该表由 JavaScprit 根据来自 API 端点的数据动态构建。
您可以查询该端点并重建表。
方法如下：
from datetime import datetime
import requests
from tabulate import tabulate
endpoint = "https://api.weather.com/v1/location/KDCA:9:US/observations/historical.json?apiKey=e1f10a1e78da46f5b10a1e78da96f525&units=e&startDate=20190125&endDate=20190125"
response = requests.get(endpoint).json()["observations"]
weather_data = sorted(response, key=lambda k: k["valid_time_gmt"])
header = [
"Time", "Temperature", "Dew Point", "Humidity", "Wind",
"Wind Speed", "Wind Gust", "Pressure", "Percip.", "Conditions",
]
table = []
for item in weather_data:
row = [
datetime.fromtimestamp(item["valid_time_gmt"]).strftime('%I:%M %p'),
item["temp"],
f'{item["dewPt"]} °F',
f'{item["rh"]} %',
item["wdir_cardinal"],
item["wspd"],
f'{item["gust"] if item["gust"] else 0} mph',
f'{item["pressure"]} in',
f'{item["precip_total"] if item["precip_total"] else "0.0 in"}',
item["wx_phrase"],
]
table.append(row)
print(tabulate(table, headers=header, tablefmt="pretty"))
示例输出：
+----------+-------------+-----------+----------+------+------------+-----------+----------+---------+---------------+
| Time | Temperature | Dew Point | Humidity | Wind | Wind Speed | Wind Gust | Pressure | Percip. | Conditions |
+----------+-------------+-----------+----------+------+------------+-----------+----------+---------+---------------+
| 06:52 AM | 37 | 22 °F | 54 % | WNW | 10 | 0 mph | 29.91 in | 0.0 in | Fair |
| 07:52 AM | 36 | 21 °F | 55 % | NW | 6 | 0 mph | 29.92 in | 0.0 in | Fair |
| 08:52 AM | 35 | 21 °F | 57 % | W | 7 | 0 mph | 29.95 in | 0.0 in | Fair |
| 09:26 AM | 35 | 21 °F | 57 % | W | 6 | 0 mph | 29.95 in | 0.0 in | Mostly Cloudy |
| 09:52 AM | 35 | 22 °F | 59 % | W | 5 | 0 mph | 29.96 in | 0.0 in | Mostly Cloudy |
| 10:52 AM | 36 | 22 °F | 57 % | VAR | 5 | 0 mph | 29.98 in | 0.0 in | Cloudy |
| 11:52 AM | 35 | 21 °F | 57 % | WNW | 9 | 0 mph | 30.0 in | 0.0 in | Partly Cloudy |
| 12:52 PM | 35 | 21 °F | 57 % | NW | 10 | 0 mph | 30.0 in | 0.79 | Mostly Cloudy |
| 01:52 PM | 35 | 21 °F | 57 % | WNW | 5 | 0 mph | 30.02 in | 0.0 in | Partly Cloudy |
| 02:52 PM | 37 | 22 °F | 54 % | WSW | 8 | 0 mph | 30.04 in | 0.0 in | Partly Cloudy |
| 03:52 PM | 40 | 22 °F | 49 % | W | 14 | 18 mph | 30.04 in | 0.0 in | Fair |
| 04:52 PM | 41 | 19 °F | 41 % | W | 17 | 21 mph | 30.06 in | 0.0 in | Partly Cloudy |
| 05:52 PM | 43 | 20 °F | 40 % | WNW | 14 | 23 mph | 30.06 in | 0.0 in | Partly Cloudy |
| 06:52 PM | 41 | 18 °F | 40 % | W | 16 | 0 mph | 30.07 in | 0.0 in | Mostly Cloudy |
| 07:21 PM | 42 | 19 °F | 40 % | NW | 16 | 24 mph | 30.07 in | 0.0 in | Mostly Cloudy |
| 07:52 PM | 43 | 20 °F | 40 % | NW | 18 | 24 mph | 30.08 in | 0.0 in | Partly Cloudy |
| 08:52 PM | 43 | 12 °F | 28 % | NW | 9 | 20 mph | 30.09 in | 0.0 in | Fair |
| 09:52 PM | 42 | 11 °F | 28 % | WNW | 13 | 0 mph | 30.09 in | 0.0 in | Fair |
| 10:52 PM | 40 | 10 °F | 29 % | NW | 13 | 0 mph | 30.12 in | 0.0 in | Fair |
| 11:52 PM | 37 | 12 °F | 36 % | NNW | 10 | 0 mph | 30.14 in | 0.0 in | Fair |
| 12:52 AM | 36 | 8 °F | 31 % | NNW | 12 | 0 mph | 30.16 in | 0.0 in | Fair |
| 01:52 AM | 35 | 8 °F | 33 % | NNW | 9 | 0 mph | 30.17 in | 0.0 in | Fair |
| 02:52 AM | 33 | 7 °F | 34 % | NW | 13 | 0 mph | 30.18 in | 0.0 in | Fair |
| 03:52 AM | 32 | 7 °F | 35 % | NNW | 5 | 0 mph | 30.18 in | 0.0 in | Fair |
| 04:52 AM | 32 | 7 °F | 35 % | N | 5 | 0 mph | 30.2 in | 0.0 in | Fair |
| 05:52 AM | 31 | 7 °F | 37 % | NNW | 6 | 0 mph | 30.22 in | 0.0 in | Fair |
+----------+-------------+-----------+----------+------+------------+-----------+----------+---------+---------------+