To count the number of NaN (Not a Number) values in a 2D array in Python, you can use the NumPy library, which provides efficient array operations. Here’s an example:

```
import numpy as np
# Create a 2D array with NaN values
arr = np.array([[1.0, np.nan, 3.0],
[np.nan, 5.0, 6.0],
[7.0, 8.0, np.nan]])
# Count NaN values in the array
nan_count = np.isnan(arr).sum()
# Print the count of NaN values
print(f"Number of NaN values: {nan_count}")
```

In this example, we first create a 2D NumPy array `arr`

with some NaN values. Then, we use the `np.isnan()`

function to create a boolean mask where `True`

values indicate the presence of NaN values in the array. Finally, we use the `sum()`

method on the boolean mask to count the number of `True`

values, which represents the count of NaN values in the array. The result is printed using the `print()`

statement.

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