Python 获取 TypeError: '(slice(None, None, None), 0)' 是一个无效的键
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/55291667/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Getting TypeError: '(slice(None, None, None), 0)' is an invalid key
提问by Unknown
Trying to plot the decision Boundary of the k-NN Classifier but is unable to do so getting TypeError: '(slice(None, None, None), 0)' is an invalid key`
试图绘制 k-NN 分类器的决策边界，但无法这样做得到 TypeError: '(slice(None, None, None), 0)' is an invalid key`
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()
Got this when running not very sure what it means dont think the clf.fit have a problem but I am not sure
运行时得到这个不太确定这意味着什么不要认为 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
回答by Srikanth Avadhanula
Since you are trying to access directly as array, you are getting that issue
由于您尝试直接作为数组访问，因此您遇到了该问题
Try this ::
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])
Using iloc/locwill resolve the issue.
使用iloc/loc将解决该问题。
回答by Ali Al Fatly
you need to use iloc/loc to acces df, try adding iloc to X so X.iloc[:,0]
您需要使用 iloc/loc 来访问 df，尝试将 iloc 添加到 X 所以 X.iloc[:,0]
回答by att
I had the same issue with the following
我有以下同样的问题
X = dataset.iloc[:,:-1]
Then I added .valuesproperty, after that it worked without problem
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
回答by Ghazal
回答by ebuzz168
Try run this code before your code writed above.
尝试在上面编写的代码之前运行此代码。
x_min = x_min.values
x_min = x_min.astype('float32')
x_max = x_max.values
y_test1 = x_max.astype('float32')
回答by user702846
I changed my input to a numpy array instead and it worked. I have still not been able to sort this issue with a Pandas dataframe input. If it is urgent in your case, I suggest changing your input to numpy and moving ahead.
我将我的输入改为一个 numpy 数组并且它起作用了。我仍然无法使用 Pandas 数据框输入来解决这个问题。如果您的情况很紧急，我建议您将输入更改为 numpy 并继续前进。
回答by Muhammad Hassan Dawood
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values= np.nan, strategy= 'mean')
imputer = imputer.fit(X.iloc[:, 1:3])
X = imputer.transform(X.iloc[:, 1:3])
回答by Jagmeet Singh
you have to create the array
你必须创建数组
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
This is present in the dataframe
这存在于数据框中
you have to first convert the dataframe to array by this dataframe.values then apply this
您必须首先通过此 dataframe.values 将数据帧转换为数组，然后应用此
