Synthesizers crash when column names are integers (TypeError: unsupported operand)

[npatki]

Environment Details

• SDV version: 1.12.0

Error Description

If my datafame's column names are represented with integers (instead of with strings), then the synthesizer's fit crashes with a TypeError

Steps to reproduce

import pandas as pd
import numpy as np

from sdv.metadata import SingleTableMetadata
from sdv.single_table import GaussianCopulaSynthesizer

# generate a dataframe with integer column names
num_rows = 100
num_cols = 20
values = {i+1: np.random.randint(0, 100, size=num_rows) for i in range(num_cols)}
data = pd.DataFrame(values)

# create metadata
metadata = SingleTableMetadata()
metadata_dict = {'columns': {}}
for i in range(num_cols):
  metadata_dict['columns'][i+1] = { 'sdtype': 'numerical' }
metadata = SingleTableMetadata.load_from_dict(metadata_dict)

# try to model it
synth = GaussianCopulaSynthesizer(metadata)
synth.fit(data)

TypeError: unsupported operand type(s) for +=: 'int' and 'str'

stack_trace.txt

Additional Context

• This is replicable for any synthesizer with a fit function (CTGAN, etc.)
• The metadata auto detection doesn't work for these dataframes either -- see Metadata detection crashes when the column names are integers (AttributeError: 'int' object has no attribute 'lower') #1933
• When would you have a DataFrame with integer column names? One use case is if I have a CSV that doesn't have any column names listed. If I tell pandas that there is no header in my data, then it will create column names with index values 0, 1, ...

df = pd.read_csv('my_data.csv', header=None)

Workaround

A simple workaround would be to rename the columns using strings instead of integers.