TimedeltaIndex.is_unique = None

TimedeltaIndex.get_indexer_non_unique(target) [source] return an indexer suitable for taking from a non unique index return the labels in the same order as the target, and return a missing indexer into the target (missing are marked as -1 in the indexer); target must be an iterable

Series.cat.codes

Panel.iteritems() [source] Iterate over (label, values) on info axis This is index for Series, columns for DataFrame, major_axis for Panel, and so on.

We use the standard convention for referencing the matplotlib API: In [1]: import matplotlib.pyplot as plt The plots in this document are made using matplotlib?s ggplot style (new in version 1.4): import matplotlib matplotlib.style.use('ggplot') We provide the basics in pandas to easily create decent looking plots. See the ecosystem section for visualization libraries that go beyond the basics documented here. Note All calls to np.random are seeded with 123456. Basic Plotting: plot See t

TimedeltaIndex.ndim return the number of dimensions of the underlying data, by definition 1

DataFrame.to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, bold_rows=True, classes=None, escape=True, max_rows=None, max_cols=None, show_dimensions=False, notebook=False, decimal='.', border=None) [source] Render a DataFrame as an HTML table. to_html-specific options: bold_rows : boolean, default TrueMake the row labels bold in the output classes : str or list or tuple,

MultiIndex.to_series(**kwargs) [source] Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index Returns: Series : dtype will be based on the type of the Index values.

pandas.set_option(pat, value) = Sets the value of the specified option. Available options: display.[chop_threshold, colheader_justify, column_space, date_dayfirst, date_yearfirst, encoding, expand_frame_repr, float_format, height, large_repr] display.latex.[escape, longtable, repr] display.[line_width, max_categories, max_columns, max_colwidth, max_info_columns, max_info_rows, max_rows, max_seq_items, memory_usage, mpl_style, multi_sparse, notebook_repr_html, pprint_nest_depth, precision,

GroupBy.groups dict {group name -> group labels}