Last updated on January 21st, 2025 at 11:00 pm
Here, we see the Rising Temperature LeetCode Solution. This Leetcode problem is solved using MySQL and Pandas.
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Level of Question
Easy

Rising Temperature LeetCode Solution
Table of Contents
1. Problem Statement
Column Name | Type |
id | int |
recordDate | date |
temperature | int |
Weather
id is the column with unique values for this table. There are no different rows with the same recordDate. This table contains information about the temperature on a certain day.
Write a solution to find all dates’ Id
with higher temperatures compared to its previous dates (yesterday).
Return the result table in any order.
The result format is in the following example.
Example 1:
Input:
id | recordDate | temperature |
1 | 2015-01-01 | 10 |
2 | 2015-01-02 | 25 |
3 | 2015-01-03 | 20 |
4 | 2015-01-04 | 30 |
Output:
id |
2 |
4 |
Explanation: In 2015-01-02, the temperature was higher than the previous day (10 -> 25). In 2015-01-04, the temperature was higher than the previous day (20 -> 30).
2. Code Implementation in Different Languages
2.1 Rising Temperature MySQL
select w1.Id from Weather as w1, Weather as w2 where datediff(w1.RecordDate, w2.RecordDate) = 1 and w1.Temperature > w2.Temperature;
2.2 Rising Temperature Pandas
import pandas as pd def rising_temperature(weather: pd.DataFrame) -> pd.DataFrame: weather['recordDate'] = pd.to_datetime(weather['recordDate']) weather_shifted = weather.copy() weather_shifted['recordDate'] = weather_shifted['recordDate'] + pd.to_timedelta(1, unit='D') merged_df = pd.merge(weather, weather_shifted, on='recordDate', suffixes=('_today', '_yesterday')) result = merged_df[merged_df['temperature_today'] > merged_df['temperature_yesterday']][['id_today']].rename(columns={'id_today': 'Id'}) return result