shashank88 on master
Pandas: use sort_index instead … Create df from structured lists… Fix another multi index read_st… and 2 more (compare)
NaN
s would still have a 'ones' bitmask?
rowmask
only gets written if you pass in a list, not when you pass in a dataframe.
numpy.int32
are written as numpy.int64
into arctic. And using my custom scala driver, if I store them as int32
, then reading them using your python driver I get float64
back! 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: