136python可视化图

灵感 ：来自朋友让我帮它弄可视化图，持续更新，后期可直接套用

EXCEL文件

例子：

excel布局

效果：

实现代码：

import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

df = pd.read_excel(r"C:\\Users\43701\\Desktop\可视化练习\\test01.xlsx")
# 输入折线图数据
plt.plot(df["物品编号"], df["库存量"], label='库存量', linewidth=1, color='c', marker='o', markerfacecolor='blue', markersize=5)
plt.plot(df["物品编号"], df["进货价格"], label='进货价格', linewidth=1, color='y', marker='o', markerfacecolor='blue', markersize=5)
plt.plot(df["物品编号"], df["出售价格"], label='出售价格', linewidth=1, color='r', marker='v', markerfacecolor='blue', markersize=5)
plt.plot(df["物品编号"], df["收益"], label='收益', linewidth=1, color='m', marker='1', markerfacecolor='blue', markersize=5)
plt.xlabel("物品编号")
plt.ylabel('各类指标')
plt.title("商品详细信息")

# 添加数据点注释
for x, y in zip(df["物品编号"], df["库存量"]):
    plt.annotate(str(y), xy=(x, y), xytext=(-10, 10), textcoords="offset points")


for x, y in zip(df["物品编号"], df["进货价格"]):
    plt.annotate(str(y), xy=(x, y), xytext=(-10, 10), textcoords="offset points")

for x, y in zip(df["物品编号"], df["出售价格"]):
    plt.annotate(str(y), xy=(x, y), xytext=(-10, 10), textcoords="offset points")

for x, y in zip(df["物品编号"], df["收益"]):
    plt.annotate(str(y), xy=(x, y), xytext=(-10, 10), textcoords="offset points")

# 图例说明
plt.legend()
# 显示网格
plt.grid()
# 保存折线图
plt.savefig('line_chartdemo3.png')
plt.show()

CSV文件

例子1

csv布局

效果：

代码如下：

# @author： zhc
# @Time: 2023/4/29
# @FileName: demo2


import pandas as pd


class Bar_429:

    def start(self):
        self.__testA_wy()
        self.__testB_img()

    def __testA_wy(self):
        from bokeh.plotting import figure, output_file, show
        from bokeh.models import CustomJS, ColumnDataSource
        from bokeh.models.tools import HoverTool

        # 读csv文件
        data = pd.read_csv('中国历年甘蔗甜菜产量(1949年-2021年).csv')
        years = data['年份']
        sugarcane = data['甘蔗产量']
        beets = data['甜菜产量']

        # 创建交互式图表
        p = figure(title='中国历年甘蔗甜菜产量(1949年-2021年)', x_axis_label='年份', y_axis_label='产量', tools='tap')

        # 添加柱状图
        source = ColumnDataSource(data=dict(years=years, sugarcane=sugarcane, beets=beets))
        p.vbar(x='years', top='sugarcane', color='red', width=0.5, legend_label='甘蔗', source=source)
        p.vbar(x='years', top='beets', color='green', width=0.5, legend_label='甜菜', source=source)

        # 添加鼠标悬停工具
        p.add_tools(HoverTool(tooltips=[('年份', '@years'), ('甘蔗产量', '@sugarcane'), ('甜菜产量', '@beets')]))

        # 设置图表样式
        p.legend.location = "top_left"
        p.legend.orientation = "horizontal"
        p.legend.label_text_font_size = "10pt"
        p.legend.background_fill_alpha = 0.3
        p.xaxis.axis_label_text_font_size = "12pt"
        p.yaxis.axis_label_text_font_size = "12pt"
        p.title.text_font_size = "16pt"

        # 添加回调函数
        callback = CustomJS(args=dict(source=source), code="""
            var selected_index = source.selected.indices[0];
            var data = source.data;
            var year = data['years'][selected_index];
            var sugarcane = data['sugarcane'][selected_index];
            var beets = data['beets'][selected_index];
            alert("年份：" + year + "\n甘蔗产量：" + sugarcane + "\n甜菜产量：" + beets);
        """)
        p.js_on_event('tap', callback)

        # 输出图表到HTML文件
        output_file('chart.html')
        show(p)

    def __testB_img(self):
        # 2
        import pandas as pd
        import matplotlib.pyplot as plt

        # 读取数据
        data = pd.read_csv('中国历年甘蔗甜菜产量(1949年-2021年).csv')
        years = data['年份']
        sugarcane = data['甘蔗产量']
        beets = data['甜菜产量']

        # 创建画布
        fig, ax = plt.subplots()

        # 添加柱状图
        ax.bar(years, sugarcane, color='red', label='甘蔗')
        ax.bar(years, beets, color='green', label='甜菜')

        # 设置坐标轴标签和图例
        ax.set_xlabel('年份')
        ax.set_ylabel('产量')
        ax.set_title('中国历年甘蔗甜菜产量(1949年-2021年)')
        ax.legend()

        # 添加鼠标悬停事件
        def onclick(event):
            index = event.xdata
            if index is not None:
                index = int(index)
                print(f'年份: {years[index]}, 甘蔗产量: {sugarcane[index]}, 甜菜产量: {beets[index]}')

        cid = fig.canvas.mpl_connect('button_press_event', onclick)

        # 保存图形并显示
        plt.savefig('chart.png')
        plt.show()


if __name__ == '__main__':
    bar = Bar_429()
    bar.start()