Sorting and Ranking

Sort by Column

Pick the minimum-score row from the remaining pool repeatedly until the pool is empty, collecting names in order. With pandas, df.sort_values('score') returns a new DataFrame with all rows in ascending order by that column.

By hand

With pandas

df.sort_values('score') returns a new sorted DataFrame — the original df is unchanged. The snapshot shows names, scores, and the preserved (non-reset) original index.

naive.py 
# trace: ignore records
records = [
    {'name': 'al', 'score': 80},
    {'name': 'bo', 'score': 55},
    {'name': 'cy', 'score': 90},
    {'name': 'di', 'score': 70},
]
scores = [r['score'] for r in records]
names = [r['name'] for r in records]
remaining = list(range(len(records)))
result = []
for _ in range(len(records)):
    best = min(remaining, key=lambda i: scores[i])
    result.append(names[best])
    remaining.remove(best)
print('RESULT:', result)
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

records = [
    {'name': 'al', 'score': 80},
    {'name': 'bo', 'score': 55},
    {'name': 'cy', 'score': 90},
    {'name': 'di', 'score': 70},
]
df = pd.DataFrame(records)
sorted_df = df.sort_values('score')
print('names:', sorted_df['name'].tolist())
print('scores:', sorted_df['score'].tolist())
print('index:', sorted_df.index.tolist())
print('RESULT:', sorted_df['name'].tolist())

names: ['bo', 'di', 'al', 'cy']
scores: [55, 70, 80, 90]
index: [1, 3, 0, 2]
RESULT: ['bo', 'di', 'al', 'cy']

Implementation notes

  • sort_values returns a new DataFrame; the original is unchanged. Use inplace=True to sort in place, but prefer the default for readable pipelines.
  • The sorted result keeps the original row index ([1, 3, 0, 2] above). Add .reset_index(drop=True) to renumber from 0.
  • Default order is ascending. Pass ascending=False for descending: df.sort_values('score', ascending=False).
  • Cross-reference: sort-by-key (python-data-basics) for the pure-Python version; sort-array (python-numpy ch08) for the NumPy array version.