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.

Practice

1.

What is a pandas.Series best described as?

easy

A. A two-dimensional table with rows and columns

B. A one-dimensional labeled array that can hold any data type

C. A function to perform mathematical operations

D. A method to read data from a file

Solution

1. Step 1: Understand the definition of Series

   A pandas Series is a one-dimensional array with labels called an index.

2. Step 2: Compare options with definition

   A one-dimensional labeled array that can hold any data type matches the definition exactly, others describe different pandas objects or unrelated concepts.

3. Final Answer:

   A one-dimensional labeled array that can hold any data type -> Option B

4. Quick Check:

   Series = one-dimensional labeled array [OK]

Hint: Remember: Series = 1D labeled array in pandas [OK]

Common Mistakes:

• Confusing Series with DataFrame (2D)
• Thinking Series is a function or method
• Mixing Series with file operations

2.

Which of the following is the correct way to create a pandas.Series with values [10, 20, 30] and index labels ['a', 'b', 'c']?

import pandas as pd
# Choose the correct code

easy

A. pd.Series(data=[10, 20, 30], labels=['a', 'b', 'c'])

B. pd.Series(index=[10, 20, 30], data=['a', 'b', 'c'])

C. pd.Series([10, 20, 30], index=['a', 'b', 'c'])

D. pd.Series([10, 20, 30], labels=['a', 'b', 'c'])

Solution

1. Step 1: Recall Series constructor parameters

   The Series constructor uses data as first argument and index for labels.

2. Step 2: Match options with correct parameter names

   pd.Series([10, 20, 30], index=['a', 'b', 'c']) uses correct syntax: data list and index list. Others use wrong parameter names or order.

3. Final Answer:

   pd.Series([10, 20, 30], index=['a', 'b', 'c']) -> Option C

4. Quick Check:

   Use 'index' for labels in Series constructor [OK]

Hint: Use 'index' keyword for labels when creating Series [OK]

Common Mistakes:

• Using 'labels' instead of 'index'
• Swapping data and index arguments
• Passing index as 'data' parameter

3.

What will be the output of the following code?

import pandas as pd
s = pd.Series([5, 10, 15], index=['x', 'y', 'z'])
print(s['y'])

medium

A. KeyError

B. y

C. 15

D. 10

Solution

1. Step 1: Understand Series indexing by label

   Series allows access by index label. Here, 'y' corresponds to value 10.

2. Step 2: Check the printed value

   Accessing s['y'] returns 10, so print outputs 10.

3. Final Answer:

   10 -> Option D

4. Quick Check:

   Access by label 'y' returns 10 [OK]

Hint: Access Series values by index label like dictionary keys [OK]

Common Mistakes:

• Confusing label with value
• Expecting index position access with label
• Mistaking output for error

4.

Identify the error in the following code snippet:

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

medium

A. KeyError because 'd' is not in index

B. SyntaxError due to wrong index syntax

C. No error, prints 0

D. TypeError because index labels must be integers

Solution

1. Step 1: Check index labels in Series

   The Series has index labels 'a', 'b', 'c'. 'd' is not present.

2. Step 2: Understand what happens when accessing missing label

   Accessing s['d'] raises KeyError because label 'd' does not exist.

3. Final Answer:

   KeyError because 'd' is not in index -> Option A

4. Quick Check:

   Accessing missing label causes KeyError [OK]

Hint: Accessing missing label in Series raises KeyError [OK]

Common Mistakes:

• Expecting default 0 or None for missing label
• Confusing KeyError with SyntaxError
• Thinking index labels must be integers

5.

Given a pandas.Series s = pd.Series([100, 200, 300], index=['a', 'b', 'c']), how can you add a new element with label 'd' and value 400?

hard

A. s['d'] = 400

B. s.append({'d': 400})

C. s.add('d', 400)

D. s.insert('d', 400)

Solution

1. Step 1: Recall how to add elements to Series

   You can add a new label and value by assignment using s[label] = value.

2. Step 2: Check options for correct syntax

   s['d'] = 400 uses s['d'] = 400 which adds new element. Others are invalid or incorrect methods.

3. Final Answer:

   s['d'] = 400 -> Option A

4. Quick Check:

   Assign new label with s['label'] = value [OK]

Hint: Add new item by assigning s['new_label'] = value [OK]

Common Mistakes:

• Using non-existent methods like add or insert
• Trying to append with dict without reassignment
• Confusing Series with DataFrame methods