分析n0~n14与违约率相关性排序

def solve():
    from pyodide.http import open_url
    from io import StringIO
    loans_raw_csv = open_url(____).read()
    import pandas as pd
    from io import StringIO
    df = pd.read_csv(StringIO(____))
    n_cols=[f'____' for i in range(____)]
    n_cols=[c for c in n_cols if c in df.columns]
    rows=[{'____':c,'____':round(df[c].corr(____),____)} for c in n_cols]
    rdf = pd.DataFrame(____).assign(abs_corr=lambda x:x['____'].abs()).sort_values(____)[['____','____']]
    print(rdf.to_string(____))