【Python数据分析】可视化图表分析拉钩网招聘数据

超、凢脫俗 2022-09-16 15:20 254阅读 0赞

# 前言 #

上次我们爬了拉钩网的数据，现在我们来分析下，看看哪些公司的招聘信息具体需求都是哪些，让我们用可视化图表展示

# 导入所需模块 #

import numpy as np
    import pandas as pd
    from pyecharts.charts import *
    from pyecharts import options as opts
    import matplotlib.pyplot as plt
    plt.rcParams['font.sans-serif'] = ['SimHei']
    from pyecharts.globals import ThemeType

# [对于文章有疑问，或者想要Python相关资料、源码的同学也可以点这里][Python] #

# 清洗数据 #

### 导入数据 ###

df = pd.read_csv('data.csv', encoding='UTF-8')
    df.head()

![ed9473a6d7b7fae44cb77d4a2dc19510.png][]

### 查看整体性描述 ###

df.info()

![11505c603cedb580c44fd7ede4b75aba.png][]

df.describe()

![6b73c88fb0779fd4ffec443b9f735d28.png][]

### 删除重复值 ###

df.drop_duplicates(inplace = True)
    df.duplicated().sum()

### 获取 ###

df['地区'] = df['地区'].apply(lambda x:x.split('-')[0])
    df['地区'].unique()
    
    df['经验'].unique()
    
    df['薪资'].unique()
    
    df['m_max'] = df['薪资'].str.extract('(\d+)')#提取出最低薪资
    df['m_min'] = df['薪资'].str.extract('(\d+)K')#提取出最高薪资
    df['m_max'] = df['m_max'].apply('float64')#转换数据类型
    df['m_min'] = df['m_min'].apply('float64')
    df['平均薪资'] = (df['m_max']+df['m_min'])/2
    df.head()

![b28be192407e27f25fe2fa05a8cd4dc4.png][]

# 可视化 #

### 每个地区的招聘数量 ###

dq = df.groupby('地区').count()['标题']
    dq_index = dq.index.tolist()
    dq_value = dq.values.tolist()
    
    bar1 = (Bar(init_opts=opts.InitOpts(width='800px', height='400px',theme=ThemeType.MACARONS))
           .add_xaxis(dq_index)
           .add_yaxis('', dq_value,category_gap="50%")
           .set_global_opts(title_opts=opts.TitleOpts(title="每个地区的招聘数量"),
                           xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-50)),
                            visualmap_opts=opts.VisualMapOpts(max_=80),#彩色块
                            datazoom_opts=[opts.DataZoomOpts()]#拉动条形轴
                            )
          )
    bar1.render_notebook()

![6865e69b6b3a76170b89736b1307ec4c.png][]

### 经验学历需求图 ###

pair_1 = [(i, int(j)) for i, j in zip(jingyan.index,jingyan.values)]
    pie = (
        Pie(init_opts=opts.InitOpts(theme=ThemeType.MACARONS,width='1000px',height='600px'))
        
        .add('', pair_1, radius=['40%', '70%'])
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%"))
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title="经验学历需求图", 
                pos_left='center', 
                pos_top='center',
                title_textstyle_opts=opts.TextStyleOpts(
                    color='black', 
                    font_size=20, 
                    font_weight='bold'
                ),
            )
        )
        
    )
    pie.render_notebook()

![eb878fd0a30c8acdf76101aab059ccd6.png][]

### 招聘公司所在领域 ###

gongsi = df.groupby('经验').count()['标题']
    pie1 = (
        Pie(init_opts=opts.InitOpts(theme=ThemeType.MACARONS,width='1500px',height='600px'))
        .add(
            "",
            [list(z) for z in zip(gongsi.index.tolist(), gongsi.values.tolist())],
            radius=["20%", "80%"],
            center=["25%", "70%"],
            rosetype="radius",
            label_opts=opts.LabelOpts(is_show=False),
        ).set_global_opts(title_opts=opts.TitleOpts(title="招聘公司所在领域"))
    )
    pie1.render_notebook()

![fabfee21561b450cdc61e4de610763be.png][]

【Python爬虫】拉钩网职业招聘数据采集（发送请求、解析数据、保存数据）详细过程讲解

# [点这里即可免费在线观看][Link 1] #

[Python]: https://link.csdn.net/?target=https%3A%2F%2Fjq.qq.com%2F%3F_wv%3D1027%26k%3DSuR89RD4
[ed9473a6d7b7fae44cb77d4a2dc19510.png]: /images/20220828/6da41be828364175855b448270606988.png
[11505c603cedb580c44fd7ede4b75aba.png]: /images/20220828/c5b7eb50632541ccb345c133b913e863.png
[6b73c88fb0779fd4ffec443b9f735d28.png]: /images/20220828/ec7d77bca28e42acbb98ef86fe9bd7b8.png
[b28be192407e27f25fe2fa05a8cd4dc4.png]: /images/20220828/b6f6fa88659f4a4b94044b995925a111.png
[6865e69b6b3a76170b89736b1307ec4c.png]: /images/20220828/0499000a047d421fa3777d54af109215.png
[eb878fd0a30c8acdf76101aab059ccd6.png]: /images/20220828/d59b77e7428340d484bccb2bda836dee.png
[fabfee21561b450cdc61e4de610763be.png]: /images/20220828/576636cf58bb4c50a296edab524e96ca.png
[Link 1]: https://www.bilibili.com/video/BV1BL4y1B7yo/