nlargest() and nsmallest() in Pandas - Step-by-Step Execution

Concept Flow - nlargest() and nsmallest()

Start with DataFrame

↓

Call nlargest(n, column)

→Sort column descending

↓

Select top n rows

↓

Call nsmallest(n, column)

→Sort column ascending

↓

Select bottom n rows

↓

Return subset DataFrame

Start with a DataFrame, then use nlargest() to get top n rows by a column sorted descending, or nsmallest() to get bottom n rows sorted ascending, returning those rows.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'Name': ['A', 'B', 'C', 'D'], 'Score': [88, 92, 85, 90]})

# Top 2 scores
top_rows = df.nlargest(2, 'Score')
print(top_rows)

# Bottom 2 scores
bottom_rows = df.nsmallest(2, 'Score')
print(bottom_rows)

This code creates a DataFrame with names and scores, assigns and prints the top 2 and bottom 2 rows by Score.

Execution Table

Step	Action	DataFrame State	Result
1	Create DataFrame	[{'A',88}, {'B',92}, {'C',85}, {'D',90}]	DataFrame with 4 rows
2	top_rows = df.nlargest(2, 'Score')	Sort 'Score' descending	Rows with scores 92 (B), 90 (D)
3	print(top_rows)	Subset DataFrame	Rows: B(92), D(90)
4	bottom_rows = df.nsmallest(2, 'Score')	Sort 'Score' ascending	Rows with scores 85 (C), 88 (A)
5	print(bottom_rows)	Subset DataFrame	Rows: C(85), A(88)
6	End	No change	Execution complete

💡 All steps executed; nlargest and nsmallest returned correct subsets.

Variable Tracker

Variable	Start	After nlargest	After nsmallest	Final
df	[{'A',88}, {'B',92}, {'C',85}, {'D',90}]	Unchanged	Unchanged	Unchanged
top_rows	N/A	[{'B',92}, {'D',90}]	[{'B',92}, {'D',90}]	[{'B',92}, {'D',90}]
bottom_rows	N/A	N/A	[{'C',85}, {'A',88}]	[{'C',85}, {'A',88}]

Key Moments - 2 Insights

Why does nlargest(2, 'Score') return rows with scores 92 and 90, not 88?

Because nlargest sorts the 'Score' column in descending order and picks the top 2 rows, which are 92 and 90, as shown in execution_table step 2 and 3.

Does nsmallest change the original DataFrame?

No, nsmallest returns a new subset DataFrame without modifying the original, as shown in variable_tracker where 'df' remains unchanged.

Visual Quiz - 3 Questions

Look at the execution table, what rows does nlargest(2, 'Score') return at step 3?

A Rows with scores 92 and 90

B Rows with scores 85 and 88

C All rows sorted ascending

D Rows with scores 88 and 85

💡 Hint

Check execution_table row 3 under 'Result' column.

At which step does the DataFrame get sorted ascending by 'Score'?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at execution_table row 4 'Action' column.

If we change nlargest(2, 'Score') to nlargest(3, 'Score'), what changes in variable_tracker?

A df will be modified

B bottom_rows will change to 3 rows

C top_rows will include 3 rows with highest scores

D No change at all

💡 Hint

Refer to variable_tracker row for 'top_rows' and how nlargest affects it.

Concept Snapshot

nlargest(n, column) returns top n rows sorted descending by column.
nsmallest(n, column) returns bottom n rows sorted ascending by column.
Both return new DataFrames without changing original.
Use to quickly find highest or lowest values in data.

Full Transcript

We start with a DataFrame of names and scores. Calling nlargest(2, 'Score') sorts the scores descending and picks top 2 rows with scores 92 and 90. Calling nsmallest(2, 'Score') sorts ascending and picks bottom 2 rows with scores 85 and 88. Neither method changes the original DataFrame. The output is subsets of the original data showing highest or lowest values as requested.