Traceback (most recent call last): File "pandas\_libs\tslibs\timedeltas.pyx", line 354, in pandas._libs.tslibs.timedeltas.array_to_timedelta64 File "pandas\_libs\tslibs\timedeltas.pyx", line 409, in pandas._libs.tslibs.timedeltas.parse_timedelta_string ValueError: only leading negative signs are allowed During handling of the above exception, another exception occurred: Traceback (most recent call last): File "D:\desktop\st_dbscan-master\demo\ais.py", line 32, in <module> df['BaseDateTime'] = pd.to_timedelta(df['BaseDateTime']) File "C:\ProgramData\Anaconda3\envs\tf2.6\lib\site-packages\pandas\core\tools\timedeltas.py", line 124, in to_timedelta values = _convert_listlike(arg._values, unit=unit, errors=errors) File "C:\ProgramData\Anaconda3\envs\tf2.6\lib\site-packages\pandas\core\tools\timedeltas.py", line 173, in _convert_listlike td64arr = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0] File "C:\ProgramData\Anaconda3\envs\tf2.6\lib\site-packages\pandas\core\arrays\timedeltas.py", line 991, in sequence_to_td64ns data = objects_to_td64ns(data, unit=unit, errors=errors) File "C:\ProgramData\Anaconda3\envs\tf2.6\lib\site-packages\pandas\core\arrays\timedeltas.py", line 1100, in objects_to_td64ns result = array_to_timedelta64(values, unit=unit, errors=errors) File "pandas\_libs\tslibs\timedeltas.pyx", line 368, in pandas._libs.tslibs.timedeltas.array_to_timedelta64 File "pandas\_libs\tslibs\timedeltas.pyx", line 359, in pandas._libs.tslibs.timedeltas.array_to_timedelta64 File "pandas\_libs\tslibs\timedeltas.pyx", line 300, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64 File "pandas\_libs\tslibs\timedeltas.pyx", line 409, in pandas._libs.tslibs.timedeltas.parse_timedelta_string ValueError: only leading negative signs are allowed 进程已结束,退出代码1
时间: 2023-08-11 07:07:11 浏览: 1162
这个错误是由于`pd.to_timedelta`函数无法解析具有非法格式的时间间隔字符串导致的。根据错误信息,只允许在时间间隔字符串的开头使用负号。
为了解决这个问题,你需要确保`df['BaseDateTime']`列中的时间间隔字符串的格式是正确的。请检查该列中的所有时间间隔字符串,并确保它们满足`pd.to_timedelta`函数的要求。
如果你发现某些时间间隔字符串具有非法格式,可以尝试使用其他方法进行处理。例如,你可以使用`pd.to_datetime`函数将日期时间字符串转换为日期时间类型,然后计算时间间隔。
另外,请确保你正在使用的pandas版本是最新的,以确保没有由于bug引起的问题。
希望这可以帮到你!如果还有其他问题,请随时提问。
相关问题
raceback (most recent call last): File "pandas/_libs/parsers.pyx", line 1119, in pandas._libs.parsers.TextReader._convert_tokens File "pandas/_libs/parsers.pyx", line 1244, in pandas._libs.parsers.TextReader._convert_with_dtype File "pandas/_libs/parsers.pyx", line 1259, in pandas._libs.parsers.TextReader._string_convert File "pandas/_libs/parsers.pyx", line 1450, in pandas._libs.parsers._string_box_utf8 UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc9 in position 0: invalid continuation byte During handling of the above exception, another exception occurred: Traceback (most recent call last): File "4.1-stu.py", line 8, in <module> master=pd.read_csv('Training_Master.csv') File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 688, in read_csv return _read(filepath_or_buffer, kwds) File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 460, in _read data = parser.read(nrows) File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 1198, in read ret = self._engine.read(nrows) File "/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py", line 2157, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 847, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 862, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 941, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 1073, in pandas._libs.parsers.TextReader._convert_column_data File "pandas/_libs/parsers.pyx", line 1126, in pandas._libs.parsers.TextReader._convert_tokens File "pandas/_libs/parsers.pyx", line 1244, in pandas._libs.parsers.TextReader._convert_with_dtype File "pandas/_libs/parsers.pyx", line 1259, in pandas._libs.parsers.TextReader._string_convert File "pandas/_libs/parsers.pyx", line 1450, in pandas._libs.parsers._string_box_utf8 UnicodeDecodeError: 'utf-8' codec
### 解决 Pandas `read_csv` 出现 `UnicodeDecodeError` 的问题
当使用 Pandas 的 `read_csv()` 方法读取 CSV 文件时,如果遇到 `UnicodeDecodeError: 'utf-8' codec can't decode byte...` 错误,通常是因为文件的实际编码与默认的 UTF-8 编码不匹配所致。以下是详细的解决方案:
#### 1. 明确文件的真实编码
在尝试加载文件之前,应先确认目标文件的具体编码方式。可以借助 Python 中的第三方库如 `chardet` 或者 `cchardet` 来检测文件编码。
```python
import chardet
with open('data.csv', 'rb') as f:
result = chardet.detect(f.read())
print(result['encoding'])
```
通过上述代码可获取到文件的确切编码格式[^1]。
#### 2. 设置正确的编码参数
一旦明确了文件的编码类型,则可以在调用 `pd.read_csv()` 方法时指定该编码作为参数传入。例如,假设检测结果显示文件采用的是 GBK 编码,则应该这样操作:
```python
import pandas as pd
df = pd.read_csv('data.csv', encoding='GBK')
```
对于其他可能的情况,比如 ISO-8859-1 编码, 则需相应调整为如下形式[^5]:
```python
df = pd.read_csv('data.csv', encoding='ISO-8859-1')
```
#### 3. 使用错误处理机制忽略或替换非法字符
有时即使指定了正确编码仍会遭遇少量无法解析的数据项引发异常。此时可以通过设置额外选项来规避此类情况的发生。具体做法是在函数调用中加入 `errors='ignore'` 参数以跳过有问题的部分;或者利用 `errors='replace'` 将其替换成替代符(通常是问号 ?)。注意这两种策略都可能导致部分信息丢失或变形,仅适用于对数据质量要求不高场景下作为一种权宜之计[^4]。
```python
# 忽略错误字节序列
df_ignore = pd.read_csv('data.csv', encoding='UTF-8', errors='ignore')
# 替换错误字节序列为特殊标记(一般显示为?)
df_replace = pd.read_csv('data.csv', encoding='UTF-8', errors='replace')
```
以上便是针对 Pandas 在执行 `read_csv` 过程当中可能出现的 `UnicodeDecodeError` 提供的一系列应对措施总结][^[^23]。
### 注意事项
尽管提供了多种解决办法,但在实际应用过程中还是建议尽可能保持源数据本身的纯净度以及一致性,从而减少不必要的麻烦。
C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\Scripts\python.exe C:\Users\26921\PycharmProjects\pythonProject\six.py Traceback (most recent call last): File "C:\Users\26921\PycharmProjects\pythonProject\six.py", line 3, in <module> import pandas as pd File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\__init__.py", line 22, in <module> from pandas.compat import is_numpy_dev as _is_numpy_dev # pyright: ignore # noqa:F401 File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\compat\__init__.py", line 25, in <module> from pandas.compat.numpy import ( File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\compat\numpy\__init__.py", line 4, in <module> from pandas.util.version import Version File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\util\__init__.py", line 2, in <module> from pandas.util._decorators import ( # noqa:F401 File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\util\_decorators.py", line 14, in <module> from pandas._libs.properties import cache_readonly File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\_libs\__init__.py", line 13, in <module> from pandas._libs.interval import Interval File "pandas\_libs\interval.pyx", line 1, in init pandas._libs.interval File "pandas\_libs\hashtable.pyx", line 1, in init pandas._libs.hashtable File "pandas\_libs\missing.pyx", line 1, in init pandas._libs.missing File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\pandas\_libs\tslibs\__init__.py", line 39, in <module> from pandas._libs.tslibs.conversion import localize_pydatetime File "pandas\_libs\tslibs\conversion.pyx", line 1, in init pandas._libs.tslibs.conversion File "pandas\_libs\tslibs\offsets.pyx", line 1, in init pandas._libs.tslibs.offsets File "pandas\_libs\tslibs\timestamps.pyx", line 1, in init pandas._libs.tslibs.timestamps File "pandas\_libs\tslibs\timedeltas.pyx", line 1, in init pandas._libs.tslibs.timedeltas File "pandas\_libs\tslibs\timezones.pyx", line 24, in init pandas._libs.tslibs.timezones File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\dateutil\tz\__init__.py", line 2, in <module> from .tz import * File "C:\Users\26921\PycharmProjects\pythonProject\jsonxinhuanjing\lib\site-packages\dateutil\tz\tz.py", line 19, in <module> import six File "C:\Users\26921\PycharmProjects\pythonProject\six.py", line 30, in <module> df = pd.read_excel('/Users/26921/Desktop/shuju/10000-20000.xlsx') AttributeError: partially initialized module 'pandas' has no attribute 'read_excel' (most likely due to a circular import) Process finished with exit code 1
这是一个Python的报错信息,看起来是因为在你的代码中出现了循环导入(circular import)的问题导致的。具体来说,pandas模块和six模块之间出现了循环导入的问题,导致pandas模块没有正确地导入read_excel函数。建议你检查一下你的代码,看看是否有导入模块的顺序出现了问题,或者是否有不必要的循环导入。
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