The best month to travel to Jeju Island: data to help you find out! (feat. Python)

Hey, travel lovers! Are you planning a trip to Jeju Island? Ah, the excitement, I know it all too well, but you've been thinking about Wondering which is the best month to travel to Jeju Island? If you're worried about bad weather ruining your trip, listen up! Today, we're going to show you how to use data to learn about the Best time to visit Jeju IslandLet's take a look at them together.

제주도 여행하기 좋은 달 포스트 그림1
(Example illustration for Jeju Island's Best Months to Travel post, source: pieliny )

When is the best month to travel to Jeju Island?

Finding the best month to visit Jeju Island is really important because the weather can make or break your trip! So I analyzed the weather data for the past year on Jeju Island, looking at temperature, wind speed, and cloud cover.

It is generally accepted that the best weather conditions for traveling are as follows. Let's analyze the data to find a month that fits these conditions.

  • Average temperatures between 20°C and 28°C.
  • Maximum wind speed is less than 15 km/h.
  • Average cloud cover less than 501 TP3T.

The best time to travel to Jeju Island according to data

Okay, so now we've got PythonTo find the best months to travel to Jeju Island, I wrote the following code. For reference, I used the WeatherAPI.com API to retrieve the data. You can find the API key in the Dashboard after signing up.

import requests
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from time import sleep

# WeatherAPI.com API key (replace with your actual key)
api_key = 'YOUR_API_KEY'

# List to store weather data
full_weather_data = []

# Collect data for the last year
end_date = datetime.now()
start_date = end_date - timedelta(days=365)

# Iterate over date range
for date in pd.date_range(start=start_date, end=end_date):
    url = f"http://api.weatherapi.com/v1/history.json?key={api_key}&q=Jeju&dt={date.strftime('%Y-%m-%d')}"
    
    try:
        response = requests.get(url)
        if response.status_code == 200:
            data = response.json()
            forecast_day = data['forecast']['forecastday'][0]['day']
            
            # Add weather data
            full_weather_data.append({
                'date': date.strftime('%Y-%m-%d'),
                'temp': forecast_day['avgtemp_c'],
                'wind_speed': forecast_day['maxwind_kph'],
                'cloud_cover': forecast_day['avghumidity']
            })
            
            print(f"Data for {date.strftime('%Y-%m-%d')} successfully added")
        else:
            print(f"Failed to fetch data for {date.strftime('%Y-%m-%d')}. Status code: {response.status_code}")
    
    except Exception as e:
        print(f"Error occurred while requesting data for {date.strftime('%Y-%m-%d')}: {e}")
    
    # Wait time to consider API request limits
    sleep(1)

# Create DataFrame
df = pd.DataFrame(full_weather_data)
df['date'] = pd.to_datetime(df['date'])

# Define good weather conditions
df['good_weather'] = (df['temp'].between(20, 28) &
                      (df['wind_speed'] < 15) &
                      (df['cloud_cover']  0]

# Temperature plot
plt.subplot(3, 1, 1)
plt.plot(monthly_data['date'], monthly_data['temp'], marker='o')
plt.title('Monthly Average Temperature')
plt.ylabel('Temperature (°C)')
plt.axhline(y=20, color='r', linestyle='--', label='Lower Bound (20°C)')
plt.axhline(y=28, color='r', linestyle='--', label='Upper Bound (28°C)')
for idx, row in best_months.iterrows():
    plt.annotate(row['date'].strftime('%b'), (row['date'], row['temp']),
                 textcoords="offset points", xytext=(0,10), ha='center',
                 fontsize=10, color='green')

# Wind speed plot
plt.subplot(3, 1, 2)
plt.plot(monthly_data['date'], monthly_data['wind_speed'], marker='o', color='green')
plt.title('Monthly Average Wind Speed')
plt.ylabel('Wind Speed (km/h)')
plt.axhline(y=15, color='r', linestyle='--', label='Wind Speed Threshold (15 km/h)')
for idx, row in best_months.iterrows():
    plt.annotate(row['date'].strftime('%b'), (row['date'], row['wind_speed']),
                 textcoords="offset points", xytext=(0,10), ha='center',
                 fontsize=10, color='green')

# Cloud cover plot
plt.subplot(3, 1, 3)
plt.plot(monthly_data['date'], monthly_data['cloud_cover'], marker='o', color='blue')
plt.title('Monthly Average Cloud Cover')
plt.ylabel('Cloud Cover (%)')
plt.axhline(y=50, color='r', linestyle='--', label='Cloud Cover Threshold (50%)')
for idx, row in best_months.iterrows():
    plt.annotate(row['date'].strftime('%b'), (row['date'], row['cloud_cover']),
                 textcoords="offset points", xytext=(0,10), ha='center',
                 fontsize=10, color='green')

plt.tight_layout()

# Final plot with legends and display
plt.legend(loc='upper right')
plt.show()

# Print best months for travel
print("Best months for travel:")
print(best_months[['date', 'good_weather']]])

Code commentary

This code is a program that uses WeatherAPI.com to collect and analyze a year's worth of weather data for Jeju Island to find the best months to travel. The main features are as follows

1. collect data via API

  • Request weather data from WeatherAPI.com for Jeju Island for the past year by day.
  • For each day, collect the average temperature, maximum wind speed, and average humidity.

2. data processing

  • Convert the collected data into a pandas DataFrame.
  • Define 'good weather' conditions and evaluate them for each date (temperature 20-28°C, wind speed less than 15 km/h, cloud cover less than 501 TP3T).

3. analyze your data

  • Calculate average monthly temperature, wind speed, and cloud cover.
  • Aggregate the number of days that meet the "good weather" condition by month.

4. visualize

  • Graph average monthly temperature, wind speed, and cloud cover using matplotlib.
  • Add a baseline to each graph to visualize "good weather" conditions.
  • Plot the best months to travel on a graph.

5. output the results

  • Output the best months to travel and the number of "good weather" days in each month. In the output below, you can see that January, November/December, February, and March have the highest number of good weather days, in that order.

제주도 여행하기 좋은 달 그림2
(Visualize the best months to travel to Jeju Island)

Results for best months to travel to Jeju Island

Now it's time to see the results! Looking at the graph, the best months for traveling to Jeju Island stand out. (By the way, the average monthly temperature seems to be a little different for us.) Based on the picture above, it looks like November is about the right time to go.

1. average temperature by month (first graph)

  • Travel-friendly temperature range: 20°C to 28°C (indicated by the red dotted line)
  • Result:
    • 2024 January, February, March, November, December's average temperature falls within this range.
    • In particular, the Decemberand Januaryis a comfortable temperature of around 24°C.

2. average wind speed by month (second graph)

  • Wind speed threshold for travel: less than 15 km/h (indicated by red dotted line)
  • Result:
    • For months with wind speeds of 15 km/h or less, use the December 2023, January 2024, February, November, December.
    • In particular, the Novemberand Decemberhad the lowest wind speeds.

3. average cloud cover by month (third graph)

  • Cloudiness threshold for travel: less than 501 TP3T (indicated by red dotted line)
  • Result:
    • For months with less than 501 TP3T of cloud cover, the December 2023, January 2024, February, March, November, December.
    • January, February, Marchwas particularly sunny with very low cloud cover.

Finalize

So there you have it, the best months to travel to Jeju Island, and while you should still consider other factors like personal preferences and special events, this data should help you plan your trip.

If you're planning a trip to Jeju Island, use these tips to make the most of your time there! We hope you have a fantastic trip to Jeju Island, and we'll be back with another travel tip.

For another example of using APIs to analyze and visualize data, check out the Analyzing a 30-day Bitcoin Dollar chart with Python and the CryptoCompare APIfor more information.

# Code Explained in Detail

import requests
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from time import sleep

Import the necessary libraries: requests is used for API requests, pandas for data processing, matplotlib for visualization, datetime for date processing, and time for latency between API requests.

api_key = 'YOUR_API_KEY'

Set the API key issued by WeatherAPI.com. You will need to replace this with your own API key for actual use.

full_weather_data = []

Initialize an empty list to store weather data.

end_date = datetime.now()
start_date = end_date - timedelta(days=365)

Set the data collection period. This covers the period from the current date to one year ago.

for date in pd.date_range(start=start_date, end=end_date):

Repeats by date for the set time period.

    url = f"http://api.weatherapi.com/v1/history.json?key={api_key}&q=Jeju&dt={date.strftime('%Y-%m-%d')}"

Generate an API request URL for each date.

    try:
        response = requests.get(url)
        if response.status_code == 200:

Send a request to the API and verify that the response is successful.

            data = response.json()
            forecast_day = data['forecast']['forecastday'][0]['day']

Parse the response data into JSON format and extract the daily forecast data you need.

            full_weather_data.append({
                'date': date.strftime('%Y-%m-%d'),
                'temp': forecast_day['avgtemp_c'],
                'wind_speed': forecast_day['maxwind_kph'],
                'cloud_cover': forecast_day['avghumidity']
            })

Add the extracted data to the full_weather_data list as a dictionary.

            print(f"Data for {date.strftime('%Y-%m-%d')} successfully added")
        else:
            print(f"Failed to fetch data for {date.strftime('%Y-%m-%d')}. Status code: {response.status_code}")

    except Exception as e:
        print(f"Error occurred while requesting data for {date.strftime('%Y-%m-%d')}: {e}")

Outputs whether the data was added successfully or not, and an error message if it failed.

    sleep(1)

Wait 1 second between each request to account for API request limits.

df = pd.DataFrame(full_weather_data)
df['date'] = pd.to_datetime(df['date'])

Convert the collected data to a pandas DataFrame, and convert the date column to datetime format.

df['good_weather'] = (df['temp'].between(20, 28) &
                      (df['wind_speed'] < 15) &
                      (df['cloud_cover'] < 50))

Define "good weather" conditions and evaluate them for each day.

monthly_data = df.groupby(df['date'].dt.to_period('M')).agg({
    'temp': 'mean',
    'wind_speed': 'mean',
    'cloud_cover': 'mean',
    'good_weather': 'sum'
}).reset_index()

monthly_data['date'] = monthly_data['date'].dt.to_timestamp()

Calculate the monthly average data and count the number of days that satisfy the 'good_weather' condition.

plt.figure(figsize=(15, 12))

Set the size of the entire graph.

best_months = monthly_data[monthly_data['good_weather'] > 0]

Select months with at least one 'good_weather' day as the 'best month'.

The code that follows creates three subplots (temperature, wind speed, and cloud cover), plots the data on each, and displays the 'best month'. Finally, it displays the graph and outputs the 'best month' information.

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