Bug: Type Mismatch in Dataset Mapping
By the way, following code is working. This show the inconsistentcy.
```python
from datasets import Dataset

# Original data
data = {
    'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],
    'label': [0, 1, 0, 1, 1, 0]
}

# Creating a Dataset object
dataset = Dataset.from_dict(data)

# Mapping function to convert label to string
def add_one(example):
    example['label'] += 1
    return example

# Applying the mapping function
dataset = dataset.map(add_one)

# Iterating over the dataset to show results
for item in dataset:
    print(item)
    print(type(item['label']))
``` Hello, thanks for submitting an issue.

FWIU, the issue is that `datasets` tries to limit casting [ref](https://github.com/huggingface/datasets/blob/ca58154bba185c1916ca5eea4e33b27258642044/src/datasets/arrow_writer.py#L526) and as such will try to convert your strings back to int to preserve the `Features`. 

A quick solution would be to use `dataset.cast` or to supply `features` when calling `dataset.map`.


```python
# using Dataset.cast
dataset = dataset.cast_column('label', Value('string'))

# Alternative, supply features
dataset = dataset.map(add_one, features=Features({**dataset.features, 'label': Value('string')}))
``` LGTM! Thanks for the review.

Just to clarify, is this intended behavior, or is it something that might be addressed in a future update?
I'll leave this issue open until it's fixed if this is not the intended behavior.
# Issue: Type Mismatch in Dataset Mapping

## Description
There is an issue with the `map` function in the `datasets` library where the mapped output does not reflect the expected type change. After applying a mapping function to convert an integer label to a string, the resulting type remains an integer instead of a string.

## Reproduction Code
Below is a Python script that demonstrates the problem:

```python
from datasets import Dataset

# Original data
data = {
    'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],
    'label': [0, 1, 0, 1, 1, 0]
}

# Creating a Dataset object
dataset = Dataset.from_dict(data)

# Mapping function to convert label to string
def add_one(example):
    example['label'] = str(example['label'])
    return example

# Applying the mapping function
dataset = dataset.map(add_one)

# Iterating over the dataset to show results
for item in dataset:
    print(item)
    print(type(item['label']))
```

## Expected Output
After applying the mapping function, the expected output should have the `label` field as strings:

```plaintext
{'text': 'Hello', 'label': '0'}
<class 'str'>
{'text': 'world', 'label': '1'}
<class 'str'>
{'text': 'this', 'label': '0'}
<class 'str'>
{'text': 'is', 'label': '1'}
<class 'str'>
{'text': 'a', 'label': '1'}
<class 'str'>
{'text': 'test', 'label': '0'}
<class 'str'>
```

## Actual Output
The actual output still shows the `label` field values as integers:

```plaintext
{'text': 'Hello', 'label': 0}
<class 'int'>
{'text': 'world', 'label': 1}
<class 'int'>
{'text': 'this', 'label': 0}
<class 'int'>
{'text': 'is', 'label': 1}
<class 'int'>
{'text': 'a', 'label': 1}
<class 'int'>
{'text': 'test', 'label': 0}
<class 'int'>
```

## Why necessary
In the case of Image process we often need to convert PIL to tensor with same column name.

Thank for every dev who review this issue. 🤗