DataFrame from Records - Pandas Step by Step

Pivot a list of row dicts into per-column lists by looping over the records and appending each field to its column accumulator. The pandas version passes the same list-of-dicts directly to pd.DataFrame, which performs the same pivot internally and returns a two-dimensional structure with an aligned index.

By hand

Loop over each record r, appending each field to its dedicated column list. After the loop, the three lists collectively form a columnar table.

                            naive.py
                        
Replay: real traced execution (multi-file project)

                            # trace: ignore records
records = [
    {'name': 'al', 'age': 20, 'score': 80},
    {'name': 'bo', 'age': 25, 'score': 90},
    {'name': 'cy', 'age': 22, 'score': 70},
    {'name': 'di', 'age': 28, 'score': 85},
]
names = []
ages = []
scores = []
for r in records:
    names.append(r['name'])
    ages.append(r['age'])
    scores.append(r['score'])
print('RESULT:', sorted(['name', 'age', 'score']))

                        
1{'name': 'al', 'age': 20, 'score': 80},⋯2records = [3    {'name': 'al', 'age': 20, 'score': 80},4    {'name': 'bo', 'age': 25, 'score': 90},⋯
2{'name': 'bo', 'age': 25, 'score': 90},⋯3{'name': 'al', 'age': 20, 'score': 80},4{'name': 'bo', 'age': 25, 'score': 90},5{'name': 'cy', 'age': 22, 'score': 70},⋯
3{'name': 'cy', 'age': 22, 'score': 70},⋯4{'name': 'bo', 'age': 25, 'score': 90},5{'name': 'cy', 'age': 22, 'score': 70},6{'name': 'di', 'age': 28, 'score': 85},⋯
4{'name': 'di', 'age': 28, 'score': 85},⋯5    {'name': 'cy', 'age': 22, 'score': 70},6    {'name': 'di', 'age': 28, 'score': 85},7]⋯
5records = [1# trace: ignore records2records = [3    {'name': 'al', 'age': 20, 'score': 80},⋯
6names ← []⋯7]8names = []9ages = []⋯values this step[]names
7ages ← []⋯8names = []9ages = []10scores = []⋯values this step[]ages
8scores ← []⋯9ages = []10scores = []11for r in records:⋯values this step[]scores
9r ← {'name': 'al', 'age': 20, 'score': 80}⋯10scores = []11for r in records:12    names.append(r['name'])⋯values this step{'name': 'al', 'age': 20, 'score': 80}r
10names ← ['al']⋯11for r in records:12    names.append(r['name'])13    ages.append(r['age'])⋯values this step[] → ['al']names
11ages ← [20]⋯12names.append(r['name'])13ages.append(r['age'])14scores.append(r['score'])⋯values this step[] → [20]ages
12scores ← [80]⋯13    ages.append(r['age'])14    scores.append(r['score'])15print('RESULT:', sorted(['name', 'age', 'score']))values this step[] → [80]scores
13r ← {'name': 'bo', 'age': 25, 'score': 90}⋯10scores = []11for r in records:12    names.append(r['name'])⋯values this step{'name': 'al', 'age': 20, 'score': 80} → {'name': 'bo', 'age': 25, 'score': 90}r
14names ← ['al', 'bo']⋯11for r in records:12    names.append(r['name'])13    ages.append(r['age'])⋯values this step['al'] → ['al', 'bo']names
15ages ← [20, 25]⋯12names.append(r['name'])13ages.append(r['age'])14scores.append(r['score'])⋯values this step[20] → [20, 25]ages
16scores ← [80, 90]⋯13    ages.append(r['age'])14    scores.append(r['score'])15print('RESULT:', sorted(['name', 'age', 'score']))values this step[80] → [80, 90]scores
17r ← {'name': 'cy', 'age': 22, 'score': 70}⋯10scores = []11for r in records:12    names.append(r['name'])⋯values this step{'name': 'bo', 'age': 25, 'score': 90} → {'name': 'cy', 'age': 22, 'score': 70}r
18names ← ['al', 'bo', 'cy']⋯11for r in records:12    names.append(r['name'])13    ages.append(r['age'])⋯values this step['al', 'bo'] → ['al', 'bo', 'cy']names
19ages ← [20, 25, 22]⋯12names.append(r['name'])13ages.append(r['age'])14scores.append(r['score'])⋯values this step[20, 25] → [20, 25, 22]ages
20scores ← [80, 90, 70]⋯13    ages.append(r['age'])14    scores.append(r['score'])15print('RESULT:', sorted(['name', 'age', 'score']))values this step[80, 90] → [80, 90, 70]scores
21r ← {'name': 'di', 'age': 28, 'score': 85}⋯10scores = []11for r in records:12    names.append(r['name'])⋯values this step{'name': 'cy', 'age': 22, 'score': 70} → {'name': 'di', 'age': 28, 'score': 85}r
22names ← ['al', 'bo', 'cy', 'di']⋯11for r in records:12    names.append(r['name'])13    ages.append(r['age'])⋯values this step['al', 'bo', 'cy'] → ['al', 'bo', 'cy', 'di']names
23ages ← [20, 25, 22, 28]⋯12names.append(r['name'])13ages.append(r['age'])14scores.append(r['score'])⋯values this step[20, 25, 22] → [20, 25, 22, 28]ages
24scores ← [80, 90, 70, 85]⋯13    ages.append(r['age'])14    scores.append(r['score'])15print('RESULT:', sorted(['name', 'age', 'score']))values this step[80, 90, 70] → [80, 90, 70, 85]scores
25for r in records:⋯10scores = []11for r in records:12    names.append(r['name'])⋯
26stdout ← RESULT: ['age', 'name', 'score']⋯14    scores.append(r['score'])15print('RESULT:', sorted(['name', 'age', 'score']))values this stepRESULT: ['age', 'name', 'score']stdout

With pandas

pd.DataFrame(records) accepts a list of dicts and builds a DataFrame with one column per key. The snapshot shows the column names, shape, and each column's values.

                            library.py
                        

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

records = [
    {'name': 'al', 'age': 20, 'score': 80},
    {'name': 'bo', 'age': 25, 'score': 90},
    {'name': 'cy', 'age': 22, 'score': 70},
    {'name': 'di', 'age': 28, 'score': 85},
]
df = pd.DataFrame(records)
print('columns:', df.columns.tolist())
print('shape:', df.shape)
print('name:', df['name'].tolist())
print('age:', df['age'].tolist())
print('score:', df['score'].tolist())
print('RESULT:', sorted(df.columns.tolist()))

                        

columns: ['name', 'age', 'score']
shape: (4, 3)
name: ['al', 'bo', 'cy', 'di']
age: [20, 25, 22, 28]
score: [80, 90, 70, 85]
RESULT: ['age', 'name', 'score']

Implementation notes

A DataFrame is a collection of Series columns sharing one index. Each column is a typed 1D array (dtype per column); the shared index aligns rows across columns, just as the record position did in the naive loop.
df.shape returns (rows, columns) — the 2D analogue of len() on a Series.
pd.DataFrame also accepts a dict of column-lists ({'name': [...], ...}), which mirrors the columnar layout of the hand-written names, ages, scores lists.
The records variable is excluded from the trace with # trace: ignore records because its repr (160 chars) exceeds the 80-char display limit; each per-record dict r remains visible in the loop.