Add solutions and explanations for problems 955, 3784, 3783, 3781, 3780, 3779, 3776, 3775, 3774, 3716 #127
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…80, 3779, 3776, 3775, 3774, 3716 - Problem 955: Delete Columns to Make Sorted II (Accepted) - Problem 3784: Minimum Deletion Cost to Make All Characters Equal - Problem 3783: Mirror Distance of an Integer - Problem 3781: Maximum Score After Binary Swaps - Problem 3780: Maximum Sum of Three Numbers Divisible by Three - Problem 3779: Minimum Number of Operations to Have Distinct Elements - Problem 3776: Minimum Moves to Balance Circular Array - Problem 3775: Reverse Words With Same Vowel Count - Problem 3774: Absolute Difference Between Maximum and Minimum K Elements - Problem 3716: Find Churn Risk Customers (SQL - fixed solution) All problems include: - Python solutions (SQL problems also include .sql and .py files) - Detailed explanations following the template structure - Updated metadata in leetcode-problems.json
Reviewer's GuideAdds 10 new LeetCode solutions (9 Python + 1 SQL with Python wrapper), detailed English explanations for each, updates problem metadata, and refactors some existing implementations (notably for problems 3776, 3775, 3774, and 3716) for clarity, correctness, and efficiency. Class diagram for new and updated LeetCode solution implementationsclassDiagram
class Solution_955 {
+minDeletionSize(strs: List[str]) int
}
class Solution_3784 {
+minCost(s: str, cost: List[int]) int
}
class Solution_3783 {
+mirrorDistance(n: int) int
}
class Solution_3781 {
+maximumScore(nums: List[int], s: str) int
}
class Solution_3780 {
+maximumSum(nums: List[int]) int
}
class Solution_3779 {
+minOperations(nums: List[int]) int
}
class Solution_3776 {
+minMoves(balance: List[int]) int
}
class Solution_3775 {
+reverseWords(s: str) str
}
class Solution_3774 {
+absDifference(nums: List[int], k: int) int
}
class ChurnRiskService_3716 {
+find_churn_risk_customers(subscription_events: DataFrame) DataFrame
}
%% All Solution_* classes are independent, problem-specific implementations
Solution_955 ..> Solution_3784 : independent
Solution_955 ..> Solution_3783 : independent
Solution_955 ..> Solution_3781 : independent
Solution_955 ..> Solution_3780 : independent
Solution_955 ..> Solution_3779 : independent
Solution_955 ..> Solution_3776 : independent
Solution_955 ..> Solution_3775 : independent
Solution_955 ..> Solution_3774 : independent
Solution_955 ..> ChurnRiskService_3716 : independent
Flow diagram for SQL-based churn risk customer detection (problem 3716)flowchart TD
A["subscription_events table"] --> B["Compute query_cte with per-user aggregates
- max_event_date
- current_plan and monthly_amount
- max_historical_amount
- days_as_subscriber"]
B --> C["Filter to latest events per user
(event_date = max_event_date)"]
C --> D["Keep only active subscriptions
(monthly_amount > 0)"]
D --> E["Ensure user has at least one downgrade
(EXISTS downgrade in query_cte)"]
E --> F["Require tenure >= 60 days
(days_as_subscriber >= 60)"]
F --> G["Require current MRR < 50% of historical max
(monthly_amount / max_historical_amount < 0.5)"]
G --> H["Output churn risk customers
user_id, current_plan,
current_monthly_amount,
max_historical_amount,
days_as_subscriber
(sorted by days_as_subscriber DESC, user_id)"]
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Important Review skippedMore than 25% of the files skipped due to max files limit. The review is being skipped to prevent a low-quality review. 172 files out of 279 files are above the max files limit of 100. Please upgrade to Pro plan to get higher limits. You can disable this status message by setting the Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. WalkthroughThe PR extends the dataset and solutions: adds new LeetCode problem entries (IDs 3779–3784), creates multiple explanation documents, adds several new Python solution implementations and a SQL-backed churn query, and refactors existing solution files for problems 3774, 3775, and 3776. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20–30 minutes Areas that may need extra attention:
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Hey - I've found 4 issues, and left some high level feedback:
- The new
minMovesimplementation for 3776 never decrements neighbor balances and adds the fullstoragetoA[j]instead of just thetransferamount, which can both undercount cost and potentially cause an infinite loop whenstorageis 0; consider explicitly reducing neighbor balances bytransferand only addingtransfertoA[j](or otherwise reworking the logic) so the simulation is consistent with the explanation. - In
solutions/3716/01.py,pd.read_sql(query, con=None)will fail at runtime if invoked; if this is intended only as a display wrapper, consider replacing the call with a clear placeholder (e.g., raisingNotImplementedError) to avoid misleading behavior.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- The new `minMoves` implementation for 3776 never decrements neighbor balances and adds the full `storage` to `A[j]` instead of just the `transfer` amount, which can both undercount cost and potentially cause an infinite loop when `storage` is 0; consider explicitly reducing neighbor balances by `transfer` and only adding `transfer` to `A[j]` (or otherwise reworking the logic) so the simulation is consistent with the explanation.
- In `solutions/3716/01.py`, `pd.read_sql(query, con=None)` will fail at runtime if invoked; if this is intended only as a display wrapper, consider replacing the call with a clear placeholder (e.g., raising `NotImplementedError`) to avoid misleading behavior.
## Individual Comments
### Comment 1
<location> `solutions/3776/01.py:20-29` </location>
<code_context>
- transfer = min(remaining, amount)
- res += transfer * dist
- remaining -= transfer
+ # Work with a copy to avoid modifying input
+ A = balance[:]
+ res = 0
+ d = 0
+
+ # Expand outward from negative index
+ while A[j] < 0:
+ d += 1
+ # Get storage from neighbors at distance d
+ storage = A[(j + d) % n] + A[(j - d) % n]
+ transfer = min(-A[j], storage)
+ res += transfer * d
+ A[j] += storage
return res
</code_context>
<issue_to_address>
**issue (bug_risk):** Negative-balance adjustment loop does not conserve balances and can miscompute transfers or loop incorrectly.
Inside the `while A[j] < 0:` loop, you compute `storage` from both neighbors but only ever change `A[j]` (`A[j] += storage`), leaving the neighbor balances untouched. This breaks conservation and over-credits `A[j]`. Also, you calculate `transfer = min(-A[j], storage)` but then add `storage` instead of `transfer`, so `A[j]` can flip positive in one step when `storage > -A[j]`, and if `storage <= 0`, `A[j]` will not change, risking an infinite loop. You likely want to debit neighbors and credit `A[j]` by `transfer`, or redesign the loop so each step represents a valid transfer and guarantees progress toward zero.
</issue_to_address>
### Comment 2
<location> `solutions/3716/01.py:14-23` </location>
<code_context>
+ query = """
</code_context>
<issue_to_address>
**issue (bug_risk):** Using `pd.read_sql` with `con=None` will raise at runtime; consider making the placeholder safer or injectable.
`pd.read_sql(query, con=None)` will always error because `con` must be a real DB-API connection or SQLAlchemy engine. Since this function appears callable, consider either accepting a `con` parameter, raising `NotImplementedError` instead of invoking `read_sql`, or wrapping the call in a placeholder mechanism to avoid surprising runtime failures.
</issue_to_address>
### Comment 3
<location> `solutions/3716/01.py:5-14` </location>
<code_context>
+ """
</code_context>
<issue_to_address>
**question (bug_risk):** The "active subscription" definition in the docstring and query have slightly different semantics.
The docstring requires “last event is not cancel”, but the SQL instead uses `monthly_amount > 0` to infer an active subscription. If `cancel` (or other terminal events) can appear with non-zero or zero `monthly_amount`, this may diverge from the documented behavior. If those cases exist in your data model, consider aligning the condition with the docstring (e.g., also filtering on `event_type`).
</issue_to_address>
### Comment 4
<location> `explanations/955/en.md:54` </location>
<code_context>
+| Step | Column | Check Pair | Comparison | Breaks Order? | Action | res | sorted_pairs |
+| ---- | ------ | ---------- | ---------- | ------------- | ------ | --- | ------------ |
+| 1 | 0 | "ca"-"bb" | 'c' > 'b' | Yes | Delete column 0 | 1 | [False, False] |
+| 2 | 1 | "ca"-"bb" | 'a' < 'b' | No | Keep column 1, update sorted | 1 | [True, False] |
+| 2 | 1 | "bb"-"ac" | 'b' < 'c' | No | Keep column 1, update sorted | 1 | [True, True] |
+
+Detailed execution:
</code_context>
<issue_to_address>
**issue (typo):** The Step column in the trace table for problem 955 repeats the value 2; the last row should likely be Step 3.
In the **2.3 Trace Walkthrough** table for `explanations/955/en.md`, both rows for column 1 are labeled `Step 2`. Please renumber the second one to `Step 3` (or otherwise fix the sequence) so the progression is unambiguous.
```suggestion
| 3 | 1 | "bb"-"ac" | 'b' < 'c' | No | Keep column 1, update sorted | 1 | [True, True] |
```
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| query = """ | ||
| WITH query_cte AS ( | ||
| SELECT | ||
| user_id, | ||
| event_date, | ||
| MAX(event_date) OVER(PARTITION BY user_id) max_event_date, | ||
| event_type, | ||
| plan_name current_plan, | ||
| monthly_amount, | ||
| MAX(monthly_amount) OVER(PARTITION BY user_id) max_historical_amount, |
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issue (bug_risk): Using pd.read_sql with con=None will raise at runtime; consider making the placeholder safer or injectable.
pd.read_sql(query, con=None) will always error because con must be a real DB-API connection or SQLAlchemy engine. Since this function appears callable, consider either accepting a con parameter, raising NotImplementedError instead of invoking read_sql, or wrapping the call in a placeholder mechanism to avoid surprising runtime failures.
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Actionable comments posted: 8
🧹 Nitpick comments (4)
solutions/3716/01.py (1)
4-4: Remove or utilize the unusedsubscription_eventsparameter.The
subscription_eventsparameter is declared but never used in the function body. Since this is a SQL-based solution that operates viapd.read_sql, either:
- Remove the parameter entirely, or
- Use it to populate a temporary table for the query (if that's the intended design)
🔎 Proposed fix to remove unused parameter
-def find_churn_risk_customers(subscription_events: pd.DataFrame) -> pd.DataFrame: +def find_churn_risk_customers() -> pd.DataFrame:explanations/3781/en.md (1)
1-65: Documentation is comprehensive and technically accurate.The explanation clearly describes the greedy max-heap strategy with good complexity analysis and a detailed trace example.
Optional style polish: Line 5 has three consecutive sentences beginning with "We". Consider rewording for variety:
🔎 Suggested rephrase
-**Restate the problem:** We are given an integer array `nums` and a binary string `s` of the same length. Initially, score is 0. Each index where `s[i] = '1'` contributes `nums[i]` to the score. We can swap '0' and '1' if they are adjacent and '0' is before '1' (swap "01" to "10"). We can do this any number of times. We need to find the maximum possible score. +**Restate the problem:** Given an integer array `nums` and a binary string `s` of the same length, we start with a score of 0. Each index where `s[i] = '1'` contributes `nums[i]` to the score. Adjacent '0' and '1' can be swapped if '0' precedes '1' (swap "01" to "10"), and this operation can be repeated any number of times. The goal is to find the maximum possible score.solutions/3784/01.py (1)
3-3: Consider moving import to module level.While importing inside the function works, it's more conventional and slightly more efficient to place imports at the module level.
🔎 Proposed refactor
from typing import List +from collections import defaultdict class Solution: def minCost(self, s: str, cost: List[int]) -> int: - from collections import defaultdict - # Sum up costs for each character char_to_cost = defaultdict(int)explanations/955/en.md (1)
1-71: LGTM! Comprehensive explanation with clear trace walkthrough.The documentation provides excellent coverage of the greedy column-deletion strategy. The trace table with detailed step-by-step execution is particularly helpful for understanding the sorted-pairs tracking mechanism.
Note: There's a minor style suggestion from LanguageTool about "all of the" → "all" on line 5, but this is purely stylistic and optional.
📜 Review details
Configuration used: defaults
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (21)
data/leetcode-problems.json(1 hunks)explanations/3774/en.md(1 hunks)explanations/3775/en.md(1 hunks)explanations/3776/en.md(1 hunks)explanations/3779/en.md(1 hunks)explanations/3780/en.md(1 hunks)explanations/3781/en.md(1 hunks)explanations/3783/en.md(1 hunks)explanations/3784/en.md(1 hunks)explanations/955/en.md(1 hunks)solutions/3716/01.py(1 hunks)solutions/3716/01.sql(1 hunks)solutions/3774/01.py(1 hunks)solutions/3775/01.py(1 hunks)solutions/3776/01.py(1 hunks)solutions/3779/01.py(1 hunks)solutions/3780/01.py(1 hunks)solutions/3781/01.py(1 hunks)solutions/3783/01.py(1 hunks)solutions/3784/01.py(1 hunks)solutions/955/01.py(1 hunks)
🧰 Additional context used
🪛 LanguageTool
explanations/955/en.md
[style] ~5-~5: Consider removing “of” to be more concise
Context: ...em:** We are given an array of strings, all of the same length. We can delete any columns ...
(ALL_OF_THE)
explanations/3781/en.md
[style] ~5-~5: Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
Context: ..."). We can do this any number of times. We need to find the maximum possible score...
(ENGLISH_WORD_REPEAT_BEGINNING_RULE)
🪛 Ruff (0.14.8)
solutions/3784/01.py
2-2: Undefined name List
(F821)
7-7: zip() without an explicit strict= parameter
Add explicit value for parameter strict=
(B905)
solutions/3781/01.py
2-2: Undefined name List
(F821)
solutions/3780/01.py
2-2: Undefined name List
(F821)
solutions/3779/01.py
2-2: Undefined name List
(F821)
solutions/3716/01.py
4-4: Unused function argument: subscription_events
(ARG001)
solutions/955/01.py
2-2: Undefined name List
(F821)
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- GitHub Check: Sourcery review
🔇 Additional comments (21)
solutions/3716/01.sql (1)
28-29: LGTM! Thresholds are clear and mathematically sound.The 60-day duration threshold and 50% revenue ratio are well-defined churn risk indicators. The division is safe because
max_historical_amount(the MAX of allmonthly_amountvalues) must be ≥ currentmonthly_amount > 0, ensuring no division by zero.solutions/3716/01.py (2)
35-35: Verify thatmonthly_amount > 0reliably identifies active subscriptions.This Python implementation uses the same filter logic as the SQL file. As noted in the SQL review, the assumption that
monthly_amount > 0means "not cancelled" should be verified against the data schema to ensure cancelled subscriptions with positive amounts aren't incorrectly included.
46-48: Placeholdercon=Noneis appropriately documented.The comment clearly explains that
con=Noneis a placeholder for the LeetCode environment. This is acceptable for a solution wrapper file intended for frontend display.solutions/3774/01.py (1)
1-13: LGTM! Clean and efficient implementation.The logic correctly sorts the array, extracts the k smallest and k largest elements, and returns the absolute difference. The slicing approach is idiomatic Python.
Two minor observations:
Listtype hint requiresfrom typing import List(though LeetCode typically provides this).nums.sort()mutates the input. If immutability is desired, usesorted(nums)instead.solutions/3775/01.py (2)
9-16: Verify case sensitivity for vowel counting.The vowel check only includes lowercase vowels (
'aeiou'). If the problem allows uppercase letters, words like "APPLE" would count 0 vowels instead of 2.If uppercase vowels should be counted, consider:
c.lower() in 'aeiou'
1-24: Logic is clean and correct for the stated problem.The implementation correctly:
- Handles empty input by returning
""- Counts vowels in the first word as the reference
- Reverses only subsequent words with matching vowel counts
- Preserves the first word unchanged
solutions/3776/01.py (1)
5-18: Assumption of single negative balance may not hold.The algorithm finds only the first negative balance and processes it. If the problem guarantees at most one negative balance, this is fine. However, if multiple negatives can exist, this approach would miss them.
Please verify the problem constraints to confirm there's at most one negative balance in the input.
explanations/3774/en.md (1)
1-61: Clear and well-structured explanation.The documentation accurately describes the approach, matches the implementation, and provides a helpful trace walkthrough. The complexity analysis is correct.
explanations/3776/en.md (1)
52-63: Trace walkthrough correctly shows the intended algorithm behavior.The explanation accurately describes adding
transfer(4) to the balance, resulting in-4 + 4 = 0. Note: the current code implementation has a bug where it addsstorageinstead oftransfer(flagged separately in the code review).explanations/3775/en.md (1)
1-62: Documentation is well-structured and clear.The explanation follows a consistent template with clear problem restatement, complexity analysis, strategy breakdown, and a detailed trace walkthrough. The content accurately describes the vowel-counting and word-reversal algorithm.
solutions/3781/01.py (1)
3-19: Algorithm implementation is correct.The max-heap approach using negation with Python's
heapqis properly implemented. The logic correctly adds all values to the heap and pops the maximum when encountering '1' in the string.solutions/3783/01.py (1)
1-8: Clean and correct implementation.The digit reversal using string slicing is straightforward and matches the documented approach. The logic correctly computes the absolute difference between the number and its reverse.
explanations/3783/en.md (1)
1-62: Clear and thorough documentation.The explanation effectively describes the mirror distance problem with accurate complexity analysis and a helpful trace walkthrough using n=25 as an example.
solutions/3780/01.py (1)
3-30: Algorithm correctly implements remainder-grouping strategy.The solution properly groups numbers by remainder (mod 3), sorts each group in descending order, and evaluates all four valid combinations to find the maximum sum divisible by 3.
solutions/3779/01.py (1)
3-13: Implementation correctly processes right-to-left with duplicate detection.The solution appropriately creates a copy to avoid input mutation, maintains a seen set while processing from right to left, and applies the formula
(len(nums) + 2) // 3when a duplicate is encountered.explanations/3784/en.md (1)
1-71: Excellent documentation with clear strategy explanation.The explanation effectively describes the greedy approach of keeping the character with maximum deletion cost. The trace walkthrough with the example clearly demonstrates how the algorithm identifies 'c' as the character to keep, resulting in a minimum cost of 11.
explanations/3780/en.md (1)
1-69: LGTM! Well-structured explanation.The documentation provides clear strategy rationale, comprehensive step-by-step walkthrough with a trace table, proper complexity analysis, and edge case coverage. The explanation effectively guides readers through the modulo-grouping approach.
explanations/3779/en.md (1)
1-68: LGTM! Clear and thorough explanation.The documentation effectively explains the right-to-left processing strategy with set-based duplicate detection. The trace walkthrough clearly demonstrates the algorithm flow, and the complexity analysis is accurate.
solutions/955/01.py (1)
3-24: LGTM! Correct greedy implementation.The algorithm correctly implements the greedy approach with sorted-pair tracking:
- Processes columns left to right
- Checks for ordering violations only on unsorted pairs
- Updates sorted status when columns establish strict ordering
- Properly handles the early break when a violation is found
solutions/3784/01.py (1)
7-7: This suggestion cannot be applied without establishing Python 3.10+ as the repository's minimum version requirement.The
strict=Trueparameter forzip()was introduced in Python 3.10, but this repository does not specify a minimum Python version. Adding this parameter would break compatibility with Python versions earlier than 3.10. Before adopting this suggestion, ensure the project explicitly requires Python 3.10+ in its configuration (pyproject.toml, setup.py, .python-version, or similar).Likely an incorrect or invalid review comment.
data/leetcode-problems.json (1)
26448-26482: Clarify whether problem 3782 was intentionally excluded.The sequence jumps from 3781 to 3783, omitting 3782. Please confirm whether this is intentional (problem doesn't exist on LeetCode) or if it should be included in this PR.
| - **Input Size:** Up to 10^5 elements in the array. | ||
| - **Time Complexity:** O(n) where n is the length - we find the negative index once, then expand outward at most n/2 steps. | ||
| - **Space Complexity:** O(1) - we only use a few variables. | ||
| - **Edge Case:** If all balances are already non-negative, return 0. If the total sum is negative, return -1. |
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Space complexity is inconsistent with the implementation.
The explanation states O(1) space, but the solution creates a copy with A = balance[:], which is O(n) space. Consider updating the documentation to reflect the actual space usage.
🔎 Suggested fix
-- **Space Complexity:** O(1) - we only use a few variables.
+- **Space Complexity:** O(n) - we create a copy of the balance array.📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - **Input Size:** Up to 10^5 elements in the array. | |
| - **Time Complexity:** O(n) where n is the length - we find the negative index once, then expand outward at most n/2 steps. | |
| - **Space Complexity:** O(1) - we only use a few variables. | |
| - **Edge Case:** If all balances are already non-negative, return 0. If the total sum is negative, return -1. | |
| - **Input Size:** Up to 10^5 elements in the array. | |
| - **Time Complexity:** O(n) where n is the length - we find the negative index once, then expand outward at most n/2 steps. | |
| - **Space Complexity:** O(n) - we create a copy of the balance array. | |
| - **Edge Case:** If all balances are already non-negative, return 0. If the total sum is negative, return -1. |
🤖 Prompt for AI Agents
In explanations/3776/en.md around lines 9 to 12, the doc claims O(1) space but
the implementation creates a copy with A = balance[:] (O(n)); update the Space
Complexity line to O(n) to match the code, or alternatively remove the array
copy in the implementation and operate in-place to legitimately claim O(1)
space—pick one approach and make the corresponding change (update the text to
O(n) if keeping the copy, or change code to avoid copying if you want O(1)).
| while A[j] < 0: | ||
| d += 1 | ||
| # Get storage from neighbors at distance d | ||
| storage = A[(j + d) % n] + A[(j - d) % n] | ||
| transfer = min(-A[j], storage) | ||
| res += transfer * d | ||
| A[j] += storage |
There was a problem hiding this comment.
Critical bug: incorrect balance update causes wrong results.
Line 32 adds storage to A[j], but it should add transfer. When transfer < storage, the debt is incorrectly over-credited, and subsequent loop iterations may behave unexpectedly.
Additionally, at larger distances d, the same neighbors may be visited multiple times (when d >= n//2), leading to double-counting of available storage.
🔎 Proposed fix
while A[j] < 0:
d += 1
# Get storage from neighbors at distance d
storage = A[(j + d) % n] + A[(j - d) % n]
transfer = min(-A[j], storage)
res += transfer * d
- A[j] += storage
+ A[j] += transferAlso consider adding a guard to stop expansion when d > n // 2 to avoid overlapping neighbors in the circular array.
🤖 Prompt for AI Agents
In solutions/3776/01.py around lines 26 to 32, the loop that tries to fill
negative A[j] incorrectly adds the full neighbor storage to A[j] (A[j] +=
storage) instead of only the actual amount transferred, and it can double-count
neighbors when d grows large; change the update to add transfer (A[j] +=
transfer) and after computing d increment/add a guard to break once d > n//2 (or
when neighbors overlap) to avoid revisiting the same pair of neighbors in the
circular array.
| class Solution: | ||
| def minOperations(self, nums: List[int]) -> int: |
There was a problem hiding this comment.
Missing typing import causes undefined name error.
The type hint List[int] on line 2 requires importing List from the typing module.
🔎 Proposed fix
+from typing import List
+
class Solution:
def minOperations(self, nums: List[int]) -> int:🧰 Tools
🪛 Ruff (0.14.8)
2-2: Undefined name List
(F821)
🤖 Prompt for AI Agents
In solutions/3779/01.py around lines 1 to 2, the type hint List[int] is used but
List is not imported; add "from typing import List" (or switch to built-in
"list[int]" for Python 3.9+) at the top of the file so the type name is defined
and the annotation no longer raises a NameError.
| class Solution: | ||
| def maximumSum(self, nums: List[int]) -> int: |
There was a problem hiding this comment.
Missing typing import causes undefined name error.
The type hint List[int] on line 2 requires importing List from the typing module. This will cause type checking failures and potential runtime errors.
🔎 Proposed fix
+from typing import List
+
class Solution:
def maximumSum(self, nums: List[int]) -> int:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| class Solution: | |
| def maximumSum(self, nums: List[int]) -> int: | |
| from typing import List | |
| class Solution: | |
| def maximumSum(self, nums: List[int]) -> int: |
🧰 Tools
🪛 Ruff (0.14.8)
2-2: Undefined name List
(F821)
🤖 Prompt for AI Agents
In solutions/3780/01.py around lines 1 to 2, the function annotation uses
List[int] but List is not imported; add the typing import by importing List from
the typing module (e.g., from typing import List) at the top of the file so the
type hint is defined and type checking/runtime name resolution succeeds.
| class Solution: | ||
| def maximumScore(self, nums: List[int], s: str) -> int: |
There was a problem hiding this comment.
Missing typing import causes undefined name error.
The type hint List[int] on line 2 requires importing List from the typing module. Without this import, the code will fail type checking and may raise a NameError at runtime depending on the Python version.
🔎 Proposed fix
+from typing import List
+
class Solution:
def maximumScore(self, nums: List[int], s: str) -> int:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| class Solution: | |
| def maximumScore(self, nums: List[int], s: str) -> int: | |
| from typing import List | |
| class Solution: | |
| def maximumScore(self, nums: List[int], s: str) -> int: |
🧰 Tools
🪛 Ruff (0.14.8)
2-2: Undefined name List
(F821)
🤖 Prompt for AI Agents
In solutions/3781/01.py around lines 1 to 2, the type hint uses List[int] but
List is not imported, causing an undefined name error; add the import from
typing (e.g., `from typing import List`) at the top of the file so the List
symbol is defined and type hints work correctly.
| @@ -0,0 +1,16 @@ | |||
| class Solution: | |||
| def minCost(self, s: str, cost: List[int]) -> int: | |||
There was a problem hiding this comment.
Add missing import for List type annotation.
The List type is used in the method signature but not imported, which will cause a NameError at runtime.
🔎 Proposed fix
+from typing import List
+
class Solution:
def minCost(self, s: str, cost: List[int]) -> int:Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.14.8)
2-2: Undefined name List
(F821)
🤖 Prompt for AI Agents
In solutions/3784/01.py around line 2, the function signature uses the type name
List but the symbol is not imported; add the missing import by adding "from
typing import List" at the top of the file (or replace List with built-in list
in the signature) so the type annotation resolves and no NameError occurs.
| @@ -0,0 +1,25 @@ | |||
| class Solution: | |||
| def minDeletionSize(self, strs: List[str]) -> int: | |||
There was a problem hiding this comment.
Add missing import for List type annotation.
The List type is used in the method signature but not imported, which will cause a NameError at runtime.
🔎 Proposed fix
+from typing import List
+
class Solution:
def minDeletionSize(self, strs: List[str]) -> int:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def minDeletionSize(self, strs: List[str]) -> int: | |
| from typing import List | |
| class Solution: | |
| def minDeletionSize(self, strs: List[str]) -> int: |
🧰 Tools
🪛 Ruff (0.14.8)
2-2: Undefined name List
(F821)
🤖 Prompt for AI Agents
In solutions/955/01.py around lines 2 to 2, the function signature uses the
typing name List but the module does not import it; add the missing import by
importing List from the typing module at the top of the file (e.g., from typing
import List) so the type annotation resolves and avoids a NameError.
…to match community solution
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (5)
solutions/3779/01.py (1)
1-2: MissingListimport (already flagged).This issue was already identified in previous reviews. The type hint
List[int]requires importingListfrom thetypingmodule (or use the built-inlist[int]for Python 3.9+).solutions/3716/01.py (1)
5-5: Address the unused parameter and runtime-error placeholder.Two issues remain from previous reviews:
Unused parameter (line 5): The
subscription_eventsDataFrame parameter is never used. The embedded SQL queries a table namedsubscription_eventsdirectly, creating confusion about the function's contract.Runtime error (lines 49-51):
pd.read_sql(query, con=None)will always raise an exception sinceconmust be a valid database connection.Consider one of these approaches:
- Option 1 (Recommended): Remove the misleading parameter and raise
NotImplementedErrorwith a clear message that this is a SQL-only solution.- Option 2: Accept a
conparameter and pass it through topd.read_sql.- Option 3: Use the
subscription_eventsDataFrame by creating an in-memory SQLite connection (viasqlite3orsqlalchemy), loading the DataFrame into it, then executing the query.🔎 Proposed fix using Option 1
-def find_churn_risk_customers(subscription_events: pd.DataFrame) -> pd.DataFrame: +def find_churn_risk_customers() -> pd.DataFrame: """ Find customers at risk of churning. A customer is at churn risk if they: 1. Currently have an active subscription (last event is not cancel) 2. Have performed at least one downgrade 3. Current plan revenue < 50% of historical maximum 4. Have been a subscriber for at least 60 days + + Note: This is a SQL-only solution for LeetCode. + The SQL should be executed directly in LeetCode's SQL environment. """ - query = """ + raise NotImplementedError( + "This is a SQL-only solution. Execute the query below in LeetCode SQL environment:\n\n" + """ WITH query_cte AS ( SELECT user_id, @@ -44,10 +47,5 @@ AND monthly_amount / CAST(max_historical_amount AS FLOAT) <= 0.5 ORDER BY days_as_subscriber DESC, user_id """ - # Note: This is a SQL solution. In actual LeetCode environment, - # this would be executed as raw SQL, not via pandas - return pd.read_sql( - query, con=None - ) # Placeholder - actual execution depends on LeetCode environment + )Also applies to: 49-51
solutions/3776/01.py (2)
32-32: Critical bug: incorrect balance update causes wrong results.Line 32 adds
storagetoA[j], but should addtransfer. Whentransfer < storage, the debt is incorrectly over-credited, and subsequent iterations behave unexpectedly.🔎 Proposed fix
- A[j] += storage + A[j] += transfer
26-32: Critical bug: neighbor overlap causes double-counting and incorrect results.When
dreachesn/2(for evenn) or exceeds(n-1)/2, the indices(j+d)%nand(j-d)%neither point to the same element or revisit previously accessed neighbor pairs.Examples:
n=6, j=0, d=3:(j+d)%n = 3and(j-d)%n = 3→storage = 2*A[3](same element counted twice)n=6, j=0, d=4: visits neighbors(4, 2), but these were already visited atd=2as(2, 4)This leads to incorrect storage calculations and inflated costs.
🔎 Proposed fix
Add a guard to prevent overlap and handle the edge case when neighbors converge:
# Expand outward from negative index while A[j] < 0: d += 1 + # Stop if we've covered all unique neighbor pairs + if d > n // 2: + break # Get storage from neighbors at distance d - storage = A[(j + d) % n] + A[(j - d) % n] + left = (j - d) % n + right = (j + d) % n + # Handle case when neighbors converge to same index + storage = A[left] + (A[right] if left != right else 0) transfer = min(-A[j], storage) res += transfer * d A[j] += transferNote: This fix also incorporates the correction from the previous comment (using
transferinstead ofstorage).solutions/3781/01.py (1)
1-2: MissingListimport (previously flagged).This critical issue has already been identified in a previous review. The
List[int]type hint requires importingListfrom thetypingmodule.
🧹 Nitpick comments (1)
solutions/3781/01.py (1)
3-3: Consider moving import to module level.Importing
heapqinside the method works but is non-idiomatic. Module-level imports improve readability and follow PEP 8 conventions.🔎 Suggested refactor
Move the import to the top of the file alongside the
Listimport:+from typing import List +import heapq + class Solution: def maximumScore(self, nums: List[int], s: str) -> int: - import heapq - # Use max heap (negate values for Python's min heap) heap = []
📜 Review details
Configuration used: defaults
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (11)
solutions/3716/01.py(1 hunks)solutions/3716/01.sql(1 hunks)solutions/3768/01.py(2 hunks)solutions/3769/01.py(1 hunks)solutions/3774/01.py(1 hunks)solutions/3775/01.py(1 hunks)solutions/3776/01.py(1 hunks)solutions/3779/01.py(1 hunks)solutions/3780/01.py(1 hunks)solutions/3781/01.py(1 hunks)solutions/3783/01.py(1 hunks)
✅ Files skipped from review due to trivial changes (2)
- solutions/3768/01.py
- solutions/3769/01.py
🚧 Files skipped from review as they are similar to previous changes (2)
- solutions/3783/01.py
- solutions/3774/01.py
🧰 Additional context used
🪛 Ruff (0.14.8)
solutions/3716/01.py
5-5: Unused function argument: subscription_events
(ARG001)
solutions/3781/01.py
2-2: Undefined name List
(F821)
solutions/3780/01.py
2-2: Undefined name List
(F821)
solutions/3779/01.py
2-2: Undefined name List
(F821)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Sourcery review
🔇 Additional comments (10)
solutions/3779/01.py (1)
4-5: Verify empty list handling.If
numsis empty,nums.pop()on line 5 will raise anIndexError. Please confirm whether the problem constraints guarantee at least one element, or add a guard clause if empty lists are valid inputs.🔎 Proposed defensive check (if needed)
def minOperations(self, nums: List[int]) -> int: + if not nums: + return 0 + # Process from right to left using a copy nums = nums[:] # Work with a copy to avoid modifying inputsolutions/3716/01.sql (1)
21-21: LGTM! The filter now correctly excludes cancelled subscriptions.The change from
monthly_amount > 0toevent_type <> 'cancel'properly addresses the previous review feedback. This ensures that cancelled subscriptions are excluded based on their event type rather than amount, which is the correct semantics for identifying active subscribers.solutions/3716/01.py (1)
15-46: SQL logic correctly matches the standalone solution.The embedded SQL query is consistent with
solutions/3716/01.sqland correctly usesevent_type <> 'cancel'(line 36) to filter out cancelled subscriptions. The churn-risk criteria are properly implemented.solutions/3780/01.py (1)
3-30: Algorithm logic is correct and well-implemented.The solution correctly identifies all four valid patterns for selecting three numbers with sum divisible by 3:
- Three numbers with remainder 0
- One from each remainder (0, 1, 2)
- Three numbers with remainder 1
- Three numbers with remainder 2
The greedy approach of selecting the largest values from each group maximizes the sum, and the implementation matches the stated O(n log n) time and O(n) space complexity.
Note: The missing
Listimport flagged in the previous review comment must be addressed for the code to run correctly.solutions/3775/01.py (4)
5-6: LGTM: Empty input handling is correct.Returning an empty string for empty input is appropriate.
8-12: Verify first word reversal logic and handle uppercase vowels.Two concerns:
First word consistency: The first word is never reversed (Line 12), even though it has the same vowel count as itself. If the problem expects ALL words with matching vowel counts to be reversed, the first word should also be reversed. Please verify whether the first word should remain unchanged or be reversed like other matching words.
Uppercase vowels not counted: Line 9 checks
c in "aeiou", which only counts lowercase vowels. If the input contains uppercase vowels ('A', 'E', 'I', 'O', 'U'), they will be ignored, leading to incorrect vowel counts and potentially wrong word reversals.🔎 Proposed fix for uppercase vowel handling
If uppercase vowels should be counted, update the vowel check:
- first_word_vowels = sum(1 for c in words[0] if c in "aeiou") + first_word_vowels = sum(1 for c in words[0] if c.lower() in "aeiou")
14-22: Handle uppercase vowels in word processing loop.Line 16 has the same issue:
c in "aeiou"only counts lowercase vowels. If the input contains uppercase vowels, they will be ignored, leading to incorrect comparisons and word reversals.🔎 Proposed fix for uppercase vowel handling
If uppercase vowels should be counted:
- vowel_count = sum(1 for c in word if c in "aeiou") + vowel_count = sum(1 for c in word if c.lower() in "aeiou")
24-24: LGTM: String joining is correct.The result is properly joined with spaces to reconstruct the output string.
solutions/3776/01.py (1)
5-10: No action needed. The problem guarantees that initially at most one index has a negative balance. The algorithm correctly handles this constraint by finding and processing the single negative index.solutions/3781/01.py (1)
9-17: The algorithm correctly assumeslen(nums) == len(s)based on the problem constraints, which explicitly state that both inputs have the same length. No input validation is needed.Likely an incorrect or invalid review comment.
romankurnovskii
deleted the
problems-955-3784-3783-3781-3780-3779-3776-3775-3774-3716
branch
2 participants
Summary
This PR adds solutions and explanations for 10 LeetCode problems, including one accepted solution and comprehensive documentation for all problems.
Problems Added
✅ Accepted Solutions
Solutions with Explanations
Problem 3784: Minimum Deletion Cost to Make All Characters Equal (Medium)
Problem 3783: Mirror Distance of an Integer (Easy)
Problem 3781: Maximum Score After Binary Swaps (Medium)
Problem 3780: Maximum Sum of Three Numbers Divisible by Three (Medium)
Problem 3779: Minimum Number of Operations to Have Distinct Elements (Medium)
Problem 3776: Minimum Moves to Balance Circular Array (Medium)
Problem 3775: Reverse Words With Same Vowel Count (Medium)
Problem 3774: Absolute Difference Between Maximum and Minimum K Elements (Easy)
Problem 3716: Find Churn Risk Customers (Medium) - SQL
event_type <> 'cancel'tomonthly_amount > 0Changes
data/leetcode-problems.jsonwith metadata for all problemsNotes
Summary by Sourcery
Add implemented solutions and explanations for several LeetCode problems, including one SQL/Python hybrid, and update existing implementations and explanations for clarity and correctness.
New Features:
Bug Fixes:
Enhancements:
Summary by CodeRabbit
New Features
Documentation
Chores / Style
Behavioral tweaks
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