以下内容来自百度百科：

金字塔图表是由两套柱状统计图表相对组合而成，因其形似金字塔而得名。

数据准备

数据来自中国统计年鉴2021年 —— 2-17 按年龄和性别分人口数（2020年）

小贴士：为便于后续制作，对 % Female 做了特殊处理（乘-1），这也是最重要的技巧。

以下分别通过Excel、R ggplot2、Python matplotlib进行制图。

Excel

1）选中数据，插入“簇状条形图”

2）修改标题，删除水平轴，选中垂直轴，选择“逆序类别”，标签位置选择“低”

3）添加标签，% Female 标签数字格式代码改为“;##.##%;”

4）选中 % Male，调整系列重叠和间隙宽度，至此完成制作。

R ggplot2

library(readxl)library(ggplot2)library(dplyr)library(tidyr)library(magrittr)df <- read_xlsx("population.xlsx")df$group <- as.factor(nrow(df):1)df1 <- df %>%  select(group, `% Male`, `% Female`) %>%   pivot_longer(cols = 2:3, names_to = "Gender", values_to = "Rate")  df1ggplot(data = df1, aes(x = group, y = Rate, fill = Gender)) +  geom_bar(stat = "identity") +  scale_y_continuous(labels = abs, limits = c(-0.05, 0.05)) +  scale_x_discrete(breaks = df$group, labels = df$Age) +  labs(title = "China Population (2020)", x = NULL, y = "Rate") +  # geom_label() +  geom_text(aes(label = paste0(round(abs(Rate)*100,2), "%"), y = Rate), position = "dodge") +  coord_flip() +   theme_light()

import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsdf = pd.read_excel("population.xlsx", sheet_name=0)df.head()bar_plot = sns.barplot(y = "Age", x = "% Male", color = "red", data = df)bar_plot = sns.barplot(y = "Age", x = "% Female", color = "blue", data = df)bar_plot.set(xlabel="Rate", ylabel=None, title = "China Population (2020)")