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import dask.dataframe as dd
converters = {f'Spread{t}': lambda s: float(s.rstrip('%'))/100 if s else np.nan \
for t in ['6m'] + [f'{y}y' for y in [1, 2, 3, 4, 5, 7, 10, 15, 20, 30]]}
df = dd.read_csv("*.csv", skiprows=2, converters=converters,
dtype={'CompositeLevel4y': 'object', 'Rating4y': 'object',
'CompositeDepth5y': 'object', 'AvRating': 'object',
'ImpliedRating':'object'},
parse_dates=['Date'])
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