SkyCoord is very slow with pandas dataframe

Description

Defining a SkyCoord object using a pandas dataframe is hundreds of times slower than simply using a numpy array

Expected behavior

Same performance

Steps to Reproduce

import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
import pandas as pd
import time as t

N = 100000
ra = np.random.uniform(1, 200, N)
de = np.random.uniform(-70, 70, N)

df = pd.DataFrame()
df['ra'], df['dec'] = ra, de

# Very slow
s = t.time()
gc = SkyCoord(ra=df['ra'] * u.degree, dec=df['dec'] * u.degree)
print(t.time() - s)

# Not slow
s = t.time()
gc = SkyCoord(ra=df['ra'].values * u.degree, dec=df['dec'].values * u.degree)
print(t.time() - s)

System Details

Linux-5.5.0-050500-generic-x86_64-with-glibc2.17 Python 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] Numpy 1.21.2 pyerfa 2.0.0 astropy 5.0 Scipy 1.7.3 Matplotlib 3.5.0

[taldcroft]

I removed the Close? and wont-fix labels. This is something that is worth addressing albeit at moderate priority.

[eerovaher]

The underlying issue is described in more detail in #11247, and it has nothing to do with SkyCoord.

A second solution would be to use << instead of * because pandas does not think it can do that so astropy gets the chance of creating a Quantity.