Series.duplicated(*args, **kwargs) [source] Return boolean Series denoting duplicate values Parameters: keep : {?first?, ?last?, False}, default ?first? first : Mark duplicates as True except for the first occurrence. last : Mark duplicates as True except for the last occurrence. False : Mark all duplicates as True. take_last : deprecated Returns: duplicated : Series

Panel4D.astype(dtype, copy=True, raise_on_error=True, **kwargs) [source] Cast object to input numpy.dtype Return a copy when copy = True (be really careful with this!) Parameters: dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame?s columns to column-specific t

CategoricalIndex.map(mapper) [source] Apply mapper function to its categories (not codes). Parameters: mapper : callable Function to be applied. When all categories are mapped to different categories, the result will be Categorical which has the same order property as the original. Otherwise, the result will be np.ndarray. Returns: applied : Categorical or np.ndarray.

Styler.set_precision(precision) [source] Set the precision used to render. New in version 0.17.1. Parameters: precision: int Returns: self : Styler

Panel4D.fromDict(data, intersect=False, orient='items', dtype=None) [source] Construct Panel from dict of DataFrame objects Parameters: data : dict {field : DataFrame} intersect : boolean Intersect indexes of input DataFrames orient : {?items?, ?minor?}, default ?items? The ?orientation? of the data. If the keys of the passed dict should be the items of the result panel, pass ?items? (default). Otherwise if the columns of the values of the passed DataFrame objects should be the items

Rolling.skew(**kwargs) [source] Unbiased rolling skewness Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

Series.asof(where, subset=None) [source] The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) New in version 0.19.0: For DataFrame If there is no good value, NaN is returned. Parameters: where : date or array of dates subset : string or list of strings, default None if not None use these columns for NaN propagation Returns: where is scalar value or NaN if input is Series Series if input is DataFrame whe

Styler.applymap(func, subset=None, **kwargs) [source] Apply a function elementwise, updating the HTML representation with the result. New in version 0.17.1. Parameters: func : function func should take a scalar and return a scalar subset : IndexSlice a valid indexer to limit data to before applying the function. Consider using a pandas.IndexSlice kwargs : dict pass along to func Returns: self : Styler

Panel4D.kurtosis(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1 Parameters: axis : {labels (0), items (1), major_axis (2), minor_axis (3)} 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), cou

Series.cat.categories The categories of this categorical. Setting assigns new values to each category (effectively a rename of each individual category). The assigned value has to be a list-like object. All items must be unique and the number of items in the new categories must be the same as the number of items in the old categories. Assigning to categories is a inplace operation! Raises: ValueError If the new categories do not validate as categories or if the number of new categories is