Sorting and Ranking

nLargest Top Rows

Pick the top-N values from a pool by repeated max-pick, shrinking the pool each round. With pandas, nlargest(n, col) returns the top-N rows in descending order without sorting the entire DataFrame.

By hand

With pandas

df.nlargest(3, 'score') returns a new DataFrame with the 3 highest-scoring rows in descending order. The snapshot shows that the original row index is preserved ([2, 0, 3]), just like sort_values.

naive.py 
scores = [80, 55, 90, 70]
remaining = list(range(len(scores)))
result = []
for _ in range(3):
    best = max(remaining, key=lambda i: scores[i])
    result.append(scores[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)
top3 = df.nlargest(3, 'score')
print('names:', top3['name'].tolist())
print('scores:', top3['score'].tolist())
print('index:', top3.index.tolist())
print('RESULT:', top3['score'].tolist())

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

Implementation notes

  • nlargest(n, col) is more efficient than sort_values(col, ascending=False).head(n) for large DataFrames — it uses a partial selection algorithm that avoids fully ordering the DataFrame.
  • Use nsmallest(n, col) for the bottom-N rows.
  • The result preserves the original row index. Add .reset_index(drop=True) to renumber from 0.
  • Cross-reference: top-k-select (python-data-basics) for the pure-Python heapq version; sort-by-column (this chapter) for full-sort comparison.