问题

我想创建一个包含一些函数的类，其中一个函数在类中调用另一个函数，类似这样：

```python
import pandas as pd

class Prep:
    def __init__(self, data):
        self.data = data

    def slicing(self):
        sliceInput = self.data.iloc[:, 1:8]
        sliceTarget = self.data.iloc[:, 8]
        return sliceInput, sliceTarget

    def convert(self):
        sliceInput, sliceTarget = self.slicing()
        convertInput = sliceInput.to_numpy(float)
        convertTarget = sliceTarget.to_numpy(int)
        return convertInput, convertTarget

if __name__ == "__main__":
    data_frame = pd.read_csv('data_manual.csv', sep=';')

    tes = Prep(data_frame)
    print(tes.convert())

我得到了以下错误：
NameError: name 'sliceInput' is not defined

如何在函数中调用 convertInput 和 convertTarget？
为什么我会得到这个错误，我完全不理解。

<details>
<summary>英文:</summary>

I want to create a class that includes some functions, that one of them call another one in class, something like that:

import pandas as pd

class Prep:
def init(self, data):
self.data = data

def slicing(self):
    sliceInput = self.data.iloc[:, 1:8]
    sliceTarget = self.data.iloc[:, 8]
    return sliceInput, sliceTarget

def convert(self):
    convertInput = sliceInput.to_numpy(float)
    convertTarget = sliceTarget.to_numpy(int)
    return convertInput, convertTarget

if name == "main":
data_frame = pd.read_csv('data_manual.csv', sep=';')

tes = Prep(data_frame)
print(tes.convert())

i got error like this
`NameError: name 'sliceInput' is not defined`

how to call ` convertInput ` and `convertTarget` in function
why I get an error, I don't understand at all.

</details>


# 答案1
**得分**: 2

你需要在`convert`函数中添加`sliceInput, sliceTarget = self.slicing()`。

```python
import pandas as pd

class Prep:
    def __init__(self, data):
        self.data = data

    def slicing(self):
        sliceInput = self.data.iloc[:, 1:8]
        sliceTarget = self.data.iloc[:, 8]

        return sliceInput, sliceTarget

    def convert(self):
        sliceInput, sliceTarget = self.slicing()
        convertInput = sliceInput.to_numpy(float)
        convertTarget = sliceTarget.to_numpy(int)
        return convertInput, convertTarget

if __name__ == "__main__":
    data_frame = pd.read_csv('data_manual.csv', sep=';')

    tes = Prep(data_frame)
    print(tes.convert())

import pandas as pd


class Prep:
    def __init__(self, data):
        self.data = data

    def slicing(self):
        sliceInput = self.data.iloc[:, 1:8]
        sliceTarget = self.data.iloc[:, 8]

        return sliceInput, sliceTarget

    def convert(self):
        sliceInput, sliceTarget = self.slicing()
        convertInput = sliceInput.to_numpy(float)
        convertTarget = sliceTarget.to_numpy(int)
        return convertInput, convertTarget

if __name__ == "__main__":
    data_frame = pd.read_csv('data_manual.csv', sep=';')

    tes = Prep(data_frame)
    print(tes.convert())

答案2

得分: 2

只需将self添加到已定义的属性中。
此外，因为在实例化Prep对象后，data是不可变的，所以最好在__init__()方法中只初始化切片变量一次。

import pandas as pd

class Prep:
    def __init__(self, data):
        self.data = data
        self.sliceInput = data.iloc[:, 1:8]
        self.sliceTarget = data.iloc[:, 8]

    def slicing(self):
        return self.sliceInput, self.sliceTarget

    def convert(self):
        convertInput = self.sliceInput.to_numpy(float)
        convertTarget = self.sliceTarget.to_numpy(int)
        return convertInput, convertTarget

if __name__ == "__main__":
    data_frame = pd.read_csv('data_manual.csv', sep=';')
    
    tes = Prep(data_frame)
    print(tes.convert())

注意：代码中的if __name__ == "__main__": 已被更正为正常的Python语法。

import pandas as pd


class Prep:
    def __init__(self, data):
        self.data = data
        self.sliceInput = data.iloc[:, 1:8]
        self.sliceTarget = data.iloc[:, 8]

    def slicing(self):
        return self.sliceInput, self.sliceTarget


    def convert(self):
        convertInput = self.sliceInput.to_numpy(float)
        convertTarget = self.sliceTarget.to_numpy(int)
        return convertInput, convertTarget

if __name__ == "__main__":
    data_frame = pd.read_csv('data_manual.csv', sep=';')

    tes = Prep(data_frame)
    print(tes.convert())