How to Extract Weather Data from Google in Python

Scraping Google weather search page to extract useful information such as current weather, temperature, next days' weather and much more in Python using requests and Beautiful Soup.
  · 8 min read · Updated jun 2020 · Web Scraping

As you may know, Web scraping is essentially extracting data from websites. Doing such task in a high-level programming language like Python is very handy and powerful. In this tutorial, you will learn how to use requests and BeautifulSoup to scrape weather data from Google search engine.

Although, this is not the perfect and official way to get the actual weather for a specific location, because there are hundreds of weather APIs out there to use. However, it is a great exercise for you to get familiar with scraping.

Related: How to Make an Email Extractor in Python.

Alright, let's get started, installing the required dependencies:

pip3 install requests bs4

First, let's experiment a little bit, open up a Google search bar and type for instance: "weather london", you'll see the official weather, let's right click and inspect HTML code as shown in the following figure:

Inspect Element on Google Weather Region

Note: Google does not have its appropriate weather API, as it also scrapes weather data from, so we are essentially scraping from it.

You'll be forwarded to HTML code that is responsible for displaying the region, day and hour, and the actual weather:

HTML tags to extract in Python

Great, let's try to extract these information in a Python interactive shell quickly:

In [7]: soup = BeautifulSoup(requests.get("").content)

In [8]: soup.find("div", attrs={'id': 'wob_loc'}).text
Out[8]: 'London, UK'

Don't worry about how we created the soup object, all you need to worry about right now is how you can grab that information from HTML code, all you have to specify to soup.find() method is the HTML tag name and the matched attributes, in this case, a div element with an id of "wob_loc" will get us the location.

Similarly, let's extract current day and time:

In [9]: soup.find("div", attrs={"id": "wob_dts"}).text
Out[9]: 'Wednesday 3:00 PM'

The actual weather:

In [10]: soup.find("span", attrs={"id": "wob_dc"}).text
Out[10]: 'Sunny'

Alright, now you are familiar with it, let's create our quick script for grabbing more information about the weather ( as much information as we can ). Open up a new Python script and follow with me.

First, let's import necessary modules:

from bs4 import BeautifulSoup as bs
import requests

It is worth to note that Google tries to prevent us to scrape its website programmatically, as it is an unofficial way to get data, because it provides us with a convenient alternative, which is The Custom Search Engine, but just for educational purposes, we gonna pretend that we are a legitimate web browser, let's define the user agent:

USER_AGENT = "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36"
# US english
LANGUAGE = "en-US,en;q=0.5"

Let's define a function that given a URL, it tries to extract all useful weather information and return it in a dictionary:

def get_weather_data(url):
    session = requests.Session()
    session.headers['User-Agent'] = USER_AGENT
    session.headers['Accept-Language'] = LANGUAGE
    session.headers['Content-Language'] = LANGUAGE
    html = session.get(url)
    # create a new soup
    soup = bs(html.text, "html.parser")

All we did here, is to create a session with that browser and language, and then download the HTML code using session.get(url) from the web, and finally creating the BeautifulSoup object with an HTML parser.

Let's get current region, weather, temperature and actual day and hour:

    # store all results on this dictionary
    result = {}
    # extract region
    result['region'] = soup.find("div", attrs={"id": "wob_loc"}).text
    # extract temperature now
    result['temp_now'] = soup.find("span", attrs={"id": "wob_tm"}).text
    # get the day and hour now
    result['dayhour'] = soup.find("div", attrs={"id": "wob_dts"}).text
    # get the actual weather
    result['weather_now'] = soup.find("span", attrs={"id": "wob_dc"}).text

Since the current precipitation, humidity and wind are displayed, why not grab them ?

    # get the precipitation
    result['precipitation'] = soup.find("span", attrs={"id": "wob_pp"}).text
    # get the % of humidity
    result['humidity'] = soup.find("span", attrs={"id": "wob_hm"}).text
    # extract the wind
    result['wind'] = soup.find("span", attrs={"id": "wob_ws"}).text

Let's try to get weather information about the next few days, if you take some time finding the HTML code of it, you'll find something similar to this:

<div class="wob_df"
    data-wob-di="3" role="button" tabindex="0" data-ved="2ahUKEwifm-6c6NrkAhUBdBQKHVbBADoQi2soAzAAegQIDBAN">
    <div class="vk_lgy" style="padding-top:7px;line-height:15px" aria-label="Saturday">Sat</div>
    <div style="display:inline-block"><img style="margin:1px 4px 0;height:48px;width:48px" alt="Sunny"
            src="//" data-atf="1"></div>
    <div style="font-weight:normal;line-height:15px;font-size:13px">
        <div class="vk_gy" style="display:inline-block;padding-right:5px"><span class="wob_t"
                style="display:inline">25</span><span class="wob_t" style="display:none">77</span>°</div>
        <div class="vk_lgy" style="display:inline-block"><span class="wob_t" style="display:inline">17</span><span
                class="wob_t" style="display:none">63</span>°</div>

Not human readable, I know, but this parent div contains all information about one next day, which is "Saturday" as shown in the first child div element with the class of vk_lgy in the aria-label attribute, the weather information is within the alt attribute in the img element, in this case "Sunny". The temperature however, there is a max and min with both Celsius and Fahrenheit, this lines of code takes care of everything:

    # get next few days' weather
    next_days = []
    days = soup.find("div", attrs={"id": "wob_dp"})
    for day in days.findAll("div", attrs={"class": "wob_df"}):
        # extract the name of the day
        day_name = day.find("div", attrs={"class": "vk_lgy"}).attrs['aria-label']
        # get weather status for that day
        weather = day.find("img").attrs["alt"]
        temp = day.findAll("span", {"class": "wob_t"})
        # maximum temparature in Celsius, use temp[1].text if you want fahrenheit
        max_temp = temp[0].text
        # minimum temparature in Celsius, use temp[3].text if you want fahrenheit
        min_temp = temp[2].text
        next_days.append({"name": day_name, "weather": weather, "max_temp": max_temp, "min_temp": min_temp})
    # append to result
    result['next_days'] = next_days
    return result

Now result dictionary got everything we need, let's finish up the script by parsing command line arguments using argparse:

if __name__ == "__main__":
    URL = ""
    import argparse
    parser = argparse.ArgumentParser(description="Quick Script for Extracting Weather data using Google Weather")
    parser.add_argument("region", nargs="?", help="""Region to get weather for, must be available region.
                                        Default is your current location determined by your IP Address""", default="")
    # parse arguments
    args = parser.parse_args()
    region = args.region
    URL += region
    # get data
    data = get_weather_data(URL)

Displaying everything:

    # print data
    print("Weather for:", data["region"])
    print("Now:", data["dayhour"])
    print(f"Temperature now: {data['temp_now']}°C")
    print("Description:", data['weather_now'])
    print("Precipitation:", data["precipitation"])
    print("Humidity:", data["humidity"])
    print("Wind:", data["wind"])
    print("Next days:")
    for dayweather in data["next_days"]:
        print("="*40, dayweather["name"], "="*40)
        print("Description:", dayweather["weather"])
        print(f"Max temperature: {dayweather['max_temp']}°C")
        print(f"Min temperature: {dayweather['min_temp']}°C")

If you run this script, it will automatically grab the weather of your current region determined by your IP address. However, if you want a different region, you can pass it as arguments:

C:\weather-extractor>python "New York"

This will show weather data of New York state in the US:

Weather for: New York, NY, USA
Now: wednesday 2:00 PM
Temperature now: 20°C
Description: Mostly Cloudy
Precipitation: 0%
Humidity: 52%
Wind: 13 km/h
Next days:
======================================== wednesday ========================================
Description: Mostly Cloudy
Max temperature: 21°C
Min temperature: 12°C
======================================== thursday ========================================
Description: Sunny
Max temperature: 22°C
Min temperature: 14°C
======================================== friday ========================================
Description: Partly Sunny
Max temperature: 28°C
Min temperature: 18°C
======================================== saturday ========================================
Description: Sunny
Max temperature: 30°C
Min temperature: 19°C
======================================== sunday ========================================
Description: Partly Sunny
Max temperature: 29°C
Min temperature: 21°C
======================================== monday ========================================
Description: Partly Cloudy
Max temperature: 30°C
Min temperature: 19°C
======================================== tuesday ========================================
Description: Mostly Sunny
Max temperature: 26°C
Min temperature: 16°C
======================================== wednesday ========================================
Description: Mostly Sunny
Max temperature: 25°C
Min temperature: 19°C

Alright, we are done with this tutorial, I hope this was helpful for you to understand how you can combine requests and BeautifulSoup to grab data from web pages.

By the way, there is another tutorial for extracting YouTube videos data in Python or accessing wikipedia pages in Python !

Read also: How to Extract All Website Links in Python.

Happy Scraping ♥

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