sort_values() by single column in Pandas - Step-by-Step Execution

Concept Flow - sort_values() by single column

Start with DataFrame

↓

Choose column to sort

↓

Call sort_values() on column

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DataFrame rows rearranged

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Sorted DataFrame output

We start with a table, pick one column to sort by, then rearrange rows based on that column's values, producing a sorted table.

Execution Sample

Pandas

import pandas as pd

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

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

Execution Table

Step	DataFrame State	Action	Resulting DataFrame
1	{'Name': ['Bob', 'Alice', 'Eve'], 'Age': [25, 30, 22]}	Call df.sort_values('Age')	Name Age 2 Eve 22 0 Bob 25 1 Alice 30
2	Sorted DataFrame ready	Print sorted_df	Name Age 2 Eve 22 0 Bob 25 1 Alice 30
3	End	No further action	Sorting complete

💡 All rows rearranged by ascending 'Age' values, sorting finished

Variable Tracker

Variable	Start	After sort_values()	Final
df	{'Name': ['Bob', 'Alice', 'Eve'], 'Age': [25, 30, 22]}	Unchanged	Unchanged
sorted_df	Not defined	{'Name': ['Eve', 'Bob', 'Alice'], 'Age': [22, 25, 30]}	Same

Key Moments - 2 Insights

Why does the original DataFrame 'df' not change after sort_values()?

Because sort_values() returns a new sorted DataFrame and does not modify 'df' unless you use inplace=True. See execution_table step 1 where 'df' stays the same.

What determines the order of rows after sorting?

The values in the chosen column ('Age' here) determine the new row order, smallest to largest by default, as shown in execution_table step 1.

Visual Quiz - 3 Questions

Look at the execution_table step 1, what is the first row in the sorted DataFrame?

A Bob, Age 25

B Alice, Age 30

C Eve, Age 22

D None of these

💡 Hint

Check the 'Resulting DataFrame' column in step 1 for the first row after sorting.

At which step does the DataFrame get printed?

A Step 2

B Step 1

C Step 3

D It is never printed

💡 Hint

Look at the 'Action' column to find when print(sorted_df) happens.

If we want to sort the original DataFrame 'df' without creating a new one, what should we add?

A by='Age'

B inplace=True

C ascending=False

D axis=1

💡 Hint

Refer to key_moments about why 'df' does not change after sort_values().

Concept Snapshot

sort_values(column_name)
- Sorts DataFrame rows by one column
- Returns a new sorted DataFrame by default
- Use inplace=True to modify original
- Default order is ascending
- Useful to reorder data by values

Full Transcript

We start with a DataFrame containing names and ages. We want to sort rows by the 'Age' column. Calling df.sort_values('Age') returns a new DataFrame with rows ordered from youngest to oldest. The original DataFrame stays the same unless we use inplace=True. We print the sorted DataFrame to see the result. This process helps us organize data by any column's values easily.

Practice

1. What does the sort_values() function do in pandas when used on a DataFrame?

easy

A. It filters rows based on a condition.

B. It changes the data type of a column.

C. It sorts the DataFrame rows based on the values in a specified column.

D. It adds a new column to the DataFrame.

Solution

1. Step 1: Understand the purpose of sort_values()

   This function is used to reorder rows in a DataFrame based on the values in one or more columns.

2. Step 2: Compare with other options

   Changing data types, filtering rows, or adding columns are done by different functions, not sort_values().

3. Final Answer:

   It sorts the DataFrame rows based on the values in a specified column. -> Option C

4. Quick Check:

   sort_values() = reorder rows by column [OK]

Hint: Remember: sort_values() orders rows by column values [OK]

Common Mistakes:

• Confusing sorting with filtering
• Thinking it changes data types
• Assuming it adds columns

2. Which of the following is the correct syntax to sort a DataFrame df by the column 'age' in ascending order?

easy

A. df.sort_values(by='age', ascending=True)

B. df.sort_values('age', ascending=False)

C. df.sort(by='age', ascending=True)

D. df.sort_values(by='age', ascending='yes')

Solution

1. Step 1: Check the correct function and parameters

   The function is sort_values() and it requires the parameter by='column_name'. The ascending parameter should be a boolean.

2. Step 2: Validate each option

   df.sort_values(by='age', ascending=True) uses correct function and parameters. df.sort_values('age', ascending=False) has ascending=False (descending). df.sort(by='age', ascending=True) uses wrong function sort(). df.sort_values(by='age', ascending='yes') uses a string instead of boolean for ascending.

3. Final Answer:

   df.sort_values(by='age', ascending=True) -> Option A

4. Quick Check:

   Correct syntax = df.sort_values(by='age', ascending=True) [OK]

Hint: Use by='column' and ascending=True/False in sort_values() [OK]

Common Mistakes:

• Using sort() instead of sort_values()
• Passing ascending as string instead of boolean
• Omitting 'by' keyword

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'name': ['Anna', 'Bob', 'Cara'], 'score': [88, 92, 85]})
sorted_df = df.sort_values(by='score')

What is the order of names in sorted_df?

medium

A. ['Cara', 'Anna', 'Bob']

B. ['Bob', 'Anna', 'Cara']

C. ['Anna', 'Bob', 'Cara']

D. ['Cara', 'Bob', 'Anna']

Solution

1. Step 1: Understand sorting by 'score'

   The scores are 88 (Anna), 92 (Bob), and 85 (Cara). Sorting ascending means smallest to largest.

2. Step 2: Order names by ascending scores

   Sorted scores: 85 (Cara), 88 (Anna), 92 (Bob). So names order is ['Cara', 'Anna', 'Bob'].

3. Final Answer:

   ['Cara', 'Anna', 'Bob'] -> Option A

4. Quick Check:

   Ascending sort by score = ['Cara', 'Anna', 'Bob'] [OK]

Hint: Sort ascending means smallest value first [OK]

Common Mistakes:

• Assuming descending order by default
• Mixing up names and scores
• Not using 'by' parameter

4. What is wrong with this code snippet?

df = pd.DataFrame({'A': [3, 1, 2]})
df.sort_values('A', ascending='False')

medium

A. The column name should be passed as a list.

B. The ascending parameter should be a boolean, not a string.

C. sort_values() does not accept the ascending parameter.

D. The DataFrame must be sorted inplace.

Solution

1. Step 1: Check the ascending parameter type

   The ascending parameter expects a boolean (True or False), but here it is passed as a string 'False'.

2. Step 2: Validate other options

   Passing column name as string is allowed. sort_values() accepts ascending. Sorting inplace is optional.

3. Final Answer:

   The ascending parameter should be a boolean, not a string. -> Option B

4. Quick Check:

   ascending must be True/False boolean [OK]

Hint: Use True/False, not 'True'/'False' for ascending [OK]

Common Mistakes:

• Passing ascending as string
• Thinking column name must be list
• Forcing inplace sorting unnecessarily

5. You have a DataFrame df with columns 'product' and 'price'. You want to sort the products by price descending but keep the original DataFrame unchanged. Which code achieves this?

hard

A. df.sort_values(by='price', ascending=False, inplace=True)

B. df.sort_values('price', inplace=False)

C. df.sort_values(by='price', ascending=True)

D. sorted_df = df.sort_values(by='price', ascending=False)

Solution

1. Step 1: Understand sorting without changing original

   To keep original DataFrame unchanged, do not use inplace=True. Instead, assign the sorted result to a new variable.

2. Step 2: Check options for descending order

   sorted_df = df.sort_values(by='price', ascending=False) sorts by 'price' descending and assigns to sorted_df. df.sort_values(by='price', ascending=False, inplace=True) changes original due to inplace=True. df.sort_values(by='price', ascending=True) sorts ascending, not descending. df.sort_values('price', inplace=False) sorts ascending by default and does not assign result.

3. Final Answer:

   sorted_df = df.sort_values(by='price', ascending=False) -> Option D

4. Quick Check:

   Assign sorted result without inplace for original unchanged [OK]

Hint: Assign sorted DataFrame to new variable, avoid inplace=True [OK]

Common Mistakes:

• Using inplace=True and losing original order
• Sorting ascending instead of descending
• Not assigning sorted DataFrame to a variable