xpipe.tools.catalogs.to_pandas

xpipe.tools.catalogs.to_pandas(recarr)[source]

Converts potentially nested record array (such as a FITS Table) into Pandas DataFrame

FITS tables sometimes have multidimensional columns, which are not supported for DataFrames Pandas DataFrames however provide many nice features, such as SQL speed database matchings.

The approach is to flatten out multidimensional column [[COL]] into [COL_1, COL_2, …, COL_N]

Examples

Just pass the loaded FITS table:

import fitsio as fio
import xpipe.io.catalogs as catalogs

raw_data = fio.read("data.fits")
data = catalogs.to_pandas(raw_data)
Parameters

recarr (numpy.array) – array to be converted to DataFrame

Returns

array as DataFrame

Return type

pandas.DataFrame