数据分析小案例：招聘数据可视化，查看领域最需技术~

前言

嗨喽~大家好呀，这里是魔王呐

在前一章：让我们用python来采集数据看看找工作都要会什么吧~

我们讲了如何采集zhaopin网站数据，现在~

我们来把数据可视化，更好的查看在自己领域最需的技术是什么~

下面，我们直接上代码~

目录（可点击自己想去得地方哦~）

- 前言
- 代码
- 效果（部分）
- 尾语

代码提供者：青灯教育-自游老师

代码

import pandas as pdfrom pyecharts.charts import *from pyecharts import options as optsimport refrom pyecharts.globals import ThemeTypefrom pyecharts.commons.utils import JsCode

完整可视化代码可查看并点击网页主页（文章）左侧的流动文字免费获取哦~（可能需要往下划一下呐）

也可以直接查看文章下方推广加助理小姐姐V免费获取呐~

# 读取数据df = pd.read_csv("招聘数据.csv")df.head()

df.info()

df['薪资'].unique()df['bottom']=df['薪资'].str.extract('^(\d+).*')df['top']=df['薪资'].str.extract('^.*?-(\d+).*')df['top'].fillna(df['bottom'],inplace=True)df['commision_pct']=df['薪资'].str.extract('^.*?·(\d{2})薪')df['commision_pct'].fillna(12,inplace=True)df['commision_pct']=df['commision_pct'].astype('float64')df['commision_pct']=df['commision_pct']/12df.dropna(inplace=True)df['bottom'] = df['bottom'].astype('int64')df['top'] = df['top'].astype('int64')df['平均薪资'] = (df['bottom']+df['top'])/2*df['commision_pct']df['平均薪资'] = df['平均薪资'].astype('int64')df.head()

df['薪资'] = df['薪资'].apply(lambda x:re.sub('.*千/月', '0.3-0.7万/月', x))df["薪资"].unique()

df['bottom'] = df['薪资'].str.extract('^(.*?)-.*?')df['top'] = df['薪资'].str.extract('^.*?-(\d\.\d|\d)')df.dropna(inplace=True)df['bottom'] = df['bottom'].astype('float64')df['top'] = df['top'].astype('float64')df['平均薪资'] = (df['bottom']+df['top'])/2 * 10df.head()

mean = df.groupby('学历')['平均薪资'].mean().sort_values()x = mean.index.tolist()y = mean.values.tolist()c = (Bar().add_xaxis(x).add_yaxis("学历",y).set_global_opts(title_opts=opts.TitleOpts(title="不同学历的平均薪资"),datazoom_opts=opts.DataZoomOpts()).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))c.render_notebook()

color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0,[{offset: 0, color: '#63e6be'}, {offset: 1, color: '#0b7285'}], false)"""color_js1 = """new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#ed1941'}, {offset: 1,color: '#009ad6'}], false)"""dq = df.groupby('城市')['职位'].count().to_frame('数量').sort_values(by='数量',ascending=False).reset_index()x_data = dq['城市'].values.tolist()[:20]y_data = dq['数量'].values.tolist()[:20]b1 = (Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK,bg_color=JsCode(color_js1),width='1000px',height='600px')).add_xaxis(x_data).add_yaxis('', y_data , category_gap="50%", label_opts=opts.LabelOpts(font_size=12,color='yellow',font_weight='bold', font_family='monospace',position='insideTop',formatter = '{b}\n{c}'),).set_series_opts(itemstyle_opts={"normal": {"color": JsCode(color_js),"barBorderRadius": [15, 15, 0, 0],"shadowColor": "rgb(0, 160, 221)",}}).set_global_opts(title_opts=opts.TitleOpts(title='招 聘 数 量 前 20 的 城 市 区 域', title_textstyle_opts=opts.TextStyleOpts(color="yellow"), pos_top='7%',pos_left = 'center' ),legend_opts=opts.LegendOpts(is_show=False),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),yaxis_opts=opts.AxisOpts(name="", name_location='middle', name_gap=40, name_textstyle_opts=opts.TextStyleOpts(font_size=16)), datazoom_opts=[opts.DataZoomOpts(range_start=1,range_end=50)]))b1.render_notebook()

boss = df['学历'].value_counts()x = boss.index.tolist()y = boss.values.tolist()data_pair = [list(z) for z in zip(x, y)]c = (Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c")).add(series_name="学历需求占比",data_pair=data_pair,label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} 
{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),).set_global_opts(title_opts=opts.TitleOpts(title="学历需求占比",pos_left="center",pos_top="20",title_textstyle_opts=opts.TextStyleOpts(color="#fff"),),legend_opts=opts.LegendOpts(is_show=False),).set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"]))c.render_notebook()

boss = df['经验'].value_counts()x = boss.index.tolist()y = boss.values.tolist()data_pair = [list(z) for z in zip(x, y)]c = (Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c")).add(series_name="经验需求占比",data_pair=data_pair,label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} 
{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),).set_global_opts(title_opts=opts.TitleOpts(title="经验需求占比",pos_left="center",pos_top="20",title_textstyle_opts=opts.TextStyleOpts(color="#fff"),),legend_opts=opts.LegendOpts(is_show=False),).set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"]))c.render_notebook()

boss = df['公司领域'].value_counts()x = boss.index.tolist()y = boss.values.tolist()data_pair = [list(z) for z in zip(x, y)]c = (Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c")).add(series_name="公司领域占比",data_pair=data_pair,label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} 
{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),).set_global_opts(title_opts=opts.TitleOpts(title="公司领域占比",pos_left="center",pos_top="20",title_textstyle_opts=opts.TextStyleOpts(color="#fff"),),legend_opts=opts.LegendOpts(is_show=False),).set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"]))c.render_notebook()

from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Fakerboss = df['经验'].value_counts()x = boss.index.tolist()y = boss.values.tolist()data_pair = [list(z) for z in zip(x, y)]c = (Pie().add("", data_pair).set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"]).set_global_opts(title_opts=opts.TitleOpts(title="经验要求占比")).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))c.render_notebook()

from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Fakerboss = df['经验'].value_counts()x = boss.index.tolist()y = boss.values.tolist()data_pair = [list(z) for z in zip(x, y)]c = (Pie().add("",data_pair,radius=["40%", "55%"],label_opts=opts.LabelOpts(position="outside",formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c}{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="python招聘经验要求")))c.render_notebook()

gsly = df['公司领域'].value_counts()[:10]x1 = gsly.index.tolist()y1 = gsly.values.tolist()c = (Bar().add_xaxis(x1).add_yaxis("公司领域",y1).set_global_opts(title_opts=opts.TitleOpts(title="公司领域"),datazoom_opts=opts.DataZoomOpts()).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))c.render_notebook()

gsgm = df['公司规模'].value_counts()[1:10]x2 = gsgm.index.tolist()y2 = gsgm.values.tolist()c = (Bar().add_xaxis(x2).add_yaxis("公司规模",y2).set_global_opts(title_opts=opts.TitleOpts(title="公司规模"),datazoom_opts=opts.DataZoomOpts()).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))c.render_notebook()

import stylecloudfrom PIL import Imagewelfares = df['福利'].dropna(how='all').values.tolist()welfares_list = []for welfare in welfares:welfares_list += welfare.split('，')pic_name = '福利词云.png'stylecloud.gen_stylecloud(text=' '.join(welfares_list),font_path='msyh.ttc',palette='cartocolors.qualitative.Bold_5',max_font_size=100,icon_name='fas fa-yen-sign',background_color='#212529',output_name=pic_name,)Image.open(pic_name)