Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly
Sign up
Appearance settings

Commit 28b3e81

Browse files
Merge pull request iamAntimPal#89 from iamAntimPal/Branch-1
Branch 1
2 parents fcc3719 + a37cf6b commit 28b3e81

File tree

5 files changed

+790
-0
lines changed

5 files changed

+790
-0
lines changed
Lines changed: 119 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,119 @@
1+
# πŸ“° Article Views I - LeetCode 1148
2+
3+
## πŸ“Œ Problem Statement
4+
You are given the **Views** table that records article views.
5+
6+
### Views Table
7+
| Column Name | Type |
8+
| ----------- | ---- |
9+
| article_id | int |
10+
| author_id | int |
11+
| viewer_id | int |
12+
| view_date | date |
13+
14+
- The table **may contain duplicate rows**.
15+
- Each row indicates that **some viewer viewed an article** written by some author on a specific date.
16+
- If `author_id = viewer_id`, it means **the author viewed their own article**.
17+
18+
### Task:
19+
Find **all authors** who have viewed **at least one of their own articles**.
20+
- **Return the result sorted by `id` in ascending order**.
21+
22+
---
23+
24+
## πŸ“Š Example 1:
25+
### Input:
26+
**Views Table**
27+
| article_id | author_id | viewer_id | view_date |
28+
| ---------- | --------- | --------- | ---------- |
29+
| 1 | 3 | 5 | 2019εΉ΄08月01ζ—₯ |
30+
| 1 | 3 | 6 | 2019εΉ΄08月02ζ—₯ |
31+
| 2 | 7 | 7 | 2019εΉ΄08月01ζ—₯ |
32+
| 2 | 7 | 6 | 2019εΉ΄08月02ζ—₯ |
33+
| 4 | 7 | 1 | 2019εΉ΄07月22ζ—₯ |
34+
| 3 | 4 | 4 | 2019εΉ΄07月21ζ—₯ |
35+
| 3 | 4 | 4 | 2019εΉ΄07月21ζ—₯ |
36+
37+
### Output:
38+
| id |
39+
| --- |
40+
| 4 |
41+
| 7 |
42+
43+
### Explanation:
44+
- **Author 4** viewed their own article (`viewer_id = author_id`).
45+
- **Author 7** viewed their own article (`viewer_id = author_id`).
46+
- The result is sorted in **ascending order**.
47+
48+
---
49+
50+
## πŸ–₯ SQL Solutions
51+
52+
### 1️⃣ Standard MySQL Solution
53+
#### Explanation:
54+
- **Filter rows** where `author_id = viewer_id`.
55+
- Use `DISTINCT` to **remove duplicates**.
56+
- **Sort the result** in ascending order.
57+
58+
```sql
59+
SELECT DISTINCT author_id AS id
60+
FROM Views
61+
WHERE author_id = viewer_id
62+
ORDER BY id ASC;
63+
```
64+
65+
---
66+
67+
### 2️⃣ Alternative Solution Using `GROUP BY`
68+
#### Explanation:
69+
- **Group by** `author_id` and **filter authors** who have viewed at least one of their own articles.
70+
71+
```sql
72+
SELECT author_id AS id
73+
FROM Views
74+
WHERE author_id = viewer_id
75+
GROUP BY author_id
76+
ORDER BY id ASC;
77+
```
78+
79+
---
80+
81+
## 🐍 Pandas Solution (Python)
82+
#### Explanation:
83+
- **Filter rows** where `author_id == viewer_id`.
84+
- **Select distinct author IDs**.
85+
- **Sort the result** in ascending order.
86+
87+
```python
88+
import pandas as pd
89+
90+
def authors_who_viewed_own_articles(views: pd.DataFrame) -> pd.DataFrame:
91+
# Filter rows where author_id == viewer_id
92+
filtered = views[views["author_id"] == views["viewer_id"]]
93+
94+
# Select unique author IDs and sort
95+
result = pd.DataFrame({"id": sorted(filtered["author_id"].unique())})
96+
97+
return result
98+
```
99+
100+
---
101+
102+
## πŸ“ File Structure
103+
```
104+
πŸ“‚ Article-Views-I
105+
│── πŸ“œ README.md
106+
│── πŸ“œ solution.sql
107+
│── πŸ“œ solution_group_by.sql
108+
│── πŸ“œ solution_pandas.py
109+
│── πŸ“œ test_cases.sql
110+
```
111+
112+
---
113+
114+
## πŸ”— Useful Links
115+
- πŸ“– [LeetCode Problem](https://leetcode.com/problems/article-views-i/)
116+
- πŸ“š [SQL DISTINCT vs GROUP BY](https://www.w3schools.com/sql/sql_distinct.asp)
117+
- 🐍 [Pandas Unique Function](https://pandas.pydata.org/docs/reference/api/pandas.Series.unique.html)
118+
119+
## Let me know if you need any changes! πŸš€
Lines changed: 126 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,126 @@
1+
# 🏦 Monthly Transactions I - LeetCode 1193
2+
3+
## πŸ“Œ Problem Statement
4+
You are given the **Transactions** table that records financial transactions.
5+
6+
### Transactions Table
7+
| Column Name | Type |
8+
| ----------- | ------- |
9+
| id | int |
10+
| country | varchar |
11+
| state | enum |
12+
| amount | int |
13+
| trans_date | date |
14+
15+
- **id** is the **primary key**.
16+
- The **state** column is an `ENUM` type with values **"approved"** and **"declined"**.
17+
- Each row **records a transaction** with an amount and a transaction date.
18+
19+
### Task:
20+
Find **monthly statistics** for each country:
21+
- Total **number of transactions**.
22+
- Total **amount of transactions**.
23+
- Total **number of approved transactions**.
24+
- Total **amount of approved transactions**.
25+
26+
The **month format should be `YYYY-MM`**.
27+
28+
---
29+
30+
## πŸ“Š Example 1:
31+
### Input:
32+
**Transactions Table**
33+
| id | country | state | amount | trans_date |
34+
| --- | ------- | -------- | ------ | ---------- |
35+
| 121 | US | approved | 1000 | 2018εΉ΄12月18ζ—₯ |
36+
| 122 | US | declined | 2000 | 2018εΉ΄12月19ζ—₯ |
37+
| 123 | US | approved | 2000 | 2019εΉ΄01月01ζ—₯ |
38+
| 124 | DE | approved | 2000 | 2019εΉ΄01月07ζ—₯ |
39+
40+
### Output:
41+
| month | country | trans_count | approved_count | trans_total_amount | approved_total_amount |
42+
| ------- | ------- | ----------- | -------------- | ------------------ | --------------------- |
43+
| 2018-12 | US | 2 | 1 | 3000 | 1000 |
44+
| 2019-01 | US | 1 | 1 | 2000 | 2000 |
45+
| 2019-01 | DE | 1 | 1 | 2000 | 2000 |
46+
47+
### Explanation:
48+
- **December 2018 (US)**:
49+
- **2 transactions** (1000 + 2000).
50+
- **1 approved transaction** (1000).
51+
- **January 2019 (US)**:
52+
- **1 transaction** (2000).
53+
- **1 approved transaction** (2000).
54+
- **January 2019 (DE)**:
55+
- **1 transaction** (2000).
56+
- **1 approved transaction** (2000).
57+
58+
---
59+
60+
## πŸ–₯ SQL Solution
61+
62+
### 1️⃣ Standard MySQL Solution
63+
#### Explanation:
64+
- **Extract the month** from `trans_date` using `DATE_FORMAT()`.
65+
- **Count transactions** for each `month` and `country`.
66+
- **Sum transaction amounts**.
67+
- **Filter only approved transactions** separately using `CASE WHEN`.
68+
69+
```sql
70+
SELECT
71+
DATE_FORMAT(trans_date, '%Y-%m') AS month,
72+
country,
73+
COUNT(id) AS trans_count,
74+
SUM(CASE WHEN state = 'approved' THEN 1 ELSE 0 END) AS approved_count,
75+
SUM(amount) AS trans_total_amount,
76+
SUM(CASE WHEN state = 'approved' THEN amount ELSE 0 END) AS approved_total_amount
77+
FROM Transactions
78+
GROUP BY month, country
79+
ORDER BY month, country;
80+
```
81+
82+
---
83+
84+
## 🐍 Pandas Solution (Python)
85+
#### Explanation:
86+
- **Extract the month (`YYYY-MM`)** from `trans_date`.
87+
- **Group by month and country**.
88+
- **Compute counts and sums** using `.agg()`.
89+
90+
```python
91+
import pandas as pd
92+
93+
def monthly_transactions(transactions: pd.DataFrame) -> pd.DataFrame:
94+
# Extract 'YYYY-MM' from the trans_date
95+
transactions['month'] = transactions['trans_date'].dt.strftime('%Y-%m')
96+
97+
# Aggregate transaction counts and sums
98+
result = transactions.groupby(['month', 'country']).agg(
99+
trans_count=('id', 'count'),
100+
approved_count=('state', lambda x: (x == 'approved').sum()),
101+
trans_total_amount=('amount', 'sum'),
102+
approved_total_amount=('amount', lambda x: x[transactions['state'] == 'approved'].sum())
103+
).reset_index()
104+
105+
return result.sort_values(['month', 'country'])
106+
```
107+
108+
---
109+
110+
## πŸ“ File Structure
111+
```
112+
πŸ“‚ Monthly-Transactions
113+
│── πŸ“œ README.md
114+
│── πŸ“œ solution.sql
115+
│── πŸ“œ solution_pandas.py
116+
│── πŸ“œ test_cases.sql
117+
```
118+
119+
---
120+
121+
## πŸ”— Useful Links
122+
- πŸ“– [LeetCode Problem](https://leetcode.com/problems/monthly-transactions-i/)
123+
- πŸ“š [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
124+
- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/groupby.html)
125+
126+
## Let me know if you need any modifications! πŸš€
Lines changed: 149 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,149 @@
1+
# 🚌 Last Person to Fit in the Bus - LeetCode 1204
2+
3+
## πŸ“Œ Problem Statement
4+
You are given the **Queue** table, which contains information about people waiting for a bus.
5+
6+
### Queue Table
7+
| Column Name | Type |
8+
| ----------- | ------- |
9+
| person_id | int |
10+
| person_name | varchar |
11+
| weight | int |
12+
| turn | int |
13+
14+
- **person_id** contains unique values.
15+
- The **turn** column determines the order in which people will board (`turn = 1` means the first person to board).
16+
- The **bus has a weight limit of 1000 kg**.
17+
- Only **one person can board at a time**.
18+
19+
### Task:
20+
Find **the last person** who can board the bus **without exceeding the 1000 kg weight limit**.
21+
22+
---
23+
24+
## πŸ“Š Example 1:
25+
### Input:
26+
**Queue Table**
27+
| person_id | person_name | weight | turn |
28+
| --------- | ----------- | ------ | ---- |
29+
| 5 | Alice | 250 | 1 |
30+
| 4 | Bob | 175 | 5 |
31+
| 3 | Alex | 350 | 2 |
32+
| 6 | John Cena | 400 | 3 |
33+
| 1 | Winston | 500 | 6 |
34+
| 2 | Marie | 200 | 4 |
35+
36+
### Output:
37+
| person_name |
38+
| ----------- |
39+
| John Cena |
40+
41+
### Explanation:
42+
Ordering by `turn`:
43+
| Turn | ID | Name | Weight | Total Weight |
44+
| ---- | --- | --------- | ------ | ------------ |
45+
| 1 | 5 | Alice | 250 | 250 |
46+
| 2 | 3 | Alex | 350 | 600 |
47+
| 3 | 6 | John Cena | 400 | 1000 | βœ… (last person to board) |
48+
| 4 | 2 | Marie | 200 | 1200 | ❌ (exceeds limit) |
49+
| 5 | 4 | Bob | 175 | ❌ |
50+
| 6 | 1 | Winston | 500 | ❌ |
51+
52+
---
53+
54+
## πŸ–₯ SQL Solution
55+
56+
### 1️⃣ Standard MySQL Solution
57+
#### Explanation:
58+
- **Use a self-join** to accumulate the total weight up to each person's turn.
59+
- **Filter out** people whose cumulative weight exceeds **1000**.
60+
- **Find the last person** who can board.
61+
62+
```sql
63+
SELECT a.person_name
64+
FROM
65+
Queue AS a,
66+
Queue AS b
67+
WHERE a.turn >= b.turn
68+
GROUP BY a.person_id
69+
HAVING SUM(b.weight) <= 1000
70+
ORDER BY a.turn DESC
71+
LIMIT 1;
72+
```
73+
74+
---
75+
76+
### πŸ“ Step-by-Step Breakdown:
77+
78+
1️⃣ **Self-Join on the Table**
79+
```sql
80+
FROM Queue AS a, Queue AS b
81+
WHERE a.turn >= b.turn
82+
```
83+
- This pairs each row `a` with all rows `b` where `b.turn` is less than or equal to `a.turn`.
84+
- Allows us to calculate the **cumulative sum of weights** for each person.
85+
86+
2️⃣ **Group by Each Person**
87+
```sql
88+
GROUP BY a.person_id
89+
```
90+
- Groups all rows by `person_id` so we can perform calculations per person.
91+
92+
3️⃣ **Compute the Cumulative Weight**
93+
```sql
94+
HAVING SUM(b.weight) <= 1000
95+
```
96+
- Filters out people whose cumulative boarding weight exceeds **1000 kg**.
97+
98+
4️⃣ **Find the Last Person Who Can Board**
99+
```sql
100+
ORDER BY a.turn DESC
101+
LIMIT 1;
102+
```
103+
- **Sorts by turn in descending order** so that we find the **last person** who can board.
104+
- **Limits to 1 row** to return only the last eligible person.
105+
106+
---
107+
108+
## 🐍 Pandas Solution (Python)
109+
#### Explanation:
110+
- **Sort by `turn`** to simulate the boarding order.
111+
- **Compute the cumulative sum** of weights.
112+
- **Find the last person** whose weight sum **does not exceed 1000**.
113+
114+
```python
115+
import pandas as pd
116+
117+
def last_person_to_fit(queue: pd.DataFrame) -> pd.DataFrame:
118+
# Sort by turn
119+
queue = queue.sort_values(by="turn")
120+
121+
# Compute cumulative weight sum
122+
queue["cumulative_weight"] = queue["weight"].cumsum()
123+
124+
# Filter those who fit on the bus
125+
queue = queue[queue["cumulative_weight"] <= 1000]
126+
127+
# Return the last person to fit
128+
return queue.tail(1)[["person_name"]]
129+
```
130+
131+
---
132+
133+
## πŸ“ File Structure
134+
```
135+
πŸ“‚ Last-Person-Fit
136+
│── πŸ“œ README.md
137+
│── πŸ“œ solution.sql
138+
│── πŸ“œ solution_pandas.py
139+
│── πŸ“œ test_cases.sql
140+
```
141+
142+
---
143+
144+
## πŸ”— Useful Links
145+
- πŸ“– [LeetCode Problem](https://leetcode.com/problems/last-person-to-fit-in-the-bus/)
146+
- πŸ“š [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
147+
- 🐍 [Pandas cumsum() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumsum.html)
148+
149+
## Let me know if you need any modifications! πŸš€

0 commit comments

Comments
(0)

AltStyle γ«γ‚ˆγ£γ¦ε€‰ζ›γ•γ‚ŒγŸγƒšγƒΌγ‚Έ (->γ‚ͺγƒͺγ‚ΈγƒŠγƒ«) /