Question

Choose Your Own Data: The World Bank maintains many data sets that are broken down for information on each country worldwide. Go to http://data.worldbank.org/indicator/ and find a data set that looks interesting. Click the data set, click the Download Data link, and choose CSV. You’ll receive three CSV files, two of which are labeled Metadata; use the third CSV file. Write a program that generates a dictionary with Pygal’s two-letter country codes as its keys and your chosen data from the file as its values. Plot the data on a Worldmap and style the map as you like.

Short answer

Choose data, download it, process CSV with pandas, create a dictionary with Pygal country codes, and generate a styled Pygal Worldmap.

Step-by-step solution

Access the World Bank Data

Go to the World Bank Indicators page at http://data.worldbank.org/indicator/. Browse through the various datasets available until you find one that interests you. Note the name of the dataset as you will be using its information shortly.

Download the Dataset

Click on the dataset you have selected to view its details. Once on the dataset's page, locate the 'Download Data' button and click it. Choose to download the CSV format, which will give you three files. Identify the main CSV file (the one that is not labeled 'Metadata') as this contains the dataset you want to use.

Install Required Libraries

Ensure you have the necessary libraries installed in your Python environment. For this exercise, you'll need Pygal for creating maps and panda for handling CSV files. You can install these using pip: `pip install pygal pandas`.

Read the CSV File

Use pandas to read the main CSV file into a DataFrame. This will make it easier to manipulate the data. Example: `import pandas as pd; data = pd.read_csv('your_data.csv')`. Inspect the DataFrame using `data.head()` to understand its structure and identify the columns you are interested in.

Create a Data Dictionary with Country Codes

Prepare your data for plotting by mapping the Pygal two-letter country codes to the relevant data values. Pygal comes with utilities to help align country names with codes: `from pygal.maps.world import COUNTRIES`. Create a dictionary comprehension to associate each country's Pygal code with its respective data value from your DataFrame.

Generate the World Map

Import Pygal and create a Worldmap chart object. Feed your data dictionary to the Worldmap object to populate the map. Customize your map with any styles you prefer, such as colors and labels.

Render the Map

Render the map to a file so you can view the results in a web browser. Use `worldmap.render_to_file('world_data.svg')` to output an SVG file. Open this file with a browser to view your data visualization.

Key concepts

World Bank Data

The World Bank is a treasure trove of data, offering extensive datasets that cover a variety of global topics. By visiting their indicators page, students and researchers can explore and download datasets ranging from economic indicators to social statistics for countries around the globe. Each dataset is meticulously organized to provide insights for each country, and you can easily filter data based on your field of interest. Understanding how to access and make sense of this data is a valuable skill in today's data-driven world. Once you find a dataset that interests you from the World Bank site, you can download it in CSV format for further analysis. This is the first step in using data visualization to tell compelling stories with facts and figures. Working with World Bank data requires attention to detail and a clear understanding of the dataset you are working with. Always make sure to download and use the correct CSV file, which is not labeled 'Metadata', for your analyses.

CSV Handling

Handling CSV files is a fundamental skill in data analysis and manipulation. CSV, which stands for Comma-Separated Values, is a popular format for storing tabular data, such as spreadsheets or databases. This format is simple, yet versatile, making it a staple for data professionals. In Python, the pandas library is a powerful tool that makes handling CSV files straightforward. To start, import pandas and use the `pd.read_csv()` function to load your data into a DataFrame. This structure allows for easy access and manipulation of your data. For instance, using `data.head()` lets you preview the first few rows and understand the data's layout. With pandas, you can filter, sort, and perform complex transformations on your data with just a few commands. This capability is essential when preparing data for visualization and analysis. Effective CSV handling is about ensuring accuracy in data extraction and transformation, paving the way for accurate and insightful visualizations.

Country Codes

Country codes are crucial when dealing with global datasets, providing a standardized system to identify countries and regions. In the context of Pygal, which is used for mapping and charting data, two-letter country codes are essential. Pygal uses ISO 3166-1 alpha-2 codes, which are two-letter codes representing countries worldwide. These codes simplify the process of mapping datasets to geographical regions. For example, the United States is represented as `US`, and France as `FR`. By aligning your dataset's country names with Pygal's codes, you ensure that your visualization tool can accurately map the data geographically. To achieve this, you can use the `COUNTRIES` dictionary from `pygal.maps.world`, which contains these codes. This step is crucial for ensuring your data aligns correctly with its visual representation in maps.

Pygal Mapping

Pygal is a dynamic JavaScript and SVG charting library that makes creating interactive charts easy and intuitive. It is especially useful for generating beautiful and informative world maps. With Pygal, you can visualize your data in various forms, including bar charts, line graphs, and, importantly for this exercise, world maps. To create a world map, start by importing Pygal and generating a `Worldmap` chart object. Populate this object with your data dictionary, which maps Pygal country codes to data values. This dictionary acts as the link between your data analysis and visualization. Pygal allows for extensive customization, enabling you to choose colors, adjust map styles, and add labels, enhancing both aesthetics and clarity. Finally, use the `render_to_file` method to save your map as an SVG file, ready for viewing in any web browser. Pygal is a flexible choice for developers looking to create interactive and visually appealing data visualizations.

Pandas Library

The pandas library is fundamental for data manipulation in Python. It's a powerful tool that simplifies handling complex datasets, performing data wrangling tasks smoothly and efficiently. Using pandas, you can load data from CSV files into DataFrames, which are easily navigable data structures that mimic spreadsheets. Once your data is in a DataFrame, you can clean it, filter necessary columns, and perform calculations with ease. Functions such as `data.describe()` provide a quick statistical summary, whereas `data.info()` gives you an overview of data types and missing values. Pandas enables you to reshape data, handle missing data, and perform group operations, which are critical for data preparation prior to visualization. It also integrates well with other libraries, such as Pygal, for creating seamless data analysis pipelines. In essence, mastering pandas is key to unlocking the full potential of your datasets and making informed, data-driven decisions.