正在获取TypeError：'（slice（None，None，None），0）'是无效的密钥

Question

试图绘制k-NN分类器的决策边界，但无法这样做，但得到TypeError：'（slice（None，None，None），0）'是无效键`

    h = .01  # step size in the mesh

    # Create color maps
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])
    cmap_bold  = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])

    for weights in ['uniform', 'distance']:
        # we create an instance of Neighbours Classifier and fit the data.
        clf = KNeighborsClassifier(n_neighbors=6, weights=weights)
        clf.fit(X_train, y_train)

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, x_max]x[y_min, y_max].
        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                             np.arange(y_min, y_max, h))
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.figure()
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.title("4-Class classification (k = %i, weights = '%s')"
                  % (n_neighbors, weights))

    plt.show()

运行时不太清楚这是什么意思，不要认为clf.fit有问题，但我不确定

  TypeError                                 Traceback (most recent call last)
<ipython-input-394-bef9b05b1940> in <module>
     12         # Plot the decision boundary. For that, we will assign a color to each
     13         # point in the mesh [x_min, x_max]x[y_min, y_max].
---> 14         x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
     15         y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
     16         xx, yy = np.meshgrid(np.arange(x_min, x_max, h),

~\Miniconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2925             if self.columns.nlevels > 1:
   2926                 return self._getitem_multilevel(key)
-> 2927             indexer = self.columns.get_loc(key)
   2928             if is_integer(indexer):
   2929                 indexer = [indexer]

~\Miniconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2654                                  'backfill or nearest lookups')
   2655             try:
-> 2656                 return self._engine.get_loc(key)
   2657             except KeyError:
   2658                 return self._engine.get_loc(self._maybe_cast_indexer(key))

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

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

TypeError: '(slice(None, None, None), 0)' is an invalid key

Answer 1

您需要使用iloc/loc来访问 df。尝试将 iloc 添加到 X 所以X.iloc[:, 0]

Answer 2

由于您尝试直接以数组形式访问，因此遇到了这个问题

尝试这个 ：：

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean',verbose=0)
imputer = imputer.fit(X.iloc[:, 1:3])
X.iloc[:, 1:3] = imputer.transform(X.iloc[:, 1:3])

使用iloc / loc将解决问题。

Answer 3

我有以下同样的问题

X = dataset.iloc[:,:-1]

然后我添加了.values属性，之后它就可以正常工作了

X = dataset.iloc[:,:-1].values

Answer 4

当您尝试使用 pandas 获取数据集时，请使用以下代码：

dataset = pd.read_csv("path or file name")
x = dataset.iloc[:,:-1].values
y = dataset.iloc[:,-1].values

Answer 5

导入数据集时，使用.values。

改变：

X = dataset.iloc[:, 1:3]

到：

X = dataset.iloc[:, 1:3].values

Answer 6

我通过将 pandas 数据帧转换为 numpy 数组来修复它。从这里得到帮助