简介

PyeCharts 是一个用于生成 Echarts 图表的类库，

用其生成的图可视化效果非常棒，而且使用起来非常简单。

下面是一些常用图的pyecharts实现方法

柱状图

bar = pye.Bar("柱状图")#新建柱状图

bar.add("服装", #图例名称

["衬衫", "羊毛衫", "雪纺衫", "裤子" , "高跟鞋" , "袜子"],#x

[5, 20, 36, 10, 75, 90],#y

bar_category_gap="20%",

is_more_utils=True,#右边工具栏显示更多按钮

)#添加数据

# bar.render("bar.html")#渲染到文件

bar

输出:

堆叠柱状图

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子" , "高跟鞋" , "袜子"]

v1 = [5, 20, 36, 10, 75, 90]

v2 = [10, 25, 8, 60, 20, 80]

bar = pye.Bar("堆叠柱状图")

bar.add("商家A", attr, v1, is_stack=True)

bar.add("商家B", attr, v2, is_stack=True)

bar

输出:

条形图

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子" , "高跟鞋" , "\

袜子"]

v1 = [5, 20, 36, 10, 75, 90]

v2 = [10, 25, 8, 60, 20, 80]

bar = pye.Bar()

bar.add("商家A", attr, v1, mark_point=["average", "max", "min"], is_stack=True)

bar.add("商家B", attr, v2, mark_line=["max"], is_convert=True, is_stack=True)

bar

输出:

折线图

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子" , "高跟鞋" , "\

袜子"]

v1 = [5, 20, 36, 10, 75, 90]

v2 = [10, 25, 8, 60, 20, 80]

bar = pye.Line("折线图")

bar.add("商家A", attr, v1,

mark_point=["average"],

mark_point_symbol="arrow",

mark_point_textcolor="#cf0",

mark_point_symbolsize=30,

is_fill=True,#是否填充

line_opacity=0.2,#线的不透明度

area_opacity=0.4,#填充区域的不透明度

)

bar.add("商家B", attr, v2,

mark_point=["max"],

mark_point_symbol="diamond",

is_smooth=True, #绘制平滑曲线

is_fill=True,

#line_opacity=1,

area_opacity=0.3,

area_color="#000",

symbol=None

)

bar

输出:

饼图

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子" , "高跟鞋" , "\

袜子"]

v1 = [5, 20, 36, 10, 75, 90]

pie = pye.Pie("饼图", title_pos="center")

pie.add("服装销量", attr, v1,

is_label_show=True,

center=[50, 50],#中心点位置

# rosetype="area",#南丁格尔图

radius=[40, 75],#内环,外环,

legend_orient="vertical",

legend_pos="left"

)

pie

输出:

散点图

v1 = v2 = np.arange(0, 70, 10)#散点图

scatter = pye.Scatter()

scatter.add("", v1, v2, )

scatter.add("", v1[::-1],

v2, is_visualmap=True, #是否使用第三维度

visual_type="size", #第三维度是点的大小

visual_range_size=[10, 60])#取值范围

scatter

输出:

地图

map = pye.Map()

attr = ["浦东新区", "闵行区"]

value = [50, 180]

map.add("", attr, value,

maptype="上海",#地图类型

is_visualmap=True,

is_label_show=True#显示地区标注

)

map

输出:

云词图

import pandas as pd

from bs4 import BeautifulSoup as bs

import re

df = pd.read_csv("labeledTrainData.tsv", sep='\t', escapechar='\\')#读取文件

reviews = df["review"].head(1000).tolist()#转换成list

review_str = "".join(reviews)#合并成str

bs_text = str.lower(bs(review_str, "lxml").get_text())#1去掉html标签并且转为小写

only_words_list = re.sub(r"[^\w\s]", "", bs_text).replace(" ", " ").split(" ")#删除特殊字符 重新拆分成list

stop_words_list = open("stopwords.txt").read().split(" \n")#获取停用词列表

no_stop_words_list = [w for w in only_words_list if w not in stop_words_list]#删除所有停用词

from collections import Counter

only_words_dict = Counter(no_stop_words_list)#转换成字典

# only_words_dict

word_cloud = pye.WordCloud()

word_cloud.add("", only_words_dict.keys(), only_words_dict.values(), word_size_range=[10, 100])

word_cloud.render("ccc.html")#存在文件中

输出:

组合图

使用Overlap

attr = ['A' , 'B' , 'C' , 'D' , 'E' , 'F']

v1 = [10, 20, 30, 40, 50, 60]

v2 = [38, 28, 58, 48, 78, 68]

bar = pye.Bar("Line - Bar 示例")

bar.add("bar" , attr, v1)

line = pye.Line()

line.add("line" , attr, v2)

overlap = pye.Overlap()

overlap.add(bar)

overlap.add(line)

overlap

输出: