我试图使用Numpy和Pandas库在Pycharm中修改Python中的列的数据类型,但是我收到以下错误。
dataset.fillna(1E6).astype(int)的
D:\Softwares\Python3.6.1\python.exe D:/PythonPractice/DataPreprocessing/DataPreprocessing_1.py
Traceback (most recent call last):
Country Age Salary Purchased
File "D:/PythonPractice/DataPreprocessing/DataPreprocessing_1.py", line 6, in <module>
dataset.fillna(1e6).astype(int)
0 France 44.0 72000.0 No
1 Spain 27.0 48000.0 Yes
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\util\_decorators.py", line 91, in wrapper
2 Germany 30.0 54000.0 No
3 Spain 38.0 61000.0 No
return func(*args, **kwargs)
4 Germany 40.0 NaN Yes
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\core\generic.py", line 3299, in astype
**kwargs)
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\core\internals.py", line 3224, in astype
5 France 35.0 58000.0 Yes
return self.apply('astype', dtype=dtype, **kwargs)
6 Spain NaN 52000.0 No
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\core\internals.py", line 3091, in apply
7 France 48.0 79000.0 Yes
applied = getattr(b, f)(**kwargs)
8 Germany 50.0 83000.0 No
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\core\internals.py", line 471, in astype
9 France 37.0 67000.0 Yes
**kwargs)
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\core\internals.py", line 521, in _astype
values = astype_nansafe(values.ravel(), dtype, copy=True)
File "D:\Softwares\Python3.6.1\lib\site-packages\pandas\core\dtypes\cast.py", line 625, in astype_nansafe
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
File "pandas\_libs\lib.pyx", line 917, in pandas._libs.lib.astype_intsafe (pandas\_libs\lib.c:16260)
File "pandas\_libs\src\util.pxd", line 93, in util.set_value_at_unsafe (pandas\_libs\lib.c:73093)
ValueError: invalid literal for int() with base 10: 'France'
任何帮助都将受到高度赞赏。谢谢:))
答案 0 :(得分:1)
您的错误消息 - ValueError: invalid literal for int() with base 10: 'France'
- 建议您使用Country
列,其内容为字符串,不能更改为整数。尝试调整范围。
答案 1 :(得分:0)
你不能将'France'转换为整数,你应该:
dataset['Country'] = dataset['Country'].map({'France': 0, 'Spain': 1, 'Germany': 2})]
然后:
dataset['Country'].astype(int)
如果仍然有这样的错误:
ValueError: Cannot convert non-finite values (NA or inf) to integer
这是因为NaN
中有一些dataset['Country']
。
按NaN
或fillna()
等处理这些drop()
,您将解决此问题。