用来自lis的值替换pandas数据框中的索引值

Question

我有一个数据框和两个列表。

第一个列表提供了一组要替换的数据帧的索引值

第二个列表给出了我想要使用的值

我不想触及其他任何价值观

以下是数据框：

df =  pd.DataFrame.from_dict({u'Afghanistan': 6532.0,
 u'Albania': 662.0,
 u'Andorra': 2.0,
 u'Angola': 2219.0,
 u'Antigua and Barbuda': 0.0,
 u'Argentina': 6.0,
 u'Armenia': 15.0,
 u'Australia': 108.0,
 u'Azerbaijan': 210.0,
 u'Bahamas': 0.0,
 u'Bahrain': 6.0,
 u'Bangladesh': 5098.0,
 u'Barbados': 0.0,
 u'Belarus': 21.0,
 u'Belize': 0.0,
 u'Benin': 4244.0,
 u'Bhutan': 418.0,
 u'Bolivia (Plurinational State of)': 122.0,
 u'Bosnia and Herzegovina': 43.0,
 u'Botswana': 2672.0,
 u'Brazil': 36.0,
 u'Brunei Darussalam': 42.0,
 u'Bulgaria': 46.0,
 u'Burkina Faso': 6074.0,
 u'Burundi': 18363.0,
 u'Cabo Verde': 2.0,
 u'Cambodia': 12237.0,
 u'Cameroon': 14629.0,
 u'Canada': 206.0,
 u'Central African Republic': 3207.0,
 u'Chad': 3546.0,
 u'Chile': 0.0,
 u'China': 71093.0,
 u'Colombia': 1.0,
 u'Congo': 1678.0,
 u'Cook Islands': 2.0,
 u'Costa Rica': 0.0,
 u'Croatia': 9.0,
 u'Cuba': 0.0,
 u'Cyprus': 0.0,
 u'Czechia': 9.0,
 u"C\xf4te d'Ivoire": 5729.0,
 u'Democratic Republic of the Congo': 8282.0,
 u'Denmark': 14.0,
 u'Djibouti': 183.0,
 u'Dominica': 0.0,
 u'Dominican Republic': 253.0,
 u'Ecuador': 0.0,
 u'Egypt': 2633.0,
 u'El Salvador': 0.0,
 u'Eritrea': 789.0,
 u'Estonia': 9.0,
 u'Ethiopia': 1660.0,
 u'France': 10000.0,
 u'Gabon': 15.0,
 u'Gambia': 336.0,
 u'Georgia': 50.0,
 u'Ghana': 23068.0,
 u'Greece': 56.0,
 u'Grenada': 0.0,
 u'Guatemala': 0.0,
 u'Guinea': 11294.0,
 u'Guyana': 0.0,
 u'Haiti': 992.0,
 u'Honduras': 0.0,
 u'Hungary': 1.0,
 u'Iceland': 0.0,
 u'India': 38835.0,
 u'Indonesia': 3344.0,
 u'Iran (Islamic Republic of)': 11874.0,
 u'Iraq': 726.0,
 u'Israel': 36.0,
 u'Italy': 1457.0,
 u'Jamaica': 0.0,
 u'Japan': 22497.0,
 u'Jordan': 32.0,
 u'Kazakhstan': 245.0,
 u'Kenya': 21002.0,
 u'Kiribati': 0.0,
 u'Kuwait': 6.0,
 u'Kyrgyzstan': 16.0,
 u"Lao People's Democratic Republic": 332.0,
 u'Latvia': 0.0,
 u'Lebanon': 5.0,
 u'Lesotho': 660.0,
 u'Liberia': 5977.0,
 u'Lithuania': 19.0,
 u'Luxembourg': 0.0,
 u'Madagascar': 35256.0,
 u'Malawi': 304.0,
 u'Malaysia': 6187.0,
 u'Maldives': 20.0,
 u'Mali': 1578.0,
 u'Malta': 2.0,
 u'Marshall Islands': 0.0,
 u'Mauritius': 0.0,
 u'Mexico': 30.0,
 u'Micronesia (Federated States of)': 0.0,
 u'Mongolia': 925.0,
 u'Morocco': 7368.0,
 u'Mozambique': 7375.0,
 u'Myanmar': 845.0,
 u'Namibia': 469.0,
 u'Nauru': 0.0,
 u'Nepal': 9397.0,
 u'Netherlands': 1019.0,
 u'New Zealand': 65.0,
 u'Nicaragua': 0.0,
 u'Niger': 21319.0,
 u'Nigeria': 212183.0,
 u'Niue': 0.0,
 u'Norway': 0.0,
 u'Oman': 15.0,
 u'Pakistan': 2064.0,
 u'Palau': 0.0,
 u'Panama': 0.0,
 u'Papua New Guinea': 7135.0,
 u'Paraguay': 0.0,
 u'Peru': 1.0,
 u'Philippines': 7120.0,
 u'Poland': 77.0,
 u'Portugal': 45.0,
 u'Qatar': 46.0,
 u'Republic of Korea': 32647.0,
 u'Republic of Moldova': 687.0,
 u'Romania': 35.0,
 u'Russian Federation': 4800.0,
 u'Rwanda': 2095.0,
 u'Saint Kitts and Nevis': 0.0,
 u'Saint Lucia': 0.0,
 u'Saint Vincent and the Grenadines': 0.0,
 u'San Marino': 1.0,
 u'Sao Tome and Principe': 0.0,
 u'Senegal': 5839.0,
 u'Serbia': 38.0,
 u'Sierra Leone': 3575.0,
 u'Singapore': 141.0,
 u'Slovakia': 0.0,
 u'Somalia': 3965.0,
 u'South Africa': 1459.0,
 u'Spain': 152.0,
 u'Sri Lanka': 16527.0,
 u'Sudan': 2875.0,
 u'Suriname': 0.0,
 u'Swaziland': 10.0,
 u'Sweden': 59.0,
 u'Syrian Arab Republic': 146.0,
 u'Tajikistan': 192.0,
 u'Thailand': 4074.0,
 u'The former Yugoslav republic of Macedonia': 36.0,
 u'Togo': 3578.0,
 u'Tonga': 0.0,
 u'Trinidad and Tobago': 0.0,
 u'Tunisia': 47.0,
 u'Turkey': 16244.0,
 u'Turkmenistan': 113.0,
 u'Uganda': 42554.0,
 u'Ukraine': 817.0,
 u'United Arab Emirates': 69.0,
 u'United Kingdom of Great Britain and Northern Ireland': 104.0,
 u'United Republic of Tanzania': 14649.0,
 u'United States of America': 85.0,
 u'Uruguay': 0.0,
 u'Uzbekistan': 80.0,
 u'Vanuatu': 9.0,
 u'Venezuela (Bolivarian Republic of)': 22.0,
 u'Viet Nam': 16512.0,
 u'Zambia': 30930.0,
 u'Zimbabwe': 1483.0}, orient = 'index')

以下是第一个列表：

list1 = [u'Bolivia (Plurinational State of)', u'Brunei Darussalam', u'Cabo Verde', u'China',
    u'Congo', u'Cook Islands', u'Czechia', u"C\xf4te d'Ivoire", 
    u"Democratic People's Republic of Korea", u'France', u'Iran (Islamic Republic of)', 
    u"Lao People's Democratic Republic", u'Micronesia (Federated States of)', u'Niue', 
    u'Republic of Korea', u'Republic of Moldova', u'Russian Federation', u'Sao Tome and Principe', 
    u'Serbia', u'Somalia', u'Syrian Arab Republic', u'The former Yugoslav republic of Macedonia', 
    u'United Kingdom of Great Britain and Northern Ireland', u'United Republic of Tanzania', 
    u'United States of America', u'Venezuela (Bolivarian Republic of)', u'Viet Nam']

这是第二份名单

list2 = [u'Bolivia', u'Brunei', u'Cape Verde', u'China[1]', u'Democratic Republic of the Congo', 
    u'Cook Islands (NZ)', u'Czech Republic', u'Ivory Coast', u'North Korea', u'France[2]', 
    u'Iran', u'Laos', u'Federated States of Micronesia', u'Niue (NZ)', u'South Korea', 
    u'Moldova[3]', u'Russia', u'S\xe3o Tom\xe9 and Pr\xedncipe', u'Serbia[5]', 
    u'Somalia[6]', u'Syria', u'Macedonia', u'United Kingdom', u'Tanzania', 
    u'United States', u'Venezuela', u'Vietnam']

很明显，这是python擅长的——我怀疑一个简单的for循环可以做到这一点，但是我还不能完全理解这个逻辑

感谢您的帮助！

Answer 1

使用

df = df.rename(index=dict(zip(list1,list2)))

Answer 2

压缩这两个列表以创建将旧名称映射到新名称的字典。

使用函数pandas.DataFrame.rename with替换字典和所有其他默认参数

replacements = {l1:l2 for l1, l2 in zip(list1, list2)}

df2 = df.rename(replacements)