Astropy TimeSeries Required Columns Error

You are working in the astropy/astropy repository at commit 298ccb478e6bf092953bca67a3d29dc6c35f6752.

Problem

TimeSeries produces a misleading exception when required-column validation fails for objects with more than one required leading column.

For a TimeSeries object that has additional required columns beyond time, removing one of those required columns should produce an error that tells the user which required leading columns were expected and which leading columns were actually found.

Currently, the message assumes time is the only required column. For example, when TimeSeries._required_columns is set to ["time", "flux"] and flux is removed, the error can look like this:

ValueError: TimeSeries object is invalid - expected 'time' as the first columns but found 'time'

That message is confusing because it reports the same visible column on both sides and hides the missing required column.

Reproduction

import numpy as np
from astropy.time import Time
from astropy.timeseries import TimeSeries

time = Time(np.arange(100000, 100003), format="jd")
ts = TimeSeries(time=time, data={"flux": [99.9, 99.8, 99.7]})
ts._required_columns = ["time", "flux"]
ts.remove_column("flux")

Expected Behavior

The exception should identify the full required leading-column prefix and the actual leading columns that were found.

For a two-column requirement like ["time", "flux"], the message should be shaped like:

TimeSeries object is invalid - expected ['time', 'flux'] as the first columns but found ['time', ...]

Keep the existing one-required-column behavior readable. Existing tests around TimeSeries initialization and required columns should continue to pass.

Constraints

• •Do not hard-code this one example.
• •Preserve existing valid TimeSeries behavior.
• •Keep the patch focused on required-column validation and error reporting.
• •Add or update tests if useful, but the hidden grader will apply its own test patch.

Scoring

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