Profiling with line_profiler in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the line_profiler module.

Data Analysis Python

import [1]

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

AcProfile

Bline_profiler

Ctimeit

Dprofile

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'cProfile' instead of 'line_profiler'.

Using 'profile' which is a decorator but not the module name.

The line_profiler module is imported to profile code line-by-line.

2fill in blank

medium

Complete the code to create a LineProfiler object.

Data Analysis Python

profiler = [1]()

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

ALineProfiler

BProfile

CcProfile

DTimer

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Profile' which is from a different module.

Using 'Timer' which is unrelated.

The LineProfiler class creates a profiler object for line-by-line profiling.

3fill in blank

hard

Fix the error in the code to add a function to the profiler.

Data Analysis Python

profiler.[1](my_function)

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

Aadd_function

Brun

Cadd_function_to_profile

Dadd_func

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'run' which executes code but does not add functions.

Using 'add_func' which is not a valid method.

The method add_function adds a function to the profiler to be tracked.

4fill in blank

hard

Fill both blanks to run the profiler on a function and print the stats.

Data Analysis Python

profiler.[1](my_function)
profiler.[2]()

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

Arun

Bprint_stats

Cstart

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'start' and 'stop' which are not methods of LineProfiler.

Using 'run' twice or 'print_stats' first.

Use run to execute the function under profiling, then print_stats to show results.

5fill in blank

hard

Fill all three blanks to decorate a function, run it, and print profiling results.

Data Analysis Python

@profiler.[1]
def my_function():
    total = 0
    for i in range(1000):
        total += i
    return total

my_function()
profiler.[2](my_function)
profiler.[3]()

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

Aprofile

Badd_function

Cprint_stats

Drun

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'run' as decorator instead of 'profile'.

Not adding the function before printing stats.

Use @profiler.profile to decorate the function, add_function to add it, then print_stats to show results.

Practice

1. What is the main purpose of using line_profiler in Python data analysis?

easy

A. To measure the time each line of a function takes to execute

B. To visualize data distributions with graphs

C. To clean missing data automatically

D. To convert data types in a DataFrame

Solution

1. Step 1: Understand profiling purpose

   line_profiler is designed to measure execution time per line in functions.

2. Step 2: Compare options to profiling goal

   Options A, B, and D relate to data cleaning, visualization, and type conversion, not profiling.

3. Final Answer:

   To measure the time each line of a function takes to execute -> Option A

4. Quick Check:

   Profiling = measuring line execution time [OK]

Hint: Profiling finds slow lines by timing code line-by-line [OK]

Common Mistakes:

• Confusing profiling with data visualization
• Thinking profiling cleans or transforms data
• Assuming profiling measures overall script time only

2. Which of the following is the correct way to create a LineProfiler object and add a function my_func for profiling?

easy

A. profiler = LineProfiler(my_func); profiler.run()

B. profiler = LineProfiler(); profiler.add_function('my_func')

C. profiler = LineProfiler(); profiler.add(my_func)

D. profiler = LineProfiler(); profiler.add_function(my_func)

Solution

1. Step 1: Recall LineProfiler usage

   The correct method to add a function is add_function() with the function object.

2. Step 2: Check each option

   profiler = LineProfiler(); profiler.add_function(my_func) uses add_function(my_func) correctly. profiler = LineProfiler(my_func); profiler.run() wrongly passes function to constructor. profiler = LineProfiler(); profiler.add(my_func) uses non-existent add(). profiler = LineProfiler(); profiler.add_function('my_func') passes function name as string, which is incorrect.

3. Final Answer:

   profiler = LineProfiler(); profiler.add_function(my_func) -> Option D

4. Quick Check:

   Add function with add_function(func) [OK]

Hint: Use add_function(func) with function object, not string [OK]

Common Mistakes:

• Passing function name as string instead of function object
• Using wrong method like add() instead of add_function()
• Passing function to LineProfiler constructor

3. Given the code below, what will be printed by profiler.print_stats()?

from line_profiler import LineProfiler

def slow_func():
    total = 0
    for i in range(3):
        total += i
    return total

profiler = LineProfiler()
profiler.add_function(slow_func)
profiler.enable()
slow_func()
profiler.disable()
profiler.print_stats()

medium

A. No output because profiler.enable() was called before add_function()

B. SyntaxError because LineProfiler requires run() method

C. Detailed timing per line of slow_func showing time spent on the loop lines

D. Only total execution time without line-by-line details

Solution

1. Step 1: Understand profiling flow

   Profiler is created, function added, enabled, function called, then disabled and stats printed.

2. Step 2: Analyze output of print_stats()

   print_stats() shows detailed line-by-line timing for added functions, here slow_func, including loop lines.

3. Final Answer:

   Detailed timing per line of slow_func showing time spent on the loop lines -> Option C

4. Quick Check:

   print_stats() = detailed line timing [OK]

Hint: print_stats() shows line-by-line timing for added functions [OK]

Common Mistakes:

• Thinking print_stats() shows only total time
• Believing enable() must be after add_function() to work
• Confusing run() method requirement

4. Identify the error in the following code snippet that uses line_profiler:

from line_profiler import LineProfiler

def compute():
    s = 0
    for i in range(5):
        s += i
    return s

profiler = LineProfiler()
profiler.add_function('compute')
profiler.enable()
compute()
profiler.disable()
profiler.print_stats()

medium

A. Passing function name as string to add_function instead of function object

B. Missing call to profiler.run() to execute the function

C. Not importing line_profiler correctly

D. For loop range is invalid syntax

Solution

1. Step 1: Check add_function argument

   add_function requires the function object, not its name as a string.

2. Step 2: Identify error cause

   Passing 'compute' as string causes profiler not to track the function properly.

3. Final Answer:

   Passing function name as string to add_function instead of function object -> Option A

4. Quick Check:

   add_function(func) needs function object [OK]

Hint: Always pass function object, not string, to add_function() [OK]

Common Mistakes:

• Passing function name as string
• Thinking profiler.run() is mandatory
• Assuming import error without checking code

5. You want to profile two functions func_a and func_b in your data analysis script. You also want to compare which function is slower line-by-line. Which of the following code snippets correctly profiles both functions and prints their stats separately?

hard

A. profiler = LineProfiler()
profiler.add_function(func_a)
profiler.add_function(func_b)
profiler.enable()
func_a()
func_b()
profiler.disable()
profiler.print_stats()

B. profiler_a = LineProfiler()
profiler_a.add_function(func_a)
profiler_b = LineProfiler()
profiler_b.add_function(func_b)
profiler_a.enable()
func_a()
profiler_a.disable()
profiler_b.enable()
func_b()
profiler_b.disable()
profiler_a.print_stats()
profiler_b.print_stats()

C. profiler = LineProfiler(func_a, func_b)
profiler.run()
profiler.print_stats()

D. profiler = LineProfiler()
profiler.add_function('func_a')
profiler.add_function('func_b')
profiler.enable()
func_a()
func_b()
profiler.disable()
profiler.print_stats()

Solution

1. Step 1: Understand profiling multiple functions separately

   To compare functions separately, create separate LineProfiler instances for each.

2. Step 2: Analyze each option

   profiler = LineProfiler() profiler.add_function(func_a) profiler.add_function(func_b) profiler.enable() func_a() func_b() profiler.disable() profiler.print_stats() profiles both with one profiler but prints combined stats, not separate. profiler_a = LineProfiler() profiler_a.add_function(func_a) profiler_b = LineProfiler() profiler_b.add_function(func_b) profiler_a.enable() func_a() profiler_a.disable() profiler_b.enable() func_b() profiler_b.disable() profiler_a.print_stats() profiler_b.print_stats() profiles separately with two profilers and prints stats separately. profiler = LineProfiler(func_a, func_b) profiler.run() profiler.print_stats() wrongly passes functions to constructor and calls run() without arguments. profiler = LineProfiler() profiler.add_function('func_a') profiler.add_function('func_b') profiler.enable() func_a() func_b() profiler.disable() profiler.print_stats() passes function names as strings, which is incorrect.

3. Final Answer:

   profiler_a = LineProfiler() profiler_a.add_function(func_a) profiler_b = LineProfiler() profiler_b.add_function(func_b) profiler_a.enable() func_a() profiler_a.disable() profiler_b.enable() func_b() profiler_b.disable() profiler_a.print_stats() profiler_b.print_stats() -> Option B

4. Quick Check:

   Separate profilers for separate stats [OK]

Hint: Use separate LineProfiler instances to profile functions separately [OK]

Common Mistakes:

• Passing function names as strings
• Using one profiler for separate stats
• Misusing LineProfiler constructor arguments