尝试随机化数据帧的列时出现 KeyError

Question

最小示例：
考虑这个数据框 temp：

temp = pd.DataFrame({"A":[1,2,3,4,5,6,7,8,9,10],"B":[2,3,4,5,6,7,8,9,10,11],"C":[3,4,5,6,7,8,9,10,11,12]})
>>> temp
    A   B   C
0   1   2   3
1   2   3   4
2   3   4   5
3   4   5   6
4   5   6   7
5   6   7   8
6   7   8   9
7   8   9  10
8   9  10  11
9  10  11  12

现在，尝试在 for 循环中一次打乱每一列。

>>> for i in temp.columns:
...     np.random.shuffle(temp.loc[:,i])
...     print(temp)
...
    A   B   C
0   8   2   3
1   3   3   4
2   9   4   5
3   6   5   6
4   4   6   7
5  10   7   8
6   7   8   9
7   1   9  10
8   2  10  11
9   5  11  12
    A   B   C
0   8   7   3
1   3   9   4
2   9   8   5
3   6  10   6
4   4   4   7
5  10  11   8
6   7   5   9
7   1   3  10
8   2   2  11
9   5   6  12
    A   B   C
0   8   7   6
1   3   9   8
2   9   8   4
3   6  10  10
4   4   4   7
5  10  11  11
6   7   5   5
7   1   3   3
8   2   2  12
9   5   6   9

这非常有效。
具体示例：

现在，如果我想获取此数据框的一部分，用于训练和测试目的，那么我将使用 sklearn.model_selection 中的 train_test_split 函数。

>>> from sklearn.model_selection import train_test_split
>>> temp = pd.DataFrame({"A":[1,2,3,4,5,6,7,8,9,10],"B":[2,3,4,5,6,7,8,9,10,11],"C":[3,4,5,6,7,8,9,10,11,12]})
>>> y = [i for i in range(16,26)]
>>> len(y)
10
>>> X_train,X_test,y_train,y_test = train_test_split(temp,y,test_size=0.2)
>>> X_train
    A   B   C
2   3   4   5
6   7   8   9
8   9  10  11
0   1   2   3
7   8   9  10
3   4   5   6
1   2   3   4
9  10  11  12

现在，我们已经获得了 X_train 数据框。为了洗牌它的每一列：

>>> for i in X_train.columns:
...     np.random.shuffle(X_train.loc[:,i])
...     print(X_train)
...

不幸的是，这会导致错误。
错误：

sys:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
    Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "mtrand.pyx", line 4852, in mtrand.RandomState.shuffle
  File "mtrand.pyx", line 4855, in mtrand.RandomState.shuffle
  File "C:\Users\H.P\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\series.py", line 623, in __getitem__
    result = self.index.get_value(self, key)
  File "C:\Users\H.P\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\indexes\base.py", line 2560, in get_value
    tz=getattr(series.dtype, 'tz', None))
  File "pandas\_libs\index.pyx", line 83, in pandas._libs.index.IndexEngine.get_value
  File "pandas\_libs\index.pyx", line 91, in pandas._libs.index.IndexEngine.get_value
  File "pandas\_libs\index.pyx", line 139, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 811, in pandas._libs.hashtable.Int64HashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 817, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 4

跟踪问题及其解决方案：

在 SettingWithCopyWarning 下，我找到了 this 问题，第一个答案下面有一行：

However it could create a copy which updates a copy of data['amount'] which you would not see. Then you would be wondering why it is not updating.

但是，如果是这种情况，那么为什么代码对第一种情况有效？

答案中还给出了：

Pandas returns a copy of an object in almost all method calls. The inplace operations are a convience operation which work, but in general are not clear that data is being modified and could potentially work on copies.

因此，我们可以使用 np.random.permutation 而不是 np.random.shuffle，如 this 答案所示。所以：

>>> for i in X_train.columns:
...     X_train.loc[:,i] = np.random.permutation(X_train.loc[:,i])
...     print(X_train)
...

但是，我又得到了 SettingWithCopyWarning，也得到了答案。

C:\Users\H.P\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\indexing.py:621: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[item_labels[indexer[info_axis]]] = value
    A   B   C
2  10   4   5
6   9   8   9
8   2  10  11
0   8   2   3
7   1   9  10
3   3   5   6
1   4   3   4
9   7  11  12
    A   B   C
2  10   5   5
6   9  11   9
8   2   4  11
0   8   9   3
7   1   3  10
3   3   8   6
1   4  10   4
9   7   2  12
    A   B   C
2  10   5  10
6   9  11   5
8   2   4  11
0   8   9   3
7   1   3   4
3   3   8   6
1   4  10  12
9   7   2   9

这可能是一个解决方法。

问题：

当我使用 train_test_split 时，为什么代码适用于第一种情况，而不适用于第二种情况？
为什么我没有使用就地洗牌器 np.random.shuffle 时仍然得到 SettingWithCopyWarning？

请求建议：

是否有更好的（易于 use/error free/faster）的方法来进行列改组？

Answer 1

1.Why does the code work for the first case, and not the second case, when I use train_test_split?

因为 train_test_split 打乱了 X_train 的行。因此每列的索引不是一个范围而是一组值

你可以通过检查 temp 和 X_train

的索引看到这一点

X_train.index
Int64Index([6, 8, 9, 5, 0, 2, 3, 4], dtype='int64')

temp.index
RangeIndex(start=0, stop=10, step=1)

在第一种情况下，与第二种情况不同，可以将列安全地视为数组。如果您将第二种情况下的代码更改为

for i in X_train.columns:
    np.random.shuffle(X_train.loc[:,i].values)
    print(X_train)

这不会导致错误。

请注意，在您提供的案例中，洗牌将导致每列的洗牌不同。即数据点会混淆。

2.Why do I still get the SettingWithCopyWarning when I'm not using the inplace shuffler np.random.shuffle?

我在使用最新版本 pandas (0.22.0)

时没有收到警告

Requests for Suggestions:

Is there a better (easy to use/error free/faster) method to do column shuffling?

我建议在axis=1时使用sample，它会打乱列数，samples数应该是列数。即 X_train.shape[1]

X_train = X_train.sample(X_train.shape[1],axis=1)

In []: X_train.sample(X_train.shape[1],axis=1)
Out[]: 
    B   A   C
6   8   7   9
9  11  10  12
8  10   9  11
4   6   5   7
5   7   6   8
0   2   1   3
2   4   3   5
3   5   4   6

Answer 2

我也运行和 train_test_split 一起解决了这个问题。我改用这个：

np.random.shuffle(x.iloc[:, i].values)

不确定它为什么有效，但它似乎解决了问题

尝试随机化数据帧的列时出现 KeyError

KeyError when trying to randomize a column of a dataframe

python

numpy

pandas

train-test-split

问题：

请求建议：