为了矢量化此代码，您将需要对完整的数据框进行操作，而不要对单个经纬度进行操作。我对此做了尝试。我需要结果df和一个新函数h2，
import numpy as npdef h2(df, p): inrad = df.applymap(radians) dlon = inrad.longitude-inrad.longitude[p] dlat = inrad.latitude-inrad.latitude[p] lat1 = pd.Series(index = df.index, data = [df.latitude[p] for i in range(len(df.index))]) a = np.sin(dlat/2)*np.sin(dlat/2) + np.cos(df.latitude) * np.cos(lat1) * np.sin(dlon/2)**2 c = 2 * 1/np.sin(np.sqrt(a)) km = 6367 * c return kmdf = df.set_index('Provider ID')df = df.astype(float)df2 = pd.Dataframe(index = df.index, columns = df.index)for c in df2.columns: df2[c] = h2(df, c)print (df2)这应该产生（我不确定我是否有正确答案……我的目标是对代码进行矢量化处理）
Provider ID 10001 10005 10006 10007 Provider ID 10001 inf 5.021936e+05 5.270062e+05 1.649088e+06 10005 5.021936e+05inf 9.294868e+05 4.985233e+05 10006 5.270062e+05 9.294868e+05inf 4.548412e+05 10007 1.649088e+06 4.985233e+05 4.548412e+05inf 10008 1.460299e+06 5.777248e+05 5.246954e+05 3.638231e+06 10011 6.723581e+05 2.004199e+06 1.027439e+06 6.394402e+05 10012 4.559090e+05 3.265536e+06 7.573411e+05 4.694125e+05 10016 7.680036e+05 1.429573e+06 9.105474e+05 7.517467e+05 10018 7.096548e+05 1.733554e+06 1.020976e+06 6.701920e+05 10019 5.436342e+05 9.278739e+05 2.891822e+07 4.638858e+05Provider ID 10008 10011 10012 10016 Provider ID 10001 1.460299e+06 6.723581e+05 4.559090e+05 7.680036e+05 10005 5.777248e+05 2.004199e+06 3.265536e+06 1.429573e+06 10006 5.246954e+05 1.027439e+06 7.573411e+05 9.105474e+05 10007 3.638231e+06 6.394402e+05 4.694125e+05 7.517467e+05 10008 inf 7.766998e+05 5.401081e+05 9.496953e+05 10011 7.766998e+05inf 1.341775e+06 4.220911e+06 10012 5.401081e+05 1.341775e+06inf 1.119063e+06 10016 9.496953e+05 4.220911e+06 1.119063e+06inf 10018 8.236437e+05 1.242451e+07 1.226941e+06 5.866259e+06 10019 5.372119e+05 1.051748e+06 7.514774e+05 9.362341e+05Provider ID 10018 10019 Provider ID 10001 7.096548e+05 5.436342e+05 10005 1.733554e+06 9.278739e+05 10006 1.020976e+06 2.891822e+07 10007 6.701920e+05 4.638858e+05 10008 8.236437e+05 5.372119e+05 10011 1.242451e+07 1.051748e+06 10012 1.226941e+06 7.514774e+05 10016 5.866259e+06 9.362341e+05 10018 inf 1.048895e+06 10019 1.048895e+06inf[10 rows x 10 columns]
