# How to Check for Nan Values

In pandas, you can check for NaN (Not a Number) values using the `isna()` or `isnull()` methods. These methods return a DataFrame of the same shape as the original DataFrame, where each element is `True` if it's NaN and `False` otherwise. Here's how you can do it:

```python
import pandas as pd
import numpy as np

# Create a DataFrame with NaN values
data = {'A': [1, np.nan, 3], 'B': [np.nan, 5, np.nan], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

# Check for NaN values using isna()
nan_values = df.isna()

print("DataFrame:")
print(df)
print("\\nNaN values:")
print(nan_values)
```

This will output:

```
DataFrame:
     A    B  C
0  1.0  NaN  7
1  NaN  5.0  8
2  3.0  NaN  9

NaN values:
       A      B      C
0  False   True  False
1   True  False  False
2  False   True  False
```

In this example, `nan_values` is a DataFrame where each `True` value indicates a NaN value in the corresponding position of the original DataFrame `df`.