关于使用给定数据绘制1999年与2019年收入对比箱线图的技术咨询

First off, this is a perfect use case for boxplots! Boxplots are built to visualize and compare the distribution of numerical data across different groups—exactly what you’re aiming to do here (comparing income distributions between 1999 and 2019).

Step 1: Reshape Your Data (Critical!) #

Your current data is in "wide" format (each year is a separate column). Most plotting tools work best with "long" (tidy) format, where each row represents a single observation. Here’s how to restructure your data:

Original wide format:

Land Income 1999 Income 2019
Spain 5 7
France 4 8
Greece 3 5

Reshaped long format:

Land Year Income
Spain 1999 5
Spain 2019 7
France 1999 4
France 2019 8
Greece 1999 3
Greece 2019 5

This structure makes it easy for plotting libraries to group data by year and generate your boxplots smoothly.

Step 2: Choosing Your Axes #

This is straightforward for your comparison:

• X-axis: Use the categorical variable Year (with two categories: 1999 and 2019). This places each boxplot side by side, making direct visual comparison simple.
• Y-axis: Use the numerical variable Income. This axis will display the range of income values, with each boxplot summarizing the median, quartiles, and outliers for that year.

Quick Example Code Snippets #

In Python (using Seaborn) #

import seaborn as sns
import matplotlib.pyplot as plt

# Assume your reshaped data is stored in a pandas DataFrame called df
sns.boxplot(x="Year", y="Income", data=df)
plt.title("Income Distribution: 1999 vs 2019")
plt.show()

In R (using ggplot2) #

library(ggplot2)

# Assume reshaped data is in a data frame called df
ggplot(df, aes(x = factor(Year), y = Income)) +
  geom_boxplot() +
  labs(title = "Income Distribution: 1999 vs 2019", x = "Year", y = "Income")

Both snippets will generate side-by-side boxplots that let you easily compare central tendency, spread, and outliers of incomes between the two years.

内容的提问来源于stack exchange，提问作者WeareR