Sorting by values in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to sort the DataFrame df by the column 'age' in ascending order.

Pandas

sorted_df = df.sort_values(by=[1])

Drag options to blanks, or click blank then click option'

A'age'

B'name'

C'height'

D'weight'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in the DataFrame.

Not passing the column name as a string.

The sort_values function sorts the DataFrame by the column specified in the 'by' parameter. Here, we want to sort by 'age'.

2fill in blank

medium

Complete the code to sort the DataFrame df by the column 'score' in descending order.

Pandas

sorted_df = df.sort_values(by='score', ascending=[1])

Drag options to blanks, or click blank then click option'

ATrue

B'False'

C'True'

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the boolean value as a string instead of a boolean.

Setting ascending to True which sorts in ascending order.

Setting ascending=False sorts the DataFrame in descending order.

3fill in blank

hard

Fix the error in the code to sort the DataFrame df by the column 'height'.

Pandas

sorted_df = df.sort_values(by=[1])

Drag options to blanks, or click blank then click option'

Aheight

B['height']

C'height'

Dheight'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column name without quotes causing a NameError.

Using a list instead of a string when only one column is needed.

The 'by' parameter requires the column name as a string, so it must be enclosed in quotes.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

Pandas

lengths = {word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length as the value.

Using the word in the condition instead of its length.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each uppercase word to its score only if the score is greater than 50.

Pandas

result = { [1]: [2] for word, score in data.items() if [3] }

Drag options to blanks, or click blank then click option'

Aword.upper()

Bscore

Cscore > 50

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper().

Not filtering scores correctly.

The comprehension maps the uppercase version of each word to its score. The condition filters scores greater than 50.

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