‘1500’ 是什么？

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英文:

What is '\u200d1500'?

问题

以下是代码部分的翻译：

I crawled data from a website which was in string format I replaced string character and now data only contains number. But when I want to convert this column to numeric I get that error. I have two columns which first is previous_prices other is now_prices. If now the product is not on sale program fill nas with previous_prices. Previous_prices type is int64, now_prices type is object. Error is: ValueError: invalid literal for int() with base 10: '\u200d1500&#39.

实际上，我看到了一个类似的问题，但那个问题与 ''\u200d1500'' 不相关。

now_prices_after_fillna
1450
‍1500
700
1700
2090

当我将 now_prices 更改为整数，然后使用 previous_prices 填充缺失值时，一般数据类型变为整数。但当我尝试将数据导出到 Excel 时，出现了此错误。我无法理解问题。

‘1500’ 是什么？

英文:

I crawled data from a website which was in string format I replaced string character and now data only contains number. But when I want to convert this column to numeric I get that error. I have two columns which first is previous_prices other is now_prices. If now the product is not on sale program fill nas with previous_prices. Previous_prices type is int64, now_prices type is object. Error is: ValueError: invalid literal for int() with base 10: '\u200d1500'.

Actually I saw a similiar question but that question is not relevant to '\u200d1500'.

now_prices_after_fillna
1450
‍1500
700
1700
2090

There are strange situation when When I change now_prices to integer and then fill na with previous_prices general data type was int. But when I want to export that data to excel I get this error. I can not understand problem.

‘1500’ 是什么？

答案1

得分: 2

因为\u200d是不可打印字符，以下是去除它并将其转换为整数的解决方案：

  1. df = pd.DataFrame({'now_prices_after_fillna':['1450', u'\u200d1500']})
  3. print(df)
  4.   now_prices_after_fillna
  5. 0                    1450
  6. 1                   ‍1500
  8. # https://stackoverflow.com/a/54451873/2901002
  9. import sys
  11. # 构建一个将所有不可打印字符映射到None的表
  12. NOPRINT_TRANS_TABLE = {
  13.     i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
  14. }
  16. def make_printable(s):
  17.     """替换字符串中的不可打印字符。"""
  19.     # str的translate方法从字符串中删除映射到None的字符
  20.     return s.translate(NOPRINT_TRANS_TABLE)
  22. df['now_prices_after_fillna'] = (df['now_prices_after_fillna'].apply(make_printable)
  23.                                                               .astype(int))
  24. print(df)
  25.  now_prices_after_fillna
  26. 0                     1450
  27. 1                     1500

如果混合了数字和字符串值，可以尝试使用try和except语句：

  1. df = pd.DataFrame({'now_prices_after_fillna':['1450', u'\u200d1500', 1000]})
  3. print(df)
  5. # https://stackoverflow.com/a/54451873/2901002
  6. import sys
  8. # 构建一个将所有不可打印字符映射到None的表
  9. NOPRINT_TRANS_TABLE = {
  10.     i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
  11. }
  13. def make_printable(s):
  14.     """替换字符串中的不可打印字符。"""
  16.     # str的translate方法从字符串中删除映射到None的字符
  17.     try:
  18.         return s.translate(NOPRINT_TRANS_TABLE)
  19.     except AttributeError:
  20.         return s
  22. df['now_prices_after_fillna'] = (df['now_prices_after_fillna'].apply(make_printable)
  23.                                                               .astype(int))
  24. print(df)
  25.  now_prices_after_fillna
  26. 0                     1450
  27. 1                     1500
  28. 2                     1000

测试你的真实数据：

  1. df = pd.read_excel('your_updated_file2222.xlsx')
  3. # https://stackoverflow.com/a/54451873/2901002
  4. import sys
  6. # 构建一个将所有不可打印字符映射到None的表
  7. NOPRINT_TRANS_TABLE = {
  8.     i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
  9. }
  11. def make_printable(s):
  12.     """替换字符串中的不可打印字符。"""
  14.     # str的translate方法从字符串中删除映射到None的字符
  15.     try:
  16.         return s.translate(NOPRINT_TRANS_TABLE)
  17.     except AttributeError:
  18.         return s
  20. df['price'] = df['price'].apply(make_printable).astype(int)
  22. print(df)
  23.      price
  24. 0     1450
  25. 1     1500
  26. 2      700
  27. 3     1700
  28. 4     2090
  29. ..     ...
  30. 206   1500
  31. 207   1290
  32. 208   1500
  33. 209   1560
  34. 210   1800
  36. [211 行 x 1 列]
英文:

Because \u200d is not printable character, here is solution for remove it and converting to integers:

  1. df = pd.DataFrame({'now_prices_after_fillna':['1450', u'\u200d1500']})
  2.     
  3. print (df)
  4.   now_prices_after_fillna
  5. 0                    1450
  6. 1                   ‍1500
  8. #https://stackoverflow.com/a/54451873/2901002
  9. import sys
  11. # build a table mapping all non-printable characters to None
  12. NOPRINT_TRANS_TABLE = {
  13.     i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
  14. }
  16. def make_printable(s):
  17.     """Replace non-printable characters in a string."""
  19.     # the translate method on str removes characters
  20.     # that map to None from the string
  21.     return s.translate(NOPRINT_TRANS_TABLE)
  24. df['now_prices_after_fillna'] = (df['now_prices_after_fillna'].apply(make_printable)
  25.                                                               .astype(int))
  26. print (df)
  27.    now_prices_after_fillna
  28. 0                     1450
  29. 1                     1500

Another idea if mixed numeric with strings values add try with except statement:

  1. df = pd.DataFrame({'now_prices_after_fillna':['1450', u'\u200d1500', 1000]})
  2.     
  3. print (df)
  5. #https://stackoverflow.com/a/54451873/2901002
  6. import sys
  8. # build a table mapping all non-printable characters to None
  9. NOPRINT_TRANS_TABLE = {
  10.     i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
  11. }
  13. def make_printable(s):
  14.     """Replace non-printable characters in a string."""
  16.     # the translate method on str removes characters
  17.     # that map to None from the string
  18.     try:
  19.         return s.translate(NOPRINT_TRANS_TABLE)
  20.     except AttributeError:
  21.         return s
  24. df['now_prices_after_fillna'] = (df['now_prices_after_fillna'].apply(make_printable)
  25.                                                               .astype(int))
  26. print (df)
  27.    now_prices_after_fillna
  28. 0                     1450
  29. 1                     1500
  30. 2                     1000

Test your real data:

  1. df = pd.read_excel('your_updated_file2222.xlsx')
  3. #https://stackoverflow.com/a/54451873/2901002
  4. import sys
  6. # build a table mapping all non-printable characters to None
  7. NOPRINT_TRANS_TABLE = {
  8.     i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()
  9. }
  11. def make_printable(s):
  12.     """Replace non-printable characters in a string."""
  14.     # the translate method on str removes characters
  15.     # that map to None from the string
  16.     try:
  17.         return s.translate(NOPRINT_TRANS_TABLE)
  18.     except AttributeError:
  19.         return s
  1. df['price'] = df['price'].apply(make_printable).astype(int)
  3. print (df)
  4.      price
  5. 0     1450
  6. 1     1500
  7. 2      700
  8. 3     1700
  9. 4     2090
  10. ..     ...
  11. 206   1500
  12. 207   1290
  13. 208   1500
  14. 209   1560
  15. 210   1800
  17. [211 rows x 1 columns]

huangapple
  • 本文由 发表于 2023年3月7日 13:43:21
  • 转载请务必保留本文链接：https://go.coder-hub.com/75658406.html
  • dataframe
  • integer
  • pandas
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