Learn Practice Projects Free datasets Resources Toolkit Pricing

Loading this exercise…

Loading…

Hands-on analytics engineering training with browser-based practice, guided projects, and a human-reviewed capstone.

support@analyticsengineering.com

Learn

The course
Roadmap
Practice
Projects
Free datasets
Resources
Templates
Open-source toolkit
Jobs
Job market data
Guides
Glossary
Compare tools
Starting-point quiz

Topics

Analytics Engineering
SQL
dbt
Data Modeling
BigQuery
Snowflake
Looker
Python
Career
Interview Prep

Company

About
1-on-1 coaching
Pricing
Log in
Start free

Terms Privacy Built by Routeless

Loading this exercise…

List Comprehension Vs. For Loop · Python: Writing Efficient…

Home / Practice / Python: Writing Efficient Code (Time Complexity, Vectorization)

List Comprehension Vs. For Loop

#3 · 3 of 20Free sample

Python: Writing Efficient Code (Time Complexity, Vectorization)

20 exercises

01Measure Time Of A Small LoopFree
02Vectorize A Computation With NumPy (Fixed Seed)Free
03List Comprehension Vs. For LoopFree
04Use CProfile On A Small Summation FunctionLocked
05Identify Bottlenecks And Print Optimized ResultLocked
06Demonstrate Built-In Sum() Vs. Manual LoopLocked
07Preallocate A List And Print Final SumLocked
08Compare Dictionary Vs. List Lookup, Then Print ResultsLocked
09Use Map() On 1..50, Doubling Each Number. Print 'MAPSUM:' Plus The Sum Of Mapped Results.Locked
10Document Code & Show Performance Gains, Print Final ResultLocked
11Set Vs. List Membership Test With 0..9999, Print Times In A Single StringLocked
12Itertools For Summation, Print 'ISLICE_SUM:' Plus SumLocked
13Vectorize String Uppercase With Pandas, Print 'STRING_VEC:' Plus The First Item Of The Transformed SeriesLocked
14Compare Function Calls In A Loop, Print 'FCALL_SUM:' Plus Final SumLocked
15Generator Expression Summation, Print 'GEN_SUM:' Plus ResultLocked
16Measure A Snippet With Timeit, Then Print 'TIMEIT_DONE'Locked
17Memoized Fibonacci(10), Then Print 'FIB:' Plus ResultLocked
18Avoid Repeated Computations By Caching A Sum, Then Print 'CACHED:' Plus Final IntegerLocked
19Compare Slicing Vs. Entire List, Print 'SLICE_SUM:' Plus Sum( Arr[:50] )Locked
20Demonstrate NumPy Array Performance And Print 'NUMPY_SUM:' Plus A Stable SumLocked

Instructions

List Comprehension Vs. For Loop

Compute the sum of the squares of 1..1000 using a list comprehension. Then print 'LCSUM:' plus the sum. (The sum of squares of 1..1000 is 333833500.)

solution.py⌘/Ctrl + Enter

Loading editor…

The reference answer is one attempt away.

Create a free account to save your progress and reveal the worked answer for this free exercise. The complete practice library is available with Practice Pass or Mastery.

Save attempts and completed exercises
Reveal this free exercise's answer after an attempt
Return to your progress from any device
Free forever — no credit card

Create free account →Already a member? Log in →

The reference answer is one attempt away.

Create a free account to save your progress and reveal the worked answer for this free exercise. The complete practice library is available with Practice Pass or Mastery.

Save attempts and completed exercises
Reveal this free exercise's answer after an attempt
Return to your progress from any device
Free forever — no credit card

Create free account →Already a member? Log in →

← Previousk

Vectorize A Computation With NumPy (Fixed Seed)

jNext →

Use CProfile On A Small Summation Function

All exercises in Python: Writing Efficient Code (Time Complexity, Vectorization)20

Python: Writing Efficient Code (Time Complexity, Vectorization)

20 exercises

01Measure Time Of A Small LoopFree
02Vectorize A Computation With NumPy (Fixed Seed)Free
03List Comprehension Vs. For LoopFree
04Use CProfile On A Small Summation FunctionLocked
05Identify Bottlenecks And Print Optimized ResultLocked
06Demonstrate Built-In Sum() Vs. Manual LoopLocked
07Preallocate A List And Print Final SumLocked
08Compare Dictionary Vs. List Lookup, Then Print ResultsLocked
09Use Map() On 1..50, Doubling Each Number. Print 'MAPSUM:' Plus The Sum Of Mapped Results.Locked
10Document Code & Show Performance Gains, Print Final ResultLocked
11Set Vs. List Membership Test With 0..9999, Print Times In A Single StringLocked
12Itertools For Summation, Print 'ISLICE_SUM:' Plus SumLocked
13Vectorize String Uppercase With Pandas, Print 'STRING_VEC:' Plus The First Item Of The Transformed SeriesLocked
14Compare Function Calls In A Loop, Print 'FCALL_SUM:' Plus Final SumLocked
15Generator Expression Summation, Print 'GEN_SUM:' Plus ResultLocked
16Measure A Snippet With Timeit, Then Print 'TIMEIT_DONE'Locked
17Memoized Fibonacci(10), Then Print 'FIB:' Plus ResultLocked
18Avoid Repeated Computations By Caching A Sum, Then Print 'CACHED:' Plus Final IntegerLocked
19Compare Slicing Vs. Entire List, Print 'SLICE_SUM:' Plus Sum( Arr[:50] )Locked
20Demonstrate NumPy Array Performance And Print 'NUMPY_SUM:' Plus A Stable SumLocked