Series arithmetic and alignment in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add two pandas Series s1 and s2.

Data Analysis Python

result = s1 [1] s2

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

A+

B/

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' instead of '+' causes subtraction.

Using '*' or '/' will multiply or divide instead of adding.

Adding two Series uses the + operator to sum values by index.

2fill in blank

medium

Complete the code to subtract Series s2 from s1.

Data Analysis Python

result = s1 [1] s2

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

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' adds instead of subtracting.

Using '*' or '/' changes the operation to multiply or divide.

Subtracting s2 from s1 uses the '-' operator.

3fill in blank

hard

Fix the error in the code to multiply two Series s1 and s2 element-wise.

Data Analysis Python

result = s1 [1] s2

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

A+

B-

C/

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' or '-' changes the operation to addition or subtraction.

Using '/' divides instead of multiplying.

Element-wise multiplication uses the '*' operator between Series.

4fill in blank

hard

Fill both blanks to create a new Series with squares of values from s for indices where values are greater than 3.

Data Analysis Python

result = {word: s[word][1]2 for word in s.index if s[word] [2] 3}

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

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '**' does multiplication, not power.

Using '<' instead of '>' filters wrong values.

Use '**' to square the value and '>' to filter values greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of index labels in uppercase with their values, filtering values less than 5.

Data Analysis Python

result = [1]: [2] for [3], val in s.items() if val < 5}}

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

Aidx.upper()

Bval

Cidx

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using item instead of idx causes undefined variable error.

Using val as key or idx.upper() as loop variable causes errors.

Use idx.upper() for keys, val for values, and idx as the loop variable for index labels.

Practice

1. What happens when you add two pandas Series with different labels using + operator?

easy

A. The result is a Series with all zeros

B. Values are added ignoring labels, just by position

C. An error is raised due to label mismatch

D. Values are added only where labels match; others become NaN

Solution

1. Step 1: Understand pandas Series label alignment

   When adding two Series, pandas aligns data by their labels (index) before performing arithmetic.

2. Step 2: Effect of label mismatch on addition

   If a label is missing in one Series, the result for that label is NaN because pandas cannot add missing data.

3. Final Answer:

   Values are added only where labels match; others become NaN -> Option D

4. Quick Check:

   Series addition aligns labels = Values are added only where labels match; others become NaN [OK]

Hint: Remember: pandas adds by matching labels, not positions [OK]

Common Mistakes:

• Assuming addition ignores labels and adds by position
• Expecting an error on label mismatch
• Thinking missing labels fill with zero automatically

2. Which of the following is the correct syntax to add two pandas Series s1 and s2 with label alignment, allowing specification of fill_value for missing labels?

easy

A. s1 + s2

B. s1 + s2.values

C. s1.add(s2)

D. s1.append(s2)

Solution

1. Step 1: Check syntax for adding Series with alignment and fill_value

   Using s1.add(s2) adds Series with label alignment and allows fill_value for missing labels.

2. Step 2: Compare other options

   s1 + s2 adds with alignment but no fill_value option. s1 + s2.values ignores labels (positional). s1.append(s2) concatenates.

3. Final Answer:

   s1.add(s2) -> Option C

4. Quick Check:

   Use .add() for aligned addition with fill_value [OK]

Hint: Use .add() method for safe label-aligned addition [OK]

Common Mistakes:

• Using + with raw values ignoring labels
• Confusing append with addition
• Not knowing .add() exists for Series

3. Given the code:

import pandas as pd
s1 = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
s2 = pd.Series([1, 3, 3], index=['b', 'c', 'd'])
result = s1 + s2
print(result)

What is the output?

medium

A. a NaN
b 21.0
c 33.0
d NaN

B. a 10
b 22
c 33
d 3

C. a 10
b 21
c 33
d 3

D. a NaN
b 22.0
c 33.0
d NaN

Solution

1. Step 1: Align Series by labels

   s1 has 'a':10, 'b':20, 'c':30; s2 has 'b':1, 'c':3, 'd':3. Union index: 'a','b','c','d'.

2. Step 2: Add values where labels match, NaN otherwise

   'a': 10 + NaN = NaN, 'b':20+1=21.0, 'c':30+3=33.0, 'd': NaN + 3 = NaN. Floats due to NaN.

3. Final Answer:

   a NaN b 21.0 c 33.0 d NaN -> Option A

4. Quick Check:

   Series addition aligns labels, unmatched get NaN [OK]

Hint: Add Series and check labels; unmatched labels become NaN [OK]

Common Mistakes:

• Adding values by position ignoring labels
• Expecting no NaN for unmatched labels
• Misreading index labels and values

4. Consider the code:

import pandas as pd
s1 = pd.Series([5, 10], index=['x', 'y'])
s2 = pd.Series([2, 4, 6], index=['x', 'y', 'z'])
result = s1 + s2
print(result)

The result contains NaN. What is the problem and how to fix it to treat missing values as zero?

medium

A. No error; code runs fine

B. NaN due to different lengths; fix by using s1.add(s2, fill_value=0)

C. Error because + cannot add Series; use concat instead

D. Error due to missing index in s1; fix by reindexing s1 to s2's index

Solution

1. Step 1: Check the code execution

   The code runs but produces NaN for 'z' (missing in s1). Alignment by labels causes NaN for unmatched.

2. Step 2: Fix with fill_value

   s1.add(s2, fill_value=0) fills missing labels with 0: 'x':7, 'y':14, 'z':6.

3. Final Answer:

   NaN due to different lengths; fix by using s1.add(s2, fill_value=0) -> Option B

4. Quick Check:

   Use .add() with fill_value to avoid NaN [OK]

Hint: Use .add(fill_value=0) to handle missing labels safely [OK]

Common Mistakes:

• Thinking + raises error on length mismatch
• Using concat instead of arithmetic
• Ignoring missing labels causing NaN

5. You have two Series representing sales in two stores:

store1 = pd.Series([100, 200, 300], index=['apple', 'banana', 'orange'])
store2 = pd.Series([50, 150, 100], index=['banana', 'orange', 'grape'])

How do you create a Series showing total sales per fruit, treating missing fruits as zero?

hard

A. store1.add(store2, fill_value=0)

B. store1 + store2

C. store1.append(store2).sum()

D. store1.combine(store2, lambda x, y: x + y)

Solution

1. Step 1: Understand the problem

   We want to add sales per fruit, but some fruits are missing in one store's Series.

2. Step 2: Use addition with fill_value=0

   Using store1.add(store2, fill_value=0) adds values label-wise, treating missing labels as zero, so no NaN appears.

3. Final Answer:

   store1.add(store2, fill_value=0) -> Option A

4. Quick Check:

   Add Series with fill_value=0 to handle missing labels [OK]

Hint: Use .add(fill_value=0) to sum with missing labels as zero [OK]

Common Mistakes:

• Using + causing NaN for missing labels
• Using append and sum incorrectly
• Using combine without fill_value causing NaN