Sorting by values in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas method sort_values() do?

It sorts a DataFrame or Series by the values in one or more columns or the Series itself, arranging data in ascending or descending order.

beginner

How do you sort a DataFrame by a single column named 'age' in ascending order?

Use df.sort_values(by='age'). This sorts the DataFrame rows so that the smallest age is first and the largest last.

intermediate

How can you sort a DataFrame by multiple columns, for example 'age' ascending and 'score' descending?

Use df.sort_values(by=['age', 'score'], ascending=[True, False]). This sorts first by age ascending, then by score descending within each age.

beginner

What does the parameter inplace=True do in sort_values()?

It changes the original DataFrame directly without creating a new sorted copy. If inplace=False (default), it returns a new sorted DataFrame.

beginner

How do you sort a pandas Series in descending order?

Use series.sort_values(ascending=False). This arranges the Series values from largest to smallest.

Which pandas method sorts a DataFrame by column values?

A sort_values()

B sort_index()

C groupby()

D filter()

sort_values() sorts data by column or Series values. sort_index() sorts by index labels.

How do you sort a DataFrame df by column 'score' in descending order?

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

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

C df.sort_index(ascending=False)

D df.sort(by='score')

Set ascending=False to sort from highest to lowest values.

What happens if you use inplace=True in sort_values()?

A Sorts the DataFrame and returns the original unsorted DataFrame

B Returns a new sorted DataFrame

C Deletes the DataFrame

D Sorts the DataFrame and returns None

inplace=True sorts the original DataFrame and returns nothing (None).

How do you sort a DataFrame by two columns, 'age' ascending and 'score' descending?

A df.sort_values(by=['age', 'score'], ascending=[True, False])

B df.sort_values(by=['age', 'score'], ascending=True)

C df.sort_values(by=['score', 'age'], ascending=[False, True])

D df.sort_index()

Use a list for by and a matching list of booleans for ascending to control order per column.

Which of these is NOT a valid argument for sort_values()?

A by

B ascending

C groupby

D inplace

groupby is a separate pandas method, not an argument for sort_values().

Explain how to sort a pandas DataFrame by one or more columns with different ascending/descending orders.

Think about how to control sorting order for each column separately.

Describe the effect of the 'inplace' parameter in the sort_values() method.

Consider whether the original data changes or a copy is made.

Practice

1. What does the sort_values() function do in pandas?

easy

A. It filters rows based on a condition.

B. It groups data by a column.

C. It sorts the DataFrame by one or more columns.

D. It merges two DataFrames.

Solution

1. Step 1: Understand the function purpose

   The sort_values() function is designed to reorder rows in a DataFrame based on the values in one or more columns.

2. Step 2: Compare with other functions

   Filtering, grouping, and merging are done by different pandas functions like filter(), groupby(), and merge().

3. Final Answer:

   It sorts the DataFrame by one or more columns. -> Option C

4. Quick Check:

   sort_values() = reorder rows by column values [OK]

Hint: Remember: sort_values orders rows by column data [OK]

Common Mistakes:

• Confusing sorting with filtering
• Thinking it groups data
• Mixing it up with merging DataFrames

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

easy

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

B. df.sort_values('age', descending=True)

C. df.sort('age', ascending=False)

D. df.sort_values('age', ascending=True)

Solution

1. Step 1: Identify correct parameter names

   The correct parameter to specify the column is by='age', and to sort descending, use ascending=False.

2. Step 2: Check syntax correctness

   df.sort_values(by='age', ascending=False) uses the correct function name sort_values and correct parameters. df.sort_values('age', ascending=True) sorts ascending, C uses a wrong function sort, and B uses a wrong parameter descending=True.

3. Final Answer:

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

4. Quick Check:

   sort_values(by=col, ascending=False) = descending sort [OK]

Hint: Use ascending=False for descending order in sort_values [OK]

Common Mistakes:

• Using ascending=True for descending sort
• Using wrong parameter names like descending=True
• Using non-existent sort() method

3. Given the DataFrame:

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

What will be the output?

medium

A. Original DataFrame without sorting

B. Rows sorted by 'score' descending: Bob, Anna, Cara

C. Error because 'score' is not a column

D. Rows sorted by 'score' ascending: Cara, Anna, Bob

Solution

1. Step 1: Understand the DataFrame content

   The DataFrame has names and scores: Anna(88), Bob(92), Cara(85).

2. Step 2: Apply sort_values on 'score'

   Sorting by 'score' ascending orders rows as Cara(85), Anna(88), Bob(92).

3. Final Answer:

   Rows sorted by 'score' ascending: Cara, Anna, Bob -> Option D

4. Quick Check:

   sort_values('score') sorts ascending by default [OK]

Hint: Default sort_values sorts ascending unless told otherwise [OK]

Common Mistakes:

• Assuming descending order by default
• Thinking original order is preserved
• Believing 'score' column does not exist

4. What is wrong with this code snippet?

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

medium

A. The column name 'age' must 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 function should be sort_by_values, not sort_values.

Solution

1. Step 1: Check parameter types

   The ascending parameter expects a boolean value True or False, not a string.

2. Step 2: Validate other parts

   The column name can be a string or list, sort_values is the correct function, and it accepts ascending.

3. Final Answer:

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

4. Quick Check:

   ascending must be True/False boolean, not 'False' string [OK]

Hint: Use True/False without quotes for ascending parameter [OK]

Common Mistakes:

• Passing 'False' as a string instead of boolean
• Thinking column name must be a list
• Using wrong function name

5. You have a DataFrame df with columns 'city' and 'temperature'. You want to sort it first by 'city' alphabetically ascending, then by 'temperature' descending within each city. Which code correctly does this?

hard

A. df.sort_values(by=['city', 'temperature'], ascending=[True, False])

B. df.sort_values(by=['temperature', 'city'], ascending=[False, True])

C. df.sort_values(by=['city', 'temperature'], ascending=False)

D. df.sort_values(['city', 'temperature'], ascending=[False, True])

Solution

1. Step 1: Understand multi-column sorting

   To sort by multiple columns, pass a list to by and a list of booleans to ascending matching each column.

2. Step 2: Match sorting order requirements

   We want 'city' ascending (True) and 'temperature' descending (False), so ascending=[True, False].

3. Step 3: Check options

   df.sort_values(by=['city', 'temperature'], ascending=[True, False]) matches the correct order and ascending list. df.sort_values(by=['temperature', 'city'], ascending=[False, True]) reverses columns. df.sort_values(by=['city', 'temperature'], ascending=False) uses a single boolean for two columns. df.sort_values(['city', 'temperature'], ascending=[False, True]) has wrong ascending order.

4. Final Answer:

   df.sort_values(by=['city', 'temperature'], ascending=[True, False]) -> Option A

5. Quick Check:

   Multi-column sort uses lists for by and ascending [OK]

Hint: Use lists for by and ascending to sort multiple columns [OK]

Common Mistakes:

• Using single boolean for multiple columns
• Mixing column order in by list
• Wrong ascending order list