Analyze Historical Weather Data With Python

AlfredSeptember 7, 2023

221 6 minutes read

Across the annals of human existence, weather has consistently occupied a central place in our existence. It intricately weaves itself into our daily rituals, sculpts our agricultural methods, wields sway over our financial systems, and indelibly etches its signature upon our diverse cultures. As we navigate the ever-changing climate, historical weather data emerges as a treasure trove of knowledge. Comprehending past weather patterns and trends not only illuminates our planet’s climate history but also empowers us to make informed choices for the future. This is where the utility of a weather data API becomes evident.

In the forthcoming discussion, we will delve into the fascinating realm of historical weather data analysis and unveil how Python serves as a powerful instrument for extracting valuable insights from these weather records. Our journey commences with a profound understanding of the significance of historical weather data in today’s context. As we continue, we will uncover the myriad advantages of utilizing Python for such analytical endeavors, elucidating why it has become the tool of choice for numerous data scientists and researchers. Subsequently, we shall provide a step-by-step guide on procuring historical weather data through a weather data API.

Following that, our exploration will lead us to harness Python’s charting libraries to dissect and interpret the historical weather data we’ve acquired. Let’s embark on this enlightening journey.

What Is Historical Weather Data?

Historical weather data encompasses a comprehensive archive of past weather conditions and atmospheric parameters meticulously gathered over time. It encompasses a wide spectrum of information, including temperature, precipitation, wind speed, humidity, and various other meteorological elements. For instance, this invaluable resource might unveil intriguing temperature trends within a city spanning several decades, shedding light on the evolving climate.

Additionally, it has the capacity to spotlight extreme weather phenomena such as hurricanes or blistering heatwaves that have left their mark on diverse regions. This reservoir of information holds immense significance for a diverse array of stakeholders, including researchers, meteorologists, and policymakers. Beyond that, it aids in unraveling intricate climate patterns, evaluating risks associated with weather-related events, and crafting strategic plans for the future.

Why Should We Use Python to Analyse Historical Weather Data?

Python has undoubtedly ascended to prominence as the preferred language for historical weather data analysis, driven by its remarkable versatility, user-friendliness, and the robust arsenal of data analysis libraries at its disposal. Below, we explore several compelling reasons why Python has risen to the forefront in this domain:

Rich Ecosystem of Libraries

Python offers a diverse array of specialized libraries like NumPy, Pandas, and Matplotlib, all meticulously crafted to streamline data management, making data manipulation, analysis, and visualization a breeze. They serve as indispensable tools for simplifying complex data operations, thereby facilitating the extraction of valuable insights from voluminous historical weather datasets.

User-Friendly and Readable Syntax

Python’s simple and intuitive syntax enables analysts and researchers to focus on the data analysis rather than getting bogged down by intricate code. This readability is especially beneficial when collaborating on data analysis projects.

Active Community Support

Python’s popularity has led to a large and active community of developers. This means that others will likely solve any challenges encountered during data analysis. Hence making it easier to find solutions online.

Data Visualization Capabilities

Analyzing historical weather data often involves visualizing trends and patterns. Python’s Matplotlib and Seaborn offer a wide range of options for creating insightful visualizations that aid in better understanding the data.

Integration with APIs and Web Scraping

Python’s versatility extends to integrating with APIs, making fetching historical weather data from various sources convenient. Additionally, Python’s web scraping capabilities allow researchers to gather data from websites with weather archives.

Machine Learning and Predictive Modeling

Python’s integration with popular machine learning libraries opens possibilities for developing predictive models for historical weather data. This can be invaluable for forecasting future weather patterns and understanding climate trends.

How to Get Historical Weather Data?

Multiple sources can help you get historical weather data. However, APIs are the most reliable sources for retrieving historical weather data. Check out the most popular API in 2023 to get historical weather data.

Weatherstack

Weatherstack is developed and managed by APILayer. It is a company responsible for renowned developer tools, SaaS offerings, and APIs, such as ipstack, mailboxlayer, and currencylayer. Weatherstack is a reliable service for accessing live weather data at an affordable rate.

With Weather API as our trusted ally, we gain access to a wide range of weather information. It includes international weather data, historical records, and real-time updates. The API follows a REST architecture and provides data responses in JSON format. Hence, accommodating callbacks in JSONP format as well. While free usage is available, there are also paid subscriptions with different pricing plans.

Therefore, catering to various needs from individual users to enterprise-level requirements. This flexibility in pricing makes it accessible for users with diverse demands and budget considerations.

How to Analyse Historical Weather Data Using Python?

First, get an API key from Weatherstack.

Next, write code to fetch the historical weather data from Weatherstack in Python.

Here is how we do it:

First, run the following two commands to install the necessary libraries:

pip install requests
pip install matplotlib

The next step is to write our code. We will begin by importing our libraries as under:

import requests

import pandas as pd

import matplotlib.pyplot as plt

Then, we will create parameters to fetch the historical data from Weatherstack.

params = {

‘access_key’: ‘YOURAPIKEY’, # Replace with your actual API key

‘query’: ‘New York’,

‘historical_date’: ‘2015-01-21’,

‘hourly’: ‘1’

}

Then, we add functionality to display the JSON response fetched from the Weatherstack.

api_result = requests.get(‘https://api.weatherstack.com/historical’, params)

response = api_result.json()

Finally, we will plot our data on a chart using the following code.

try:

# Extract hourly temperature data

temperature_data = response[‘historical’][‘2015-01-21’][‘hourly’]

# Convert the data into a pandas DataFrame for easier manipulation

df = pd.DataFrame(temperature_data)

# Convert the ‘time’ column to numeric format

df[‘time’] = df[‘time’].astype(int) // 100 # Convert time to hours for plotting

# Plot the hourly temperature data using matplotlib

plt.figure(figsize=(10, 6))

plt.plot(df[‘time’], df[‘temperature’], marker=’o’)

plt.xlabel(‘Time (Hours)’)

plt.ylabel(‘Temperature (°C)’)

plt.title(‘Hourly Temperature in New York on 2015-01-21’)

plt.grid(True)

plt.xticks(df[‘time’])

plt.tight_layout()

# Display the chart

plt.show()

except KeyError:

print(“Hourly temperature data not available for the specified date and location.”)

The final code should look like below:

import requests

import pandas as pd

import matplotlib.pyplot as plt

params = {

‘access_key’: ‘YOURAPIKEY’, # Replace with your actual API key

‘query’: ‘New York’,

‘historical_date’: ‘2015-01-21’,

‘hourly’: ‘1’

}

api_result = requests.get(‘https://api.weatherstack.com/historical’, params)

response = api_result.json()

try:

# Extract hourly temperature data

temperature_data = response[‘historical’][‘2015-01-21’][‘hourly’]

# Convert the data into a pandas DataFrame for easier manipulation

df = pd.DataFrame(temperature_data)

# Convert the ‘time’ column to numeric format

df[‘time’] = df[‘time’].astype(int) // 100 # Convert time to hours for plotting

# Plot the hourly temperature data using matplotlib

plt.figure(figsize=(10, 6))

plt.plot(df[‘time’], df[‘temperature’], marker=’o’)

plt.xlabel(‘Time (Hours)’)

plt.ylabel(‘Temperature (°C)’)

plt.title(‘Hourly Temperature in New York on 2015-01-21’)

plt.grid(True)

plt.xticks(df[‘time’])

plt.tight_layout()

# Display the chart

plt.show()

except KeyError:

print(“Hourly temperature data not available for the specified date and location.”)

Here is the response that you will get after running the above code:

Note that you may change the date in the code. Now, you can analyze the weather data hourly using Weatherstack and Python charting libraries.

Conclusion

Analyzing historical weather data with Python provides valuable insights into past climate patterns and trends. Python, with its powerful libraries like requests, pandas, and matplotlib, allows us to fetch, process, and visualize weather data efficiently. By utilizing APIs like Weatherstack, we can access a vast repository of historical weather information.

Python’s versatile data manipulation features empower us to retrieve precise weather statistics. Furthermore, it assists us in crafting meaningful graphs to illustrate temperature fluctuations, rainfall amounts, and other variables across different time periods. This examination can be valuable for meteorologists, scholars, and climate scientists seeking to grasp enduring climate trends and formulate well-informed forecasts for the upcoming years.