sort_values() by single column in Pandas

Introduction

We use sort_values() to arrange data in order based on one column. This helps us see data from smallest to largest or vice versa.

When you want to see the top or bottom values in a list, like highest sales.

When you need to organize data by date to see events in order.

When preparing data for reports where order matters.

When cleaning data and checking for outliers by sorting values.

When comparing values easily by sorting them.

Syntax

Pandas

DataFrame.sort_values(by='column_name', ascending=True)

by is the column name to sort by.

ascending=True sorts from smallest to largest. Use False for largest to smallest.

Examples

Sorts the DataFrame df by the column age in ascending order.

Pandas

df.sort_values(by='age')

Sorts df by score from highest to lowest.

Pandas

df.sort_values(by='score', ascending=False)

Sample Program

This code creates a small table of names and ages. Then it sorts the table by age from youngest to oldest and prints the result.

Pandas

import pandas as pd

data = {'name': ['Anna', 'Bob', 'Charlie', 'Diana'],
        'age': [28, 22, 35, 30]}
df = pd.DataFrame(data)

# Sort by age ascending
sorted_df = df.sort_values(by='age')
print(sorted_df)

Important Notes

Sorting does not change the original DataFrame unless you use inplace=True.

If the column has missing values, they appear at the end by default.

Summary

sort_values() helps order data by one column.

Use ascending=True for smallest to largest, False for largest to smallest.

It returns a new sorted DataFrame unless inplace=True is set.

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