Hands-On Exploratory Data Analysis with Python

Mathematical operations with NaN

The pandas and numpy libraries handle NaN values differently for mathematical operations.

Consider the following example:

ar1 = np.array([100, 200, np.nan, 300])ser1 = pd.Series(ar1)ar1.mean(), ser1.mean()

The output of the preceding code is the following:

(nan, 200.0)

Note the following things:

• When a NumPy function encounters NaN values, it returns NaN.
• Pandas, on the other hand, ignores the NaN values and moves ahead with processing. When performing the sum operation, NaN is treated as 0. If all the values are NaN, the result is also NaN.

Let's compute the total quantity of fruits sold by store4:

ser2 = dfx.store4ser2.sum()

The output of the preceding code is as follows:

38.0

Note that store4 has five ...