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.