DataFrame.product(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the product 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 onl

DataFrame.boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds) [source] Make a box plot from DataFrame column optionally grouped by some columns or other inputs Parameters: data : the pandas object holding the data column : column name or list of names, or vector Can be any valid input to groupby by : string or sequence Column in the DataFrame to group by ax : Matplotlib axes object, optional fontsize : int or stri

Panel.consolidate(inplace=False) [source] Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray). Mainly an internal API function, but available here to the savvy user Parameters: inplace : boolean, default False If False return new object, otherwise modify existing object Returns: consolidated : type of caller

CategoricalIndex.categories

classmethod Series.from_csv(path, sep=', ', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False) [source] Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a time Series. This method only differs from pandas.read_csv() in some defaults: index_c

Series.str.partition(pat=' ', expand=True) [source] Split the string at the first occurrence of sep, and return 3 elements containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 elements containing the string itself, followed by two empty strings. Parameters: pat : string, default whitespace String to split on. expand : bool, default True If True, return DataFrame/MultiIndex expanding dimensionality. If F

Panel.ix A primarily label-location based indexer, with integer position fallback. .ix[] supports mixed integer and label based access. It is primarily label based, but will fall back to integer positional access unless the corresponding axis is of integer type. .ix is the most general indexer and will support any of the inputs in .loc and .iloc. .ix also supports floating point label schemes. .ix is exceptionally useful when dealing with mixed positional and label based hierachical indexes

Panel4D.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs) [source] Percent change over given number of periods. Parameters: periods : int, default 1 Periods to shift for forming percent change fill_method : str, default ?pad? How to handle NAs before computing percent changes limit : int, default None The number of consecutive NAs to fill before stopping freq : DateOffset, timedelta, or offset alias string, optional Increment to use from time series API (e.g.

DataFrame.memory_usage(index=True, deep=False) [source] Memory usage of DataFrame columns. Parameters: index : bool Specifies whether to include memory usage of DataFrame?s index in returned Series. If index=True (default is False) the first index of the Series is Index. deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: sizes : Series A series with column names as index and memory usage of columns with units of bytes. S

DataFrame.truediv(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator truediv). 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 thi