shashank88 on master
Pandas: use sort_index instead … Create df from structured lists… Fix another multi index read_st… and 2 more (compare)
NaNs would still have a 'ones' bitmask?
rowmaskonly gets written if you pass in a list, not when you pass in a dataframe.
numpy.int32are written as
numpy.int64into arctic. And using my custom scala driver, if I store them as
int32, then reading them using your python driver I get
def _set_or_promote_dtype(self, column_dtypes, c, dtype): existing_dtype = column_dtypes.get(c) if existing_dtype is None or existing_dtype != dtype: # Promote ints to floats - as we can't easily represent NaNs if np.issubdtype(dtype, int): dtype = np.dtype('f8') column_dtypes[c] = np.promote_types(column_dtypes.get(c, dtype), dtype)
Hi, I'm a PhD student considering exploring using arctic for some time series storage and analysis. However, I'm not gonna store financial data. It'll essentially be health time series w/ metadata, possibly multivariate.
Looked online and didn't seem to find anyone ever exploring this.
Was wondering if: