Series Basics

Series with Label Index

Map string labels to values with a dict loop, then look up by label. A Series with a named index works the same way but also preserves order, supports arithmetic alignment, and integrates with DataFrames. The trace shows data filling label by label before lookup isolates a single entry.

By hand

Build data as a dict mapping each label to its value. Look up data['b'] to retrieve the value for label 'b'.

naive.py
Replay: real traced execution (multi-file project)
labels = ['a', 'b', 'c', 'd']
values = [10, 20, 30, 40]
data = {}
for i in range(len(labels)):
    data[labels[i]] = values[i]
lookup = data['b']
print('RESULT:', (lookup, values))

  1. labels ← ['a', 'b', 'c', 'd']

    1labels = ['a', 'b', 'c', 'd']2values = [10, 20, 30, 40]
    values this step['a', 'b', 'c', 'd']labels

  2. values ← [10, 20, 30, 40]

    1labels = ['a', 'b', 'c', 'd']2values = [10, 20, 30, 40]3data = {}
    values this step[10, 20, 30, 40]values

  3. data ← {}

    2values = [10, 20, 30, 40]3data = {}4for i in range(len(labels)):
    values this step{}data

  4. i ← 0

    3data = {}4for i in range(len(labels)):5    data[labels[i]] = values[i]
    values this step0i

  5. data ← {'a': 10}

    4for i in range(len(labels)):5    data[labels[i]] = values[i]6lookup = data['b']
    values this step{} → {'a': 10}data

  6. i ← 1

    3data = {}4for i in range(len(labels)):5    data[labels[i]] = values[i]
    values this step0 → 1i

  7. data ← {'a': 10, 'b': 20}

    4for i in range(len(labels)):5    data[labels[i]] = values[i]6lookup = data['b']
    values this step{'a': 10} → {'a': 10, 'b': 20}data

  8. i ← 2

    3data = {}4for i in range(len(labels)):5    data[labels[i]] = values[i]
    values this step1 → 2i

  9. data ← {'a': 10, 'b': 20, 'c': 30}

    4for i in range(len(labels)):5    data[labels[i]] = values[i]6lookup = data['b']
    values this step{'a': 10, 'b': 20} → {'a': 10, 'b': 20, 'c': 30}data

  10. i ← 3

    3data = {}4for i in range(len(labels)):5    data[labels[i]] = values[i]
    values this step2 → 3i

  11. data ← {'a': 10, 'b': 20, 'c': 30, 'd': 40}

    4for i in range(len(labels)):5    data[labels[i]] = values[i]6lookup = data['b']
    values this step{'a': 10, 'b': 20, 'c': 30} → {'a': 10, 'b': 20, 'c': 30, 'd': 40}data

  12. for i in range(len(labels)):

    3data = {}4for i in range(len(labels)):5    data[labels[i]] = values[i]

  13. lookup ← 20

    5    data[labels[i]] = values[i]6lookup = data['b']7print('RESULT:', (lookup, values))
    values this step20lookup

  14. stdout ← RESULT: (20, [10, 20, 30, 40])

    6lookup = data['b']7print('RESULT:', (lookup, values))
    values this stepRESULT: (20, [10, 20, 30, 40])stdout

With pandas

pd.Series(values, index=labels) assigns the string labels as the index. s['b'] retrieves the value by label — the same dict-like lookup, but on a typed, ordered structure.

library.py 
import pandas as pd
from dalib.display import set_display
set_display()

labels = ['a', 'b', 'c', 'd']
values = [10, 20, 30, 40]
s = pd.Series(values, index=labels)
lookup = int(s['b'])
print('index:', s.index.tolist())
print('values:', s.tolist())
print('dtype:', s.dtype)
print('s[b]:', lookup)
print('RESULT:', (lookup, s.tolist()))

index: ['a', 'b', 'c', 'd']
values: [10, 20, 30, 40]
dtype: int64
s[b]: 20
RESULT: (20, [10, 20, 30, 40])

Implementation notes

  • A Series goes beyond a plain dict in four ways: values are typed and homogeneous (single dtype); the index is an explicit Index object that participates in alignment; both label-based (s['b'], s.loc['b']) and positional access (s.iloc[1]) are available; and arithmetic between two Series aligns on index labels automatically (see series-arithmetic-align).
  • int(s['b']) converts the returned numpy scalar to a plain Python int for a stable RESULT, avoiding repr differences across numpy versions.
  • When two Series share the same index, pandas aligns them by label during arithmetic: s1 + s2 adds elements with matching labels, filling mismatches with NaN.
  • Index and values are shown via .tolist() here because raw Series.__repr__ output varies with pandas display options.