Sorting by values in Pandas - Step-by-Step Execution

Concept Flow - Sorting by values

Start with DataFrame

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Choose column(s) to sort

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Call sort_values()

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Sort rows by chosen column(s)

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Return sorted DataFrame

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End

We start with a DataFrame, pick columns to sort by, call sort_values(), and get a new DataFrame sorted by those columns.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'Name': ['Anna', 'Bob', 'Cara'], 'Age': [25, 30, 22]})
sorted_df = df.sort_values(by='Age')
print(sorted_df)

This code sorts the DataFrame rows by the 'Age' column in ascending order.

Execution Table

Step	Action	DataFrame state (Name, Age)	Result
1	Create DataFrame	[('Anna', 25), ('Bob', 30), ('Cara', 22)]	Original order
2	Call sort_values(by='Age')	Same as step 1	Sorting triggered
3	Sort rows by Age ascending	[('Cara', 22), ('Anna', 25), ('Bob', 30)]	Rows reordered
4	Return sorted DataFrame	[('Cara', 22), ('Anna', 25), ('Bob', 30)]	Sorted DataFrame ready

💡 Sorting complete, DataFrame rows ordered by Age ascending

Variable Tracker

Variable	Start	After sort_values	Final
df	[('Anna', 25), ('Bob', 30), ('Cara', 22)]	[('Anna', 25), ('Bob', 30), ('Cara', 22)]	[('Anna', 25), ('Bob', 30), ('Cara', 22)]
sorted_df	N/A	[('Cara', 22), ('Anna', 25), ('Bob', 30)]	[('Cara', 22), ('Anna', 25), ('Bob', 30)]

Key Moments - 2 Insights

Why does the original DataFrame 'df' not change after sorting?

Because sort_values() returns a new sorted DataFrame and does not modify 'df' in place unless you use inplace=True. See execution_table step 3 and variable_tracker where 'df' stays the same.

What happens if we sort by a column with duplicate values?

Rows with the same value in the sorted column keep their original order (stable sort). This is shown by the sorting step in execution_table where order is preserved for ties.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, which row comes first after sorting by 'Age'?

A ('Bob', 30)

B ('Cara', 22)

C ('Anna', 25)

D ('Bob', 22)

💡 Hint

Check the 'DataFrame state' column at step 3 in execution_table

According to variable_tracker, what is the value of 'df' after sorting?

A [('Anna', 25), ('Bob', 30), ('Cara', 22)]

B N/A

C [('Cara', 22), ('Anna', 25), ('Bob', 30)]

D Empty DataFrame

💡 Hint

Look at the 'df' row in variable_tracker after sort_values

If we want to sort the DataFrame by 'Age' descending, what parameter should we add?

A ascending=True

B by='Age_desc'

C ascending=False

D reverse=True

💡 Hint

Recall sort_values() has an 'ascending' parameter controlling sort order

Concept Snapshot

pandas.DataFrame.sort_values(by=column_name)
- Sorts rows by column values
- Returns a new sorted DataFrame
- Default is ascending order
- Use inplace=True to modify original
- Can sort by multiple columns

Full Transcript

We start with a DataFrame containing names and ages. We want to sort it by the 'Age' column. Using df.sort_values(by='Age'), pandas sorts the rows in ascending order by age. The original DataFrame 'df' remains unchanged because sort_values returns a new DataFrame. The new sorted DataFrame has rows ordered from youngest to oldest. This process is stepwise: create DataFrame, call sort_values, sort rows, return sorted DataFrame. Remember, to change the original DataFrame, use inplace=True. Sorting is stable, so rows with equal values keep their order.