Opérations vectorisées sur les Series

Like NumPy, Pandas Series support vectorized operations that apply arithmetic, comparisons, and functions to every element without Python loops. This provides both conciseness and speed. Under the hood each operation delegates to NumPy's C-level routines, but Pandas adds a critical bonus: automatic index alignment before performing the operation.

When you add, subtract, multiply, or divide two Series, Pandas aligns them by index label first. Only positions where both Series have a label produce a result; unmatched labels produce NaN. This behaviour prevents hidden bugs from mis-aligned data — for example, adding January figures from two data sources that happen to be stored in different orders.

To avoid NaN propagation in misaligned operations, use the arithmetic methods like .add(), .sub(), .mul(), and .div() with the fill_value= parameter. This substitutes a given value for any label that is missing in one of the Series before performing the operation — commonly 0 for addition and 1 for multiplication.

Arithmetic with a scalar applies the operation to every element and produces a new Series with the same index. This is the simplest form of vectorization: s + 5, s * 100, s ** 2. Scalar arithmetic never introduces NaN and is used for unit conversion, normalisation, and offsetting time series.

NumPy ufuncs work seamlessly on Pandas Series and return a Series with the same index. This means you can call np.sqrt(s), np.log1p(s), or np.exp(s) directly on a Series without converting to an array first. The result is a new Series, preserving the original labels.

Comparison operators (==, !=, >, <, >=, <=) return a boolean Series element-wise. These are the building blocks for filtering: you pass the boolean Series to .loc to select the matching rows. Pandas also aligns by index when comparing two Series.

Because each operation returns a new Series, you can chain multiple transformations in a single expression. This is readable and avoids creating unnecessary intermediate variables. For complex pipelines, break the chain across lines using parentheses. Chaining is the Pandas equivalent of functional programming with immutable data.

NaN is contagious: any arithmetic involving NaN produces NaN. Use s.fillna(value) to substitute before operating, or use the skipna=True parameter on aggregation methods. The pd.isna(s) function returns a boolean Series marking NaN positions — use it to audit data before computing statistics.

Pandas Series expose many NumPy-like methods directly: s.abs(), s.clip(lower, upper), and s.round(decimals) all return new Series with the transformation applied element-wise. These are equivalent to the NumPy ufunc calls but use method syntax and preserve the Series index and name.

s.pct_change() computes the percentage change between consecutive elements: (current - previous) / previous. The first element is NaN since there is no previous value. This is the standard operation for computing month-over-month growth rates, daily returns in finance, and rate-of-change features for time series models.

s.cumsum() returns a Series of running totals and s.cumprod() returns running products. These are used to compute cumulative revenue, compound investment returns, or running word counts. They preserve the original index labels, making it easy to plot the cumulative series over time or category.

Test your understanding of vectorized operations on Series from this lesson.

In this lesson you learned: arithmetic operators apply element-wise to Series without loops, Pandas automatically aligns two Series by index label before operating, placing NaN at unmatched positions, and methods like fill_value, pct_change, and cumsum extend vectorized operations for analytical tasks. Next up we explore useful Series methods like describe, value_counts, and map for rapid summarisation.