我有一个数据框和 2 个列表。
第一个列表给出了我想要替换的数据帧中的一组索引值
第二个列表给出了我想要使用的值
我不想触及任何其他价值观
这是数据框：
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')
这是第一个列表：
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']
这是第二份清单
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']
这显然是 python 擅长的事情 - 我怀疑一个简单的 for 循环就可以做到这一点，但我还不能完全理解逻辑（还）
任何帮助，不胜感激！
使用，
df = df.rename(index=dict(zip(list1,list2)))
压缩两个列表以创建一个将旧名称映射到新名称的字典。
将函数 pandas.DataFrame.rename 与替换字典和所有其他默认参数一起使用
replacements = {l1:l2 for l1, l2 in zip(list1, list2)}
df2 = df.rename(replacements)
我相信现在有一个更简单的方法：pandas.DataFrame.set_index()
用途：
df.set_index(list1)
或
# Use this if you wanna assing one of the existing DataFrame columns as Index
df.set_index(df_column_id)
如果新标签在列表中：
df.set_index(array)如果新标签位于列中：
df.set_index(column_label)如果索引是 MultiIndex，则分别使用 2D 数组和标签列表。
将 pandas 导入为 pd 将 numpy 导入为 np
df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
index=list('abc'),
columns=['A', 'B'])
new_labels = np.array(list('uvw'))
df = df.set_index(new_labels)
A B
a 1 2
b 3 4
c 5 6
A B
u 1 2
v 3 4
w 5 6