Scrape and plot historical weather data

Scraping and plotting historical weather data involves several steps:

1. Identify a reliable source of historical weather data

2. Scrape or download the data

3. Process the data

4. Plot the data

Here’s a detailed walkthrough with Python code to demonstrate this.

Step 1: Identify a Data Source

Some popular sources for historical weather data include:

• NOAA Climate Data Online (requires API or manual download)

• Weather websites with public historical data like Weather Underground, Visual Crossing, or World Weather Online

• OpenWeatherMap API (provides historical data with API key)

• Meteostat Python library (provides access to historical weather data for free)

For scraping directly from a website, you need to check their policy and if they allow scraping.

For this example, I’ll use Meteostat because it’s free, easy to use, and doesn’t require scraping from web pages.

Step 2: Install required libraries

bash
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pip install meteostat matplotlib pandas

Step 3: Fetch and Plot Historical Weather Data with Meteostat

python
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from meteostat import Point, Daily
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# Define location (example: New York City)
location = Point(40.7128, -74.0060)  # latitude, longitude

# Define time period
start = datetime(2023, 1, 1)
end = datetime(2023, 4, 1)

# Get daily data for the location and period
data = Daily(location, start, end)
data = data.fetch()

# Inspect data columns: temperature, precipitation, wind, etc.
print(data.head())

# Plot daily average temperature (tavg)
plt.figure(figsize=(12,6))
plt.plot(data.index, data['tavg'], label='Average Temperature (°C)')
plt.plot(data.index, data['tmin'], label='Min Temperature (°C)', linestyle='--')
plt.plot(data.index, data['tmax'], label='Max Temperature (°C)', linestyle='--')

plt.title('Daily Temperature in New York City (Jan-Apr 2023)')
plt.xlabel('Date')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.grid(True)
plt.show()

What the code does:

• Defines a geographic location using latitude and longitude.

• Fetches daily historical weather data for that location and date range.

• Plots average, minimum, and maximum daily temperatures over time.

Alternative: Scraping Weather Data from Websites

If you want to scrape data from a website (e.g., Weather Underground), you would:

• Use requests to fetch the page.

• Use BeautifulSoup to parse HTML.

• Extract the relevant data (dates, temperature, precipitation).

• Store it in a DataFrame.

• Plot similarly using matplotlib.

Note: Many websites prohibit scraping or require you to use their API, so always verify legal usage.

If you want, I can also provide a sample scraping script for a specific weather site or explain how to use APIs like OpenWeatherMap or NOAA for historical weather data. Just let me know!