Series as labeled one-dimensional array in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Series with custom index

What is the output of this code snippet?

Pandas

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

A 20

B 10

C 30

D KeyError

💡 Hint

Remember that the index labels are used to access values in a Series.

The Series has values [10, 20, 30] with index labels ['a', 'b', 'c']. Accessing s['b'] returns the value 20.

❓ data_output

intermediate

Length of Series after filtering

What is the length of the Series after filtering values greater than 15?

Pandas

import pandas as pd
s = pd.Series([5, 15, 25, 35], index=['w', 'x', 'y', 'z'])
filtered = s[s > 15]
print(len(filtered))

A 2

B 1

C 4

D 3

💡 Hint

Count how many values are strictly greater than 15.

Values greater than 15 are 25 and 35, so the filtered Series has length 2.

🔧 Debug

advanced

Identify the error in Series creation

What error does this code raise?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3], index=['a', 'b'])

A No error, runs successfully

B ValueError: Length of passed values is not equal to length of index

C KeyError: 'a'

D TypeError: unsupported operand type(s)

💡 Hint

Check if the length of the data matches the length of the index.

The data list has length 3 but the index list has length 2, causing a ValueError.

🚀 Application

advanced

Sum values of Series with specific labels

Given this Series, what is the sum of values for labels 'b' and 'd'?

Pandas

import pandas as pd
s = pd.Series([100, 200, 300, 400], index=['a', 'b', 'c', 'd'])
result = s[['b', 'd']].sum()
print(result)

A 700

B 500

C 600

D KeyError

💡 Hint

Add the values corresponding to labels 'b' and 'd'.

Values at 'b' and 'd' are 200 and 400, their sum is 600.

🧠 Conceptual

expert

Effect of duplicate labels in Series

What happens when you create a Series with duplicate index labels and then select by that label?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3, 4], index=['a', 'b', 'b', 'c'])
result = s['b']
print(result)

A Raises a KeyError because of duplicate labels

B Returns the first value for label 'b': 2

C Returns the last value for label 'b': 3

D Returns a Series with all values for label 'b': [2, 3]

💡 Hint

Think about how pandas handles duplicate index labels when selecting.

When duplicate labels exist, selecting by that label returns a Series with all matching values.