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Currently sparse don't actually support dtype other than float. These must be fixed with #667
float
fill_value
IndexError
pd.SparseSeries([True, False, True, False, True], fill_value=False) # IndexError: index out of bounds
pd.set_option('display.max_rows', 3) pd.SparseSeries([True, False, True, False, True]) #0 1.0 # ... #4 1.0 # dtype: float64 # BlockIndex # Block locations: array([0], dtype=int32) # Block lengths: array([5], dtype=int32) pd.SparseDataFrame({'A': [True, False, True, False, True]}) # A #0 1.0 #1 0.0 #2 1.0 #3 0.0 #4 1.0
pd.show_versions()
on current master
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Currently sparse don't actually support dtype other than
float
. These must be fixed with #667Code Sample, a copy-pastable example if possible
1. bool dtype with
fill_value
raisesIndexError
2. truncated/DataFrame output coerces to float
output of
pd.show_versions()
on current master
The text was updated successfully, but these errors were encountered: