DataFrame.style Property returning a Styler object containing methods for building a styled HTML representation fo the DataFrame. See also pandas.formats.style.Styler

DataFrame.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument 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,

DataFrame.stack(level=-1, dropna=True) [source] Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels. The level involved will automatically get sorted. Parameters: level : int, string, or list of these, default last level Level(s) to stack, can pass level name dropna : boolean, default True Whether to drop rows in th

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last') [source] Sort by the values along either axis New in version 0.17.0. Parameters: by : str or list of str Name or list of names which refer to the axis items. axis : {0 or ?index?, 1 or ?columns?}, default 0 Axis to direct sorting ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must matc

DataFrame.squeeze(**kwargs) [source] Squeeze length 1 dimensions.

DataFrame.sort(columns=None, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', **kwargs) [source] DEPRECATED: use DataFrame.sort_values() Sort DataFrame either by labels (along either axis) or by the values in column(s) Parameters: columns : object Column name(s) in frame. Accepts a column name or a list for a nested sort. A tuple will be interpreted as the levels of a multi-index. ascending : boolean or list, default True Sort ascending vs. descending. Specif

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.sortlevel(level=0, axis=0, ascending=True, inplace=False, sort_remaining=True) [source] Sort multilevel index by chosen axis and primary level. Data will be lexicographically sorted by the chosen level followed by the other levels (in order) Parameters: level : int axis : {0 or ?index?, 1 or ?columns?}, default 0 ascending : boolean, default True inplace : boolean, default False Sort the DataFrame without creating a new instance sort_remaining : boolean, default True Sort by t

DataFrame.slice_shift(periods=1, axis=0) [source] Equivalent to shift without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. Parameters: periods : int Number of periods to move, can be positive or negative Returns: shifted : same type as caller Notes While the slice_shift is faster than shift, you may pay for it later during alignment.

DataFrame.skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased skew over requested axis Normalized by N-1 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 onl