快速掌握数据可视化工具pyecharts

怼烎@ 2023-01-02 08:20 288阅读 0赞

> Python数据可视化库pyecharts介绍

### 文章目录 ###

*   *  
     *  数据预处理
     *   *  模块安装
         *  导入模块
         *  去重
         *  对地理位置进行处理
         *  对销售量进行处理
     *  制作图表
     *   *  2.1 词云
         *  2.2 柱状图
         *  2.3 饼图
         *  2.3.1 玫瑰图
         *  2.4 地图
         *  2.5 水球图
     *  整合图表

##  ##

**绘图工具：**

使用百度开源的pyecharts库

> 可以参考它的官方文档  
> [**pyecharts官方文档**][pyecharts]

--------------------

## 数据预处理 ##

### 模块安装 ###

> pip install `pyecharts`

### 导入模块 ###

import pandas as pd
    df = pd.read_excel('taobao.xlsx')

### 去重 ###

# 删除行完全一样的值
    df.drop_duplicates(inplace=True)
    # 删除列重复的值
    df.drop_duplicates(subset=['列名','列名'])

### 对地理位置进行处理 ###

location_list = []
    for location in df['location']:
        location = location.split(' ')[0]
        location_list.append(location)
    df['location'] = location_list

### 对销售量进行处理 ###

sales_list = []
    for sale in df['sales']:
        sale = sale[:-3].replace('+', '')
        if '万' in sale:
            sale = int(float(sale.replace('万', '')) * 10000)
        sales_list.append(sale)
    
    df['sales'] = sales_list

## 制作图表 ##

\#\#\#导入模块

import jieba
    import pandas as pd
    from pyecharts import options as opts
    from pyecharts.globals import ThemeType
    from pyecharts.globals import SymbolType
    from pyecharts.charts import Pie, Bar, Map, WordCloud, Page

### 2.1 词云 ###

两种方法：

1.  `pyecharts`自带的生成词云
2.  `wordcloud` 模块生成词云（推荐

**方法一：**

stop_words_txt = 'stop_words.txt'
    # 载入停用词,即过滤词
    jieba.analyse.set_stop_words(stop_words_txt)
    # TextRank 关键词抽取，只获取固定词性
    # topK为返回权重最大的关键词，默认值为20
    # withWeight为返回权重值，默认为False
    keywords_count_list = jieba.analyse.textrank(' '.join(df1.comment), topK=100, withWeight=True)
    print(keywords_count_list)

word_cloud = (
        WordCloud()
            .add("", keywords_count_list, word_size_range=[5, 50], 
                 shape=SymbolType.TRIANGLE,
                )
            .set_global_opts(title_opts=opts.TitleOpts(title="这里输入标题"))
    )
    # 这句话是渲染成一个html文件到当前文件夹下面
    #     word_cloud.render('WordCloud.html')

**方法二：**（推荐，可自定义

> pip install `wordcloud`

import jieba
    import numpy as np
    import matplotlib.pyplot as plt
    
    from PIL import Image
    from wordcloud import WordCloud
    
    
    # 打开文本
    # text = open('1.txt',encoding='utf-8').read()
     
    # 中文分词
    text = ' '.join(jieba.cut(text))
     
    # 生成对象
    mask = np.array(Image.open("input_picture"))
    wc = WordCloud(mask=mask,font_path='C:\Windows\Fonts\SimHei.ttf',mode='RGBA').generate(text)
     
    # 显示词云
    # plt.imshow(wc, interpolation='bilinear')
    # plt.axis("off")
    # plt.show()
     
    # 保存到文件
    wc.to_file('output_picture')

### 2.2 柱状图 ###

**一般柱状图：**

bar = (
        Bar()
        .add_xaxis(Faker.days_attrs)
        .add_yaxis("商家A", Faker.days_values)
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-DataZoom（slider+inside）"),
        )
    #     .render("bar_datazoom_both.html")
    )

**横向柱状图：**

.reversal_axis()
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))

**滑块柱状图：**

datazoom_opts=[opts.DataZoomOpts()]

### 2.3 饼图 ###

数据来自：`standard_goods_comments.xlsx`

这里用cup做展示

[('B', 1909), ('C', 810), ('A', 696), ('D', 259)]

**多图显示cup：**

from pyecharts import options as opts
    from pyecharts.charts import Pie
    from pyecharts.commons.utils import JsCode
    
    
    fn = """
        function(params) {
            if(params.name == 'other')
                return '\\n\\n\\n' + params.name + ' : ' + params.value + '%';
            return params.name + ' : ' + params.value + '%';
        }
        """
    
    
    def new_label_opts():
        return opts.LabelOpts(formatter=JsCode(fn), position="center")
    
    
    pie = (
        Pie()
        .add(
            "",
            [['A_cup', round(696/total_cup, 2)*100],['other',round(1 - 696/total_cup, 2)*100]],
            center=["20%", "30%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .add(
            "",
            [['B_cup', round(1909/total_cup, 2)*100],['other',round(1 - 1909/total_cup, 2)*100]],
            center=["55%", "30%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .add(
            "",
            [['C_cup', round(810/total_cup, 2)*100],['other',round(1 - 810/total_cup, 2)*100]],
            center=["20%", "70%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .add(
            "",
            [['D_cup', round(259/total_cup * 100, 1)],['other',round(1 - 259/total_cup, 2)*100]],
            center=["55%", "70%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Cup-多饼图"),
            legend_opts=opts.LegendOpts(
                type_="scroll", pos_top="20%", pos_left="80%", orient="vertical"
            ),
        )
    #     .render("mutiple_pie.html")
    )

### 2.3.1 玫瑰图 ###

**疫情展示：**

from pyecharts import options as opts
    from pyecharts.charts import Pie
    from pyecharts.faker import Faker
    
    
    v = Faker.choose()
    pie = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(v, list(range(10,80,10)))],
            radius=["30%", "75%"],
            center=["25%", "50%"],
            rosetype="radius",
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add(
            "",
            [list(z) for z in zip(v,list(range(10,80,10))[::-1])],
            radius=["30%", "75%"],
            center=["75%", "50%"],
            rosetype="area",
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"))
    )

### 2.4 地图 ###

from pyecharts import options as opts
    from pyecharts.charts import Map
    from pyecharts.faker import Faker
    
    map = (
        Map()
        .add("店铺数量",[['广东',100],['广西',100],['湖南',19,]], "china")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="商家店铺地址分布图"),
            visualmap_opts=opts.VisualMapOpts(max_=200),
        )
    )

### 2.5 水球图 ###

**天气：**

from pyecharts import options as opts
    from pyecharts.charts import Liquid
    
    liquid = (
        Liquid()
        .add("lq", [0.45,0.5])	
     	# 第一个值为显示的值，第二个值为水的分量
        .set_global_opts(title_opts=opts.TitleOpts(title="今日湿度"))
        .render("liquid_base.html")
    )

## 整合图表 ##

[**多图表整合**][Link 1]

Page.save_resize_html('page_draggable_layout.html',cfg_file= 'chart_config.json')

参考文档：

1.  [5分钟快速掌握 pyecharts 的常用图表基本操作][5_ pyecharts]
2.  [pyecharts官方文档][pyecharts 1]