DatetimeIndex.sort_values(return_indexer=False, ascending=True) [source] Return sorted copy of Index

Panel.set_value(*args, **kwargs) [source] Quickly set 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) value : scalar takeable : interpret the passed labels as indexers, default False Returns: panel : Panel If label combo is contained, will be reference to calling Panel, otherwise a new object

DataFrame.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, by=None) [source] Sort object by labels (along an axis) Parameters: axis : index, columns to direct sorting level : int or level name or list of ints or list of level names if not None, sort on values in specified index level(s) ascending : boolean, default True Sort ascending vs. descending inplace : bool, default False if True, perform operation in-place

DataFrame.floordiv(other, axis='columns', level=None, fill_value=None) [source] Integer division of dataframe and other, element-wise (binary operator floordiv). 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 t

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

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

Series.irow(i, axis=0) [source] DEPRECATED. Use .iloc[i] or .iat[i] instead

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