├── README.md
└── leech.py


/README.md:
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 1 | # leechcode
 2 | Python script to fetch details about leetcode questions useful for planning interview preparation.
 3 | 
 4 | I have created a Google Sheet using this data, this might be useful for last moment preparation as well as revision. Hope you find it useful.
 5 | 
 6 | [:link: The Google Sheet](https://docs.google.com/spreadsheets/d/1KfZq_onP06UDqizkLOjraOfw2qIODDqMyd-3knUu3hA/edit?usp=sharing)
 7 | 
 8 | ### How to use this sheet?
 9 | 
10 | - First of all ```Make a copy``` of the sheet from the ```File``` option.
11 | - I have sorted the values according to the Wilson Score [[Source](https://www.evanmiller.org/how-not-to-sort-by-average-rating.html)] which is a better metric to rate something compared to a simple ratio.
12 | - All the best for your interviews!
13 | 
14 | 
15 | ## The script
16 | If you want to try this out on your own, keep reading.
17 | 
18 | ### How to use the script?
19 | We basically need to send requests to the server as an authenticated user so we have to find those details.
20 | 
21 | - Login to leetcode and go to any problem.
22 | - Open the Network panel ( Ctrl + Shift + E ) and reload.
23 | - Search for a ```graphql``` request in the big list, a small window will open to the right.
24 | - Click the ```Headers``` and scroll to find the ```Request Headers```.
25 | - Copy below 2 values from here and paste them in the code
26 |   - ```Cookie```
27 |   - ```x-csrftoken```
28 | - Run the code ```python3 leech.py```.
29 | 
30 | 
31 | ### How to edit/clean the data?
32 | Use the [Pandas](https://pandas.pydata.org/) library to modify the data as it is very convenient.
33 | 
34 | 


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/leech.py:
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 1 | import requests
 2 | import json
 3 | 
 4 | 
 5 | # Fill in these details to be able to send a request, more details in README
 6 | 
 7 | HEADERS = {
 8 |     'x-csrftoken': '<ENTER YOUR CSRF TOKEN',
 9 |     'Referer': 'https://leetcode.com/problems',
10 |     'Cookie': '<ENTER YOUR COOKIE ID>'
11 | }
12 | 
13 | graphql_url = "https://leetcode.com/graphql"
14 | 
15 | final_data = {}
16 | 
17 | 
18 | # Get a list of all questions on Leetcode,
19 | # we will query details for these question
20 | 
21 | def get_question_list():
22 | 
23 |     response = requests.get("https://leetcode.com/api/problems/all")
24 |     raw_data = response.json()
25 | 
26 |     stat_status_pairs = raw_data["stat_status_pairs"]
27 | 
28 |     question_title_slug = [ i["stat"]["question__title_slug"] for i in stat_status_pairs ]
29 | 
30 |     return question_title_slug
31 | 
32 | 
33 | # Post a GraphQL request for a given question,
34 | # Query has the description of all the details we want,
35 | # Query can be modified according to needs
36 | 
37 | def leech( question ):
38 | 
39 |     query = ("""
40 |     query questionData {
41 |         question(titleSlug:"%s")
42 |         {
43 |             questionId
44 |             title
45 |             titleSlug
46 |             content
47 |             isPaidOnly
48 |             difficulty
49 |             likes
50 |             dislikes
51 |             topicTags {
52 |                 name
53 |                 slug
54 |             }
55 |             stats
56 |         }
57 |     }
58 |     """% question )
59 | 
60 |     r = requests.post(
61 |         graphql_url,
62 |         json = {
63 |             'query': query
64 |         },
65 |         headers = HEADERS,
66 |     )
67 | 
68 |     r = r.json()
69 |     r = r['data']['question']
70 |     r['url'] = "https://leetcode.com/problems/" + str(r['titleSlug'])
71 | 
72 |     final_data[r['questionId']] = r
73 | 
74 | 
75 | question_list = get_question_list()
76 | 
77 | 
78 | # Call the leech function for all questions,
79 | # Prints progress
80 | 
81 | for i, question in enumerate( question_list ):
82 | 
83 |     leech( question )
84 |     print( f"Fetched {i+1}/{len(question_list)} questions" )
85 | 
86 | 
87 | # Dump our Python dictionary to a JSON file
88 | 
89 | with open("data.json", "w") as file:
90 |     json.dump( final_data, file )


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