Sorting by index in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does sorting by index mean in pandas?

Sorting by index means arranging the rows or columns of a DataFrame or Series based on their index labels in ascending or descending order.

beginner

Which pandas method is used to sort a DataFrame by its index?

The method sort_index() is used to sort a DataFrame or Series by its index labels.

intermediate

How do you sort a DataFrame by its index in descending order?

Use df.sort_index(ascending=False) to sort the DataFrame by its index in descending order.

advanced

What happens if you use sort_index() on a DataFrame with a MultiIndex?

The sort_index() method sorts the DataFrame by all levels of the MultiIndex by default, arranging rows based on the hierarchical index labels.

intermediate

Can you sort columns by their index labels using sort_index()?

Yes, by setting the parameter axis=1 in sort_index(), you can sort the columns of a DataFrame by their index labels.

Which pandas method sorts a DataFrame by its index?

A sort_index()

B sort_values()

C order_by()

D index_sort()

The correct method to sort by index in pandas is sort_index().

How do you sort a DataFrame's columns by their index labels?

A df.sort_values(axis=1)

B df.sort_index(axis=0)

C df.sort_columns()

D df.sort_index(axis=1)

Setting axis=1 in sort_index() sorts the columns by their index labels.

What does df.sort_index(ascending=False) do?

A Sorts the DataFrame by index in descending order

B Sorts the DataFrame by values in descending order

C Sorts the DataFrame by columns in ascending order

D Sorts the DataFrame by index in ascending order

The parameter ascending=False sorts the index in descending order.

If a DataFrame has a MultiIndex, how does sort_index() behave by default?

A Sorts only the first level of the MultiIndex

B Sorts by all levels of the MultiIndex

C Does not sort MultiIndex DataFrames

D Sorts only the last level of the MultiIndex

By default, sort_index() sorts all levels of a MultiIndex.

What is the default sorting order of sort_index()?

A Random order

B Descending order

C Ascending order

D No sorting

The default sorting order for sort_index() is ascending.

Explain how to sort a pandas DataFrame by its index and how to change the sorting order.

Think about the method name and the parameter that controls ascending or descending.

Describe how to sort the columns of a DataFrame by their index labels.

Remember axis=0 is rows, axis=1 is columns.

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