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

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

DatetimeIndex.take(indices, axis=0, allow_fill=True, fill_value=None, **kwargs) [source] return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. Parameters: indices : list Indices to be taken axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True fill_value : bool, default None If allow_fill=True and fill_value is not None, indices specified by -1 is regarded as NA. If Index doesn?t hold N

DataFrame.between_time(start_time, end_time, include_start=True, include_end=True) [source] Select values between particular times of the day (e.g., 9:00-9:30 AM). Parameters: start_time : datetime.time or string end_time : datetime.time or string include_start : boolean, default True include_end : boolean, default True Returns: values_between_time : type of caller

Series.str.rjust(width, fillchar=' ') [source] Filling left side of strings in the Series/Index with an additional character. Equivalent to str.rjust(). Parameters: width : int Minimum width of resulting string; additional characters will be filled with fillchar fillchar : str Additional character for filling, default is whitespace Returns: filled : Series/Index of objects

Series.squeeze(**kwargs) [source] Squeeze length 1 dimensions.

Panel4D.last(offset) [source] Convenience method for subsetting final periods of time series data based on a date offset. Parameters: offset : string, DateOffset, dateutil.relativedelta Returns: subset : type of caller Examples ts.last(?5M?) -> Last 5 months

Series.rtruediv(other, level=None, fill_value=None, axis=0) [source] Floating division of series and other, element-wise (binary operator rtruediv). Equivalent to other / series, 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.to_msgpack(path_or_buf=None, encoding='utf-8', **kwargs) [source] msgpack (serialize) object to input file path THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release. Parameters: path : string File path, buffer-like, or None if None, return generated string append : boolean whether to append to an existing msgpack (default is False) compress : type of compressor (zlib or blosc), default to None (no compression)

DatetimeIndex.fillna(value=None, downcast=None) [source] Fill NA/NaN values with the specified value Parameters: value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string ?infer? which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) Returns: filled : %(klass)s