leetscrape 1.0.1

Introducing LeetScrape - a powerful and efficient Python package designed to scrape problem statements and their topic and company tags, difficulty, test cases, hints, and code stubs from LeetCode.com. Easily download and save LeetCode problems to your local machine, making it convenient for offline practice and studying. It is perfect for anyone preparing for coding interviews. With the LeetScrape, you can boost your coding skills and improve your chances of landing your dream job.

LeetScrape

Introducing the LeetScrape - a powerful and efficient Python package designed to scrape problem statements and basic test cases from LeetCode.com. With this package, you can easily download and save LeetCode problems to your local machine, making it convenient for offline practice and studying. It is perfect for software engineers and students preparing for coding interviews. The package is lightweight, easy to use and can be integrated with other tools and IDEs. With the LeetScrape, you can boost your coding skills and improve your chances of landing your dream job.

Use this package to get the list of Leetcode questions, their topic and company tags, difficulty, question body (including test cases, constraints, hints), and code stubs in any of the available programming languages.

Detailed documentation available here.

There is also a related Next.js web app to serve the scraped questions and your answers at leetcode-nextjs. See the demo.

Get Started

Installation

Start by installing the package from pip or conda:

# Using pip
pip install leetscrape

# Using conda:
conda install leetscrape

# Using poetry:
poetry add leetscrape

Usage

Command Line

• leetscrape list [--out OUT] - List all questions without generating code stub.

  options:
  -h, --help         show a help message and exit
  --out OUT, -o OUT  Specify the output file name to store the list of questions.
  

• leetscrape question [--out OUT] qid [qid ...] - Generate a code stub for the given question(s).

  positional arguments:
  qid                Enter Leetcode question ID(s)
  
  options:
  -h, --help         show this help message and exit
  --out OUT, -o OUT  Enter the path to the output directory
  

• leetscrape solution [-h] [--out OUT] input - Generate mdx files from solutions.

  positional arguments:
  input              Enter the path to the solution directory with solution files or to a single
                      solution file
  
  options:
  -h, --help         show this help message and exit
  --out OUT, -o OUT  Enter the path to the output directory to save solutions mdx files
  

• leetscrape ts [--out OUT] - Create the leetscrape-ts Next.js project to host the solutions.

  options:
  -h, --help         show this help message and exit
  --out OUT, -o OUT  Enter the path to the output directory to save the project
  

Python API

Get the list of problems and their information

from leetscrape import GetQuestionsList

ls = GetQuestionsList()
ls.scrape() # Scrape the list of questions
ls.questions.head() # Get the list of questions

	QID	title	titleSlug	difficulty	acceptanceRate	paidOnly	topicTags	categorySlug
0	1	Two Sum	two-sum	Easy	51.4225	False	array,hash-table	algorithms
1	2	Add Two Numbers	add-two-numbers	Medium	41.9051	False	linked-list,math,recursion	algorithms
2	3	Longest Substring Without Repeating Characters	longest-substring-without-repeating-characters	Medium	34.3169	False	hash-table,string,sliding-window	algorithms
3	4	Median of Two Sorted Arrays	median-of-two-sorted-arrays	Hard	38.8566	False	array,binary-search,divide-and-conquer	algorithms
4	5	Longest Palindromic Substring	longest-palindromic-substring	Medium	33.4383	False	string,dynamic-programming	algorithms

You can export the associated tables to a directory using the to_csv method:

ls.to_csv(directory="<dir>")

This generates 6 .csv files in the current directory:

• questions.csv - List of questions with their title, difficulty, acceptance rate, paid status, topic tags, and category.
• companies.csv - List of companies with their name, slug, and the questions count.
• topicsTags.csv - List of topic tags with their name and slug.
• categories.csv - List of categories with their name and slug.
• questionCategory.csv - An edgelist of questions and their categories.
• questionTopics.csv - An edgelist of questions and their topic tags.

Get Question statement and other information

Query individual question's information such as the body, test cases, constraints, hints, code stubs, and company tags using the GetQuestion class:

from leetscrape import GetQuestion

# Get the question body
question = GetQuestion(titleSlug="two-sum").scrape()

This returns a Question object with the following attributes:

question.QID # Question ID
question.title # Question title
question.titleSlug # Question title slug
question.difficulty # Question difficulty
question.Hints # Question hints
question.Companies # Question companies
question.topics # Question topic tags
question.SimilarQuestions # Similar questions ids
question.Code # Code stubs
question.Body # Question body / problem statement
question.isPaidOnly # Whether the question is only available to premium users of Leetcode

Generate code stubs for a question

from leetscrape import GenerateCodeStub

# Get the question body
fcs = GenerateCodeStub(titleSlug="two-sum")
fcs.generate(directory="<dir>")

This generates the following files in the given directory:

• q_0001_twoSum.py - Python file with the code stub for the given question with a function named twoSum.
• test_q_0001_twoSum.py - Python file with the test cases for the given question.

See examples for examples of the generated code stubs.

Generate mdx files from solutions

Once you have solved a question, you can generate an mdx file with the solution and the question statement using the ExtractSolutions class:

from leetscrape import ExtractSolutions

# Get the question body
solutions = ExtractSolutions(filename="<path-to-solution-file>").extract()

This outputs a list of Solution objects with the following attributes:

solution.id # Solution ID
solution.code # Solution code
solution.docs # Docstrings associated with the solution
solution.problem_statement # Question body / problem statement

Alternatively, you can use the to_mdx method to generate the mdx file:

from leetscrape import ExtractSolutions

# Get the question body
ExtractSolutions(filename="<path-to-solution-file>").to_mdx(output_filename="<path-to-output-file>")

Serving the solutions with leetscrape-ts

You can use the leetscrape-ts Next.js template to serve the solutions. See the demo. Visit the repo for more details. You can generate the project using the leetscrape ts command:

leetscrape ts --out <path-to-output-directory>

This will bootstrap the project in the given directory. Follow the instructions in the README and create/modify the .env.local file. Then, run the following command to generate the mdx files:

leetscrape solution --out <path-to-output-directory>/src/content/solutions <path-to-your-python-solution-directory>

You can then run the project using the following command:

cd <path-to-output-directory>
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Changelog

View the changelog here.