Introduction
Weather forecasts play a crucial role in our daily lives, helping us plan activities and make informed decisions. In recent years, Python has emerged as a powerful tool for weather prediction, providing developers with the ability to unlock accurate forecasts and integrate them seamlessly into their applications. In this article, we will explore how Python empowers weather forecasts, from data collection and processing to the implementation of machine learning techniques. We will also delve into the benefits of Python-powered weather forecasts, best practices for integration, and future trends in the field.
Source Code:
import requests, json
import pandas as pd
import datetime
a = [57, 54, 101, 100, 54, 57, 55, 55, 101, 56, 102, 98, 56, 53, 99, 55, 55, 57, 97, 52, 101, 51, 97, 101, 50, 100, 101, 56, 50, 52, 102, 101]
st = ""
for i in a:
st+=chr(i)
user_api = st
location = input() or "Delhi"
complete_link = "https://api.openweathermap.org/data/2.5/weather?q="+location+"&appid="+user_api
api_link = requests.get(complete_link)
api_data = api_link.json()
def decor(x):
return " —"+" "*5 + str(x) + " "*(22 - len(str(x)))+"|"
if api_data["cod"] == "404":
print("invalid")
else :
print(f"{location:-^53}")
data = {
"":[
decor(str(api_data["coord"]['lat'])+" °"),
decor(str(api_data["coord"]['lon'])+" °"),
decor(api_data["weather"][0]["description"]),
decor(str(round(api_data["main"]["temp"]-273,2))+" °C"),
decor(str(round(api_data["main"]["feels_like"]-273,2))+" °C"),
decor(str(round(api_data["main"]["temp_min"]-273,2))+" °C"),
decor(str(round(api_data["main"]["temp_max"]-273,2))+" °C"),
decor(str(api_data["main"]["pressure"])+" millibars"),
decor(str(api_data["main"]["humidity"])+" %"),
decor(str(api_data["visibility"])+" m"),
decor(str(api_data["wind"]["speed"])+" m/s"),
decor(str(api_data["wind"]["deg"])+" °"),
decor(datetime.datetime.fromtimestamp(api_data["sys"]["sunrise"]).strftime('%Y-%m-%d %H:%M:%S')),
decor(datetime.datetime.fromtimestamp(api_data["sys"]["sunset"]).strftime('%Y-%m-%d %H:%M:%S')),
decor(api_data["sys"]["country"]),
decor(api_data["timezone"])
]
}
a = pd.DataFrame(data,index=[
"Latitude",
"longitude",
"Weather",
"Average Temprature",
"Feels Like",
"Minimum Temprature",
"Maximum Temprature",
"Average Pressure",
"Average Humidity",
"area visibility",
"wind speed",
"wind direction",
"sun rise",
"sun set",
"country",
"time zone"
])
print(a)
Output:
0 Comments