sort_index() for index sorting in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of sorting a DataFrame by index with ascending=False?

Given the DataFrame df with index [3, 1, 2] and values ["a", "b", "c"], what is the result of df.sort_index(ascending=False)?

Pandas

import pandas as pd
df = pd.DataFrame({'value': ['a', 'b', 'c']}, index=[3, 1, 2])
result = df.sort_index(ascending=False)

A {3: 'a', 2: 'c', 1: 'b'}

B {1: 'b', 2: 'c', 3: 'a'}

C {2: 'c', 3: 'a', 1: 'b'}

D {1: 'b', 3: 'a', 2: 'c'}

💡 Hint

Remember that ascending=False sorts from highest index to lowest.

The sort_index method sorts the DataFrame by its index. With ascending=False, the order is from the largest index to the smallest, so indices 3, 2, then 1.

📝 Syntax

intermediate

Which option correctly sorts a DataFrame by its index in place?

You want to sort the DataFrame df by its index and modify it directly without creating a new object. Which code does this correctly?

Pandas

import pandas as pd
df = pd.DataFrame({'val': [10, 20, 30]}, index=[2, 0, 1])

A df.sort_index(inplace=False)

B df.sort_index(inplace=True)

C df.sort_index(inplace=1)

D df.sort_index(in_place=True)

💡 Hint

Check the exact parameter name and its boolean value.

The parameter to sort_index that modifies the DataFrame in place is 'inplace=True'. Other options either use wrong parameter names or values.

❓ optimization

advanced

How to efficiently sort a MultiIndex DataFrame by the second level index?

Given a DataFrame df with a MultiIndex of two levels, which code sorts the DataFrame by the second level of the index only?

Pandas

import pandas as pd
index = pd.MultiIndex.from_tuples([(1, 'b'), (2, 'a'), (1, 'a')], names=['num', 'char'])
df = pd.DataFrame({'val': [10, 20, 30]}, index=index)

A df.sort_index(level=1)

B df.sort_index(level=0)

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

D df.sort_index(by=1)

💡 Hint

The 'level' parameter specifies which index level to sort by.

Using level=1 sorts by the second level of the MultiIndex. Option A sorts by the first level. Option A uses a non-existent parameter 'by'. Option A sorts columns, not index.

🔧 Debug

advanced

Why does this sort_index call raise an error?

Consider the code: df.sort_index(axis=2). Why does this raise an error?

Pandas

import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df.sort_index(axis=2)

A The DataFrame columns are not sorted alphabetically by default.

B The DataFrame has no index to sort.

C sort_index requires a 'level' parameter for sorting.

D Axis 2 does not exist for a DataFrame; valid axes are 0 (index) and 1 (columns).

💡 Hint

Check the valid axis values for DataFrame methods.

DataFrames have only two axes: 0 for rows (index) and 1 for columns. Axis=2 is invalid and causes an error.

🧠 Conceptual

expert

What happens when you sort a DataFrame with a non-unique index using sort_index()?

If a DataFrame has duplicate index values, what is the behavior of df.sort_index()?

A It removes duplicate index rows before sorting.

B It raises an error because index values must be unique to sort.

C It sorts all rows by index values, keeping duplicates in order as they appear.

D It sorts only the unique index values and drops duplicates.

💡 Hint

Think about whether sorting requires unique index values.

sort_index sorts rows by their index values regardless of duplicates. It does not raise errors or remove duplicates.