197_rising_temperature.md

Rising Temperature

Table: Weather

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| id            | int     |
| recordDate    | date    |
| temperature   | int     |
+---------------+---------+
id is the column with unique values for this table.
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: 
Weather table:
+----+------------+-------------+
| 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).

Solution

import pandas as pd


def rising_temperature(weather: pd.DataFrame) -> pd.DataFrame:
    weather = weather.sort_values(by=['recordDate'])
    yesterday = weather['recordDate'].diff().dt.days
    yesterday_tmp = weather['temperature'].diff()
    return weather[(yesterday == 1) & (yesterday_tmp > 0)][['id']]


if __name__ == '__main__':
    data = [[1, '2015-01-01', 10], [2, '2015-01-02', 25], [3, '2015-01-03', 20], [4, '2015-01-04', 30]]
    weather = pd.DataFrame(data, columns=['id', 'recordDate', 'temperature']).astype(
        {'id': 'Int64', 'recordDate': 'datetime64[ns]', 'temperature': 'Int64'})
    print(rising_temperature(weather))