Series

pandas.Series is a 1-D labelled array: a fixed-length homogeneous-dtype sequence with an explicit index (the labels) alongside the values. Third-party (pip install pandas), not in the standard library. Reach for it whenever the operator wants a numpy array with named positions, time-series alignment, or row-of-a-dataframe semantics.

Construct from a list; the index defaults to 0..n-1.

import pandas as pd
s = pd.Series([10, 20, 30])

Construct with an explicit index.

s = pd.Series([10, 20, 30], index=["a", "b", "c"], name="latency_ms")

Construct from a dict; the keys become the index.

s = pd.Series({"alice": 30, "bob": 25, "carol": 41})

Label-based access with .loc.

s.loc["b"]

Positional access with .iloc.

s.iloc[0]

Vectorised arithmetic broadcasts over the values.

s * 2

Boolean indexing filters by mask.

s[s > 100]

Aggregations are one-liners.

s.sum(), s.mean(), s.median(), s.std(), s.quantile(0.95)

Alignment is automatic on operations between two Series with overlapping but unequal indexes; missing labels become NaN.

a + b

Method catalog

The most-used surface.

Method	Effect
head(n=5)	First n rows.
tail(n=5)	Last n rows.
describe()	Summary statistics (count, mean, std, min, quantiles, max).
unique()	Distinct values, in order of first appearance.
value_counts()	Frequency table, descending.
sort_values(*, ascending=True)	Sort by value.
sort_index()	Sort by label.
map(f)	Element-wise transform.
apply(f)	Same, with reduce semantics on a DataFrame.
dropna()	Drop missing values.
fillna(v)	Replace missing values with v (or a method).
isna() / notna()	Boolean mask of missing / present values.
astype(dtype)	Cast every element to a dtype.
rolling(window).mean()	Rolling-window aggregation.
to_frame()	Promote to a single-column DataFrame.
to_numpy()	Underlying numpy array.
to_list()	Plain Python list.

References

• DataFrame for the 2-D tabular sibling.

• list for the one-allocation Python list these structures replace at scale.

• pandas.Series.