xxx
6 months ago
parent
4c2fcb76c4
commit
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>Awesome-pyecharts</title>
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<script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
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</head>
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<body>
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<div id="150dd4869de6437c862a2539d079fb42" class="chart-container" style="width:900px; height:500px;"></div>
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<script>
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var chart_150dd4869de6437c862a2539d079fb42 = echarts.init(
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document.getElementById('150dd4869de6437c862a2539d079fb42'), 'white', {renderer: 'canvas'});
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var option_150dd4869de6437c862a2539d079fb42 = {
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"animation": true,
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"animationThreshold": 2000,
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"animationDuration": 1000,
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"animationEasing": "cubicOut",
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"animationDelay": 0,
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"animationDurationUpdate": 300,
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"animationEasingUpdate": "cubicOut",
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"animationDelayUpdate": 0,
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"color": [
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"#9fe080",
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"#5c7bd9",
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"#ffdc60",
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"#ff7070",
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"#7ed3fc",
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"#8470FF"
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],
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"series": [
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{
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"type": "pie",
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"name": "\u516c\u53f8\u89c4\u6a21\u997c\u56fe",
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"clockwise": true,
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"data": [
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{
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"name": "100-499\u4eba",
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"value": 356
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},
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{
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"name": "1000-9999\u4eba",
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"value": 418
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},
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{
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"name": "10000\u4eba\u4ee5\u4e0a",
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"value": 235
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},
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{
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"name": "20-99\u4eba",
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"value": 423
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},
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{
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"name": "20\u4eba\u4ee5\u4e0b",
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"value": 81
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},
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{
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"name": "500-999\u4eba",
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"value": 212
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},
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{
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"name": "\u4e0a\u5e02\u516c\u53f8",
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"value": 4
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},
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{
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"name": "\u5176\u5b83",
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"value": 1
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},
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{
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"name": "\u5408\u8d44",
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"value": 4
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},
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{
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"name": "\u56fd\u4f01",
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"value": 5
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},
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{
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"name": "\u5916\u5546\u72ec\u8d44",
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"value": 3
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},
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{
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"name": "\u6c11\u8425",
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"value": 74
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},
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{
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"name": "\u80a1\u4efd\u5236\u4f01\u4e1a",
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"value": 6
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}
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],
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"radius": [
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0,
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"50%"
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],
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"center": [
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"50%",
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"50%"
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],
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"label": {
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"show": true,
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"position": "top",
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"margin": 8,
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"formatter": "{b}:{d}%"
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}
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}
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],
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"legend": [
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{
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"data": [
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"100-499\u4eba",
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"1000-9999\u4eba",
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"10000\u4eba\u4ee5\u4e0a",
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"20-99\u4eba",
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"20\u4eba\u4ee5\u4e0b",
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"500-999\u4eba",
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"\u4e0a\u5e02\u516c\u53f8",
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"\u5176\u5b83",
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"\u5408\u8d44",
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"\u56fd\u4f01",
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"\u5916\u5546\u72ec\u8d44",
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"\u6c11\u8425",
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"\u80a1\u4efd\u5236\u4f01\u4e1a"
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],
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"selected": {},
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"show": true,
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"left": "10%",
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"top": "18%",
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"orient": "vertical",
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"padding": 5,
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"itemGap": 10,
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"itemWidth": 25,
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"itemHeight": 14
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}
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],
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"tooltip": {
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"show": true,
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"trigger": "item",
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"triggerOn": "mousemove|click",
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"axisPointer": {
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"type": "line"
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},
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"showContent": true,
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"alwaysShowContent": false,
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"showDelay": 0,
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"hideDelay": 100,
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"textStyle": {
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"fontSize": 14
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},
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"borderWidth": 0,
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"padding": 5
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},
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"title": [
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{
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"text": "\u5404\u79cd\u516c\u53f8\u89c4\u6a21\u6240\u5360\u6bd4\u4f8b",
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"left": "35%",
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"top": "3%",
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"padding": 5,
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"itemGap": 10,
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"textStyle": {
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"fontSize": 25
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}
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}
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]
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};
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chart_150dd4869de6437c862a2539d079fb42.setOption(option_150dd4869de6437c862a2539d079fb42);
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</script>
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</body>
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</html>
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@ -0,0 +1,20 @@
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import pandas as pd
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from pyecharts.charts import Pie
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from pyecharts import options as opts
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data = pd.read_csv("最新数据.csv")
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d2 = data[['职位名称','公司规模']]
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d3 = d2.groupby('公司规模').count()
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gm_data = [i for i in zip(d3.index.tolist(),d3['职位名称'].tolist())]
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pie = Pie()\
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.add("公司规模饼图",gm_data,radius=[0,"50%"],
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label_opts=opts.LabelOpts(formatter='{b}:{d}%'))\
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.set_global_opts(
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title_opts=opts.TitleOpts('各种公司规模所占比例',pos_left="35%",pos_top="3%",
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title_textstyle_opts=opts.TextStyleOpts(font_size=25)),
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legend_opts=opts.LegendOpts(orient="vertical",pos_left='10%',pos_top='18%')
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)\
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.set_colors(['#9fe080','#5c7bd9','#ffdc60','#ff7070','#7ed3fc','#8470FF'])
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pie.render("公司规模饼图.html")
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@ -0,0 +1,27 @@
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import matplotlib.pyplot as plt
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import pandas as pd
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data = pd.read_csv("最新数据.csv")
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# 将工作经验这一列数据提取出来
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gzjy = data[["工作经验",'职位名称']]
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# 将工作经验去重后转化为列表变成x轴数据
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x_data = gzjy['工作经验'].drop_duplicates().tolist()
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# 将工作经验进行分组计数变为y轴数据
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y_data = gzjy.groupby('工作经验').count().reset_index()
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y_data = y_data['职位名称'].tolist()
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y_data.sort(reverse=True)
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plt.figure()
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.plot(x_data,y_data,'g^--')
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plt.xlabel('年份')
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plt.ylabel('数量')
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for i in range(len(x_data)):
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plt.text(x_data[i],y_data[i],y_data[i])
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plt.title('求职所需要的工作经验排行')
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plt.show()
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,17 @@
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import pandas as pd
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from pyecharts.charts import WordCloud
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from pyecharts import options as opts
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data = pd.read_csv("最新数据.csv")
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hy = data[['职位名称','行业']]
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hangye = hy.groupby('行业').count()
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x_data = hangye.index.tolist()
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y_data = hangye['职位名称'].tolist()
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hy_data = [i for i in zip(x_data,y_data)]
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# 绘制行业词云图
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wc = WordCloud()\
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.add(series_name="行业词云图",data_pair=hy_data)\
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.set_global_opts(title_opts=opts.TitleOpts('行业类型词云图',pos_left="45%",pos_top="7%"))
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wc.render('行业词云图.html')
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