Why Pandas performance matters - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame from a dictionary.

Pandas

import pandas as pd
data = {'name': ['Alice', 'Bob'], 'age': [25, 30]}
df = pd.[1](data)
print(df)

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

ASeries

BDataFrame

Carray

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Trying to create a list or array instead of a DataFrame

Use pd.DataFrame to create a DataFrame from a dictionary.

2fill in blank

medium

Complete the code to select the 'age' column from the DataFrame.

Pandas

ages = df[1]['age']
print(ages)

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

A.loc

B.iloc

C.select

D[

Attempts:

3 left

💡 Hint

Common Mistakes

Using .loc without brackets

Using .select which is not a pandas method

Use square brackets [] to select a column by name in pandas.

3fill in blank

hard

Fix the error in the code to filter rows where age is greater than 25.

Pandas

filtered = df[df['age'] [1] 25]
print(filtered)

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

A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of >

Using == which checks equality

Use the greater than operator > to filter ages above 25.

4fill in blank

hard

Complete the code to create a dictionary comprehension that maps names to ages for people older than 25.

Pandas

result = {person: age for person, age in zip(df['name'], df['age']) if age [1] 25}
print(result)

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

A:

B>

C<

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using = instead of : in dictionary comprehension

Using < instead of > in the condition

Use : to separate key and value in a dictionary, and > to filter ages greater than 25.

5fill in blank

hard

Fill all three blanks to create a DataFrame from a dictionary, select the 'score' column, and filter scores above 80.

Pandas

data = {'name': ['Ann', 'Ben'], 'score': [85, 75]}
df = pd.[1](data)
high_scores = df[df[[2]] [3] 80]
print(high_scores)

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

ADataFrame

B'score'

C>

DSeries

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Not quoting the column name

Using < instead of > for filtering

Create a DataFrame with pd.DataFrame, select the 'score' column with 'score', and filter with > for scores above 80.

Practice

1.

Why is it important to care about pandas performance when working with data?

easy

A. It makes the code look more complex.

B. It helps process data faster and saves time.

C. It reduces the size of the data files.

D. It changes the data values automatically.

Solution

1. Step 1: Understand the role of performance in data work

   Performance means how fast and efficiently data operations run.

2. Step 2: Connect performance to practical benefits

   Faster processing saves time and makes working with data easier.

3. Final Answer:

   It helps process data faster and saves time. -> Option B

4. Quick Check:

   Performance = Faster data work [OK]

Hint: Faster code means less waiting time [OK]

Common Mistakes:

• Thinking performance changes data content
• Believing performance only affects file size
• Confusing complexity with speed

2.

Which of the following is the correct way to use a built-in pandas function for summing a column df['A']?

?

easy

A. df['A'].sum()

B. sum(df['A'])()

C. df.sum['A']()

D. df['A'].sum

Solution

1. Step 1: Recall the syntax for pandas sum function

   The sum function is called as a method with parentheses: df['A'].sum().

2. Step 2: Check each option for correct syntax

   df['A'].sum() uses correct method call. Others have syntax errors or missing parentheses.

3. Final Answer:

   df['A'].sum() -> Option A

4. Quick Check:

   Method call with () = df['A'].sum() [OK]

Hint: Remember to use parentheses () to call functions [OK]

Common Mistakes:

• Forgetting parentheses when calling methods
• Using sum as a standalone function incorrectly
• Mixing attribute and method syntax

3.

What will be the output of this code?

import pandas as pd
df = pd.DataFrame({'A': range(1, 6)})
result = df['A'].apply(lambda x: x * 2).sum()
print(result)

medium

A. 30

B. 20

C. 10

D. 15

Solution

1. Step 1: Understand the DataFrame and apply function

   DataFrame column 'A' has values 1,2,3,4,5. The lambda doubles each value.

2. Step 2: Calculate doubled values and sum

   Doubled values: 2,4,6,8,10. Sum = 2+4+6+8+10 = 30.

3. Final Answer:

   30 -> Option A

4. Quick Check:

   Sum of doubled 1-5 = 30 [OK]

Hint: Double each number then add all [OK]

Common Mistakes:

• Summing original values instead of doubled
• Confusing apply with map or sum
• Forgetting to call sum() after apply

4.

Identify the error in this code that tries to sum column 'B' in DataFrame df:

import pandas as pd
df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})
result = df.sum['B']
print(result)

medium

A. sum() method does not exist in pandas.

B. DataFrame missing column 'B'.

C. Using square brackets instead of parentheses for sum method.

D. print() function is used incorrectly.

Solution

1. Step 1: Check how sum method is called

   sum is a method and must be called with parentheses: sum().

2. Step 2: Identify the syntax error

   Code uses df.sum['B'] which tries to index sum attribute, causing error.

3. Final Answer:

   Using square brackets instead of parentheses for sum method. -> Option C

4. Quick Check:

   Method call needs () not [] [OK]

Hint: Call methods with () not [] [OK]

Common Mistakes:

• Using [] instead of () for methods
• Assuming sum is an attribute, not a method
• Ignoring error messages about callable objects

5.

You have a large DataFrame df with 1 million rows. You want to create a new column 'C' which is double the values of column 'A'. Which approach is best for performance?

?

hard

A. Use a for loop to iterate rows and assign values one by one.

B. Use df['C'] = df['A'].apply(lambda x: x * 2).

C. Convert DataFrame to list, double values, then convert back.

D. Use df['C'] = df['A'] * 2 to vectorize the operation.

Solution

1. Step 1: Understand vectorized operations in pandas

   Vectorized operations apply to whole columns at once, very fast.

2. Step 2: Compare options for performance

   Use df['C'] = df['A'] * 2 to vectorize the operation. It uses vectorized multiplication, fastest for large data. Loops and apply are slower.

3. Final Answer:

   Use df['C'] = df['A'] * 2 to vectorize the operation. -> Option D

4. Quick Check:

   Vectorized ops = fastest for big data [OK]

Hint: Use vectorized operations, avoid loops [OK]

Common Mistakes:

• Using loops for large DataFrames
• Using apply instead of vectorized ops
• Converting DataFrame to list unnecessarily