Ascending and descending order in Pandas - Step-by-Step Execution

Concept Flow - Ascending and descending order

Start with DataFrame

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Choose column(s) to sort

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Apply sort_values()

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Set ascending=True or False

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Get sorted DataFrame

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Use or display sorted data

We start with a DataFrame, pick columns to sort, apply sort_values() with ascending True or False, and get the sorted DataFrame.

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', ascending=True)
print(sorted_df)

This code sorts the DataFrame by the 'Age' column in ascending order and prints the result.

Execution Table

Step	Action	DataFrame State	Result
1	Create DataFrame	[{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cara', 'Age': 22}]	Original DataFrame created
2	Call sort_values(by='Age', ascending=True)	Same as step 1	DataFrame sorted by Age ascending: Cara(22), Anna(25), Bob(30)
3	Print sorted DataFrame	Sorted DataFrame	Output shows rows ordered by Age ascending
4	Call sort_values(by='Age', ascending=False)	Same as step 1	DataFrame sorted by Age descending: Bob(30), Anna(25), Cara(22)
5	Print sorted DataFrame	Sorted DataFrame	Output shows rows ordered by Age descending
6	End	No change	Sorting complete

💡 Sorting ends after printing sorted DataFrames in ascending and descending order.

Variable Tracker

Variable	Start	After Step 2	After Step 4	Final
df	[Anna 25, Bob 30, Cara 22]	[Anna 25, Bob 30, Cara 22]	[Anna 25, Bob 30, Cara 22]	[Anna 25, Bob 30, Cara 22]
sorted_df	None	[Cara 22, Anna 25, Bob 30]	[Bob 30, Anna 25, Cara 22]	[Bob 30, Anna 25, Cara 22]

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' unless you use inplace=True. See execution_table steps 2 and 4 where 'df' stays the same.

What does the 'ascending' parameter control?

It controls if the sorting is from smallest to largest (True) or largest to smallest (False). See execution_table steps 2 and 4 for ascending=True and ascending=False results.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the order of names in sorted_df?

A Bob, Anna, Cara

B Anna, Bob, Cara

C Cara, Anna, Bob

D Cara, Bob, Anna

💡 Hint

Check the 'Result' column at step 2 in execution_table.

At which step does sorted_df show descending order by Age?

A Step 4

B Step 2

C Step 3

D Step 5

💡 Hint

Look for ascending=False in the 'Action' column in execution_table.

If we set inplace=True in sort_values(), what changes in variable_tracker?

A 'df' would be deleted

B 'df' would change after sorting steps

C 'sorted_df' would be None

D No change in any variable

💡 Hint

Recall that inplace=True modifies the original DataFrame, affecting 'df' in variable_tracker.

Concept Snapshot

pandas.DataFrame.sort_values(by=column, ascending=True)
- Sorts DataFrame rows by column values
- ascending=True for low to high, False for high to low
- Returns new sorted DataFrame unless inplace=True
- Use to organize data for analysis or display

Full Transcript

We start with a DataFrame containing names and ages. We use the pandas function sort_values() to order the rows by the 'Age' column. Setting ascending=True sorts from youngest to oldest. The function returns a new DataFrame sorted accordingly, leaving the original unchanged. Setting ascending=False sorts from oldest to youngest. This helps us see data in order we want without changing the original data unless we specify inplace=True. The variable tracker shows how the sorted DataFrame changes while the original stays the same. This step-by-step trace helps understand sorting in pandas clearly.