def compare(eq_dfs, columns=None, selection='<STR_LIT>'):
content = np.empty((eq_dfs[<NUM_LIT:0>].shape[<NUM_LIT:0>], len(eq_dfs)), dtype=np.float64)<EOL>rel_perf = pd.DataFrame(content, eq_dfs[<NUM_LIT:0>].index, columns, dtype=np.float64)<EOL>for i in range(len(eq_dfs)):<EOL><INDENT>rel_perf.iloc[:, i] = eq_dfs[i].loc[:, selection] / eq_dfs[i].iloc[<NUM_LIT:0>].loc[selection]<EOL><DEDENT>return rel_perf<EOL>
Get the relative performance of multiple equities.

.. versionadded:: 0.5.0

Parameters
----------
eq_dfs : list or tuple of DataFrame
    Performance data for multiple equities over
    a consistent time frame.
columns : iterable of str, default None
    Labels to use for the columns of the output DataFrame.
    The labels, if provided, should normally be the names
    of the equities whose performance is being compared.
selection : str, default 'Adj Close'
    Column containing prices to be compared. Defaults
    to 'Adj Close'.

Returns
-------
rel_perf : DataFrame
    A DataFrame whose columns contain normalized data
    for each equity represented in `eq_dfs`. The initial
    price for each equity will be normalized to 1.0.

Examples
--------

.. code-block:: python

    import pynance as pn

    eqs = ('FSLR', 'SCTY', 'SPWR')
    eq_dfs = []
    for eq in eqs:
        eq_dfs.append(pn.data.get(eq, '2016'))
    rel_perf = pn.data.compare(eq_dfs, eqs)

Notes
-----
Each set of data passed in `eq_dfs` is assumed to have
the same start and end dates as the other data sets.