数据来源
数据来源于BOSS直聘，说实话，现在的招聘网站，做的比较好的还是BOSS直聘，其相关的数据、报告等都是比较有代表性的。今天我们就来看看相关的数据吧！
数据获取
BOSS直聘上有这么一个接口，可以很好的获取当前不同岗位，不同城市的薪资水平
https://www.zhipin.com/wapi/zpboss/h5/marketpay/statistics.json
可以很方便的获取比较详细的薪资数据
import requests headers = {'accept': 'application/json, text/plain, */*', 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.96 Safari/537.36'} querystring = {"positionId":"100109","industryId":"0","cityId":"0","companySize":"0","financingStage":"0","experienceCode":"0"} job_statics_url = 'https://www.zhipin.com/wapi/zpboss/h5/marketpay/statistics.json' job_statics_data = requests.get(job_statics_url, params=querystring, headers=headers)
这样，就可以获取到我们想要的 json 数据了
下面我们就可以简单的来分析下相关的薪资数据了
数据分析
薪资分位值
在我们获取到的数据当中，就有分位值的数据，可以方便的获取
job_statics_data_json = job_staticis_data.json() job_statics_data_json['zpData']['salaryByPoints']
接下来就可以整理横纵坐标轴了
statics_x = [] statics_y = [] for i in job_statics_data_json['zpData']['salaryByPoints']: statics_x.append(i['name'] + '\n' + i['title']) statics_y.append(i['salary'])
下面开始作图
import pyecharts.options as opts from pyecharts.charts import Line, Bar, Pie, Calendar, WordCloud from pyecharts.commons.utils import JsCode from pyecharts.globals import SymbolType x_data = statics_x y_data = statics_y background_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 0, 1, " "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)" ) area_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 0, 1, " "[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)" ) c_line = ( Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))) .add_xaxis(xaxis_data=x_data) .add_yaxis( series_name="薪资", y_axis=y_data, is_smooth=True, is_symbol_show=True, symbol="circle", symbol_size=6, linestyle_opts=opts.LineStyleOpts(color="#fff"), label_opts=opts.LabelOpts(is_show=True, position="top", color="white"), itemstyle_opts=opts.ItemStyleOpts( color="red", border_color="#fff", border_width=3 ), tooltip_opts=opts.TooltipOpts(is_show=False), areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1), ) .set_global_opts( title_opts=opts.TitleOpts( title="收入分位", pos_bottom="5%", pos_left="center", title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16), ), xaxis_opts=opts.AxisOpts( type_="category", boundary_gap=False, axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"), axisline_opts=opts.AxisLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts( is_show=True, length=25, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f") ), ), yaxis_opts=opts.AxisOpts( type_="value", position="right", axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"), axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(width=2, color="#fff") ), axistick_opts=opts.AxisTickOpts( is_show=True, length=15, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f") ), ), legend_opts=opts.LegendOpts(is_show=False), ) )
可以得到一个还不错的折线图
可以看到，业内Python的薪资水平，大部分应该都处于1万左右，这个薪资水平其实并不太高，看来纯的Python岗位并不太吃香，要想获得更高的薪资，还是需要有更多的技能傍身！
薪资区间分布
下面再来看看薪资的分布情况
statics_x = [] statics_y = [] for i in job_statics_data_json['zpData']['salaryByDistributed']: statics_y.append(i['percent']) statics_x.append(i['salaryRange']) def bar_chart(x, y) -> Bar: background_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 0, 1, " "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)" ) c = ( Bar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))) #Bar() .add_xaxis(x) # .add_xaxis(searchcount.index.tolist()[:10]) .reversal_axis() .add_yaxis("", y, label_opts=opts.LabelOpts(position='inside', formatter="{c}%"), color='plum', category_gap="60%" ) .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30, formatter="{value}%"), axisline_opts=opts.AxisLineOpts(is_show=False),), yaxis_opts=opts.AxisOpts( axislabel_opts=opts.LabelOpts(is_show=True), axisline_opts=opts.AxisLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts( is_show=True, length=25, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ),) ) .set_series_opts( itemstyle_opts={ "normal": { "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: 'rgba(255,100,97,.5)' }, { offset: 1, color: 'rgba(221,160,221)' }], false)"""), "barBorderRadius": [30, 30, 30, 30], "shadowColor": 'rgb(0, 160, 221)', }} ) ) return c
来看看薪资分布情况
可以看到，15K以上的薪资还是占了16%以上，而占比最大的薪资区间则是7-9K
工作年限薪资分布
下面我们继续来看看薪资水平和工作年限之间的关系
statics_x = [] statics_y = [] for i in job_statics_data_json['zpData']['salaryByWorkExp']: statics_y.append(i['percent']) statics_x.append(i['workExp'] + ':' + str(i['aveSalary'])) background_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 0, 1, " "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)" ) c = ( Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))) .add( "", list(zip(statics_x, statics_y)), radius=["40%", "55%"], label_opts=opts.LabelOpts( position="outside", formatter="{a|job}{abg|}\n{hr|}\n {b|{b}: }{per|{d}%} ", background_color="#eee", border_color="#aaa", border_width=1, border_radius=4, rich={ "a": {"color": "#999", "lineHeight": 22, "align": "center"}, "abg": { "backgroundColor": "#e3e3e3", "width": "100%", "align": "right", "height": 22, "borderRadius": [4, 4, 0, 0], }, "hr": { "borderColor": "#aaa", "width": "100%", "borderWidth": 0.5, "height": 0, }, "b": {"fontSize": 16, "lineHeight": 33}, "per": { "color": "#eee", "backgroundColor": "#334455", "padding": [2, 4], "borderRadius": 2, }, }, ), ) .set_global_opts(title_opts=opts.TitleOpts(title="")) )
可以看到，下面的图片还是比较直观的
1-3年的应聘者还是最多的，占比达到了50%+，这个经验段，确实是职场的主力军了！
任职年龄分布
职场的年龄也是一个热点话题，35+岁的程序员们，总是一言难尽啊
statics_x = [] statics_y = [] for i in job_statics_data_json['zpData']['salaryByAge']: statics_x.append(i['ageRange']) statics_y.append(i['people']) def bar_chart_age(x, y) -> Bar: background_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 0, 1, " "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)" ) c = ( Bar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))) #Bar() .add_xaxis(x) # .add_xaxis(searchcount.index.tolist()[:10]) # .reversal_axis() .add_yaxis("", y, label_opts=opts.LabelOpts(position='inside', formatter="{c}"), color='plum', category_gap="60%" ) .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30, formatter="{value}"), axisline_opts=opts.AxisLineOpts(is_show=False),), yaxis_opts=opts.AxisOpts( axislabel_opts=opts.LabelOpts(is_show=True), axisline_opts=opts.AxisLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts( is_show=True, length=25, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ),) ) .set_series_opts( itemstyle_opts={ "normal": { "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: 'rgba(255,100,97,.5)' }, { offset: 1, color: 'rgba(221,160,221)' }], false)"""), "barBorderRadius": [30, 30, 30, 30], "shadowColor": 'rgb(0, 160, 221)', }} ) ) return c
数据很能说明问题
可以看到，35岁以下的占据了绝大多数，可想而知，35+的程序员生存状况是多么的糟糕！
月薪环比变化
我们通过每个月的薪资变化，来看看哪个月找工作比较有机会获得更高的薪资呢
statics_x = [] statics_y = [] for i in job_statics_data_json['zpData']['salaryByMonth']: statics_x.append(i['year'] + '-' + i['month']) statics_y.append(i['monthAveSalary']) x_data = statics_x y_data = statics_y
每月薪资变化
可以看到，去年2月份的薪资水平是最高的，之后一路下滑，再之后就基本趋于稳定了，7-8K这个平均水平
薪资城市分布
通过Pycharts画地图还是蛮方便的
statics_x = [] statics_y = [] for i in job_statics_data_json['zpData']['salaryByCity']: if i['cityList']: statics_x.append(i['cityList'][0]['cityAveMonthSalary']) statics_y.append(i['provinceName']) c = ( Map() .add("全国薪资", [list(z) for z in zip(statics_y, statics_x)], "china") .set_global_opts( title_opts=opts.TitleOpts(title=""), visualmap_opts=opts.VisualMapOpts(max_=15000, min_=6000), ) )
全国薪资分布
好了，今天的分享就到这里了，希望对大家有所帮助！
