Python Data Science Handbook, 2nd Edition

Chapter 15. Operating on Data in Pandas

One of the strengths of NumPy is that it allows us to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more complicated operations (trigonometric functions, exponential and logarithmic functions, etc.). Pandas inherits much of this functionality from NumPy, and the ufuncs introduced in Chapter 6 are key to this.

Pandas includes a couple of useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will preserve index and column labels in the output, and for binary operations such as addition and multiplication, Pandas will automatically align indices when passing the objects to the ufunc. This means that keeping the context of data and combining data from different sources—both potentially error-prone tasks with raw NumPy arrays—become essentially foolproof with Pandas. We will additionally see that there are well-defined operations between one-dimensional Series structures and two-dimensional DataFrame structures.

Ufuncs: Index Preservation

Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas Series and DataFrame objects. Let’s start by defining a simple Series and DataFrame on which to demonstrate this:

In [1]: import pandas as pd
        import numpy as np

In [2]: rng = np.random.default_rng(42)
        ser = pd.Series(rng.integers(0, 10, 4))
        ser
Out[2]: 0    0
        1    7
        2    6
        3    4
        dtype: int64

In [3]: df = pd.DataFrame ...