How to Visualize Global Weather Data in Python

Global weather data is highly valuable for comprehending climate patterns, monitoring weather conditions, and examining future climate projections. Fortunately, the availability of numerous free weather APIs allows us to effortlessly obtain real-time and historical climate data to give weather forecasts. In this tutorial, our emphasis will be on using the weatherstack API, the best free weather API, to gather global weather data. Using Python and its versatile libraries, including matplotlib, pandas, numpy, and cartopy, we will provide a step-by-step guide on how to visualize and analyze global weather data effectively.

How to Import Necessary Libraries for Visualizing Global Weather Data?

One of our previous blog articles discussed how to create a weather app with HTML, CSS, and JS. You also learned how to embed weather forecast data into your website. However, visualizing weather data can be more challenging as it requires more complex libraries. To visualize global weather data, we can use the following libraries.

1. matplotlib: It provides a wide range of tools and functions to generate various types of charts, graphs, and maps.
2. pandas: pandas is a powerful library used for data manipulation and analysis. With pandas, we can clean, filter, aggregate, and perform calculations on the climatological data and model data.
3. numpy: numpy is particularly useful for performing mathematical calculations on the weather data, such as calculating averages, standard deviations, or performing statistical operations.
4. cartopy: cartopy is a specialized library designed for creating maps and projections. With cartopy, we can add weather stations, coastlines, country boundaries, and other geographical features to our visualizations.

If you have installed these libraries, you can import them using the following code.

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import cartopy.crs as ccrs

How to Retrieve Global Weather Data Using the weatherstack API?

In this tutorial, we will focus on using the weatherstack API, which offers a wide range of weather information, including details about the long-term warming trend, sea level pressure, and more.

First, sign up on the weatherstack website to obtain an API key. This key is necessary to authenticate your requests and access the weather data.

Next, import the required libraries in Python to facilitate the data retrieval process.

import requests
import json

Let’s use the requests library to send HTTP requests to the weatherstack API. Pass your API key and specify the location for which you want to retrieve weather data, such as geographical coordinates or the name of a city or region.

api_key = "YOUR_API_KEY"
location = "New York"  # Example location
url = f"http://api.weatherstack.com/current?access_key={api_key}&query={location}"

response = requests.get(url)

At this point, we need to extract the available data from the API response. Typically, the data is returned in JSON format. You can use the JSON library to load the response into a Python dictionary for further processing.

data = json.loads(response.text)

Once you have the weather data, you can access specific information based on the API’s response structure. For example, you can extract the temperature, precipitation, wind speed, and other relevant details.

temperature = data["current"]["temperature"]
precipitation = data["current"]["precip"]
wind_speed = data["current"]["wind_speed"]

As you can see, you can use weatherstack API to integrate weather data into your website.

How to Preprocess Global Weather Data for Visualization?

Once you have retrieved the global weather data using the weatherstack API or from other reliable sources such as national centers, it’s essential to preprocess the data before visualizing it. This step ensures that the data is clean, organized, and suitable for analysis. In this section, we will explore the necessary data preprocessing steps.

Start by importing the pandas library and create a DataFrame to store the weather data. This allows us to manipulate and analyze the data easily. Here’s an example of how to load the weather data into a DataFrame.

import pandas as pd

weather_df = pd.DataFrame(weather_data)

To ensure data quality, it’s important to remove any unnecessary columns or rows with missing values. Focus on the variables of interest, such as daily precipitation and mean temperature, to gain insights into the weather conditions. Use the following code to remove columns that are not needed for your analysis.

weather_df = weather_df.drop(columns=["humidity", "wind_direction"])

You can also drop rows with missing values using the dropna() function.

weather_df = weather_df.dropna()

Depending on your analysis goals, you may need to perform additional data transformations. For example, you can convert temperature units from Celsius to Fahrenheit or aggregate the data by a specific time period. Here’s an example of converting temperature from Celsius to Fahrenheit.

weather_df["temperature"] = (weather_df["temperature"] * 9/5) + 32

These steps, along with handling high-resolution data, give you valuable insights into weather conditions and uncover patterns and trends.

How to Create a Global Map for Weather Data Visualization?

To visually represent global weather data, it is essential to create an informative and visually appealing map. This section focuses on the steps involved in creating a global map using Python and the cartopy library, and then plotting the weather data onto the map.

First, begin by importing the necessary libraries, including matplotlib. Set up a figure and axis using the plt.subplots() function, which provides a blank canvas for the map.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

Then, utilize the cartopy library to define a suitable map projection, such as PlateCarree or Robinson, using the ccrs module. This allows for an accurate representation of the Earth’s surface.

import cartopy.crs as ccrs

projection = ccrs.PlateCarree()

Next, enhance the map by incorporating geographical features, such as coastlines, countries, or gridlines. This provides important context to the weather data being visualized.

ax.coastlines()
ax.add_feature(cartopy.feature.BORDERS)

ax.gridlines(draw_labels=True)

After that, tailor the appearance of the map to your preferences. Customize the background color, add a title, and include a legend or color bar to provide additional information.

ax.background_patch.set_color('lightblue')
ax.set_title('Global Weather Data')
plt.colorbar(...)

How to Plot Weather Data on the Global Map?

To do this, let’s use the scatter or plot function from matplotlib to plot the weather data on the previously created global map. These functions allow you to represent each data point as a marker on the map.

ax.scatter(lon, lat, c=temperature, s=precipitation, cmap='coolwarm', transform=projection)

To represent different weather variables, such as temperature, precipitation, or wind speed, map them to visual attributes like color, size, or shape of the markers. This provides an intuitive and visual representation of the weather data.

ax.scatter(lon, lat, c=temperature, s=precipitation, cmap='coolwarm', transform=projection)

Finally, enhance the visualization by adding a color bar or legend, providing a visual reference for the mapped weather data.

cbar = plt.colorbar(...)

By following these steps, you can create a global map using matplotlib and cartopy, and plot the weather data on the map.

Why Should You Choose weatherstack for Weather Data API?

Choosing weatherstack for your weather data API offers numerous benefits. With weatherstack, you gain access to comprehensive and accurate weather data, including real-time and historical information. The API provides global coverage, allowing you to retrieve weather information for any location worldwide. You can rely on weatherstack’s reliable infrastructure, ensuring consistent delivery of weather data. Finally, weatherstack offers developer-friendly features such as support for multiple programming languages, customizable parameters, and integration with popular libraries.

Do you want to gain valuable weather insights and elevate your projects using the weatherstack API. Try it now.

FAQs.

Can I access real-time weather data using the weatherstack API?

Yes, the weatherstack API provides real-time weather data for locations worldwide.

Does weatherstack offer historical weather data?

Yes, weatherstack API also provides historical weather data for past dates.

Can I retrieve weather data for specific locations or coordinates?

Absolutely, you can retrieve weather data for specific locations or provide geographical coordinates to get accurate weather information.

Is it possible to customize the weather data parameters I retrieve?

Yes, weatherstack API offers customizable parameters, allowing you to specify the weather variables you need for your analysis and visualization.