DataFrame.blocks Internal property, property synonym for as_blocks()

Panel.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 wher

Expanding.max(*args, **kwargs) [source] expanding maximum Parameters: how : string, default ?max? (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

DataFrame.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

DatetimeIndex.dropna(how='any') [source] Return Index without NA/NaN values Parameters: how : {?any?, ?all?}, default ?any? If the Index is a MultiIndex, drop the value when any or all levels are NaN. Returns: valid : Index

Panel.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] This method returns the minimum of the values in the object. If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin. 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 MultiInde

Series.cummax(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative max over requested axis. Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cummax : scalar

DataFrameGroupBy.cumprod(axis=0, *args, **kwargs) [source] Cumulative product for each group See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

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

DatetimeIndex.to_native_types(slicer=None, **kwargs) [source] slice and dice then format