Python数据可视化库pyecharts介绍

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

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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

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
2. pyecharts官方文档

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