Series sorting in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of sorting a Series with missing values?

Consider the following pandas Series with some missing values. What will be the output after sorting it in ascending order?

Data Analysis Python

import pandas as pd
s = pd.Series([3, None, 1, 2, None])
s_sorted = s.sort_values()
print(s_sorted)

A

1    NaN
4    NaN
2    1.0
3    2.0
0    3.0
dtype: float64

B

0    3.0
1    NaN
2    1.0
3    2.0
4    NaN
dtype: float64

C

2    1.0
3    2.0
0    3.0
1    NaN
4    NaN
dtype: float64

D

2    1.0
3    2.0
0    3.0
4    NaN
1    NaN
dtype: float64

💡 Hint

Remember that pandas places NaN values at the end by default when sorting ascending.

When sorting a Series with missing values (NaN), pandas sorts the non-NaN values in ascending order and places NaNs at the end by default. The original indices are preserved in the sorted output.

❓ data_output

intermediate

How many items are in the sorted Series after dropping duplicates?

Given the Series below, after sorting it in descending order and dropping duplicates, how many items remain?

Data Analysis Python

import pandas as pd
s = pd.Series([5, 3, 5, 2, 3, 1])
s_sorted_unique = s.sort_values(ascending=False).drop_duplicates()
print(len(s_sorted_unique))

A 4

B 6

C 5

D 3

💡 Hint

Count unique values after sorting descending.

The unique values in the Series are 5, 3, 2, 1. After sorting descending and dropping duplicates, 4 items remain.

🔧 Debug

advanced

What error does this sorting code raise?

Examine the code below. What error will it raise when executed?

Data Analysis Python

import pandas as pd
s = pd.Series(['a', 'b', 3, 'd'])
s_sorted = s.sort_values()
print(s_sorted)

A KeyError: 'sort_values'

B ValueError: Cannot sort mixed types

C No error, outputs sorted Series with mixed types

D TypeError: '<' not supported between instances of 'int' and 'str'

💡 Hint

Think about comparing strings and integers during sorting.

Sorting a Series with mixed types like strings and integers raises a TypeError because Python cannot compare these types directly.

❓ visualization

advanced

Which plot shows the Series sorted in ascending order?

You have this Series: s = pd.Series([4, 1, 3, 2]). Which plot correctly shows the Series after sorting it in ascending order?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series([4, 1, 3, 2])
s_sorted = s.sort_values()
s_sorted.plot(kind='bar')
plt.show()

A Bar plot with bars heights in order: 4, 3, 2, 1 from left to right

B Bar plot with bars heights in order: 1, 2, 3, 4 from left to right

C Bar plot with bars heights in order: 4, 1, 3, 2 from left to right

D Bar plot with bars heights all equal

💡 Hint

Sorting ascending means smallest value first on the left.

Sorting the Series in ascending order arranges values as 1, 2, 3, 4. The bar plot shows bars in this order from left to right.

🧠 Conceptual

expert

What is the effect of the 'inplace=True' parameter in Series.sort_values()?

When you use s.sort_values(inplace=True) on a pandas Series s, what happens?

A The original Series s is sorted and modified directly; the method returns None.

B A new sorted Series is returned; the original Series s remains unchanged.

C The Series is sorted but a copy is returned; the original Series s is also modified.

D The method raises an error because inplace is not supported for Series.

💡 Hint

Think about whether the original data changes or a new object is created.

Using inplace=True sorts the Series in place, modifying the original Series and returning None.

Practice

1. What does the sort_values() method do when applied to a pandas Series?

easy

A. Sorts the Series by its data values in ascending order by default.

B. Sorts the Series by its index labels in ascending order by default.

C. Removes duplicate values from the Series.

D. Converts the Series into a DataFrame.

Solution

1. Step 1: Understand the purpose of sort_values()

   This method sorts the Series based on the data values it contains, not the index labels.

2. Step 2: Default sorting order

   By default, sort_values() sorts in ascending order, meaning smallest to largest values.

3. Final Answer:

   Sorts the Series by its data values in ascending order by default. -> Option A

4. Quick Check:

   sort_values() sorts by values = D [OK]

Hint: Remember: sort_values() sorts by data values, not index [OK]

Common Mistakes:

• Confusing sort_values() with sort_index()
• Thinking it sorts by index labels
• Assuming it removes duplicates

2. Which of the following is the correct syntax to sort a pandas Series named data by its index labels in descending order?

easy

A. data.sort_index(ascending=False)

B. data.sort_values(ascending=False)

C. data.sort(ascending=False)

D. data.sort_by_index(desc=True)

Solution

1. Step 1: Identify method for sorting by index

   To sort by index labels, use sort_index(), not sort_values().

2. Step 2: Apply descending order

   The parameter ascending=False sorts in descending order.

3. Final Answer:

   data.sort_index(ascending=False) -> Option A

4. Quick Check:

   sort_index() + ascending=False = B [OK]

Hint: Use sort_index() to sort by index, add ascending=False for descending [OK]

Common Mistakes:

• Using sort_values() instead of sort_index()
• Using non-existent methods like sort_by_index()
• Missing the ascending parameter

3. Given the following pandas Series:

import pandas as pd
s = pd.Series([30, 10, 20], index=['c', 'a', 'b'])
print(s.sort_values())

What will be the output?

medium

A. a 10
c 30
b 20
dtype: int64

B. c 30
b 20
a 10
dtype: int64

C. a 10
b 20
c 30
dtype: int64

D. b 20
a 10
c 30
dtype: int64

Solution

1. Step 1: Understand the Series and sort_values()

   The Series has values [30, 10, 20] with indexes ['c', 'a', 'b']. Sorting by values arranges them as 10, 20, 30.

2. Step 2: Match sorted values with their indexes

   Value 10 corresponds to index 'a', 20 to 'b', and 30 to 'c'. So sorted Series is:
   a 10
   b 20
   c 30

3. Final Answer:

   a 10 b 20 c 30 dtype: int64 -> Option C

4. Quick Check:

   sort_values() sorts values ascending = A [OK]

Hint: Match sorted values with their original indexes carefully [OK]

Common Mistakes:

• Sorting by index instead of values
• Mixing up index labels with values
• Assuming default sort is descending

4. What is wrong with the following code snippet if the goal is to sort a pandas Series s by its index in ascending order?

sorted_s = s.sort_values(ascending=True)

medium

A. The ascending parameter should be False to sort ascending.

B. It sorts by values, not by index, so index order is not guaranteed.

C. The method sort_values() does not exist for Series.

D. The code will raise a syntax error.

Solution

1. Step 1: Identify the method used

   The code uses sort_values() which sorts by data values, not index labels.

2. Step 2: Understand the goal

   The goal is to sort by index ascending, so sort_index() should be used instead.

3. Final Answer:

   It sorts by values, not by index, so index order is not guaranteed. -> Option B

4. Quick Check:

   sort_values() sorts values, not index = C [OK]

Hint: Use sort_index() to sort by index, not sort_values() [OK]

Common Mistakes:

• Confusing sort_values() with sort_index()
• Thinking ascending=False sorts ascending
• Expecting syntax error when none occurs

5. You have a pandas Series sales with product codes as index and sales numbers as values:

product_codes = ['p3', 'p1', 'p2', 'p4']
sales_numbers = [200, 450, 300, 150]
sales = pd.Series(sales_numbers, index=product_codes)

How do you sort this Series so that the products are ordered by their codes alphabetically, but the sales numbers remain correctly matched?

hard

A. sales.sort_index(ascending=False)

B. sales.sort_values()

C. sales.sort_values(ascending=False)

D. sales.sort_index()

Solution

1. Step 1: Understand the data and goal

   The Series has product codes as index and sales numbers as values. Sorting by product codes means sorting by index labels.

2. Step 2: Choose correct sorting method

   Use sort_index() to sort by index labels alphabetically, preserving the correct sales numbers with their products.

3. Final Answer:

   sales.sort_index() -> Option D

4. Quick Check:

   Sort by index to order products alphabetically = A [OK]

Hint: Sort by index to reorder labels, not by values [OK]

Common Mistakes:

• Using sort_values() which sorts by sales numbers
• Sorting descending when ascending is needed
• Mixing up index and values sorting