Series.cat() [source] Accessor object for categorical properties of the Series values. Be aware that assigning to categories is a inplace operation, while all methods return new categorical data per default (but can be called with inplace=True). Examples >>> s.cat.categories >>> s.cat.categories = list('abc') >>> s.cat.rename_categories(list('cab')) >>> s.cat.reorder_categories(list('cab')) >>> s.cat.add_categories(['d','e']) >>> s.cat.r

Panel4D.mad(axis=None, skipna=None, level=None) [source] Return the mean absolute deviation of the values for the requested axis 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), count along a particular level, collapsing into a Panel numeric_only : boolean, default None

MultiIndex.inferred_type = None

DataFrame.select_dtypes(include=None, exclude=None) [source] Return a subset of a DataFrame including/excluding columns based on their dtype. Parameters: include, exclude : list-like A list of dtypes or strings to be included/excluded. You must pass in a non-empty sequence for at least one of these. Returns: subset : DataFrame The subset of the frame including the dtypes in include and excluding the dtypes in exclude. Raises: ValueError If both of include and exclude are empty If

DataFrame.mod(other, axis='columns', level=None, fill_value=None) [source] Modulo of dataframe and other, element-wise (binary operator mod). 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 Da

Panel.clip(lower=None, upper=None, axis=None, *args, **kwargs) [source] Trim values at input threshold(s). Parameters: lower : float or array_like, default None upper : float or array_like, default None axis : int or string axis name, optional Align object with lower and upper along the given axis. Returns: clipped : Series Examples >>> df 0 1 0 0.335232 -1.256177 1 -1.367855 0.746646 2 0.027753 -1.176076 3 0.230930 -0.679613 4 1.261967 0.570967 >>> d

Panel.kurt(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 : {items (0), major_axis (1), minor_axis (2)} 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 particu

TimedeltaIndex.append(other) [source] Append a collection of Index options together Parameters: other : Index or list/tuple of indices Returns: appended : Index

Index.memory_usage(deep=False) [source] Memory usage of my values Parameters: deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: bytes used See also numpy.ndarray.nbytes Notes Memory usage does not include memory consumed by elements that are not components of the array if deep=False

Panel.clip_upper(threshold, axis=None) [source] Return copy of input with values above given value(s) truncated. Parameters: threshold : float or array_like axis : int or string axis name, optional Align object with threshold along the given axis. Returns: clipped : same type as input See also clip