Series.swapaxes(axis1, axis2, copy=True) [source] Interchange axes and swap values axes appropriately Returns: y : same as input

DataFrame.describe(percentiles=None, include=None, exclude=None) [source] Generate various summary statistics, excluding NaN values. Parameters: percentiles : array-like, optional The percentiles to include in the output. Should all be in the interval [0, 1]. By default percentiles is [.25, .5, .75], returning the 25th, 50th, and 75th percentiles. include, exclude : list-like, ?all?, or None (default) Specify the form of the returned result. Either: None to both (default). The result wi

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

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

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None) [source] ?Unpivots? a DataFrame from wide format to long format, optionally leaving identifier variables set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are ?unpivoted? to the row axis, leaving just two non-identifier columns, ?variable? and ?va

DataFrame.rtruediv(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator rtruediv). Equivalent to other / dataframe, 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 t

Series.compound(axis=None, skipna=None, level=None) [source] Return the compound percentage of the values for the 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 numeric_only : boolean, default None Include only float, int, boolean columns. I

Panel.radd(other, axis=0) [source] Addition of series and other, element-wise (binary operator radd). Equivalent to other + panel. Parameters: other : DataFrame or Panel axis : {items, major_axis, minor_axis} Axis to broadcast over Returns: Panel See also Panel.add

Panel.get_dtype_counts() [source] Return the counts of dtypes in this object.

CategoricalIndex.where(cond, other=None) [source] New in version 0.19.0. Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. Parameters: cond : boolean same length as self other : scalar, or array-like