How to draw a stacked column chart using ECharts in Python

In the field of data visualization, stacked histograms are a common visualization method. It draws multiple data series into a bar. Each bar is composed of multiple sub-items. Each sub-item corresponds to a data series and is displayed in the same coordinate system. This kind of chart can be used to compare the total size of different categories or data series, the proportion of components of each category or data series, etc. In Python, we can use the ECharts library to draw stacked histograms, and the library is richly customizable and interactive.

1. Install and import the ECharts library

Before using the ECharts library, we need to install it first. It can be installed through the pip command:

pip install pyecharts

After the installation is completed, we need to import the required components in the Python script, for example:

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker

2. Draw a stacked histogram

Next, let's look at an example in which we will use the ECharts library to draw a stacked column chart to show sales for each month, where each column represents a month's sales, and each column is represented by Sales composition of different product categories.

1. Prepare data

First, we need to prepare the data. In this example, we randomly generated sales data for 12 months, and each month included sales of 3 product categories. The code is as follows:

import random

# 随机生成12个月份的销售额数据
months = [str(i) + "月" for i in range(1, 13)]
type1_sales = [random.randint(100, 1000) for _ in range(12)]
type2_sales = [random.randint(100, 1000) for _ in range(12)]
type3_sales = [random.randint(100, 1000) for _ in range(12)]

2. Draw the chart

Next, we add the data to the chart and customize it. The code is as follows:

# 实例化柱状图
bar = (
    Bar()
    # 添加X轴数据
    .add_xaxis(months)
    # 添加Y轴数据，并使用整数值格式化标签
    .add_yaxis("类别1", type1_sales, stack="stack1", label_opts=opts.LabelOpts(formatter="{value}元"))
    .add_yaxis("类别2", type2_sales, stack="stack1", label_opts=opts.LabelOpts(formatter="{value}元"))
    .add_yaxis("类别3", type3_sales, stack="stack1", label_opts=opts.LabelOpts(formatter="{value}元"))
    # 设置全局参数
    .set_global_opts(
        # 设置标题
        title_opts=opts.TitleOpts(title="堆叠柱状图"),
        # 设置X轴标签旋转角度
        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
        # 设置Y轴的名称和最大值
        yaxis_opts=opts.AxisOpts(name="销售额", max_=3000),
        # 设置数据标签
        series_opts=opts.SeriesOpts(
            itemstyle_opts=opts.ItemStyleOpts(border_color="black", border_width=0),
            label_opts=opts.LabelOpts(is_show=True, position="inside", color="white")
            )
    )
)

In the above code, we instantiate a histogram and use add_xaxis() to add the X-axis data to the chart. Next, we use add_yaxis() to add three types of sales data to the chart. Since we need to stack three types of sales together, we set them all to stack1. At the same time, we use label_opts to set the formatting method of the label. Finally, we use set_global_opts() to set the global parameters of the chart, including the title, X-axis label rotation angle, Y-axis name and maximum value, and data label settings.

3. Visualize and save the results

Finally, we use render() to visualize the results and display the results in Jupyter Notebook using render_notebook() or render(' filename.html') saves the results as an HTML file. The code is as follows:

# 在Jupyter Notebook中显示图表
bar.render_notebook()

# 将图表保存为HTML文件
bar.render("bar_chart.html")

After running the above code, we will get a clear stacked column chart, which shows the sales of each month and can reflect the proportion of sales of different product categories. than the situation.

3. Summary

This article introduces how to use the Bar component in the ECharts library to draw a stacked column chart, and uses specific code examples to show how to prepare data and how to add data to the chart. And how to customize and save charts. Of course, in actual operation, more detailed settings and adjustments to the parameters of specific components may be required to meet different visualization needs. But overall, ECharts provides Python users with a powerful and easy-to-use data visualization tool that helps to better present the results obtained during the data analysis process.

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