Basic pd.DataFrame arithmetic

Having now covered basic pd.Series arithmetic, you will find that the corresponding pd.DataFrame arithmetic operations are practically identical, with the lone exception being that our algorithms now work in two dimensions instead of just one. In doing so, the pandas API makes it easy to interpret data regardless of its shape, and without requiring users to write loops to interact with data. This helps significantly reduce developer effort and helps you write faster code – a win-win for developers.

How it works

Let’s create a small 3x3 pd.DataFrame using random numbers:

np.random.seed(42)
df = pd.DataFrame(
    np.random.randn(3, 3),
    columns=["col1", "col2", "col3"],
    index=["row1", "row2", "row3"],
).convert_dtypes(dtype_backend="numpy_nullable")
df

        col1         col2         col3
row1    0.496714    -0.138264     0.647689
row2    1.52303...