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 Dataframe 中的一组索引值
第二个列表给出了要使用的值
我不想碰其他任何值
下面是 Dataframe :
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')
下面是第一个列表:
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']
这显然是python擅长的事情--我猜想一个简单的for循环就可以做到,但是我还不能完全理解其中的逻辑。
任何帮助都感激不尽!
3条答案
按热度按时间bfhwhh0e1#
使用,
wxclj1h52#
压缩这两个列表以创建将旧名称Map到新名称的字典。
将函数panda.DataFrame.rename与替换字典和所有其他默认参数一起使用
bvjveswy3#
我相信现在有个更简单的方法:pandas.DataFrame.set_index()
用法:
或