Dataframe替换不工作编码= 'ISO-8859-1'，Python 3.6
Dataframe替换不工作编码= 'ISO-8859-1'，Python 3.6
提问于 2019-01-18 14:39:16
我将一些CSV文件导入到数据帧中。
Data = pd.read_csv(filePath, encoding = 'ISO-8859-1', dtype=object)我正在用一些值替换列“指示器”。
DataT['Indicator'] = DataT['Indicator'].str.replace('export(us$ mil)', 'exports (in us$ mil)')
DataT['Indicator'] = DataT['Indicator'].str.replace('import(us$ mil)', 'imports (in us$ mil)')但由于编码问题，替换无效。
请建议如何解决这个问题？
从：allyears.zip下载的文件
导入所有csv文件的代码：-
for i, file in os.listdir(sourcePath):
if file.upper().endswith('.CSV'):
filePath = os.path.join(sourcePath, file)
Data = pd.read_csv(filePath, encoding = 'ISO-8859-1', dtype=object)
Data['FileName'] = file
DataAll = pd.concat([DataAll, Data], sort=False)回答 2
Stack Overflow用户
回答已采纳
发布于 2019-01-18 15:40:00
经过多次试用，我进入了下面的解决方案，只是导入重新模块。
但是，您可以将代码简化为：
import pandas as pd
import glob
import re
for f in glob('/your_Dir_path/somefiles*.csv'):
Data = pd.read_csv(f, encoding = 'ISO-8859-1', dtype=object)数据集：
>>> Data['Indicator'].head()
0 GDP (current US$ Mil)
1 No. Of Export partners
2 No. Of Export products
3 No. Of Import partners
4 No. Of Import products
Name: Indicator, dtype: object
>>> Data['Indicator'].head(100)
0 GDP (current US$ Mil)
1 No. Of Export partners
2 No. Of Export products
3 No. Of Import partners
4 No. Of Import products
5 No. Of Tariff Agreement
6 Trade Balance (current US$ Mil)
7 Trade (US$ Mil)-Top 5 Export Partner
8 Trade (US$ Mil)-Top 5 Export Partner
9 Trade (US$ Mil)-Top 5 Export Partner
10 Trade (US$ Mil)-Top 5 Export Partner
11 Trade (US$ Mil)-Top 5 Import Partner
12 Trade (US$ Mil)-Top 5 Export Partner
13 Trade (US$ Mil)-Top 5 Import Partner
14 Trade (US$ Mil)-Top 5 Export Partner
15 Trade (US$ Mil)-Top 5 Import Partner
16 Trade (US$ Mil)-Top 5 Export Partner
17 Trade (US$ Mil)-Top 5 Export Partner
18 Trade (US$ Mil)-Top 5 Import Partner结果：
>>> Data['Indicator'].str.replace(re.escape("Trade (US$ Mil)"), "IN Trade (US$ Mil)").head(100)
0 GDP (current US$ Mil)
1 No. Of Export partners
2 No. Of Export products
3 No. Of Import partners
4 No. Of Import products
5 No. Of Tariff Agreement
6 Trade Balance (current US$ Mil)
7 IN Trade (US$ Mil)-Top 5 Export Partner
8 IN Trade (US$ Mil)-Top 5 Export Partner
9 IN Trade (US$ Mil)-Top 5 Export Partner
10 IN Trade (US$ Mil)-Top 5 Export Partner
11 IN Trade (US$ Mil)-Top 5 Import Partner
12 IN Trade (US$ Mil)-Top 5 Export Partner
13 IN Trade (US$ Mil)-Top 5 Import Partner
14 IN Trade (US$ Mil)-Top 5 Export Partner
15 IN Trade (US$ Mil)-Top 5 Import Partner
16 IN Trade (US$ Mil)-Top 5 Export Partner
17 IN Trade (US$ Mil)-Top 5 Export Partner
18 IN Trade (US$ Mil)-Top 5 Import Partner
19 IN Trade (US$ Mil)-Top 5 Import Partner
20 IN Trade (US$ Mil)-Top 5 Import Partner
21 IN Trade (US$ Mil)-Top 5 Export Partner
22 IN Trade (US$ Mil)-Top 5 Export Partner
23 IN Trade (US$ Mil)-Top 5 Export Partner
24 IN Trade (US$ Mil)-Top 5 Export Partner
25 IN Trade (US$ Mil)-Top 5 Export Partner
26 IN Trade (US$ Mil)-Top 5 Export Partner
27 IN Trade (US$ Mil)-Top 5 Export Partner
28 IN Trade (US$ Mil)-Top 5 Import Partner
29 IN Trade (US$ Mil)-Top 5 Export Partner
...
70 Partner share(%)-Top 5 Export Partner
71 Partner share(%)-Top 5 Import Partner
72 Partner share(%)-Top 5 Export Partner
73 Partner share(%)-Top 5 Import Partner
74 Partner share(%)-Top 5 Export Partner
75 Partner share(%)-Top 5 Export Partner
76 Partner share(%)-Top 5 Import Partner
77 Partner share(%)-Top 5 Import Partner
78 Partner share(%)-Top 5 Import Partner
79 Partner share(%)-Top 5 Export Partner
80 Partner share(%)-Top 5 Export Partner
81 Partner share(%)-Top 5 Export Partner
82 Partner share(%)-Top 5 Export Partner
83 Partner share(%)-Top 5 Export Partner
84 Partner share(%)-Top 5 Export Partner
85 Partner share(%)-Top 5 Export Partner
86 Partner share(%)-Top 5 Import Partner
87 Partner share(%)-Top 5 Export Partner
88 Partner share(%)-Top 5 Import Partner
89 Partner share(%)-Top 5 Export Partner
90 Country Growth (%)
91 Duty Free Tariff Lines Share (%)
92 Export Product share(%)
93 Export Product share(%)
94 Export Product share(%)
95 Export Product share(%)
96 Export Product share(%)
97 Export Product share(%)
98 Export Product share(%)
99 Export Product share(%)
Name: Indicator, Length: 100, dtype: object对于您的例子，您应该尝试如下：
import re
DataT['Indicator'] = DataT['Indicator'].str.replace(re.escape('export(us$ mil)'), 'exports (in us$ mil)')
DataT['Indicator'] = DataT['Indicator'].str.replace(re.escape('import(us$ mil)'), 'imports (in us$ mil)')Stack Overflow用户
发布于 2019-01-18 15:11:35
从数据中加载一个示例时，我注意到“指示器”列的值并不都是小写--即'Export(US$ Mil)'而不是'export(us$ mil)'。您需要使用正确的值，或者：
DataT['Indicator'] = DataT['Indicator'].str.lower().replace('export(us$ mil)',
'exports (in us$ mil)')始终可以使用df[col].unique()检查列的唯一值。
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原文链接：
https://stackoverflow.com/questions/54256149
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