TimedeltaIndex.asobject return object Index which contains boxed values this is an internal non-public method

TimedeltaIndex.astype(dtype, copy=True) [source] Create an Index with values cast to dtypes. The class of a new Index is determined by dtype. When conversion is impossible, a ValueError exception is raised. Parameters: dtype : numpy dtype or pandas type copy : bool, default True By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is return

Series.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 indexe

pandas.io.json.json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None) [source] ?Normalize? semi-structured JSON data into a flat table Parameters: data : dict or list of dicts Unserialized JSON objects record_path : string or list of strings, default None Path in each object to list of records. If not passed, data will be assumed to be an array of records meta : list of paths (string or list of strings), default None Fields to use as metadata for each

This section covers indexing with a MultiIndex and more advanced indexing features. See the Indexing and Selecting Data for general indexing documentation. Warning Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called chained assignment and should be avoided. See Returning a View versus Copy Warning In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the

CategoricalIndex.has_duplicates

Panel4D.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan) [source] Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False Parameters: labels : array-like New labels / index to conform to. Preferably an Index object to avoid duplicating data axis : {0, 1, 2, ?items?, ?major_axis?,

Series.dt.is_year_start Logical indicating if first day of year (defined by frequency)

DataFrame.get(key, default=None) [source] Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found. Parameters: key : object Returns: value : type of items contained in object

Series.get_ftype_counts() [source] Return the counts of ftypes in this object.