PandasのDataFrameをループするためには、`iterrows()`関数を利用することができます。以下に、その使用例を示します：

```python
import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Loop through the rows of the DataFrame
for index, row in df.iterrows():
    print(index, row['A'], row['B'], row['C'])

```

注意すべき点は、`iterrows()`は大きなDataFrameでは遅くなる可能性があるということです。
To loop through a Pandas DataFrame, you can utilize the `iterrows()` function, which generates an iterator that provides the index and row data for each row in the DataFrame. Here's an example of how it can be used:

```python
import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Loop through the rows of the DataFrame
for index, row in df.iterrows():
    print(index, row['A'], row['B'], row['C'])

```

It's important to note that `iterrows()` can be slow for large DataFrames, and it's recommended to use vectorized operations when feasible.