Styler.set_properties(subset=None, **kwargs) [source] Convience method for setting one or more non-data dependent properties or each cell. New in version 0.17.1. Parameters: subset: IndexSlice a valid slice for data to limit the style application to kwargs: dict property: value pairs to be set for each cell Returns: self : Styler Examples >>> df = pd.DataFrame(np.random.randn(10, 4)) >>> df.style.set_properties(color="white", align="right") >>> df.style.

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

DataFrame.plot.area(x=None, y=None, **kwds) [source] Area plot New in version 0.17.0. Parameters: x, y : label or position, optional Coordinates for each point. **kwds : optional Keyword arguments to pass on to pandas.DataFrame.plot(). Returns: axes : matplotlib.AxesSubplot or np.array of them

MultiIndex.slice_locs(start=None, end=None, step=None, kind=None) [source] For an ordered MultiIndex, compute the slice locations for input labels. They can be tuples representing partial levels, e.g. for a MultiIndex with 3 levels, you can pass a single value (corresponding to the first level), or a 1-, 2-, or 3-tuple. Parameters: start : label or tuple, default None If None, defaults to the beginning end : label or tuple If None, defaults to the end step : int or None Slice step ki

DataFrame.reindex_like(other, method=None, copy=True, limit=None, tolerance=None) [source] Return an object with matching indices to myself. Parameters: other : Object method : string or None copy : boolean, default True limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between labels of the other object and this object for inexact matches. New in version 0.17.0. Returns: reindexed : same as input Note

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