Sorting by index in Pandas - Step-by-Step Execution

Concept Flow - Sorting by index

Start with DataFrame

↓

Choose sort order: ascending or descending

↓

Apply sort_index() method

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DataFrame rows rearranged by index

↓

Return sorted DataFrame

The DataFrame is sorted by its index labels in ascending or descending order using sort_index().

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'A': [10, 20, 30]}, index=[2, 0, 1])
sorted_df = df.sort_index()

This code sorts the DataFrame rows by their index in ascending order.

Execution Table

Step	DataFrame Index Before	Action	Sort Order	DataFrame Index After
1	[2, 0, 1]	Call sort_index()	ascending (default)	[0, 1, 2]
2	[0, 1, 2]	Return sorted DataFrame	N/A	[0, 1, 2]
3	N/A	End of operation	N/A	N/A

💡 Sorting completes after rearranging rows by index in ascending order.

Variable Tracker

Variable	Start	After sort_index()	Final
df.index	[2, 0, 1]	[2, 0, 1]	[2, 0, 1]
sorted_df.index	N/A	[0, 1, 2]	[0, 1, 2]

Key Moments - 2 Insights

Why does the original DataFrame 'df' keep its index order after sort_index()?

Because sort_index() returns a new sorted DataFrame and does not modify 'df' in place unless inplace=True is used, as shown in execution_table row 1 and variable_tracker.

What happens if we set ascending=False in sort_index()?

The DataFrame rows will be sorted by index in descending order, reversing the index order, which changes the 'DataFrame Index After' in execution_table row 1.

Visual Quiz - 3 Questions

Look at the execution table, what is the index order of 'sorted_df' after step 1?

A [1, 2, 0]

B [0, 1, 2]

C [2, 0, 1]

D [2, 1, 0]

💡 Hint

Check the 'DataFrame Index After' column in execution_table row 1.

At which step does the sorting operation complete according to the execution table?

A Step 2

B Step 1

C Step 3

D Sorting never completes

💡 Hint

Look at the 'Action' column describing 'Return sorted DataFrame' in execution_table.

If we want to sort the original DataFrame 'df' in place, what should we do?

A Assign the result of sort_index() back to df

B Use df.sort_values() instead

C Use df.sort_index(inplace=True)

D Nothing, df is sorted automatically

💡 Hint

Refer to key_moments explanation about inplace parameter.

Concept Snapshot

sort_index() sorts DataFrame rows by their index labels.
Default is ascending order.
Returns a new sorted DataFrame unless inplace=True.
Use ascending=False for descending order.
Does not change original DataFrame unless inplace=True.

Full Transcript

Sorting by index in pandas means rearranging the rows of a DataFrame based on their index labels. The method sort_index() is called on the DataFrame. By default, it sorts in ascending order and returns a new DataFrame with rows reordered. The original DataFrame remains unchanged unless you specify inplace=True. You can also sort in descending order by setting ascending=False. The execution table shows the index order before and after sorting, and the variable tracker shows how the index values change in the new sorted DataFrame. Remember to assign the result back or use inplace=True to modify the original DataFrame.

Practice

1. What does sorting by index in pandas do?

easy

A. It changes the data values to alphabetical order.

B. It filters rows based on a condition.

C. It removes duplicate rows from the DataFrame.

D. It arranges data rows or columns based on their labels.

Solution

1. Step 1: Understand the meaning of sorting by index

   Sorting by index means arranging the data based on the row or column labels, not the data values.

2. Step 2: Compare options with the definition

   Only It arranges data rows or columns based on their labels. correctly describes sorting by index as arranging rows or columns by their labels.

3. Final Answer:

   It arranges data rows or columns based on their labels. -> Option D

4. Quick Check:

   Sorting by index = arrange by labels [OK]

Hint: Sorting by index means ordering by labels, not values [OK]

Common Mistakes:

• Confusing sorting by index with sorting by values
• Thinking it filters or removes data
• Assuming it changes data values

2. Which of the following is the correct syntax to sort a DataFrame df by its row index in descending order without changing the original DataFrame?

easy

A. df.sort_index(ascending=False, inplace=False)

B. df.sort_index(axis=1, ascending=False)

C. df.sort_index(ascending=True, inplace=False)

D. df.sort_index(ascending=False, inplace=True)

Solution

1. Step 1: Identify parameters for sorting by index

   To sort by row index descending, use ascending=False. To avoid changing original, use inplace=False.

2. Step 2: Check each option

   df.sort_index(ascending=False, inplace=False) uses ascending=False and inplace=False, which matches the requirement. df.sort_index(ascending=False, inplace=True) changes original (inplace=True). df.sort_index(ascending=True, inplace=False) sorts ascending. df.sort_index(axis=1, ascending=False) sorts columns (axis=1).

3. Final Answer:

   df.sort_index(ascending=False, inplace=False) -> Option A

4. Quick Check:

   Descending + no inplace = ascending=False, inplace=False [OK]

Hint: Use inplace=False to keep original unchanged [OK]

Common Mistakes:

• Using inplace=True when original should not change
• Confusing ascending=True with descending order
• Sorting columns instead of rows by mistake

3. Given the DataFrame df with index labels [3, 1, 2] and values ["a", "b", "c"], what will df.sort_index() return as the index order?

medium

A. [a, b, c]

B. [1, 2, 3]

C. [3, 2, 1]

D. [c, b, a]

Solution

1. Step 1: Understand default sort_index behavior

   By default, sort_index sorts row labels in ascending order.

2. Step 2: Apply sorting to index [3, 1, 2]

   Sorting ascending gives [1, 2, 3]. Values remain associated with their labels.

3. Final Answer:

   [1, 2, 3] -> Option B

4. Quick Check:

   Default sort_index sorts index ascending [OK]

Hint: Default sort_index sorts index ascending [OK]

Common Mistakes:

• Confusing index labels with values
• Assuming sort_index sorts values instead of index
• Thinking sort_index reverses order by default

4. You run df.sort_index(axis=2) on a DataFrame but get an error. What is the likely cause?

medium

A. axis=2 is invalid; axis must be 0 or 1.

B. sort_index does not accept axis parameter.

C. DataFrame index labels are missing.

D. inplace parameter is required.

Solution

1. Step 1: Check valid axis values for sort_index

   sort_index accepts axis=0 (rows) or axis=1 (columns) only.

2. Step 2: Identify error cause with axis=2

   axis=2 is invalid and causes an error.

3. Final Answer:

   axis=2 is invalid; axis must be 0 or 1. -> Option A

4. Quick Check:

   axis must be 0 or 1 in sort_index [OK]

Hint: axis can only be 0 (rows) or 1 (columns) [OK]

Common Mistakes:

• Using axis values other than 0 or 1
• Assuming sort_index ignores invalid axis
• Forgetting axis parameter is optional but must be valid

5. You have a DataFrame df with a multi-level index (two levels). How do you sort df by the second level of the index in ascending order without changing the original DataFrame?

hard

A. df.sort_values(level=1, ascending=True)

B. df.sort_index(level=0, ascending=True, inplace=True)

C. df.sort_index(level=1, ascending=True, inplace=False)

D. df.sort_index(axis=1, level=1)

Solution

1. Step 1: Understand sorting multi-level index

   Use sort_index with level parameter to specify which index level to sort.

2. Step 2: Choose correct parameters

   To sort by second level (level=1), ascending order, and keep original unchanged, use inplace=False.

3. Final Answer:

   df.sort_index(level=1, ascending=True, inplace=False) -> Option C

4. Quick Check:

   Sort multi-level index by level=1, inplace=False [OK]

Hint: Use level parameter to sort multi-level index [OK]

Common Mistakes:

• Using sort_values instead of sort_index for index sorting
• Sorting wrong index level (level=0 instead of 1)
• Using inplace=True when original should not change