 1 Periods to shift for forming percent change fill_method : str, default ?pad? How to handle NAs before computing percent changes limit : int, default None The number of consecutive NAs to fill before stopping freq : DateOffset, timedelta, or offset alias string, optional Increment to use from time series API (e.

Panel.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 in

Resampler.__iter__() [source] Groupby iterator Returns: Generator yielding sequence of (name, subsetted object) for each group

Panel.get_value(*args, **kwargs) [source] Quickly retrieve single value at (item, major, minor) location Parameters: item : item label (panel item) major : major axis label (panel item row) minor : minor axis label (panel item column) takeable : interpret the passed labels as indexers, default False Returns: value : scalar value

Resampler.max(_method='max', *args, **kwargs) [source] Compute max of group values See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

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

Series.xs(key, axis=0, level=None, drop_level=True) [source] Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. Defaults to cross-section on the rows (axis=0). Parameters: key : object Some label contained in the index, or partially in a MultiIndex axis : int, default 0 Axis to retrieve cross-section on level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be r