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