在 Pandas 中使用 groupby 函数时如何解决“keyerror”？

Question

我正在尝试将我的数据集与“驱动轮”、“车身样式”和“价格”进行分组。我得到了关键错误。我的代码是。 （我已经进口了熊猫）

url="https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
df=pd.read_csv(url)
df_test=df['drive-wheels:','body-style:','price:']
df_grp=df_test.groupby(['drive-wheels:','body-style:'], as_index= False).mean()
df_pivot=df_grp.pivot(index='drive-wheels:',columns='body-style')

我收到了这个错误。我尝试了各种方法，例如删除列之间的空间。我是熊猫的新手。所以如果有人可以帮助我，我会很高兴

D:\SOFTWARE\IllustratorPortable\anc\lib\site-packages\pandas\core\indexes\base.py in 
    get_loc(self,key, method, tolerance)
   2601             try:
-> 2602                 return self._engine.get_loc(key)
   2603             except KeyError:

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: ('drive-wheels:', 'body-style:', 'price:')

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-8-a14bda9f1cf1> in <module>
      1 url="https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
      2 df=pd.read_csv(url)
----> 3 df_test=df['drive-wheels:','body-style:','price:']
      4 df_grp=df_test.groupby(['drive-wheels:','body-style:'], as_index= False).mean()
      5 df_pivot=df_grp.pivot(index='drive-wheels:',columns='body-style')

D:\SOFTWARE\IllustratorPortable\anc\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2915             if self.columns.nlevels > 1:
   2916                 return self._getitem_multilevel(key)
-> 2917             indexer = self.columns.get_loc(key)
   2918             if is_integer(indexer):
   2919                 indexer = [indexer]

D:\SOFTWARE\IllustratorPortable\anc\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2602                 return self._engine.get_loc(key)
   2603             except KeyError:
-> 2604                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2605         indexer = self.get_indexer([key], method=method, tolerance=tolerance)
   2606         if indexer.ndim > 1 or indexer.size > 1:

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: ('drive-wheels:', 'body-style:', 'price:')

Answer 1

该文件不包含标题。

     Attribute:                Attribute Range:
     ------------------        -----------------------------------------------
  1. symboling:                -3, -2, -1, 0, 1, 2, 3.
  2. normalized-losses:        continuous from 65 to 256.
  3. make:                     alfa-romero, audi, bmw, chevrolet, dodge, honda,
                               isuzu, jaguar, mazda, mercedes-benz, mercury,
                               mitsubishi, nissan, peugot, plymouth, porsche,
                               renault, saab, subaru, toyota, volkswagen, volvo
  4. fuel-type:                diesel, gas.
  5. aspiration:               std, turbo.
  6. num-of-doors:             four, two.
  7. body-style:               hardtop, wagon, sedan, hatchback, convertible.
  8. drive-wheels:             4wd, fwd, rwd.
  9. engine-location:          front, rear.
 10. wheel-base:               continuous from 86.6 120.9.
 11. length:                   continuous from 141.1 to 208.1.
 12. width:                    continuous from 60.3 to 72.3.
 13. height:                   continuous from 47.8 to 59.8.
 14. curb-weight:              continuous from 1488 to 4066.
 15. engine-type:              dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
 16. num-of-cylinders:         eight, five, four, six, three, twelve, two.
 17. engine-size:              continuous from 61 to 326.
 18. fuel-system:              1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
 19. bore:                     continuous from 2.54 to 3.94.
 20. stroke:                   continuous from 2.07 to 4.17.
 21. compression-ratio:        continuous from 7 to 23.
 22. horsepower:               continuous from 48 to 288.
 23. peak-rpm:                 continuous from 4150 to 6600.
 24. city-mpg:                 continuous from 13 to 49.
 25. highway-mpg:              continuous from 16 to 54.
 26. price:                    continuous from 5118 to 45400.

你可以使用 iloc

df_test = df.iloc[[7,6,25]]

或设置列

df.columns = ['one', 'two', 'three']

Answer 2

您正在加载的数据，不包含标题：

所以

df_test = df['drive-wheels:', 'body-style:', 'price:']

失败。

UPDATE：选择多列使用：

df_test = df[['drive-wheels:', 'body-style:', 'price:']]

Answer 3

我也一直在研究同一个数据集。 我添加了标题

    path = "https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
headers = ["symboling","normalized-losses","make","fuel-type","aspiration", "num-of- 
          doors","body-style",
          "drive-wheels","engine-location","wheel-base", 
          "length","width","height","curb-weight","engine-type",
          "num-of-cylinders", "engine-size","fuel-system","bore","stroke","compression-ratio","horsepower",
     "peak-rpm","city-mpg","highway-mpg","price"]

path_read = pd.read_csv(path,names = headers) 汽车_df = pd.DataFrame(path_read) 汽车_df

在此之后，您必须首先处理数据集中缺失的数据。 之后应该可以工作了，在选择列时添加另一对方括号

temp_df = automobile_df[["body-style","drive-wheels","price"]]

现在应该不再是问题了。