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# The special case of Not-A-Number (NaN)

pandas mathematical operators and functions handle `NaN` in a special manner (compared to NumPy) that does not break the computations. pandas is lenient with missing data assuming that it is a common situation.

To demonstrate the difference, we can examine the following code, which calculates the mean of a NumPy array:

```In [54]:
# mean of numpy array values
nda = np.array([1, 2, 3, 4, 5])
nda.mean()

Out[54]:
3.0
```

The result is as expected. The following code replaces one value with a `NaN` value:

```In [55]:
# mean of numpy array values with a NaN
nda = np.array([1, 2, 3, 4, np.NaN])
nda.mean()

Out[55]:
nan
```

When encountering a `NaN` value, NumPy simply returns `NaN`. pandas changes this, so that `NaN` values are ...

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