</>
pythonbeginner

Pandas nlargest / nsmallest

Efficiently retrieve the N largest or smallest rows without sorting the full DataFrame.

python
import pandas as pd
import numpy as np

df = pd.DataFrame({'product':[f'P{i}' for i in range(1000)],'revenue': np.random.randint(100,10000,1000),'orders': np.random.randint(1,500,1000)})

top5 = df.nlargest(5, 'revenue')
bottom5 = df.nsmallest(5, 'revenue')

print('Top 5 by revenue:')
print(top5[['product','revenue']])
print('Bottom 5:')
print(bottom5[['product','revenue']])

Use Cases

  • top-N queries
  • leaderboards
  • anomaly detection

Tags

#pandas#top-n#performance#analytics

Related Snippets

Similar patterns you can reuse in the same workflow.

pythonintermediate

Pandas Vectorised Operations vs Apply

Compare apply vs vectorised pandas operations for performance-critical column transformations.

Best for: feature engineering

#pandas#vectorization
pythonintermediate

Pandas .eval() for Fast Column Computation

Use DataFrame.eval() for expressive, fast in-place column calculations using numexpr.

Best for: large DataFrame operations

#pandas#eval
pythonbeginner

Pandas Cross-Tabulation (crosstab)

Compute frequency and proportion cross-tabulations between two categorical columns.

Best for: categorical analysis

#pandas#crosstab
pythonbeginner

Pandas read_csv with Explicit Dtypes

Specify column dtypes on CSV read to avoid costly inference and prevent silent type coercion.

Best for: fast CSV loading

#pandas#csv