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Usage examples for README
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‎README.md

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@@ -26,11 +26,157 @@ which is needed in order to use LC API. If you use python you can
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just use the generated code from this repo. Otherwise you'll have to
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implement something like `fix_cookies.patch` for your target language.
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You can find examples of usage in `example.py`
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## Minimal working example
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First set up a virtualenv
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```bash
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virtualenv -p python3 leetcode
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. leetcode/bin/activate
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pip3 install python-leetcode
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```
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Then in python shell initialize the client
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```python
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import leetcode
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# Get the next two values from your browser cookies
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csrf_token = "xxx"
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leetcode_session = "yyy"
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configuration = leetcode.Configuration()
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configuration.api_key["x-csrftoken"] = csrf_token
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configuration.api_key["csrftoken"] = csrf_token
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configuration.api_key["LEETCODE_SESSION"] = leetcode_session
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configuration.api_key["Referer"] = "https://leetcode.com"
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configuration.debug = False
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api_instance = leetcode.DefaultApi(leetcode.ApiClient(configuration))
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```
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Now once the client is initilized, you can start performing actual queries
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```python
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graphql_request = leetcode.GraphqlQuery(
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query="""
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{
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user {
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username
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isCurrentUserPremium
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}
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}
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""",
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variables=leetcode.GraphqlQueryVariables(),
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)
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print(api_instance.graphql_post(body=graphql_request))
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```
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You should get something like that in the response
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```python
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{'data': {'question': None,
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'user': {'is_current_user_premium': True, 'username': 'omgitspavel'}}}
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```
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This confirms you've set up auth correctly.
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## Advanced example
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Now let's try to do something more complicated. For example calculate the percentage of the problems we've solved by topic.
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For that we have to acquire the list of all the problems we solved.
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```python
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api_response = api_instance.api_problems_topic_get(topic="algorithms")
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slug_to_solved_status = {
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pair.stat.question__title_slug: True if pair.status == "ac" else False
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for pair in api_response.stat_status_pairs
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}
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```
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Now for each problem we want to get its tags
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```python
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import time
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from collections import Counter
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topic_to_accepted = Counter()
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topic_to_total = Counter()
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# Take only the first 10 for test purposes
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for slug in list(slug_to_solved_status.keys())[:10]:
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time.sleep(1) # Leetcode has a rate limiter
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graphql_request = leetcode.GraphqlQuery(
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query="""
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query getQuestionDetail($titleSlug: String!) {
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question(titleSlug: $titleSlug) {
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topicTags {
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name
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slug
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}
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}
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}
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""",
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variables=leetcode.GraphqlQueryVariables(title_slug=slug),
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operation_name="getQuestionDetail",
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)
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api_response = api_instance.graphql_post(body=graphql_request)
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for topic in (tag.slug for tag in api_response.data.question.topic_tags):
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topic_to_accepted[topic] += int(slug_to_solved_status[slug])
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topic_to_total[topic] += 1
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print(
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list(
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sorted(
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((topic, accepted / topic_to_total[topic]) for topic, accepted in topic_to_accepted.items()),
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key=lambda x: x[1]
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)
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)
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)
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```
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The output will look like this:
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```python
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[('memoization', 0.0),
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('number-theory', 0.0),
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('binary-search-tree', 0.0),
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('quickselect', 0.0),
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('recursion', 0.0),
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('suffix-array', 0.0),
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('topological-sort', 0.0),
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('shortest-path', 0.0),
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('trie', 0.0),
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('geometry', 0.0),
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('brainteaser', 0.0),
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('combinatorics', 0.0),
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('line-sweep', 0.0),
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...
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('union-find', 0.3076923076923077),
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('linked-list', 0.3333333333333333),
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('string-matching', 0.3333333333333333),
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('segment-tree', 0.4),
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('data-stream', 0.5),
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('strongly-connected-component', 0.5),
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('minimum-spanning-tree', 0.6666666666666666),
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('merge-sort', 1.0),
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('doubly-linked-list', 1.0)]
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```
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So it is clearly visible which topics we should focus on in our preparation.
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In this case memoization topic is one of the targets for improvement, so I can go to https://leetcode.com/tag/memoization/ and choose a new memoization problem. Or use python to automate the process.
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You can find other examples of usage in `example.py`
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Autogenerated by swagger documentation can be found [here](/README.generated.md).
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[1] https://github.com/skygragon/leetcode-cli

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