HDFStore.select(key, where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, auto_close=False, **kwargs) [source] Retrieve pandas object stored in file, optionally based on where criteria Parameters: key : object where : list of Term (or convertable) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection columns : a list of columns that if not None, will limit the return c

HDFStore.put(key, value, format=None, append=False, **kwargs) [source] Store object in HDFStore Parameters: key : object value : {Series, DataFrame, Panel} format : ?fixed(f)|table(t)?, default is ?fixed? fixed(f) : Fixed format Fast writing/reading. Not-appendable, nor searchable table(t) : Table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default False This w

HDFStore.get(key) [source] Retrieve pandas object stored in file Parameters: key : object Returns: obj : type of object stored in file

HDFStore.append(key, value, format=None, append=True, columns=None, dropna=None, **kwargs) [source] Append to Table in file. Node must already exist and be Table format. Parameters: key : object value : {Series, DataFrame, Panel, Panel4D} format: ?table? is the default table(t) : table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default True, append the input data

Grouper.groups

Grouper.ax

class pandas.Grouper(key=None, level=None, freq=None, axis=0, sort=False) [source] A Grouper allows the user to specify a groupby instruction for a target object This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object. These are local specifications and will override ?global? settings, that is the parameters axis and level which are passed to the groupby itself. Parameters: key : string, defa

GroupBy.__iter__() [source] Groupby iterator Returns: Generator yielding sequence of (name, subsetted object) for each group

GroupBy.var(ddof=1, *args, **kwargs) [source] Compute variance of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters: ddof : integer, default 1 degrees of freedom See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

GroupBy.transform(func, *args, **kwargs) [source]