error_info:400 values for 387 columns]

从提供的代码片段[^1]来看，这是使用MySQLi扩展执行SQL插入操作的部分。如果遇到错误信息`error_info:400 values for 387 columns`，这可能意味着尝试插入的数据列数（387列）与实际提供的值的数量（400个值）不匹配。正确的插入语句应该是每个字段对应一个值。

修复这个问题的方法是确保提供的值数组长度等于欲插入表中的列数。这里是一个示例：

$table_name = "your_table";
$columns = "column1, column2, ..., column387"; // 替换为实际列名，逗号分隔
$values = "value1, value2, ..., value387"; // 确保值数组长度为387

$sql = "INSERT INTO `$table_name` ($columns) VALUES ($values)";
if (mysqli_query($conn, $sql)) {
    echo "Data inserted successfully.";
} else {
    echo "Error: " . $sql . "<br>" . mysqli_error($conn);
}

如果数据库返回了`Error_code: 1062`[^2]，表示有重复键错误，那么在插入时应该检查是否有唯一性约束，或者考虑更新策略，如忽略重复或使用`ON DUPLICATE KEY UPDATE`语句。

相关问题

ValueError Traceback (most recent call last) Cell In[23], line 16 13 predicted = model.predict(unknown, verbose=1) 15 # 将预测结果保存到新的 CSV 文件中 ---> 16 result = pd.DataFrame(predicted, columns=['prediction']) 17 result.to_csv('predicted_result.csv', index=False) 18 print("输入的数据为: ") File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\frame.py:757, in DataFrame.__init__(self, data, index, columns, dtype, copy) 746 mgr = dict_to_mgr( 747 # error: Item "ndarray" of "Union[ndarray, Series, Index]" has no 748 # attribute "name" (...) 754 copy=_copy, 755 ) 756 else: --> 757 mgr = ndarray_to_mgr( 758 data, 759 index, 760 columns, 761 dtype=dtype, 762 copy=copy, 763 typ=manager, 764 ) 766 # For data is list-like, or Iterable (will consume into list) 767 elif is_list_like(data): File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\construction.py:337, in ndarray_to_mgr(values, index, columns, dtype, copy, typ) 332 # _prep_ndarraylike ensures that values.ndim == 2 at this point 333 index, columns = _get_axes( 334 values.shape[0], values.shape[1], index=index, columns=columns 335 ) --> 337 _check_values_indices_shape_match(values, index, columns) 339 if typ == "array": 340 if issubclass(values.dtype.type, str): File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\construction.py:408, in _check_values_indices_shape_match(values, index, columns) 406 passed = values.shape 407 implied = (len(index), len(columns)) --> 408 raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (1, 3), indices imply (1, 1)该怎么修改代码

根据错误提示，问题出在第16行，因为传递给DataFrame的数据形状不匹配。你可以尝试修改一下代码，在第16行的DataFrame构造函数中指定传递给它的数据应该是一个一维的数组，而不是一个二维的数组。这可以通过将参数`predicted`替换为`predicted.flatten()`来实现。修改后的代码如下所示：

predicted = model.predict(unknown, verbose=1)
# 将预测结果保存到新的 CSV 文件中
result = pd.DataFrame(predicted.flatten(), columns=['prediction'])
result.to_csv('predicted_result.csv', index=False)
print("输入的数据为: ")

这样，你就可以将预测结果保存到CSV文件中了。

pvalue 2 for Parameter colsample_bylevel exceed bound [0,1] colsample_bylevel: Subsample ratio of columns, resample on each level.

As an AI language model, I do not have access to the specific context in which this error occurred. However, based on the error message provided, it seems that the parameter "colsample_bylevel" is being assigned a value of 2, which exceeds the allowed range of values (0 to 1). "colsample_bylevel" is a parameter that controls the subsampling of columns at each split level in a tree-based model.

To fix this error, the value assigned to "colsample_bylevel" needs to be adjusted to a value between 0 and 1. This can be done by either modifying the code or by passing a valid value as an argument while calling the function.