FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use frame.mean(axis=0) or just frame.mean() return
时间: 2024-02-07 16:04:11 浏览: 37
这是一个警告信息,意思是在未来的版本中,DataFrame.mean(axis=None)将会返回整个DataFrame的标量平均值。为了保留旧的行为,可以使用frame.mean(axis=0)或者直接使用frame.mean()。这个警告信息是为了提醒用户在未来的版本中可能需要改变代码,以适应新的行为。
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FutureWarning: Dropping invalid columns in DataFrameGroupBy.mean is deprecated. In a future version, a TypeError will be raised. Before calling .mean, select only columns which should be valid for the function.
这个警告是由于在groupby之后,对数据进行了聚合操作,但是有些列不能被聚合,因此被忽略掉了。在将来的版本中,这些无效的列会导致TypeError异常的抛出。为了避免这个警告和将来的异常,我们需要在进行聚合操作之前,先将需要聚合的列筛选出来,例如:
```python
import pandas as pd
# 创建数据集
data = pd.DataFrame({'group':['A', 'B', 'C', 'A', 'B', 'C'],
'value':[1, 2, 3, 4, 5, 6],
'other_column':[7, 8, 9, 10, 11, 12]})
# 按照group列进行分组,并求每组的平均值
grouped_data = data.groupby('group')['value'].mean()
# 打印分组后的结果
print(grouped_data)
```
输出结果为:
```
group
A 2.5
B 3.5
C 4.5
Name: value, dtype: float64
```
上面的代码中,我们在进行groupby之前,先将需要聚合的列筛选出来,即`data.groupby('group')['value']`,然后再使用mean函数进行聚合操作,这样就避免了警告和将来的异常。
FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
This warning message is informing the user that in future versions of pandas, the only argument that can be passed to the DataFrame.drop method without a keyword will be 'labels'. This means that any other arguments that are currently being passed without a keyword will need to be explicitly specified using their corresponding keyword argument. This is being done to improve the clarity and readability of the code. Therefore, it is recommended to update the code to include the keyword arguments as soon as possible to ensure compatibility with future versions of pandas.