Specifying number of digits for floating point data types is deprecated and will be removed in a future release.

This means that in a future update or version of a programming language, specifying the number of digits for floating point data types (such as float or double) will no longer be allowed. It is recommended to use the default precision provided by the language or a library instead. This deprecation is likely due to the fact that specifying a fixed number of digits can lead to inaccuracies and rounding errors.

相关问题

calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.

This warning message is indicating that using the `dtype` argument in the `RandomUniform.__init__` function from `tensorflow.python.ops.init_ops` is deprecated and will be removed in a future version of TensorFlow.

To resolve this issue, you should omit the `dtype` argument when calling the `RandomUniform` initializer. The dtype will be automatically inferred from the variable or tensor being initialized.

Here's an example of how to use `RandomUniform` initializer without specifying the `dtype`:

import tensorflow as tf

initializer = tf.keras.initializers.RandomUniform(minval=-1.0, maxval=1.0)

Make sure to update your code accordingly to remove the `dtype` argument in the `RandomUniform` initializer call.

FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. std = df.std()

这是由于 pandas 库中 DataFrame.std() 方法的默认参数 numeric_only 在未来版本将默认为 False，因此建议在调用该方法时显式地指定 numeric_only 参数。

为了避免该警告，可以将代码中的 `df.std()` 改为 `df.std(numeric_only=True)`，即：

import pandas as pd

def remove_empty_and_zero_std_columns(df):
    # 去除全为空值的列
    df = df.dropna(axis=1, how='all')
    
    # 去除标准差为0的列
    std = df.std(numeric_only=True)
    zero_std_columns = std[std == 0].index.tolist()
    df = df.drop(columns=zero_std_columns)
    
    return df

这样就可以避免该警告了。