DataFrame.corr(method='pearson', min_periods=1) [source] Compute pairwise correlation of columns, excluding NA/null values Parameters: method : {?pearson?, ?kendall?, ?spearman?} pearson : standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation Returns: y

Panel.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None) [source] Returns a random sample of items from an axis of object. New in version 0.16.1. Parameters: n : int, optional Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None. frac : float, optional Fraction of axis items to return. Cannot be used with n. replace : boolean, optional Sample with or without replacement. Default = False. weights : str or ndarray

Panel4D.as_blocks(copy=True) [source] Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) Parameters: copy : boolean, default True Returns: values : a dict of dtype -> Constructor Types

MultiIndex.base return the base object if the memory of the underlying data is shared

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

DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the mean of the values for the requested axis 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 only floa

MultiIndex.asof_locs(where, mask) [source] where : array of timestamps mask : array of booleans where data is not NA

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

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