leetscrape 0.1.1

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

Leetcode Questions Scraper

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

Usage

Import the relevant classes from the leetcode package:

from leetscrape.GetQuestionsList import GetQuestionsList
from leetscrape.GetQuestionInfo import GetQuestionInfo
from leetscrape.utils import combine_list_and_info, get_all_questions_body

Get the list of questions, companies, topic tags, categories using the GetQuestionsList class:

ls = GetQuestionsList()
ls.scrape() # Scrape the list of questions
ls.to_csv(directory_path="../data/") # Save the scraped tables to a directory

Warning The default ALL_JSON_URL in the GetQuestionsList class might be out-of-date. Please update it by going to https://leetcode.com/problemset/all/ and exploring the Networks tab for a query returning all.json.

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

# This table can be generated using the previous commnd
questions_info = pd.read_csv("../data/questions.csv")

# Scrape question body
questions_body_list = get_all_questions_body(
    questions_info["titleSlug"].tolist(),
    questions_info["paidOnly"].tolist(),
    save_to="../data/questionBody.pickle",
)

# Save to a pandas dataframe
questions_body = pd.DataFrame(
    questions_body_list
).drop(columns=["titleSlug"])
questions_body["QID"] = questions_body["QID"].astype(int)

Note The above code stub is time consuming (10+ minutes) since there are 2500+ questions.

Create a new dataframe with all the questions and their metadata and body information.

questions = combine_list_and_info(
    info_df = questions_body, list_df=ls.questions, save_to="../data/all.json"
)

Create a PostgreSQL database using the SQL dump and insert data using sqlalchemy.

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("<database_connection_string>", echo=True)
questions.to_sql(con=engine, name="questions", if_exists="append", index=False)
# Repeat the same for tables ls.topicTags, ls.categories,
# ls.companies, # ls.questionTopics, and ls.questionCategory

Use the queried_questions_list PostgreSQL function (defined in the SQL dump) to query for questions containy query terms:

select * from queried_questions_list('<query term>');

Use the all_questions_list PostgreSQL function (defined in the SQL dump) to query for all the questions in the database:

select * from all_questions_list();

Use the get_similar_questions PostgreSQL function (defined in the SQL dump) to query for all questions similar to a given question:

select * from get_similar_questions(<QuestionID>);

Use the extract_solutions method to extract solution code stubs from your python script. Note that the solution method should be a part of a class named Solution (see here for an example):

# Returns a dict of the form {QuestionID: solutions}
solutions = extract_solutions(filename=<path_to_python_script>)

Use the upload_solutions method to upload the extracted solution code stubs from your python script to the PosgreSQL database.

upload_solutions(engine=<sqlalchemy_engine>, row_id = <row_id_in_table>, solutions: <solutions_dict>)